Cited by 184Digital Mental Health InterventionsPsychotherapy Techniques and ApplicationsPsychiatric care and mental health services

The DOI matches its Crossref record; 355 of the 356 references it cites were found in a registry or an index.

Improving Mental Health Services: A 50-Year Journey from Randomized Experiments to Artificial Intelligence and Precision Mental Health

Open access

Bickman, Leonard·2020·Administration and Policy in Mental Health and Mental Health Services Research

DOI 10.1007/s10488-020-01065-8PMC PMC7382706

Open this paper in CoChat — free account Open PDF

Abstract

This conceptual paper describes the current state of mental health services, identifies critical problems, and suggests how to solve them. I focus on the potential contributions of artificial intelligence and precision mental health to improving mental health services. Toward that end, I draw upon my own research, which has changed over the last half century, to highlight the need to transform the way we conduct mental health services research. I identify exemplars from the emerging literature on artificial intelligence and precision approaches to treatment in which there is an attempt to personalize or fit the treatment to the client in order to produce more effective interventions.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Integrity signals

Registry

Crossref · 10.1007/s10488-020-01065-8

Cites

355 of 356 references verified1 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Dermatologist-level classification of skin cancer with deep neural networks
Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017) · Nature · doi:10.1038/nature21056 · Cited by 14,362
High-performance medicine: the convergence of human and artificial intelligence
Topol, E. J. (2019) · Nature Medicine · doi:10.1038/s41591-018-0300-7 · Cited by 10,450
Estimating the reproducibility of psychological science
Open Science Collaboration (2015) · Science · doi:10.1126/science.aac4716 · Cited by 8,515 · cited 1×
“The Open Science Collaboration (2015) systematically sampled 100 results from three top-tier journals in psychology, and only 36% of the replication efforts yielded significant findings.”
Background
Dissecting racial bias in an algorithm used to manage the health of populations
Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019) · Science · doi:10.1126/science.aax2342 · Cited by 7,137 · cited 1×
“Some research has shown that inherent bias in original data sets has produced biased (racist) decisions (Obermeyer et al. 2019; Veale and Binns 2017).”
Background
Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
Adadi, A., & Berrada, M. (2018) · IEEE Access · doi:10.1109/access.2018.2870052 · Cited by 6,294
The effects of feedback interventions on performance: A historical review, a meta-analysis, and a preliminary feedback intervention theory.
Kluger, A. N., & DeNisi, A. (1996) · Psychological Bulletin · doi:10.1037/0033-2909.119.2.254 · Cited by 5,882
Multidisciplinary research priorities for the COVID-19 pandemic: a call for action for mental health science
Holmes, E. A., O'Connor, R. C., Perry, V. H., Tracey, I., Wessely, S., Arseneault, L., Ballard, C., Christensen, H., Cohen Silver, R., Everall, I., et al. (2020) · The Lancet Psychiatry · doi:10.1016/s2215-0366(20)30168-1 · Cited by 5,715
Using Effect Size—or Why the P Value Is Not Enough
Sullivan, G. M., & Feinn, R. (2012) · Journal of Graduate Medical Education · doi:10.4300/jgme-d-12-00156.1 · Cited by 5,553
Chronic inflammation in the etiology of disease across the life span
Furman, D., Campisi, J., Verdin, E., Carrera-Bastos, P., Targ, S., Franceschi, C., Ferrucci, L., Gilroy, D. W., Fasano, A., Miller, G. W., et al. (2019) · Nature Medicine · doi:10.1038/s41591-019-0675-0 · Cited by 4,915
CAUSALITY: MODELS, REASONING AND INFERENCE
Hitchcock, C., & Pearl, J. (2001) · The Philosophical Review · doi:10.2307/3182612 · Cited by 4,827
A nationwide survey of psychological distress among Chinese people in the COVID-19 epidemic: implications and policy recommendations.
Qiu, J., Shen, B., Zhao, M., Wang, Z., Xie, B., & Xu, Y. (2020) · General Psychiatry · doi:10.1136/gpsych-2020-100213 · Cited by 4,307
CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep LearningPreprint
Rajpurkar, P., Irvin, J., Zhu, K., Yang, B., Mehta, H., Duan, T., Ding, D. Y., Bagul, A., Langlotz, C. P., Shpanskaya, K. S., et al. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1711.05225 · Cited by 4,226
Risk factors for suicidal thoughts and behaviors: A meta-analysis of 50 years of research
Franklin, J. C., Ribeiro, J. D., Fox, K. R., Bentley, K. H., Kleiman, E. M., Huang, X., Musacchio, K. M., Jaroszewski, A. C., Chang, B. P., & Nock, M. K. (2017) · Psychological Bulletin · doi:10.1037/bul0000084 · Cited by 3,784
COVID-19 and mental health: A review of the existing literature
Rajkumar, R. P. (2020) · Asian Journal of Psychiatry · doi:10.1016/j.ajp.2020.102066 · Cited by 3,574
Building machines that learn and think like people
Lake, B. M., Ullman, T. D., Tenenbaum, J. B., & Gershman, S. J. (2017) · Behavioral and Brain Sciences · doi:10.1017/s0140525x16001837 · Cited by 2,550
Causal inference in statistics: An overview
Pearl, J. (2009) · Statistics Surveys · doi:10.1214/09-ss057 · Cited by 2,336 · cited 1×
“Fortunately, there are newer AI methods that can be used to infer causes, which include well validated tests of conditional independencies based on the Causal Markov Condition (Pearl 2009; Aliferis et al. 2010; Saxe 2019).”
Method
Final Report on the Aspirin Component of the Ongoing Physicians' Health Study
Steering Committee of the Physicians' Health Study Research Group (1989) · New England Journal of Medicine · doi:10.1056/nejm198907203210301 · Cited by 2,267
From stress to inflammation and major depressive disorder: A social signal transduction theory of depression.
Slavich, G. M., & Irwin, M. R. (2014) · Psychological Bulletin · doi:10.1037/a0035302 · Cited by 2,206 · cited 1×
“Slavich and Irwin (2014) have combined diverse areas to show how stressors affect neural, physiologic, molecular, and genomic and epigenetic processes that mediate depression.”
Background
The neuroactive potential of the human gut microbiota in quality of life and depression
Valles-Colomer, M., Falony, G., Darzi, Y., Tigchelaar, E. F., Wang, J., Tito, R. Y., Schiweck, C., Kurilshikov, A., Joossens, M., Wijmenga, C., et al. (2019) · Nature Microbiology · doi:10.1038/s41564-018-0337-x · Cited by 1,963
A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis
Liu, X., Faes, L., Kale, A. U., Wagner, S. K., Fu, D. J., Bruynseels, A., Mahendiran, T., Moraes, G., Shamdas, M., Kern, C., et al. (2019) · The Lancet Digital Health · doi:10.1016/s2589-7500(19)30123-2 · Cited by 1,883
Causal Protein-Signaling Networks Derived from Multiparameter Single-Cell Data
Sachs, K., Perez, O. D., Pe’er, D., Lauffenburger, D. A., & Nolan, G. P. (2005) · Science · doi:10.1126/science.1105809 · Cited by 1,785
The Great Psychotherapy Debate: The Evidence for What Makes Psychotherapy Work
Wampold, B. E. (2015) · doi:10.4324/9780203582015 · Cited by 1,773
The alliance in adult psychotherapy: A meta-analytic synthesis
Flückiger, C., Del Re, A. C., Wampold, B. E., & Horvath, A. O. (2018) · Psychotherapy · doi:10.1037/pst0000172 · Cited by 1,763 · cited 3×
