Learning vector representation of medical objects via EMR-driven nonnegative restricted Boltzmann machines (eNRBM)

LIBSVM: A library for support vector machines

Chang, C.-C., & Lin, C.-J. (2011) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/1961189.1961199 · Cited by 40,918 · see citing works

Untitled work

(2008) · Journal of Machine Learning Research · Cited by 33,051 · see citing works

Latent dirichlet allocation

(2003) · Journal of Machine Learning Research · doi:10.5555/944919.944937 · Cited by 32,337 · see citing works

Regularization and Variable Selection Via the Elastic Net

Zou, H., & Hastie, T. (2005) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.1467-9868.2005.00503.x · Cited by 20,957 · see citing works

Reducing the dimensionality of data with neural networks

Hinton, G. E., & Salakhutdinov, R. R. (2006) · Science · doi:10.1126/science.1127647 · Cited by 20,928 · see citing works

Representation Learning: A Review and New Perspectives

Bengio, Y., Courville, A., & Vincent, P. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.50 · Cited by 13,001 · see citing works

Comorbidity measures for use with administrative data.

Elixhauser, A., Steiner, C., Harris, D. R., & Coffey, R. M. (1998) · Medical Care · doi:10.1097/00005650-199801000-00004 · Cited by 9,921 · see citing works

Training Products of Experts by Minimizing Contrastive Divergence

Hinton, G. E. (2002) · Neural Computation · doi:10.1162/089976602760128018 · Cited by 4,879 · see citing works

Factor analysis in the development and refinement of clinical assessment instruments.

Floyd, F. J., & Widaman, K. F. (1995) · Psychological Assessment · doi:10.1037/1040-3590.7.3.286 · Cited by 3,970 · see citing works

No health without mental health

Prince, M. J., Patel, V., Saxena, S., Maj, M., Maselko, J., Phillips, M. R., & Rahman, A. (2007) · The Lancet · doi:10.1016/s0140-6736(07)61238-0 · Cited by 3,835 · see citing works

Psychological stress and the human immune system: a meta-analytic study of 30 Years of inquiry

Segerstrom, S. C., & Miller, G. E. (2004) · Psychological Bulletin · doi:10.1037/0033-2909.130.4.601 · Cited by 3,334 · see citing works

From Frequency to Meaning: Vector Space Models of Semantics

Turney, P. D., & Pantel, P. (2010) · Journal of Artificial Intelligence Research · doi:10.1613/jair.2934 · Cited by 2,845 · see citing works

Mining electronic health records: towards better research applications and clinical care

Jensen, P. B., Jensen, L. J., & Brunak, S. (2012) · Nature Reviews Genetics · doi:10.1038/nrg3208 · Cited by 1,680 · see citing works

Prevalence, Correlates, and Treatment of Lifetime Suicidal Behavior Among Adolescents: Results From the National Comorbidity Survey Replication Adolescent Supplement

Nock, M. K., Green, J. G., Hwang, I., McLaughlin, K. A., Sampson, N. A., Zaslavsky, A. M., & Kessler, R. C. (2013) · JAMA Psychiatry · doi:10.1001/2013.jamapsychiatry.55 · Cited by 1,666 · see citing works

Optimizing Semantic Coherence in Topic Models

Mimno, D., Wallach, H., Talley, E. M., Leenders, M., & McCallum, A. (2011) · ScholarWorks@UMassAmherst (University of Massachusetts Amherst) · Cited by 1,566 · see citing works

Risk factors for suicide in psychiatric outpatients: A 20-year prospective study.