“There are thousands of correlational studies that have established that TA is a predictor of treatment outcomes (Flückiger et al. 2018); however, there are few studies of interventions that show a causal connection between TA and outcomes (e.”
BackgroundResultInfluential
Mental Health Strategies to Combat the Psychological Impact of Coronavirus Disease 2019 (COVID-19) Beyond Paranoia and Panic
Ho, C. S., Chee, C. Y., & Ho, R. C. (2020) · Annals of the Academy of Medicine, Singapore · doi:10.47102/annals-acadmedsg.202043 · Cited by 1,699
Automated Identification of Diabetic Retinopathy Using Deep Learning
Gargeya, R., & Leng, T. (2017) · Ophthalmology · doi:10.1016/j.ophtha.2017.02.008 · Cited by 1,410
Can machine-learning improve cardiovascular risk prediction using routine clinical data?
Weng, S. F., Reps, J., Kai, J., Garibaldi, J. M., & Qureshi, N. (2017) · PLOS ONE · doi:10.1371/journal.pone.0174944 · Cited by 1,346
Personalized medicine: Time for one-person trials
Schork, N. J. (2015) · Nature · doi:10.1038/520609a · Cited by 1,274
Internet-based vs. face-to-face cognitive behavior therapy for psychiatric and somatic disorders: an updated systematic review and meta-analysis
Carlbring, P., Andersson, G., Cuijpers, P., Riper, H., & Hedman-Lagerlöf, E. (2018) · Cognitive Behaviour Therapy · doi:10.1080/16506073.2017.1401115 · Cited by 1,232
The Role of Telehealth in Reducing the Mental Health Burden from COVID-19
Zhou, X., Snoswell, C. L., Harding, L. E., Bambling, M., Edirippulige, S., Bai, X., & Smith, A. C. (2020) · Telemedicine and e-Health · doi:10.1089/tmj.2020.0068 · Cited by 1,159
Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Models
Samek, W., Wiegand, T., & Müller, K.-R. (2017) · arXiv · doi:10.48550/arxiv.1708.08296 · Cited by 1,080
Chatbots and Conversational Agents in Mental Health: A Review of the Psychiatric Landscape
Vaidyam, A. N., Wisniewski, H., Halamka, J. D., Kashavan, M. S., & Torous, J. B. (2019) · The Canadian Journal of Psychiatry · doi:10.1177/0706743719828977 · Cited by 1,048
Machine learning in mental health: a scoping review of methods and applications
Shatte, A. B. R., Hutchinson, D. M., & Teague, S. J. (2019) · Psychological Medicine · doi:10.1017/s0033291719000151 · Cited by 1,047
Causality models, reasoning and inference
Pearl, J. 1. (2000) · Cited by 1,044
Untitled work
(2019) · Cited by 986
Machine Learning Approaches for Clinical Psychology and Psychiatry
Dwyer, D. B., Falkai, P., & Koutsouleris, N. (2018) · Annual Review of Clinical Psychology · doi:10.1146/annurev-clinpsy-032816-045037 · Cited by 966
The SAGE Handbook of Applied Social Research Methods
Bickman, L., & Rog, D. J. (2009) · Sage eBooks · doi:10.4135/9781483348858 · Cited by 940
Self-citation
Machine Learning for Precision Psychiatry: Opportunities and Challenges
Bzdok, D., & Meyer‐Lindenberg, A. (2017) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2017.11.007 · Cited by 888
What five decades of research tells us about the effects of youth psychological therapy: A multilevel meta-analysis and implications for science and practice
Weisz, J. R., Kuppens, S., Ng, M. Y., Eckshtain, D., Ugueto, A. M., Vaughn-Coaxum, R., Jensen-Doss, A., Hawley, K. M., Krumholz Marchette, L. S., Chu, B. C., et al. (2017) · American Psychologist · doi:10.1037/a0040360 · Cited by 862 · cited 1×
“For example, Weisz et al. (2017) report that in their meta-analysis, the effect of treatment type as a moderator was not statistically significant but there was a significant, but not clearly understood, treatment type by informant interaction effect.”
Result
Deep Learning: A Critical Appraisal
Marcus, G. (2018) · arXiv · doi:10.48550/arxiv.1801.00631 · Cited by 853
Is guided self-help as effective as face-to-face psychotherapy for depression and anxiety disorders? A systematic review and meta-analysis of comparative outcome studies.
Cuijpers, P., Donker, T., van Straten, A., Li, J., & Andersson, G. (2010) · Psychological Medicine · doi:10.1017/s0033291710000772 · Cited by 832
Examination of real-time fluctuations in suicidal ideation and its risk factors: Results from two ecological momentary assessment studies
Kleiman, E. M., Turner, B. J., Fedor, S., Beale, E. E., Huffman, J. C., & Nock, M. K. (2017) · Journal of Abnormal Psychology · doi:10.1037/abn0000273 · Cited by 831
Personal Sensing: Understanding Mental Health Using Ubiquitous Sensors and Machine Learning
Mohr, D. C., Zhang, M., & Schueller, S. M. (2017) · Annual Review of Clinical Psychology · doi:10.1146/annurev-clinpsy-032816-044949 · Cited by 802
The impact of artificial intelligence in medicine on the future role of the physician
Ahuja, A. S. (2019) · PeerJ · doi:10.7717/peerj.7702 · Cited by 789
The SAGE Handbook of Social Research Methods
Alasuutari, P., Bickman, L., & Brannen, J. (2008) · SAGE Publications Ltd eBooks · doi:10.4135/9781446212165 · Cited by 759
Self-citation
Annals of the Academy of Medicine, Singapore
(2020) · Annals of the Academy of Medicine Singapore · doi:10.47102/annals-acadmedsg · Cited by 742
Detecting depression and mental illness on social media: an integrative review
Guntuku, S. C., Yaden, D. B., Kern, M. L., Ungar, L. H., & Eichstaedt, J. C. (2017) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2017.07.005 · Cited by 722
What is the test–retest reliability of common task-functional MRI measures? New empirical evidence and a meta-analysis
Elliott, M. L., Knodt, A. R., Ireland, D., Morris, M. L., Poulton, R., Ramrakha, S., Sison, M. L., Moffitt, T. E., Caspi, A., & Hariri, A. R. (2020) · Psychological Science · doi:10.1177/0956797620916786 · Cited by 714
Stand-Alone Artificial Intelligence for Breast Cancer Detection in Mammography: Comparison With 101 Radiologists
Rodriguez-Ruiz, A., Lång, K., Gubern-Merida, A., Broeders, M., Gennaro, G., Clauser, P., Helbich, T. H., Chevalier, M., Tan, T., Mertelmeier, T., et al. (2019) · JNCI: Journal of the National Cancer Institute · doi:10.1093/jnci/djy222 · Cited by 692
Deep Learning—A Technology With the Potential to Transform Health Care
Hinton, G. (2018) · JAMA · doi:10.1001/jama.2018.11100 · Cited by 667
The seven tools of causal inference, with reflections on machine learning
Pearl, J. (2019) · Communications of the ACM · doi:10.1145/3241036 · Cited by 659 · cited 2×
“I have found many individual papers that describe how to solve the causality problem with AI (e.g., Kuang et al. 2020; Pearl 2019).”
Background
Artificial Intelligence in Health Care: A Report From the National Academy of Medicine
Matheny, M. E., Whicher, D., & Thadaney Israni, S. (2020) · JAMA · doi:10.1001/jama.2019.21579 · Cited by 628
Implementing Measurement-Based Care in Behavioral Health: A Review
Lewis, C. C., Boyd, M., Puspitasari, A., Navarro, E., Howard, J., Kassab, H., Hoffman, M., Scott, K., Lyon, A., Douglas, S., et al. (2019) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2018.3329 · Cited by 605
The new field of ‘precision psychiatry’
Fernandes, B. S., Williams, L. M., Steiner, J. H., Leboyer, M., Carvalho, A. F., & Berk, M. (2017) · BMC Medicine · doi:10.1186/s12916-017-0849-x · Cited by 594
Potential liability for physicians using artificial intelligence
Price, W. N., Gerke, S., & Cohen, I. G. (2019) · JAMA · doi:10.1001/jama.2019.15064 · Cited by 579
Evidence-based youth psychotherapies versus usual clinical care: A meta-analysis of direct comparisons
Weisz, J. R., Jensen-Doss, A., & Hawley, K. M. (2006) · American Psychologist · doi:10.1037/0003-066x.61.7.671 · Cited by 560
How to Read Articles That Use Machine Learning
Liu, Y., Chen, P.-H. C., Krause, J., & Peng, L. H. (2019) · JAMA · doi:10.1001/jama.2019.16489 · Cited by 547
Automated assessment of psychiatric disorders using speech: A systematic review
Low, D. M., Bentley, K. H., & Ghosh, S. (2020) · Laryngoscope Investigative Otolaryngology · doi:10.1002/lio2.354 · Cited by 545
Effectiveness and Acceptability of Cognitive Behavior Therapy Delivery Formats in Adults With Depression: A Network Meta-analysis
Cuijpers, P., Noma, H., Karyotaki, E., Cipriani, A., & Furukawa, T. A. (2019) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2019.0268 · Cited by 539
A Critical Review of Consumer Wearables, Mobile Applications, and Equipment for Providing Biofeedback, Monitoring Stress, and Sleep in Physically Active Populations
Peake, J. M., Kerr, G., & Sullivan, J. P. (2018) · Frontiers in Physiology · doi:10.3389/fphys.2018.00743 · Cited by 523
Personalized evidence based medicine: predictive approaches to heterogeneous treatment effects
Kent, D. M., Steyerberg, E. W., & Klaveren, D. V. (2018) · BMJ · doi:10.1136/bmj.k4245 · Cited by 518
Fairer machine learning in the real world: Mitigating discrimination without collecting sensitive data
Veale, M., & Binns, R. (2017) · Big Data & Society · doi:10.1177/2053951717743530 · Cited by 512
Detecting Cancer Metastases on Gigapixel Pathology Images
Liu, Y., Gadepalli, K., Norouzi, M., Dahl, G. E., Kohlberger, T., Boyko, A., Venugopalan, S., Timofeev, A., Nelson, P. Q., Corrado, G. S., et al. (2017) · arXiv · doi:10.48550/arxiv.1703.02442 · Cited by 506
Social Safety Theory: A Biologically Based Evolutionary Perspective on Life Stress, Health, and Behavior
Slavich, G. M. (2020) · Annual Review of Clinical Psychology · doi:10.1146/annurev-clinpsy-032816-045159 · Cited by 502 · cited 2×
“A key aspect of this perspective is the role of inflammatory cytokines as key mediators of the inflammatory response (Slavich 2020).”
Background
Services for Adolescents With Psychiatric Disorders: 12-Month Data From the National Comorbidity Survey–Adolescent
Costello, E. J., He, J., Sampson, N. A., Kessler, R. C., & Merikangas, K. R. (2013) · Psychiatric Services · doi:10.1176/appi.ps.201100518 · Cited by 475
Improving mental health access for low-income children and families in the primary care setting.
Hodgkinson, S., Godoy, L., Beers, L. S., & Lewin, A. (2017) · Pediatrics · doi:10.1542/peds.2015-1175 · Cited by 451
Predicting Suicides After Psychiatric Hospitalization in US Army Soldiers: The Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS)
Kessler, R. C., Warner, C. H., Ivany, C., Petukhova, M. V., Rose, S., Bromet, E. J., Brown, M., Cai, T., Colpe, L. J., Cox, K. L., et al. (2015) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2014.1754 · Cited by 447
Collecting and delivering progress feedback: A meta-analysis of routine outcome monitoring.
Lambert, M. J., Whipple, J. L., & Kleinstäuber, M. (2018) · Psychotherapy · doi:10.1037/pst0000167 · Cited by 440
Comparative efficacy and acceptability of non-surgical brain stimulation for the acute treatment of major depressive episodes in adults: systematic review and network meta-analysis
Mutz, J., Vipulananthan, V., Carter, B., Hurlemann, R., Fu, C. H. Y., & Young, A. H. (2019) · BMJ · doi:10.1136/bmj.l1079 · Cited by 427
Using learning analytics to scale the provision of personalised feedback
Pardo, A., Jovanovic, J., Dawson, S., Gašević, D., & Mirriahi, N. (2019) · British Journal of Educational Technology · doi:10.1111/bjet.12592 · Cited by 425
Effects of routine feedback to clinicians on mental health outcomes of youths: Results of a randomized trial
Bickman, L., Kelley, S. D., Breda, C., de Andrade, A. R., & Riemer, M. (2011) · Psychiatric Services · doi:10.1176/appi.ps.002052011 · Cited by 421
Self-citation
Applications of machine learning algorithms to predict therapeutic outcomes in depression: A meta-analysis and systematic review
Lee, Y. L., Ragguett, R., Mansur, R. B., Boutilier, J. J., Rosenblat, J. D., Trevizol, A. P., Brietzke, E. M., Lin, K., Pan, Z., Subramaniapillai, M., et al. (2018) · Journal of Affective Disorders · doi:10.1016/j.jad.2018.08.073 · Cited by 418
Performance of Evidence-Based Youth Psychotherapies Compared With Usual Clinical Care: A Multilevel Meta-analysis
Weisz, J. R., Kuppens, S., Eckshtain, D., Ugueto, A. M., Hawley, K. M., & Jensen-Doss, A. (2013) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2013.1176 · Cited by 412
A continuum of care: More is not always better.
Bickman, L. (1996) · American Psychologist · doi:10.1037/0003-066x.51.7.689 · Cited by 366
Self-citation
Do mental health mobile apps work: evidence and recommendations for designing high-efficacy mental health mobile apps
Chandrashekar, P. (2018) · mHealth · doi:10.21037/mhealth.2018.03.02 · Cited by 359
Compliance with mobile ecological momentary assessment protocols in children and adolescents: a systematic review and meta-analysis
Wen, C. K. F., Schneider, S., Stone, A. A., & Spruijt-Metz, D. (2017) · Journal of Medical Internet Research · doi:10.2196/jmir.6641 · Cited by 350
Everybody Lies: Big Data, New Data, and What the Internet Can Tell Us About Who We Really Are
Stephens-Davidowitz, S. I. (2017) · Cited by 347
Artificial Intelligence in Clinical Decision Support: Challenges for Evaluating AI and Practical Implications: A Position Paper from the IMIA Technology Assessment & Quality Development in Health Informatics Working Group and the EFMI Working Group for Assessment of Health Information Systems
Magrabi, F., Ammenwerth, E., McNair, J. B., De Keizer, N. F., Hyppönen, H., Nykänen, P., Rigby, M., Scott, P. J., Vehko, T., Wong, Z. S.-Y., et al. (2019) · Yearbook of Medical Informatics · doi:10.1055/s-0039-1677903 · Cited by 347
Prediction Models of Functional Outcomes for Individuals in the Clinical High-Risk State for Psychosis or With Recent-Onset Depression: A Multimodal, Multisite Machine Learning Analysis
Koutsouleris, N., Kambeitz-Ilankovic, L., Ruhrmann, S., Rosen, M., Ruef, A., Dwyer, D. B., Paolini, M., Chisholm, K., Kambeitz, J., Haidl, T., et al. (2018) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2018.2165 · Cited by 341
The Personalization of Conversational Agents in Health Care: Systematic Review
Kocaballi, A. B., Berkovsky, S., Quiroz, J. C., Laranjo, L., Tong, H. L., Rezazadegan, D., Briatore, A., & Coiera, E. (2019) · Journal of Medical Internet Research · doi:10.2196/15360 · Cited by 332
The Effectiveness of School-Based Mental Health Services for Elementary-Aged Children: A Meta-Analysis
Sanchez, A. L., Cornacchio, D., Poznanski, B., Golik, A. M., Chou, T., & Comer, J. S. (2017) · Journal of the American Academy of Child & Adolescent Psychiatry · doi:10.1016/j.jaac.2017.11.022 · Cited by 331
A million variables and more: the Fast Greedy Equivalence Search algorithm for learning high-dimensional graphical causal models, with an application to functional magnetic resonance images
Ramsey, J., Glymour, M., Sánchez-Romero, R., & Glymour, C. (2016) · International Journal of Data Science and Analytics · doi:10.1007/s41060-016-0032-z · Cited by 329
Internet Interventions for Adults with Anxiety and Mood Disorders: A Narrative Umbrella Review of Recent Meta-Analyses