Brown, G. K., Beck, A. T., Steer, R. A., & Grisham, J. R. (2000) · Journal of Consulting and Clinical Psychology · doi:10.1037/0022-006x.68.3.371 · Cited by 1,317 · see citing works

Relationships between obesity and DSM-IV major depressive disorder, suicide ideation, and suicide attempts: results from a general population study

Carpenter, K. M., Hasin, D. S., Allison, D. B., & Faith, M. S. (2000) · American Journal of Public Health · doi:10.2105/ajph.90.2.251 · Cited by 1,018 · see citing works

Assessing Quality Using Administrative Data

Iezzoni, L. I. (1997) · Annals of Internal Medicine · doi:10.7326/0003-4819-127-8_part_2-199710151-00048 · Cited by 981 · see citing works

Is Obesity Associated with Major Depression? Results from the Third National Health and Nutrition Examination Survey

Onyike, C. U. (2003) · American Journal of Epidemiology · doi:10.1093/aje/kwg275 · Cited by 827 · see citing works

Depression and obesity

Stunkard, A. J., Faith, M. S., & Allison, K. C. (2003) · Biological Psychiatry · doi:10.1016/s0006-3223(03)00608-5 · Cited by 820 · see citing works

Twelve-Month Prevalence of and Risk Factors for Suicide Attempts in the World Health Organization World Mental Health Surveys

Borges, G., Nock, M. K., Haro Abad, J. M., Hwang, I., Sampson, N. A., Alonso, J., Andrade, L. H., Angermeyer, M. C., Beautrais, A., Bromet, E., et al. (2010) · The Journal of Clinical Psychiatry · doi:10.4088/jcp.08m04967blu · Cited by 796 · see citing works

Next-generation phenotyping of electronic health records

Hripcsak, G., & Albers, D. J. (2013) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2012-001145 · Cited by 795 · see citing works

Validity of Information on Comorbidity Derived From ICD-9-CCM Administrative Data

Quan, H., Parsons, G. A., & Ghali, W. A. (2002) · Medical Care · doi:10.1097/00005650-200208000-00007 · Cited by 575 · see citing works

A Nursing Model for Chronic Illness Management Based Upon the Trajectory Framework

Corbin, J. M., & Strauss, A. (1991) · Research and theory for nursing practice · doi:10.1891/0889-7182.5.3.155 · Cited by 400 · see citing works

Stress-Induced Immune Dysregulation: Implications for Wound Healing, Infectious Disease and Cancer

Godbout, J. P., & Glaser, R. (2006) · Journal of Neuroimmune Pharmacology · doi:10.1007/s11481-006-9036-0 · Cited by 385 · see citing works

Bayesian networks in biomedicine and health-care

Lucas, P., van der Gaag, L. C., & Abu–Hanna, A. (2004) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2003.11.001 · Cited by 336 · see citing works

Computational Phenotype Discovery Using Unsupervised Feature Learning over Noisy, Sparse, and Irregular Clinical Data

Lasko, T. A., Denny, J. C., & Levy, M. A. (2013) · PLoS ONE · doi:10.1371/journal.pone.0066341 · Cited by 313 · see citing works

Multimorbidity Patterns in Primary Care: Interactions among Chronic Diseases Using Factor Analysis

Prados‐Torres, A., Poblador‐Plou, B., Calderón‐Larrañaga, A., Gimeno-Feliú, L. A., González‐Rubio, F., Poncel-Falcó, A., Sicras‐Mainar, A., & Nalvaiz, J. T. A. (2012) · PLoS ONE · doi:10.1371/journal.pone.0032190 · Cited by 280 · see citing works

Unsupervised learning of distributions on binary vectors using two layer networks

Freund, Y., & Haussler, D. H. (1991) · Cited by 267 · see citing works

Charlson scores based on ICD-10 administrative data were valid in assessing comorbidity in patients undergoing urological cancer surgery

Nuttall, M., van der Meulen, J., & Emberton, M. (2006) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2005.07.015 · Cited by 219 · see citing works

Suicidal behavior in bipolar disorder: Epidemiology, characteristics and major risk factors

Gonda, X., Pompili, M., Serafini, G., Montebovi, F., Campi, S., Döme, P., Duleba, T., Girardi, P., & Rihmer, Z. (2012) · Journal of Affective Disorders · doi:10.1016/j.jad.2012.04.041 · Cited by 219 · see citing works

Restricted Boltzmann machines for neuroimaging: An application in identifying intrinsic networks

Hjelm, R. D., Calhoun, V. D., Salakhutdinov, R., Allen, E. A., Adalı, T., & Plis, S. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.03.048 · Cited by 152 · see citing works