Andersson, G., Carlbring, P., Titov, N., & Lindefors, N. (2019) · The Canadian Journal of Psychiatry · doi:10.1177/0706743719839381 · Cited by 324 · cited 1×
“In a meta-analytic review of 9 meta-analyses, containing 166 studies of adult use of internet delivered via ICBT, the authors concluded that ICBT is as effective as face-to-face therapy (Andersson et al. 2019).”
Background
Heterogeneity in psychiatric diagnostic classification
Allsopp, K., Read, J., Corcoran, R., & Kinderman, P. (2019) · Psychiatry Research · doi:10.1016/j.psychres.2019.07.005 · Cited by 323
Psychosocial Interventions and Immune System Function
Shields, G. S., Spahr, C. M., & Slavich, G. M. (2020) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2020.0431 · Cited by 322
Comparison of Machine Learning Methods With Traditional Models for Use of Administrative Claims With Electronic Medical Records to Predict Heart Failure Outcomes
Desai, R. J., Wang, S. V., Vaduganathan, M., Evers, T., & Schneeweiß, S. (2020) · JAMA Network Open · doi:10.1001/jamanetworkopen.2019.18962 · Cited by 315
IMPACT OF HUMAN DISASTERS AND COVID-19 PANDEMIC ON MENTAL HEALTH: POTENTIAL OF DIGITAL PSYCHIATRY
Ćosić, K., Popović, S., Šarlija, M., & Kesedžić, I. (2020) · Psychiatria Danubina · doi:10.24869/psyd.2020.25 · Cited by 305
Progress in Childhood Cancer: 50 Years of Research Collaboration, a Report From the Children's Oncology Group
O’Leary, M., Krailo, M., Anderson, J. R., & Reaman, G. H. (2008) · Seminars in Oncology · doi:10.1053/j.seminoncol.2008.07.008 · Cited by 304
Everything You Wanted to Know about Smart Health Care: Evaluating the Different Technologies and Components of the Internet of Things for Better Health
Sundaravadivel, P., Kougianos, E., Mohanty, S. P., & Ganapathiraju, M. K. (2017) · IEEE Consumer Electronics Magazine · doi:10.1109/mce.2017.2755378 · Cited by 304
Is the alliance really therapeutic? Revisiting this question in light of recent methodological advances.
Zilcha-Mano, S. (2017) · American Psychologist · doi:10.1037/a0040435 · Cited by 303
Deep neural networks in psychiatry
Durstewitz, D., Koppe, G., & Meyer‐Lindenberg, A. (2019) · Molecular Psychiatry · doi:10.1038/s41380-019-0365-9 · Cited by 297
Introduction to the Special Issue: Discrepancies in Adolescent–Parent Perceptions of the Family and Adolescent Adjustment
Reyes, A. D. L., & Ohannessian, C. M. (2016) · Journal of Youth and Adolescence · doi:10.1007/s10964-016-0533-z · Cited by 296
Internet-delivered cognitive behavior therapy for children and adolescents: A systematic review and meta-analysis
Vigerland, S., Lenhard, F., Bonnert, M., Lalouni, M., Hedman, E., Ahlen, J., Olén, O., Serlachius, E., & Ljótsson, B. (2016) · Clinical Psychology Review · doi:10.1016/j.cpr.2016.09.005 · Cited by 292
Annual Research Review: Building a science of personalized intervention for youth mental health
Ng, M. Y., & Weisz, J. R. (2016) · Journal of Child Psychology and Psychiatry · doi:10.1111/jcpp.12470 · Cited by 291
Psychosocial Interventions for People With Both Severe Mental Illness and Substance Misuse
Hunt, G. E., Siegfried, N., Morley, K. C., Sitharthan, T., & Cleary, M. L. (2013) · Schizophrenia Bulletin · doi:10.1093/schbul/sbt160 · Cited by 282
A Measurement Feedback System (MFS) Is Necessary to Improve Mental Health Outcomes
Hamilton, J. D., & Bickman, L. (2008) · Journal of the American Academy of Child & Adolescent Psychiatry · doi:10.1097/chi.0b013e3181825af8 · Cited by 276
Self-citation
Conversational agents in the treatment of mental health problems: mixed-method systematic review
Gaffney, H., Mansell, W., & Tai, S. (2019) · JMIR Mental Health · doi:10.2196/14166 · Cited by 275
Annual Research Review: Expanding mental health services through novel models of intervention delivery
Kazdin, A. E. (2019) · Journal of Child Psychology and Psychiatry · doi:10.1111/jcpp.12937 · Cited by 274
Systematic effect of random error in the yoked control design.
Church, R. M. (1964) · Psychological Bulletin · doi:10.1037/h0042733 · Cited by 273
The Oxford Handbook of Stress and Mental Health
(2018) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780190681777.001.0001 · Cited by 269
Smartphone sensing methods for studying behavior in everyday life
Harari, G. M., Müller, S. R., Aung, M. H., & Rentfrow, P. J. (2017) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2017.07.018 · Cited by 263
Artificial intelligence and the future of psychiatry: Insights from a global physician survey
Doraiswamy, P. M., Blease, C., & Bodner, K. (2020) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2019.101753 · Cited by 253
Effective nurse–patient relationships in mental health care: A systematic review of interventions to improve the therapeutic alliance
Hartley, S., Raphael, J., Lovell, K., & Berry, K. (2020) · International Journal of Nursing Studies · doi:10.1016/j.ijnurstu.2019.103490 · Cited by 245
Applications of Machine Learning in Real-Life Digital Health Interventions: Review of the Literature
Triantafyllidis, A. K., & Tsanas, A. (2019) · Journal of Medical Internet Research · doi:10.2196/12286 · Cited by 243
Change What? Identifying Quality Improvement Targets by Investigating Usual Mental Health Care
Garland, A. F., Bickman, L., & Chorpita, B. F. (2010) · Administration and Policy in Mental Health and Mental Health Services Research · doi:10.1007/s10488-010-0279-y · Cited by 241
Self-citation
Feasibility of Using Real-World Data to Replicate Clinical Trial Evidence
Bartlett, V., Dhruva, S. S., Shah, N. D., Ryan, P. B., & Ross, J. S. (2019) · JAMA Network Open · doi:10.1001/jamanetworkopen.2019.12869 · Cited by 237
Artificial Intelligence in Health Care: Will the Value Match the Hype?
Emanuel, E. J., & Wachter, R. M. (2019) · JAMA · doi:10.1001/jama.2019.4914 · Cited by 237
Telepsychiatry and the Coronavirus Disease 2019 Pandemic—Current and Future Outcomes of the Rapid Virtualization of Psychiatric Care
Shore, J. H., Schneck, C. D., & Mishkind, M. C. (2020) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2020.1643 · Cited by 220
Psychological therapies versus antidepressant medication, alone and in combination for depression in children and adolescents
Cox, G., Callahan, P., Churchill, R. C., Hunot, V., Merry, S., Parker, A. G., & Hetrick, S. E. (2014) · Cochrane Database of Systematic Reviews · doi:10.1002/14651858.cd008324.pub3 · Cited by 218
Toward achieving precision health
Gambhir, S. S., Ge, T. J., Vermesh, O., & Spitler, R. (2018) · Science Translational Medicine · doi:10.1126/scitranslmed.aao3612 · Cited by 217
Evaluating Managed Mental Health Services: The Fort Bragg Experiment
Bickman, L., Guthrie, P. R., Foster, E. M., Lambert, E., Summerfelt, W. T., Breda, C. S., & Heflinger, C. A. (1995) · Medical Entomology and Zoology · Cited by 215
Self-citation
A machine learning approach to predicting psychosis using semantic density and latent content analysis
Rezaii, N., Walker, E. F., & Wolff, P. (2019) · Schizophrenia · doi:10.1038/s41537-019-0077-9 · Cited by 213
Experiencing mental health diagnosis: a systematic review of service user, clinician, and carer perspectives across clinical settings
Perkins, A., Ridler, J., Browes, D., Peryer, G., Notley, C. J., & Hackmann, C. (2018) · The Lancet Psychiatry · doi:10.1016/s2215-0366(18)30095-6 · Cited by 208
The impact of machine learning on patient care: A systematic review
Ben-Israel, D., Jacobs, W. B., Casha, S., Lang, S., Ryu, W. H. A., de Lotbiniere-Bassett, M., & Cadotte, D. W. (2020) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2019.101785 · Cited by 207
Machine learning and big data in psychiatry: toward clinical applications
Rutledge, R. B., Chekroud, A. M., & Huys, Q. J. M. (2019) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2019.02.006 · Cited by 206
What counts as credible evidence in applied research and evaluation practice?
Donaldson, S. I., Christie, C. A., & Mark, M. M. (2009) · Sage eBooks · doi:10.4135/9781412995634 · Cited by 204
Evaluating Managed Mental Health Services
Bickman, L., Guthrie, P. R., Foster, E. M., Lambert, E., Summerfelt, W. T., Breda, C. S., & Heflinger, C. A. (1995) · Springer eBooks · doi:10.1007/978-1-4899-1071-4 · Cited by 201
Self-citation
GEMS: A system for automated cancer diagnosis and biomarker discovery from microarray gene expression data
Statnikov, A., Tsamardinos, I., Dosbayev, Y., & Aliferis, C. (2005) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2005.05.002 · Cited by 197
Towards integrating personalized feedback research into clinical practice: Development of the Trier Treatment Navigator (TTN)
Lutz, W., Rubel, J. A., Schwartz, B., Schilling, V., & Deisenhofer, A.-K. (2019) · Behaviour Research and Therapy · doi:10.1016/j.brat.2019.103438 · Cited by 196
Current state and future directions of technology-based ecological momentary assessment and intervention for major depressive disorder: a systematic review
Colombo, D., Fernández-Álvarez, J., Patané, A., Semonella, M., Kwiatkowska, M., García-Palacios, A., Cipresso, P., Riva, G., & Botella, C. (2019) · Journal of Clinical Medicine · doi:10.3390/jcm8040465 · Cited by 194 · cited 2×
“In a systematic review of EMA use in major depression, Colombo et al. (2019) evaluated 33 studies that met their criteria for inclusion.”
Background
Combining clinical variables to optimize prediction of antidepressant treatment outcomes
Iniesta, R., Malki, K., Maier, W., Rietschel, M., Mors, O. N., Hauser, J., Henigsberg, N., Dernovšek, M. Z., Souery, D., Stahl, D. R., et al. (2016) · Journal of Psychiatric Research · doi:10.1016/j.jpsychires.2016.03.016 · Cited by 189
Ecological momentary assessment and intervention in the treatment of psychotic disorders: a systematic review
Bell, I. H., Lim, M. H., Rossell, S. L., & Thomas, N. (2017) · Psychiatric Services · doi:10.1176/appi.ps.201600523 · Cited by 189
A prospective study of therapist facilitative interpersonal skills as a predictor of treatment outcome.
Anderson, T., McClintock, A. S., Himawan, L. K., Song, X., & Patterson, C. L. (2015) · Journal of Consulting and Clinical Psychology · doi:10.1037/ccp0000060 · Cited by 184
The Social Power of a Uniform1
Bickman, L. (1974) · Journal of Applied Social Psychology · doi:10.1111/j.1559-1816.1974.tb02599.x · Cited by 180
Self-citation
Are Psychotherapies for Young People Growing Stronger? Tracking Trends Over Time for Youth Anxiety, Depression, Attention-Deficit/Hyperactivity Disorder, and Conduct Problems
Weisz, J. R., Kuppens, S., Ng, M. Y., Vaughn-Coaxum, R. A., Ugueto, A. M., Eckshtain, D., & Corteselli, K. A. (2019) · Perspectives on Psychological Science · doi:10.1177/1745691618805436 · Cited by 180
AI and Its New Winter: from Myths to Realities
Floridi, L. (2020) · Philosophy & Technology · doi:10.1007/s13347-020-00396-6 · Cited by 179
Extracting psychiatric stressors for suicide from social media using deep learning
Du, J., Zhang, Y., Luo, J., Jia, Y., Wei, Q., Tao, C., & Xu, H. (2018) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-018-0632-8 · Cited by 177
Barriers and facilitators of mental health programmes in primary care in low-income and middle-income countries
Esponda, G. M., Hartman, S. J., Qureshi, O., Sadler, E., Cohen, A., & Kakuma, R. (2019) · The Lancet Psychiatry · doi:10.1016/s2215-0366(19)30125-7 · Cited by 176
Machine Learning and Psychological Research: The Unexplored Effect of Measurement
Jacobucci, R., & Grimm, K. J. (2020) · Perspectives on Psychological Science · doi:10.1177/1745691620902467 · Cited by 175
Feedback from Outcome Measures and Treatment Effectiveness, Treatment Efficiency, and Collaborative Practice: A Systematic Review
Gondek, D., Edbrooke-Childs, J. H., Fink, E., Deighton, J., & Wolpert, M. (2016) · Administration and Policy in Mental Health and Mental Health Services Research · doi:10.1007/s10488-015-0710-5 · Cited by 169
Ecological momentary interventions for depression and anxiety
Schueller, S. M., Aguilera, A., & Mohr, D. C. (2017) · Depression and Anxiety · doi:10.1002/da.22649 · Cited by 168
Reductions in sugar sales from soft drinks in the UK from 2015 to 2018
Bandy, L., Scarborough, P., Harrington, R., Rayner, M., & Jebb, S. A. (2020) · BMC Medicine · doi:10.1186/s12916-019-1477-4 · Cited by 167
Key Considerations for Incorporating Conversational AI in Psychotherapy
Miner, A. S., Shah, N., Bullock, K. D., Arnow, B. A., Bailenson, J., & Hancock, J. (2019) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2019.00746 · Cited by 166
Recent Advances in Deep Learning: An OverviewPreprint
Minar, M. R., & Naher, J. (2018) · arXiv (Cornell University) · doi:10.13140/rg.2.2.24831.10403 · Cited by 164
Local Causal and Markov Blanket Induction for Causal Discovery and Feature Selection for Classification Part II: Analysis and Extensions
Aliferis, C., Statnikov, A., Tsamardinos, I., Mani, S., & Koutsoukos, X. (2010) · doi:10.5555/1756006.1756014 · Cited by 163
Responders to rTMS for depression show increased fronto-midline theta and theta connectivity compared to non-responders
Bailey, N. W., Hoy, K. E., Rogasch, N. C., Thomson, R. H., McQueen, S., Elliot, D., Sullivan, C. M., Fulcher, B. D., Daskalakis, Z. J., & Fitzgerald, P. B. (2018) · Brain Stimulation · doi:10.1016/j.brs.2017.10.015 · Cited by 162
Practitioner review: Unguided and guided self-help interventions for common mental health disorders in children and adolescents: a systematic review and meta-analysis
Bennett, S. D., Cuijpers, P., Ebert, D. D., McKenzie Smith, M., Coughtrey, A. E., Heyman, I., Manzotti, G., & Shafran, R. (2019) · Journal of Child Psychology and Psychiatry · doi:10.1111/jcpp.13010 · Cited by 162
Artificial intelligence and robotic surgery
Bhandari, M., Zeffiro, T., & Reddiboina, M. (2019) · Current Opinion in Urology · doi:10.1097/mou.0000000000000692 · Cited by 159
Therapist Expertise in Psychotherapy Revisited
Hill, C. E., Spiegel, S. B., Hoffman, M. A., Kivlighan, D. M., & Gelso, C. J. (2017) · The Counseling Psychologist · doi:10.1177/0011000016641192 · Cited by 157
Practitioner Review: Empirical evolution of youth psychotherapy toward transdiagnostic approaches
Marchette, L. K., & Weisz, J. R. (2017) · Journal of Child Psychology and Psychiatry · doi:10.1111/jcpp.12747 · Cited by 156 · cited 1×
“Weisz and his colleagues (Marchette and Weisz 2017; Ng and Weisz 2016) described several factors that contribute to the problems identified above.”
Background
Open trial of a personalized modular treatment for mood and anxiety
Fisher, A. J., Bosley, H. G., Fernandez, K. C., Reeves, J. W., Soyster, P. D., Diamond, A. E., & Barkin, J. (2019) · Behaviour Research and Therapy · doi:10.1016/j.brat.2019.01.010 · Cited by 156