Risk stratification using data from electronic medical records better predicts suicide risks than clinician assessments

Tran, T., Luo, W., Phung, D., Harvey, R., Berk, M., Kennedy, R. L., & Venkatesh, S. (2014) · BMC Psychiatry · doi:10.1186/1471-244x-14-76 · Cited by 150 · see citing works

Temporal abstraction in intelligent clinical data analysis: A survey

Stacey, M., & McGregor, C. (2007) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2006.08.002 · Cited by 146 · see citing works

Classical latent variable models for medical research

Rabe‐Hesketh, S., & Skrondal, A. (2007) · Statistical Methods in Medical Research · doi:10.1177/0962280207081236 · Cited by 142 · see citing works

Clinical Decisions in Psychiatry Should Not Be Based On Risk Assessment

Ryan, C. J., Nielssen, O. B., Paton, M. B., & Large, M. M. B. (2010) · Australasian Psychiatry · doi:10.3109/10398562.2010.507816 · Cited by 118 · see citing works

Mining high-dimensional administrative claims data to predict early hospital readmissions

He, D., Mathews, S. C., Kalloo, A. N., & Hutfless, S. M. (2013) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2013-002151 · Cited by 91 · see citing works

Data-driven approach for creating synthetic electronic medical records

Buczak, A. L., Babin, S. M., & Moniz, L. J. (2010) · BMC Medical Informatics and Decision Making · doi:10.1186/1472-6947-10-59 · Cited by 84 · see citing works

The validity and utility of risk assessment for inpatient suicide

Large, M., Ryan, C., & Nielssen, O. (2011) · Australasian Psychiatry · doi:10.3109/10398562.2011.610505 · Cited by 73 · see citing works

Clinical and epidemiological aspects of suicide in patients with schizophrenia.

Gómez‐Durán, E. L., Martín-Fumadó, C., & Hurtado-Ruíz, G. (2013) · PubMed · Cited by 41 · see citing works

Stabilized sparse ordinal regression for medical risk stratification

Tran, T., Phung, D., Luo, W., & Venkatesh, S. (2015) · Deakin Research Online (Deakin University) · Cited by 33 · see citing works

Continuous time Bayesian network classifiers

Stella, F. A., & Amer, Y. (2012) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2012.07.002 · Cited by 32 · see citing works

Mixed-Variate Restricted Boltzmann MachinesPreprint

Tran, T., Phung, D., & Venkatesh, S. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1408.1160 · Cited by 31 · see citing works

Learning Parts-based Representations with Nonnegative Restricted Boltzmann Machine

Nguyen, T. D., Tran, T., Phung, D., et al. (2013) · Cited by 27 · see citing works

Bayesian nonparametric comorbidity analysis of psychiatric disordersPreprint

Ruiz, F. J. R., Valera, I., Blanco, C., & Pérez‐Cruz, F. (2014) · arXiv (Cornell University) · doi:10.5555/2627435.2638574 · Cited by 25 · see citing works

Using electronic medical records to predict mortality in primary care patients with heart disease

Tierney, W. M., Takesue, B. Y., Vargo, D. L., & Zhou, X. (1996) · Journal of General Internal Medicine · doi:10.1007/bf02599583 · Cited by 23 · see citing works

Latent Patient Profile Modelling and Applications with Mixed-Variate Restricted Boltzmann Machine

Nguyen, T. D., Tran, T., Phung, D., & Venkatesh, S. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-37453-1_11 · Cited by 15 · see citing works

Thurstonian Boltzmann Machines: Learning from Multiple InequalitiesPreprint

Tran, T., Phung, D., & Venkatesh, S. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1408.0055 · Cited by 15 · see citing works

Deep learning for healthcare: review, opportunities and challenges

Miotto, R., Wang, F., Wang, S., Jiang, X., & Dudley, J. T. (2018) · Briefings in Bioinformatics · doi:10.1093/bib/bbx044 · Cited by 3,076 · see citing works

Deep EHR: A Survey of Recent Advances in Deep Learning Techniques for Electronic Health Record (EHR) Analysis