Applications of machine learning in addiction studies: A systematic review
Mak, K. K., Lee, K., & Park, C. (2019) · Psychiatry Research · doi:10.1016/j.psychres.2019.03.001 · Cited by 155
Outcomes From Wraparound and Multisystemic Therapy in a Center for Mental Health Services System-of-Care Demonstration Site
Stambaugh, L. F., Mustillo, S., Burns, B. J., Stephens, R. L., Baxter, B., Edwards, D., & DeKraai, M. B. (2007) · Journal of Emotional and Behavioral Disorders · doi:10.1177/10634266070150030201 · Cited by 153
A Comparison of Natural Language Processing Methods for Automated Coding of Motivational Interviewing
Tanana, M., Hallgren, K. A., Imel, Z. E., Atkins, D. C., & Srikumar, V. (2016) · Journal of Substance Abuse Treatment · doi:10.1016/j.jsat.2016.01.006 · Cited by 149
Measuring the implementation of behavioral intervention technologies: Recharacterization of established outcomes
Hermes, E. D., Lyon, A. R., Schueller, S. M., & Glass, J. E. (2019) · Journal of Medical Internet Research · doi:10.2196/11752 · Cited by 149
Causal Inference
Kuang, K., Li, L., Geng, Z., Xu, L., Zhang, K., Liao, B., Huang, H., Ding, P., Miao, W., & Jiang, Z. (2020) · Engineering · doi:10.1016/j.eng.2019.08.016 · Cited by 147
Mental Health Services in School-Based Health Centers
Bains, R. M., & Diallo, A. F. (2015) · The Journal of School Nursing · doi:10.1177/1059840515590607 · Cited by 142
Utilization of machine learning for prediction of post-traumatic stress: a re-examination of cortisol in the prediction and pathways to non-remitting PTSD
Galatzer‐Levy, I. R., Ma, S., Statnikov, A., Yehuda, R., & Shalev, A. Y. (2017) · Translational Psychiatry · doi:10.1038/tp.2017.38 · Cited by 142
Empirically Based Mean Effect Size Distributions for Universal Prevention Programs Targeting School-Aged Youth: A Review of Meta-Analyses
Tanner-Smith, E. E., Durlak, J. A., & Marx, R. A. (2018) · Prevention Science · doi:10.1007/s11121-018-0942-1 · Cited by 140
Implementing a Measurement Feedback System: A Tale of Two Sites
Bickman, L., Douglas, S. R., de Andrade, A. R. V., Tomlinson, M. M., Gleacher, A. A., Olin, S.-C. S., & Hoagwood, K. (2015) · Administration and Policy in Mental Health and Mental Health Services Research · doi:10.1007/s10488-015-0647-8 · Cited by 139
Self-citation
Causal discovery algorithms: A practical guide
Malinsky, D., & Danks, D. J. (2017) · Philosophy Compass · doi:10.1111/phc3.12470 · Cited by 135
Research Review: Multi‐informant integration in child and adolescent psychopathology diagnosis
Martel, M. M., Markon, K. E., & Smith, G. T. (2016) · Journal of Child Psychology and Psychiatry · doi:10.1111/jcpp.12611 · Cited by 134
Preventing cytokine storm syndrome in COVID-19 using α-1 adrenergic receptor antagonists
Konig, M. F., Powell, M. A., Staedtke, V., Bai, R.-Y., Thomas, D. L., Fischer, N. M., Huq, S., Khalafallah, A. M., Koenecke, A., Xiong, R., et al. (2020) · Journal of Clinical Investigation · doi:10.1172/jci139642 · Cited by 132
Behavioral and Brain Sciences
隋明爽, & ming, H. (2015) · doi:10.3760/cma.j.issn.0578-1426.2015.09.103 · Cited by 132
The future of precision medicine: potential impacts for health technology assessment
Love-Koh, J., Peel, A., Rejon-Parrilla, J. C., Ennis, K., Lovett, R., Manca, A., Chalkidou, A., Wood, H., & Taylor, M. (2018) · PharmacoEconomics · doi:10.1007/s40273-018-0686-6 · Cited by 131
Recommendations and future directions for supervised machine learning in psychiatry
Cearns, M., Hahn, T., & Baune, B. T. (2019) · Translational Psychiatry · doi:10.1038/s41398-019-0607-2 · Cited by 127
Psychosocial interventions for people with both severe mental illness and substance misuse
Hunt, G. E., Siegfried, N., Morley, K. C., Sitharthan, T., & Cleary, M. L. (2013) · Cochrane Database of Systematic Reviews · doi:10.1002/14651858.cd001088.pub3 · Cited by 125
Practitioner Review: Psychological treatments for children and adolescents with conduct disorder problems – a systematic review and meta‐analysis
Bakker, M. J., Greven, C. U., Buitelaar, J. K., & Glennon, J. C. (2016) · Journal of Child Psychology and Psychiatry · doi:10.1111/jcpp.12590 · Cited by 125
Prediction of rTMS treatment response in major depressive disorder using machine learning techniques and nonlinear features of EEG signal
Hasanzadeh, F., Mohebbi, M., & Rostami, R. (2019) · Journal of Affective Disorders · doi:10.1016/j.jad.2019.05.070 · Cited by 124
Research Handbook on the Law of Artificial Intelligence
Pagallo, W. B., & Pagallo, U. (2018) · Edward Elgar Publishing eBooks · doi:10.4337/9781786439055 · Cited by 123
Recommendations for policy and practice of telepsychotherapy and e-mental health in Europe and beyond.
Van Daele, T., Karekla, M., Kassianos, A. P., Compare, A., Haddouk, L., Salgado, J., Ebert, D. D., Trebbi, G., Bernaerts, S., Van Assche, E., et al. (2020) · Journal of Psychotherapy Integration · doi:10.1037/int0000218 · Cited by 122
If we build it, will they come? Issues of engagement with digital health interventions for trauma recovery
Yeager, C. M., & Benight, C. C. (2018) · mHealth · doi:10.21037/mhealth.2018.08.04 · Cited by 120
Applied Research Design: A Practical Guide
Hedrick, T. E., Bickman, L., & Rog, D. J. (1993) · Cited by 120
Self-citation
Comparative outcomes of emotionally disturbed children and adolescents in a system of services and usual care
Bickman, L., Summerfelt, W. T., & Noser, K. (1997) · Psychiatric Services · doi:10.1176/ps.48.12.1543 · Cited by 119
Self-citation
Diagnosis of Human Psychological Disorders using Supervised Learning and Nature-Inspired Computing Techniques: A Meta-Analysis
Kaur, P., & Sharma, M. (2019) · Journal of Medical Systems · doi:10.1007/s10916-019-1341-2 · Cited by 119
Efficacy of repetitive transcranial magnetic stimulation in treatment‐resistant depression: the evidence thus far
Somani, A., & Kar, S. K. (2019) · General Psychiatry · doi:10.1136/gpsych-2019-100074 · Cited by 118
Neurobiological mechanisms of repetitive transcranial magnetic stimulation of the dorsolateral prefrontal cortex in depression: a systematic review
Noda, Y., Silverstein, W. K., Barr, M. S., Vila‐Rodriguez, F., Downar, J. H., Rajji, T. K., Fitzgerald, P. B., Mulsant, B. H., Vigod, S. N., Daskalakis, Z. J., et al. (2015) · Psychological Medicine · doi:10.1017/s0033291715001609 · Cited by 117
Predictors of Response to Repetitive Transcranial Magnetic Stimulation in Depression: A Review of Recent Updates
Kar, S. K. (2019) · Clinical Psychopharmacology and Neuroscience · doi:10.9758/cpn.2019.17.1.25 · Cited by 117
Sensing Technologies for Monitoring Serious Mental Illnesses
Abdullah, S., & Choudhury, T. (2018) · IEEE Multimedia · doi:10.1109/mmul.2018.011921236 · Cited by 116
Practice makes perfect and other myths about mental health services.
Bickman, L. (1999) · American Psychologist · doi:10.1037/h0088206 · Cited by 115
Self-citation
15 challenges for AI: Or what AI (currently) can’t do
Hagendorff, T., & Wezel, K. (2020) · AI & SOCIETY · doi:10.1007/s00146-019-00886-y · Cited by 113
The Fort Bragg continuum of care for children and adolescents: Mental health outcomes over 5 years.
Bickman, L., Lambert, E., de Andrade, A. R. V., & Peñaloza, R. V. (2000) · Journal of Consulting and Clinical Psychology · doi:10.1037/0022-006x.68.4.710 · Cited by 111
Self-citation
Emotion Recognition Using Smart Watch Sensor Data: Mixed-Design Study