Shickel, B., Tighe, P. J., Bihorac, A., & Rashidi, P. (2018) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2017.2767063 · Cited by 1,688 · see citing works

Opportunities and challenges in developing deep learning models using electronic health records data: a systematic review

Xiao, C., Choi, E., & Sun, J. (2018) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocy068 · Cited by 792 · see citing works

BEHRT: Transformer for Electronic Health Records

Li, Y., Rao, S., Solares, J. R. A., Hassaine, A., Ramakrishnan, R., Canoy, D., Zhu, Y., Rahimi, K., & Salimi-Khorshidi, G. (2020) · Scientific Reports · doi:10.1038/s41598-020-62922-y · Cited by 595 · see citing works

Patient clustering improves efficiency of federated machine learning to predict mortality and hospital stay time using distributed electronic medical records

Huang, L., Shea, A. L., Qian, H., Masurkar, A., Deng, H., & Liu, D. (2019) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2019.103291 · Cited by 437 · see citing works

Predicting healthcare trajectories from medical records: a deep learning approach

Pham, T., Tran, T., Phung, D., & Venkatesh, S. (2017) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2017.04.001 · Cited by 447 · see citing works

DeepCare: A Deep Dynamic Memory Model for Predictive Medicine

Pham, T., Tran, T., Phung, D., & Venkatesh, S. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-31750-2_3 · Cited by 269 · see citing works

Deep learning for electronic health records: A comparative review of multiple deep neural architectures

Ayala Solares, J. R., Diletta Raimondi, F. E., Zhu, Y., Rahimian, F., Canoy, D., Tran, J., Pinho Gomes, A. C., Payberah, A. H., Zottoli, M., Nazarzadeh, M., et al. (2020) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2019.103337 · Cited by 247 · see citing works

Deep representation learning of patient data from Electronic Health Records (EHR): A systematic review

Si, Y., Du, J., Li, Z., Jiang, X., Miller, T., Wang, F., Jim Zheng, W., & Roberts, K. (2021) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2020.103671 · Cited by 235 · see citing works

MedGCN: Medication recommendation and lab test imputation via graph convolutional networks

Mao, C., Yao, L., & Luo, Y. (2022) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2022.104000 · Cited by 70 · see citing works

EMR-based medical knowledge representation and inference via Markov random fields and distributed representation learning

Zhao, C., Jiang, J., Guan, Y., Guo, X., & He, B. (2018) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2018.03.005 · Cited by 41 · see citing works

Harmonized representation learning on dynamic EHR graphs

Lee, D., Jiang, X., & Yu, H. (2020) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2020.103426 · Cited by 34 · see citing works

Multi-modal Knowledge-aware Hierarchical Attention Network for Explainable Medical Question Answering

Zhang, Y., Qian, S., Fang, Q., & Xu, C. (2019) · Proceedings of the 27th ACM International Conference on Multimedia · doi:10.1145/3343031.3351033 · Cited by 33 · see citing works

Outperforming RBM Feature-Extraction Capabilities by “Dreaming” Mechanism

Fachechi, A., Adriano Barra, A., Agliari, E., & Alemanno, F. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3182882 · Cited by 29 · see citing works

An Overview of Restricted Boltzmann Machines

Upadhya, V., & SASTRY, P. S. (2019) · Journal of the Indian Institute of Science · doi:10.1007/s41745-019-0102-z · Cited by 42 · see citing works

On the equivalence of Hopfield Networks and Boltzmann MachinesPreprint

Barra, A., Bernacchia, A., Santucci, E., et al. (2011) · arXiv (Cornell University) · doi:10.48550/arxiv.1105.2790 · Cited by 1 · see citing works

Approximate Learning Algorithm for Restricted Boltzmann Machines

Yasuda, M., & Tanaka, K. (2008) · doi:10.1109/cimca.2008.57 · Cited by 7 · see citing works

Deep Restricted Boltzmann NetworksPreprint

Hu, H., Gao, L., & Ma, Q. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1611.07917 · Cited by 9 · see citing works