Quiroz, J. C., Geangu, E., & Yong, M. H. (2018) · JMIR Mental Health · doi:10.2196/10153 · Cited by 110
Psychometrics of the Personal Questionnaire: A client-generated outcome measure.
Elliott, R., Wagner, J. A., Sales, C. M. D., Rodgers, B. C., Alves, P., & Café, M. J. (2015) · Psychological Assessment · doi:10.1037/pas0000174 · Cited by 109
A PET imaging approach for determining EGFR mutation status for improved lung cancer patient management
Sun, X., Xiao, Z., Chen, G., Han, Z., Liu, Y., Zhang, C., Sun, Y., Song, Y., Wang, K., Fang, F., et al. (2018) · Science Translational Medicine · doi:10.1126/scitranslmed.aan8840 · Cited by 108
The effects of routine outcome monitoring (ROM) on therapy outcomes in the course of an implementation process: A randomized clinical trial.
Brattland, H., Koksvik, J. M., Burkeland, O., Gråwe, R. W., Klöckner, C. A., Linaker, O. M., Ryum, T., Wampold, B. E., Lara‐Cabrera, M. L., & Iversen, V. C. (2018) · Journal of Counseling Psychology · doi:10.1037/cou0000286 · Cited by 106
Applied Research Design
Hedrick, T. E., Bickman, L., & Rog, D. J. (1993) · Sage eBooks · doi:10.4135/9781412983457 · Cited by 106
Self-citation
What do clinicians treat: Diagnoses or symptoms? The incremental validity of a symptom-based, dimensional characterization of emotional disorders in predicting medication prescription patterns
Waszczuk, M. A., Zimmerman, M., Ruggero, C., Li, K., MacNamara, A., Weinberg, A., Hajcak, G., Watson, D., & Kotov, R. (2017) · Comprehensive Psychiatry · doi:10.1016/j.comppsych.2017.04.004 · Cited by 106
Overview of the National Evaluation of the Comprehensive Community Mental Health Services for Children and Their Families Program
Holden, E. W., Friedman, R. M., & Santiago, R. L. (2001) · Journal of Emotional and Behavioral Disorders · doi:10.1177/106342660100900102 · Cited by 103
Development and Evaluation of ClientBot: Patient-Like Conversational Agent to Train Basic Counseling Skills
Tanana, M. J., Soma, C. S., Srikumar, V., Atkins, D. C., & Imel, Z. E. (2019) · Journal of Medical Internet Research · doi:10.2196/12529 · Cited by 100
Mechanism of Repetitive Transcranial Magnetic Stimulation for Depression.
Peng, Z., Zhou, C., Xue, S., Bai, J., Yu, S.-F., Li, X., Wang, H., & Tan, Q. (2018) · Europe PMC (PubMed Central) · doi:10.11919/j.issn.1002-0829.217047 · Cited by 100
Major developments in methods addressing for whom psychotherapy may work and why
Zilcha‐Mano, S. (2018) · Psychotherapy Research · doi:10.1080/10503307.2018.1429691 · Cited by 99
Achieving Precision Mental Health through Effective Assessment, Monitoring, and Feedback Processes
Bickman, L., Lyon, A. R., & Wolpert, M. (2016) · Administration and Policy in Mental Health and Mental Health Services Research · doi:10.1007/s10488-016-0718-5 · Cited by 99
Self-citation
Advancing the science and practice of precision education to enhance student outcomes
Cook, C. R., Kilgus, S. P., & Burns, M. K. (2017) · Journal of School Psychology · doi:10.1016/j.jsp.2017.11.004 · Cited by 97
Predicting Social Anxiety From Global Positioning System Traces of College Students: Feasibility Study
Boukhechba, M., Chow, P. I., Fua, K., Teachman, B. A., & Barnes, L. E. (2018) · JMIR Mental Health · doi:10.2196/10101 · Cited by 96
Technology-enhanced human interaction in psychotherapy.
Imel, Z. E., Caperton, D. D., Tanana, M., & Atkins, D. C. (2017) · Journal of Counseling Psychology · doi:10.1037/cou0000213 · Cited by 94
An Overview of Scientific Reproducibility: Consideration of Relevant Issues for Behavior Science/Analysis
Laraway, S., Snycerski, S., Pradhan, S., & Huitema, B. E. (2019) · Perspectives on Behavior Science · doi:10.1007/s40614-019-00193-3 · Cited by 93
Computer-Assisted Cognitive-Behavior Therapy and Mobile Apps for Depression and Anxiety
Wright, J. H., Mishkind, M., Eells, T. D., & Chan, S. R. (2019) · Current Psychiatry Reports · doi:10.1007/s11920-019-1031-2 · Cited by 93
Clinical or gimmickal: The use and effectiveness of mobile mental health apps for treating anxiety and depression
Marshall, J. M., Dunstan, D. A., & Bartik, W. J. (2019) · Australian & New Zealand Journal of Psychiatry · doi:10.1177/0004867419876700 · Cited by 93
Predicting Response to Repetitive Transcranial Magnetic Stimulation in Patients With Schizophrenia Using Structural Magnetic Resonance Imaging: A Multisite Machine Learning Analysis
Koutsouleris, N., Wobrock, T., Guse, B., Langguth, B., Landgrebe, M., Eichhammer, P., Frank, E., Cordes, J., Wölwer, W., Musso, F., et al. (2017) · Schizophrenia Bulletin · doi:10.1093/schbul/sbx114 · Cited by 88
Machine learning methods for developing precision treatment rules with observational data
Kessler, R. C., Bossarte, R. M., Luedtke, A. R., Zaslavsky, A. M., & Zubizarreta, J. R. (2019) · Behaviour Research and Therapy · doi:10.1016/j.brat.2019.103412 · Cited by 85
Opportunities for and Tensions Surrounding the Use of Technology-Enabled Mental Health Services in Community Mental Health Care
Lattie, E. G., Nicholas, J., Knapp, A. A., Skerl, J. J., Kaiser, S. M., & Mohr, D. C. (2019) · Administration and Policy in Mental Health and Mental Health Services Research · doi:10.1007/s10488-019-00979-2 · Cited by 85
R. A. Fisher and his advocacy of randomization
Hall, N. S. (2007) · Journal of the History of Biology · doi:10.1007/s10739-006-9119-z · Cited by 85
Big Data and the Precision Medicine Revolution
Hopp, W. J., Li, J., & Wang, G. (2018) · Production and Operations Management · doi:10.1111/poms.12891 · Cited by 84
The Effectiveness of Psychosocial Interventions Delivered by Teachers in Schools: A Systematic Review and Meta-Analysis
Franklin, C., Kim, J. S., Beretvas, S. N., Zhang, A., Guz, S., Park, S., Montgomery, K. L., Chung, S., & Maynard, B. R. (2017) · Clinical Child and Family Psychology Review · doi:10.1007/s10567-017-0235-4 · Cited by 83
Abandoning personalization to get to precision in the pharmacotherapy of depression
Perlis, R. H. (2016) · World Psychiatry · doi:10.1002/wps.20345 · Cited by 82
A systematic literature review of the clinical efficacy of repetitive transcranial magnetic stimulation (rTMS) in non-treatment resistant patients with major depressive disorder
Voigt, J. D., Carpenter, L. L., & Leuchter, A. F. (2019) · BMC Psychiatry · doi:10.1186/s12888-018-1989-z · Cited by 81
A machine learning ensemble to predict treatment outcomes following an Internet intervention for depression
Pearson, R., Pisner, D., Meyer, B., Shumake, J., & Beevers, C. G. (2019) · Psychological Medicine · doi:10.1017/s003329171800315x · Cited by 81
Predictors of longer‐term development of expressive language in two independent longitudinal cohorts of language‐delayed preschoolers with Autism Spectrum Disorder
Bal, V. H., Fok, M., Lord, C., Smith, I. M., Mirenda, P., Szatmári, P., Vaillancourt, T., Volden, J., Waddell, C., Zwaigenbaum, L., et al. (2019) · Journal of Child Psychology and Psychiatry · doi:10.1111/jcpp.13117 · Cited by 81
Machine learning methods to predict child posttraumatic stress: a proof of concept study
Saxe, G., Ma, S., Ren, J., & Aliferis, C. (2017) · BMC Psychiatry · doi:10.1186/s12888-017-1384-1 · Cited by 78
Moving Toward a Precision-Based, Personalized Framework for Prevention Science: Introduction to the Special Issue
August, G. J., & Gewirtz, A. H. (2018) · Prevention Science · doi:10.1007/s11121-018-0955-9 · Cited by 78

Flagged first, then by citations

Research works printed but not linked: 12

Bickman, L. (1995). The Fort Bragg demonstration project: A managed continuum of care. The Child, Youth, and Family Services Quarterly,18(3), 2–5.
Bickman, L., & Athay, M. I., (Eds.) (2012). Youth Mental Health Measurement (special issue) Administration and Policy in Mental Health and Mental Health Services Research, 39,1-2. ISSN: 0894-587X (Print) 1573-3289 (Online) · DOI
Bickman, L., & Rog, D. (Eds.). (2016). The evaluation handbook: An evaluator’s companion. New York: Guilford Press.
Heflinger, C. A., & Bickman, L. (1996). Family empowerment: A theoretically driven intervention and evaluation. In C. A. Heflinger & C. Nixon (Eds.), Families and mental health services for children and adolescents (pp. 96–116). Thousand Oaks: Sage.
Institute of Medicine. (2007). IOM Roundtable on evidence-based medicine: The learning healthcare system: Workshop summary. Washington, DC: National Academies Press.
King, R., & Bickman, L. (2017). Is there a future for therapists? Administration and Policy in Mental Health and Mental Health Services Research,44(5), 595–597. https://doi.org/10.1007/s10488-017-0814-1. · DOI
Kohler, S. (2018). Precision medicine: Moving away from one-size-fits-all. Quest: Science for South Africa, 14(3). Academy of Science of South Africa (ASSAf)). Available at https://hdl.handle.net/20.500.11911/103
Riemer, M., & Bickman, L. (2011). Using program theory to link social psychology and program evaluation. In M. M. Mark, S. I. Donaldson, & B. Campbell (Eds.), Social Psychology and Program/Policy Evaluation. New York: Guilford.
Stroul, B. A., & Friedman, R. (1986). A system of care for children and youth with severe emotional disturbances (Rev ed.). Washington, DC: CASSP Technical Assistance Center, Georgetown University Child Development Center.
Tan, J., Rollins, C. P. E., Israel, S., & Benrimoh, D. (2019). Primed for psychiatry: The role of artificial intelligence and machine learning in the optimization of depression treatment. University of Toronto Medical Journal,96(1), 43–47.
An understanding of AI’s limitations is starting to sink in. (2020). Technology Quarterly: Artificial Intelligence and Its Limits, The Economist.
Vinge, V. (1993). How to survive in the post-human era. In Interdisciplinary Science and Engineering in the Era of Cyberspace, Proceedings of the VISION-21 Symposium. NASA Conference Proceeding 10129. https://ntrs.nasa.gov/archive/nasa/casi.ntrs.nasa.gov/19940022855.pdf

Sorted by citations

The Lancet Psychiatry Commission on youth mental health
McGorry, P. D., Mei, C., Dalal, N., Alvarez-Jimenez, M., Blakemore, S.-J., Browne, V., Dooley, B., Hickie, I. B., Jones, P. B., McDaid, D., et al. (2024) · The Lancet Psychiatry · doi:10.1016/s2215-0366(24)00163-9 · Cited by 663
Modern views of machine learning for precision psychiatry
Chen, Z. S., Kulkarni, P., Galatzer‐Levy, I. R., Bigio, B., Nasca, C., & Zhang, Y. (2022) · Patterns · doi:10.1016/j.patter.2022.100602 · Cited by 247
“However, the amount of precision that can be modeled using ML is far beyond the first level.(181,182) During psychiatric evaluation, psychiatrists may try to build a mental model of what is going on in the patient’s life in about 30 min.”
Background
Artificial intelligence and social media on academic performance and mental well-being: student perceptions of positive impact in the age of smart learning
Shahzad, M. F., Xu, S., Lim, W. M., Yang, X., & Khan, Q. R. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e29523 · Cited by 205
“AI and social media have woven themselves deeply into the fabric of modern life, redefining paradigms in sectors ranging from education to interpersonal communication [128].”
Background
Artificial intelligence in mental health care: a systematic review of diagnosis, monitoring, and intervention applications
González, P. C., He, A., Lam, E. K. M., Ng, I. M. C., Li, M. W., Hou, R., Chan, J. N.-M., Sahni, Y., Viñas‐Guasch, N., Miller, T., et al. (2025) · Psychological Medicine · doi:10.1017/s0033291724003295 · Cited by 255
Deep learning and machine learning in psychiatry: a survey of current progress in depression detection, diagnosis and treatment
Squires, M., Tao, X., Elangovan, S., Gururajan, R., Zhou, X., Acharya, U. R., & Li, Y. (2023) · Brain Informatics · doi:10.1186/s40708-023-00188-6 · Cited by 188
“Thus, unsupervised algorithms can be used to identify groupings that transcend existing diagnostic labels [70].”
BackgroundInfluential

See all 184

Authors: Bickman, L.
Affiliations: Florida International University (US)
Corresponding author: Bickman, Leonard
ORCID: Bickman, Leonard
Venue: Administration and Policy in Mental Health and Mental Health Services Research
Publisher: Springer Science and Business Media LLC
Published: 2020-07-26
Volume: 47
Issue: 5
Pages: 795-843
ISSN: 0894-587X (ISSN-L)
1573-3289
DOI: 10.1007/s10488-020-01065-8
PMC: PMC7382706
OpenAlex: W3045482196
Cited by: 184
References: 353
Citations by year: 2026: 23 · 2025: 48 · 2024: 36 · 2023: 30 · 2022: 27 · 2021: 14 · 2020: 6
FWCI: 17.40
Type: journal article
Language: en
Open access: hybrid
doi.org
Licence: cc-by
www.springernature.com/gp/researchers/text-and-data-mining (tdm)
www.springernature.com/gp/researchers/text-and-data-mining (vor)
Locations: Administration and Policy in Mental Health and Mental Health Services Research (publishedVersion, cc-by)
PubMed Central (publishedVersion)
PubMed (publishedVersion)
Funders: National Institutes of Health
Johns Hopkins Bloomberg School of Public Health
Topics: Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
Social Sciences › Psychology › Clinical Psychology › Psychotherapy Techniques and Applications
Social Sciences › Psychology › Clinical Psychology › Psychiatric care and mental health services
Keywords: mental health services, artificial intelligence, precision mental health, personalized treatment, mental health services research, treatment effectiveness
MeSH terms: Artificial Intelligence, Health Services Research, Humans, Mental Health Services, Randomized Controlled Trials as Topic, Precision Medicine

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Improving Mental Health Services: A 50-Year Journey from Randomized Experiments to Artificial Intelligence and Precision Mental Health

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks