Artificial Intelligence for Clinical Prediction: Exploring Key Domains and Essential Functions

Artificial intelligence in radiology

Hosny, A., Parmar, C., Quackenbush, J., Schwartz, L. H., & Aerts, H. J. W. L. (2018) · Nature Reviews Cancer · doi:10.1038/s41568-018-0016-5 · Cited by 3,803

The potential for artificial intelligence in healthcare

Davenport, T., & Kalakota, R. (2019) · Future Healthcare Journal · doi:10.7861/futurehosp.6-2-94 · Cited by 3,800

Revolutionizing healthcare: the role of artificial intelligence in clinical practice

Alowais, S. A., Alghamdi, S. S., Alsuhebany, N., Alqahtani, T., Alshaya, A. I., Almohareb, S. N., Aldairem, A., Alrashed, M., Bin Saleh, K., Badreldin, H. A., et al. (2023) · BMC Medical Education · doi:10.1186/s12909-023-04698-z · Cited by 3,146

Artificial intelligence in healthcare: transforming the practice of medicine

Bajwa, J., Munir, U., Nori, A., & Williams, B. (2021) · Future Healthcare Journal · doi:10.7861/fhj.2021-0095 · Cited by 1,856

Precision medicine, AI, and the future of personalized health care

Johnson, K. B., Wei, W., Weeraratne, D., Frisse, M. E., Misulis, K., Rhee, K., Zhao, J., & Snowdon, J. L. (2021) · Clinical and Translational Science · doi:10.1111/cts.12884 · Cited by 1,854

Artificial intelligence: A powerful paradigm for scientific research

Xu, Y., Liu, X., Cao, X., Huang, C., Liu, E., Qian, S., Liu, X., Wu, Y., Dong, F., Qiu, C.-W., et al. (2021) · The Innovation · doi:10.1016/j.xinn.2021.100179 · Cited by 1,726

Artificial Intelligence in Pharmaceutical Technology and Drug Delivery Design

Vora, L. K., Gholap, A. D., Jetha, K. L., Singh, T. R. R., Solanki, H. K., & Chavda, V. P. (2023) · Pharmaceutics · doi:10.3390/pharmaceutics15071916 · Cited by 1,193

Artificial intelligence for mental health and mental illnesses: an overview

Graham, S., Depp, C., Lee, E. E., Nebeker, C., Tu, X., Kim, H.-C., & Jeste, D. V. (2019) · Current Psychiatry Reports · doi:10.1007/s11920-019-1094-0 · Cited by 970

A Review of the Role of Artificial Intelligence in Healthcare

Al Kuwaiti, A., Nazer, K., Al-Reedy, A., Al-Shehri, S., Al-Muhanna, A., Subbarayalu, A. V., Al Muhanna, D., & Al-Muhanna, F. A. (2023) · Journal of Personalized Medicine · doi:10.3390/jpm13060951 · Cited by 915

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

Significance of machine learning in healthcare: Features, pillars and applications

Javaid, M., Haleem, A., Singh, R. P., Suman, R., & Rab, S. (2022) · International Journal of Intelligent Networks · doi:10.1016/j.ijin.2022.05.002 · Cited by 760

Artificial intelligence in cancer diagnosis and prognosis: Opportunities and challenges

Huang, S., Yang, J., Fong, S. J., & Zhao, Q. (2019) · Cancer Letters · doi:10.1016/j.canlet.2019.12.007 · Cited by 724

Reporting of artificial intelligence prediction models

Collins, G. S., & Moons, K. G. M. (2019) · The Lancet · doi:10.1016/s0140-6736(19)30037-6 · Cited by 701

Artificial intelligence for diabetes management and decision support: Literature review

Contreras, I., & Vehi, J. (2018) · Journal of Medical Internet Research · doi:10.2196/10775 · Cited by 560

Artificial Intelligence and Personalized Medicine

Schork, N. J. (2019) · Cancer treatment and research · doi:10.1007/978-3-030-16391-4_11 · Cited by 531

Evaluation of artificial intelligence techniques in disease diagnosis and prediction

Nia, N. G., Kaplanoğlu, E., & Nasab, A. (2023) · Discover Artificial Intelligence · doi:10.1007/s44163-023-00049-5 · Cited by 498

Role of artificial intelligence in patient safety outcomes: systematic literature review

Choudhury, A., & Asan, O. (2020) · JMIR Medical Informatics · doi:10.2196/18599 · Cited by 440

A multilayer multimodal detection and prediction model based on explainable artificial intelligence for Alzheimer’s disease

El-Sappagh, S., Alonso, J. M., Islam, S. M. R., Sultan, A. M., & Kwak, K. S. (2021) · Scientific Reports · doi:10.1038/s41598-021-82098-3 · Cited by 363

Artificial intelligence in clinical medicine: catalyzing a sustainable global healthcare paradigm

Krishnan, G., Singh, S., Pathania, M., Gosavi, S., Abhishek, S., Parchani, A., & Dhar, M. (2023) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2023.1227091 · Cited by 339

Artificial intelligence in diabetes management: Advancements, opportunities, and challenges

Guan, Z., Li, H., Liu, R., Cai, C., Liu, Y., Li, J., Wang, X., Huang, S., Wu, L., Liu, D., et al. (2023) · Cell Reports Medicine · doi:10.1016/j.xcrm.2023.101213 · Cited by 287 · cited 1×

Artificial intelligence assists precision medicine in cancer treatment

Liao, J., Li, X., Gan, Y., Han, S., Rong, P., Wang, W., Li, W., & Zhou, L. P. (2023) · Frontiers in Oncology · doi:10.3389/fonc.2022.998222 · Cited by 280

Artificial intelligence predicts the progression of diabetic kidney disease using big data machine learning

Makino, M., Yoshimoto, R., Ono, M., Itoko, T., Katsuki, T., Koseki, A., Kudo, M., Haida, K., Kuroda, J., Yanagiya, R., et al. (2019) · Scientific Reports · doi:10.1038/s41598-019-48263-5 · Cited by 250

Predictive analytics in health care: how can we know it works?

Van Calster, B., Wynants, L., Timmerman, D., Steyerberg, E. W., & Collins, G. S. (2019) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocz130 · Cited by 223

Artificial intelligence supported patient self-care in chronic heart failure: a paradigm shift from reactive to predictive, preventive and personalised care

Barrett, M., Boyne, J. J., Brandts, J., Rocca, H. B., Maesschalck, L. D., Wit, K. D., Dixon, L., Eurlings, C., Fitzsimons, D., Golubnitschaja, O., et al. (2019) · The EPMA Journal · doi:10.1007/s13167-019-00188-9 · Cited by 221

Readmission prediction using deep learning on electronic health records

Ashfaq, A., Sant’Anna, A., Lingman, M., & Nowaczyk, S. (2019) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2019.103256 · Cited by 194

Priorities for successful use of artificial intelligence by public health organizations: a literature review

Fisher, S., & Rosella, L. C. (2022) · BMC Public Health · doi:10.1186/s12889-022-14422-z · Cited by 190

Revolutionizing the Early Detection of Alzheimer’s Disease through Non-Invasive Biomarkers: The Role of Artificial Intelligence and Deep Learning

Vrahatis, A. G., Skolariki, K., Krokidis, M. G., Lazaros, K., Exarchos, T. P., & Vlamos, P. (2023) · Sensors · doi:10.3390/s23094184 · Cited by 184

Can ChatGPT provide intelligent diagnoses? A comparative study between predictive models and ChatGPT to define a new medical diagnostic bot

Caruccio, L., Cirillo, S., Polese, G., Solimando, G., Sundaramurthy, S., & Tortora, G. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.121186 · Cited by 171

Using Machine Learning to Predict Complications in Pregnancy: A Systematic Review

Bertini, A., Salas, R., Chabert, S., Sobrevía, L., & Pardo, F. (2022) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2021.780389 · Cited by 167

Artificial Intelligence and Machine Learning in Prediction of Surgical Complications: Current State, Applications, and Implications

Hassan, A. M., Rajesh, A., Asaad, M., Nelson, J. A., Coert, J. H., Mehrara, B. J., & Butler, C. E. (2022) · The American Surgeon · doi:10.1177/00031348221101488 · Cited by 156

Interpretable deep learning model to predict the molecular classification of endometrial cancer from haematoxylin and eosin-stained whole-slide images: a combined analysis of the PORTEC randomised trials and clinical cohorts

Volinsky-Fremond, S., Andani, S., Wolf, J. B., Dijkstra, J., Melsbach, S., Jobsen, J. J., Brinkhuis, M., Roothaan, S. M., Jurgenliemk-Schulz, I. M., Lutgens, L. C. H. W., et al. (2022) · The Lancet Digital Health · doi:10.1016/s2589-7500(22)00210-2 · Cited by 150

Predicting the molecular subtype of breast cancer and identifying interpretable imaging features using machine learning algorithms

Ma, M., Liu, R., Wen, C., Xu, W., Xu, Z., Wang, S., Wu, J., Pan, D., Zheng, B., Qin, G., et al. (2021) · European Radiology · doi:10.1007/s00330-021-08271-4 · Cited by 130

Exploring the Intersection of Artificial Intelligence and Clinical Healthcare: A Multidisciplinary Review

Stafie, C. S., Șufaru, I.-G., Ghiciuc, C. M., Stafie, I.-I., Sufaru, E.-C., Solomon, S. M., & Hăncianu, M. (2023) · Diagnostics · doi:10.3390/diagnostics13121995 · Cited by 110

An Augmented Artificial Intelligence Approach for Chronic Diseases Prediction

Rashid, J., Batool, S., Kim, J., Nisar, M. W., Hussain, A., Juneja, S., & Kushwaha, R. (2022) · Frontiers in Public Health · doi:10.3389/fpubh.2022.860396 · Cited by 104

Development and validation of an interpretable machine learning-based calculator for predicting 5-year weight trajectories after bariatric surgery: a multinational retrospective cohort SOPHIA study

Saux, P., Bauvin, P., Raverdy, V., Teigny, J., Verkindt, H., Soumphonphakdy, T., Debert, M., Jacobs, A., Jacobs, D., Monpellier, V. M., et al. (2023) · The Lancet Digital Health · doi:10.1016/s2589-7500(23)00135-8 · Cited by 100

The prediction of surgical complications using artificial intelligence in patients undergoing major abdominal surgery: A systematic review

Stam, W. T., Goedknegt, L. K., Ingwersen, E. W., Schoonmade, L. J., Bruns, E. R. J., & Daams, F. D. (2021) · Surgery · doi:10.1016/j.surg.2021.10.002 · Cited by 95

Functional Outcome Prediction in Acute Ischemic Stroke Using a Fused Imaging and Clinical Deep Learning Model

Liu, Y., Yu, Y., Ouyang, J., Jiang, B., Yang, G., Ostmeier, S., Wintermark, M., Michel, P., Liebeskind, D. S., Lansberg, M. G., et al. (2023) · Stroke · doi:10.1161/strokeaha.123.044072 · Cited by 92

Implementation of Artificial Intelligence-Based Clinical Decision Support to Reduce Hospital Readmissions at a Regional Hospital

Brufau, S. R., Wyatt, K. D., Boyum, P., Mickelson, M. M., Moore, M., & Cognetta-Rieke, C. (2020) · Applied Clinical Informatics · doi:10.1055/s-0040-1715827 · Cited by 89

A Systematic Review of Transformer-Based Pre-Trained Language Models through Self-Supervised Learning

Kotei, E., & Thirunavukarasu, R. (2023) · Information · doi:10.3390/info14030187 · Cited by 89

A deep learning model integrating mammography and clinical factors facilitates the malignancy prediction of BI-RADS 4 microcalcifications in breast cancer screening

Liu, H., Chen, Y., Zhang, Y., Wang, L., Luo, R., Wu, H., Wu, C., Zhang, H., Tan, W., Yin, H., et al. (2021) · European Radiology · doi:10.1007/s00330-020-07659-y · Cited by 88

Artificial intelligence in prediction of mental health disorders induced by the COVID-19 pandemic among health care workers

Ćosić, K., Popović, S., Šarlija, M., Kesedžić, I., & Jovanović, T. (2020) · Croatian Medical Journal · doi:10.3325/cmj.2020.61.279 · Cited by 87

Development and Validation of a Deep Learning Method to Predict Cerebral Palsy From Spontaneous Movements in Infants at High Risk

Groos, D., Adde, L., Aubert, S., Boswell, L., de Regnier, R.-A. O., Fjørtoft, T., Gaebler-Spira, D. J., Haukeland, A., Loennecken, M. C., Msall, M. E., et al. (2022) · JAMA Network Open · doi:10.1001/jamanetworkopen.2022.21325 · Cited by 87

Development and testing of a random forest-based machine learning model for predicting events among breast cancer patients with a poor response to neoadjuvant chemotherapy

Jin, Y., Lan, A., Dai, Y., Jiang, L., & Liu, S. (2023) · European journal of medical research · doi:10.1186/s40001-023-01361-7 · Cited by 84

Prognostic prediction of hypertensive intracerebral hemorrhage using CT radiomics and machine learning

Xu, X., Zhang, J., Yang, K. G., Wang, Q., Chen, X., & Xu, B. (2021) · Brain and Behavior · doi:10.1002/brb3.2085 · Cited by 78

Towards computational solutions for precision medicine based big data healthcare system using deep learning models: A review

Thirunavukarasu, R., Doss, C. G. P., Ramanathan, G., Gopikrishnan, M., & Palanisamy, V. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.106020 · Cited by 75

Machine Learning for Predicting Readmission Risk Among the Frail - Explainable AI for Healthcare

Mohanty, S. D., Lekan, D. A., McCoy, T. P., Jenkins, M., & Manda, P. (2021) · Patterns · doi:10.1016/j.patter.2021.100395 · Cited by 70

Artificial intelligence in critical illness and its impact on patient care: a comprehensive review

Saqib, M., Iftikhar, M., Neha, F., Karishma, F., & Mumtaz, H. (2023) · Frontiers in Medicine · doi:10.3389/fmed.2023.1176192 · Cited by 68

Advanced machine learning for predicting individual risk of flares in rheumatoid arthritis patients tapering biologic drugs

on behalf of the RETRO study group, Vodenčarević, A., Taşçılar, K., Hartmann, F., Reiser, M., Hueber, A. J., Haschka, J., Bayat, S., Meinderink, T., Knitza, J., et al. (2021) · Arthritis Research & Therapy · doi:10.1186/s13075-021-02439-5 · Cited by 66

Artificial intelligence framework for predictive cardiovascular and stroke risk assessment models: A narrative review of integrated approaches using carotid ultrasound

Jamthikar, A. D., Gupta, D., Saba, L., Khanna, N. N., Višković, K., Mavrogeni, S. I., Laird, J. R., Sattar, N. A., Johri, A. M., Pareek, G., et al. (2020) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2020.104043 · Cited by 64

Development and validation of MRI‐based deep learning models for prediction of microsatellite instability in rectal cancer

Zhang, W., Yin, H., Huang, Z., Zhao, J., Zheng, H., He, D., Li, M., Tan, W., Tian, S., & Song, B. (2021) · Cancer Medicine · doi:10.1002/cam4.3957 · Cited by 63

Prediction of Coronary Stent Underexpansion by Pre-Procedural Intravascular Ultrasound–Based Deep Learning

Min, H., Ryu, D., Kang, S.-J., Lee, J., Yoo, J. H., Cho, H., Kang, D., Lee, P. H., Ahn, J., Park, D., et al. (2021) · JACC: Cardiovascular Interventions · doi:10.1016/j.jcin.2021.01.033 · Cited by 61

Clinical Application of Machine Learning-Based Artificial Intelligence in the Diagnosis, Prediction, and Classification of Cardiovascular Diseases

Shu, S., Ren, J., & Song, J. (2021) · Circulation Journal · doi:10.1253/circj.cj-20-1121 · Cited by 60

Machine-learning model derived gene signature predictive of paclitaxel survival benefit in gastric cancer: results from the randomised phase III SAMIT trial

Sundar, R., Kumarakulasinghe, N. B., Chan, Y. H., Yoshida, K., Yoshikawa, T., Miyagi, Y., Rino, Y., Masuda, M., Guan, J., Sakamoto, J., et al. (2021) · Gut · doi:10.1136/gutjnl-2021-324060 · Cited by 59

Development and validation of a preoperative CT-based radiomic nomogram to predict pathology invasiveness in patients with a solitary pulmonary nodule: a machine learning approach, multicenter, diagnostic study

Huang, L., Lin, W., Xie, D., Yu, Y., Cao, H., Liao, G., Wu, S., Yao, L., Wang, Z., Wang, M., et al. (2021) · European Radiology · doi:10.1007/s00330-021-08268-z · Cited by 54

AI-based clinical decision-making systems in palliative medicine: ethical challenges

Panfilis, L. D., Peruselli, C., Tanzi, S., & Botrugno, C. (2021) · BMJ Supportive & Palliative Care · doi:10.1136/bmjspcare-2021-002948 · Cited by 52

Machine-learning based prediction of prognostic risk factors in patients with invasive candidiasis infection and bacterial bloodstream infection: a singled centered retrospective study

Li, Y., Wu, Y., Gao, Y., Niu, X., Li, J., Tang, M., Fu, C., Qi, R., Song, B., Chen, H., et al. (2022) · BMC Infectious Diseases · doi:10.1186/s12879-022-07125-8 · Cited by 52

Development and validation of a deep learning signature for predicting lymph node metastasis in lung adenocarcinoma: comparison with radiomics signature and clinical-semantic model

Ma, X., Xia, L., Chen, J., Wan, W., & Zhou, W. Y. (2022) · European Radiology · doi:10.1007/s00330-022-09153-z · Cited by 52

Machine‐learning radiomics to predict early recurrence in perihilar cholangiocarcinoma after curative resection

Huan, Q., Hu, X., Zhang, J., Dai, H., He, Y., Zhao, Z., Yang, J., Xu, Z., Hu, X., & Chen, Z. (2020) · Liver International · doi:10.1111/liv.14763 · Cited by 48

Machine learning algorithm outperforms fibrosis markers in predicting significant fibrosis in biopsy‐confirmed NAFLD

Feng, G., Zheng, K. I., Li, Y., Rios, R. S., Zhu, P., Pan, X., Li, G., Ma, H., Tang, L., Byrne, C. D., et al. (2021) · Journal of Hepato-Biliary-Pancreatic Sciences · doi:10.1002/jhbp.972 · Cited by 48

Diagnostic Accuracy and Performance of Artificial Intelligence in Detecting Lung Nodules in Patients With Complex Lung Disease

Abadia, A. F., Yacoub, B., Stringer, N., Snoddy, M., Kocher, M., Schoepf, U. J., Aquino, G. J., Kabakus, I. M., Dargis, D. M., Hoelzer, P., et al. (2021) · Journal of Thoracic Imaging · doi:10.1097/rti.0000000000000613 · Cited by 47

Machine learning v. traditional regression models predicting treatment outcomes for binge-eating disorder from a randomized controlled trial

Forrest, L. N., Ivezaj, V., & Grilo, C. M. (2021) · Psychological Medicine · doi:10.1017/s0033291721004748 · Cited by 47

Machine learning analysis for the noninvasive prediction of lymphovascular invasion in gastric cancer using PET/CT and enhanced CT-based radiomics and clinical variables

Fan, L., Li, J., Zhang, H., Yin, H., Zhang, R., Zhang, J., & Chen, X. (2022) · Abdominal Radiology · doi:10.1007/s00261-021-03315-1 · Cited by 47

Dose prediction via distance-guided deep learning: Initial development for nasopharyngeal carcinoma radiotherapy

Yue, M., Xue, X., Wang, Z., Lambo, R. L., Zhao, W., Xie, Y., Cai, J., & Qin, W. (2022) · Radiotherapy and Oncology · doi:10.1016/j.radonc.2022.03.012 · Cited by 46

Construction and validation of machine learning models for sepsis prediction in patients with acute pancreatitis

Liu, F., Yao, J. J., Liu, C., & Shou, S. (2023) · BMC Surgery · doi:10.1186/s12893-023-02151-y · Cited by 46

Imaging and artificial intelligence for progression of age-related macular degeneration

Romond, K., Alam, M. N., Kravets, S., de Sisternes, L., Leng, T., Lim, J. I., Rubin, D. L., & Hallak, J. (2021) · Experimental Biology and Medicine · doi:10.1177/15353702211031547 · Cited by 45

Machine learning models predict overall survival and progression free survival of non-surgical esophageal cancer patients with chemoradiotherapy based on CT image radiomics signatures

Cui, Y., Li, Z., Xiang, M., Han, D., Yin, Y., & Ma, C. (2022) · Radiation Oncology · doi:10.1186/s13014-022-02186-0 · Cited by 44

Construction and validation of nomograms combined with novel machine learning algorithms to predict early death of patients with metastatic colorectal cancer

Zhang, Y., Zhang, Z., Wei, L., & Wei, S. (2022) · Frontiers in Public Health · doi:10.3389/fpubh.2022.1008137 · Cited by 41

Comparison of Severity of Illness Scores and Artificial Intelligence Models That Are Predictive of Intensive Care Unit Mortality: Meta-analysis and Review of the Literature

Barboi, C., Tzavelis, A., & Muhammad, L. N. (2022) · JMIR Medical Informatics · doi:10.2196/35293 · Cited by 39

Predicting Chronic Myocardial Ischemia Using CCTA-Based Radiomics Machine Learning Nomogram

Shu, Z.-Y., Cui, S., Zhang, Y.-Q., Xu, Y.-Y., Hung, S.-C., Fu, L., Pang, P., Gong, X.-Y., & Jin, Q.-Y. (2020) · Journal of Nuclear Cardiology · doi:10.1007/s12350-020-02204-2 · Cited by 37

Clinical intelligence: New machine learning techniques for predicting clinical drug response

Turki, T., & Wang, J. T. L. (2019) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2018.12.017 · Cited by 33

Interpreting Deep Machine Learning Models: An Easy Guide for Oncologists

Amorim, J. P., Abreu, P. H., Fernández, A., Reyes, M., Santos, J., & Abreu, M. H. (2021) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2021.3131358 · Cited by 33

Development and validation of machine learning-based predictive model for clinical outcome of decompression surgery for lumbar spinal canal stenosis

Yagi, M., Michikawa, T., Yamamoto, T., Iga, T., Ogura, Y., Tachibana, A., Miyamoto, A., Suzuki, S. O., Nori, S., Takahashi, Y., et al. (2022) · The Spine Journal · doi:10.1016/j.spinee.2022.06.008 · Cited by 32

Predicting outcomes in older ED patients with influenza in real time using a big data-driven and machine learning approach to the hospital information system

Tan, T.-H., Hsu, C., Chen, C., Hsu, S., Liu, T.-L., Lin, H., Wang, J., Liu, C., & Huang, C. (2021) · BMC Geriatrics · doi:10.1186/s12877-021-02229-3 · Cited by 31

Predicting N2 lymph node metastasis in presurgical stage I‐II non‐small cell lung cancer using multiview radiomics and deep learning method

Zhang, H., Liao, M., Guo, Q., Chen, J., Wang, S., Liu, S., & Xiao, F. (2022) · Medical Physics · doi:10.1002/mp.16177 · Cited by 31

Machine learning–based random forest for predicting decreased quality of life in thyroid cancer patients after thyroidectomy

Liu, Y. H., Jin, J., & Liu, Y. J. (2021) · Supportive Care in Cancer · doi:10.1007/s00520-021-06657-0 · Cited by 30

Using Machine-Learning for Prediction of the Response to Cardiac Resynchronization Therapy

Howell, S. J., Stivland, T., Steín, K. M., Ellenbogen, K. A., & Tereshchenko, L. G. (2021) · JACC. Clinical electrophysiology · doi:10.1016/j.jacep.2021.06.009 · Cited by 29

Combining machine learning with radiomics features in predicting outcomes after mechanical thrombectomy in patients with acute ischemic stroke

Li, Y., Liu, Y., Hong, Z., Wang, Y., & Lu, X. (2022) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2022.107093 · Cited by 29

A DWI-based radiomics-clinical machine learning model to preoperatively predict the futile recanalization after endovascular treatment of acute basilar artery occlusion patients

Luo, Y., Sun, X., Kong, X., Tong, X., Xi, F., Yu, M., Miao, Z., & Ma, J. (2023) · European Journal of Radiology · doi:10.1016/j.ejrad.2023.110731 · Cited by 28

Prediction of repeated-dose intravenous ketamine response in major depressive disorder using the GWAS-based machine learning approach

Bao, Z., Zhao, X., Li, J., Zhang, G., Wu, H., Ning, Y., Li, M. D., & Yang, Z. (2021) · Journal of Psychiatric Research · doi:10.1016/j.jpsychires.2021.04.014 · Cited by 27

Deep learning for predicting the risk of immune checkpoint inhibitor-related pneumonitis in lung cancer

Cheng, M., Lin, R., Bai, N., Zhang, Y., Wang, H., Guo, M.-Z., Duan, X., Zheng, J., Qiu, Z., & Zhao, Y. (2023) · Clinical Radiology · doi:10.1016/j.crad.2022.12.013 · Cited by 27

Using deep learning to predict survival outcome in non-surgical cervical cancer patients based on pathological images

Zhang, K., Sun, K., Zhang, C., Ren, K., Li, C., Shen, L., & Jing, D. (2023) · Journal of Cancer Research and Clinical Oncology · doi:10.1007/s00432-022-04446-8 · Cited by 27

Improving palliative care with machine learning and routine data: a rapid review

Storick, V., O’Herlihy, A., Abdelhafeez, S., Ahmed, R., & May, P. (2019) · HRB Open Research · doi:10.12688/hrbopenres.12923.2 · Cited by 26

Exploratory application of machine learning methods on patient reported data in the development of supervised models for predicting outcomes

Verma, D., Jansen, D., Bach, K., Poel, M., Mork, P. J., & Nijeweme-d’Hollosy, W. O. (2022) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-022-01973-9 · Cited by 26

Using machine learning methods to predict 28-day mortality in patients with hepatic encephalopathy

Zhang, Z., Wang, J., Han, W., & Li, Z. (2023) · BMC Gastroenterology · doi:10.1186/s12876-023-02753-z · Cited by 26

Application of machine learning algorithms in electronic medical records to predict amputation-free survival after first revascularization in patients with peripheral artery disease

Liu, Y., Xue, J.-S., & Jiang, J. (2023) · International Journal of Cardiology · doi:10.1016/j.ijcard.2023.04.040 · Cited by 26

Deep learning-based postoperative visual acuity prediction in idiopathic epiretinal membrane

Wen, D., Yu, Z., Yang, Z., Zheng, C., Shao, Y., Ren, X., Gu, T., & Li, X. (2023) · BMC Ophthalmology · doi:10.1186/s12886-023-03079-w · Cited by 25

Design of an interface to communicate artificial intelligence-based prognosis for patients with advanced solid tumors: a user-centered approach

Staes, C. J., Beck, A. C., Chalkidis, G., Scheese, C. H., Taft, T., Guo, J., Newman, M. G., Kawamoto, K., Sloss, E. A., & McPherson, J. P. (2023) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocad201 · Cited by 25

Deep Learning Radiomics Nomogram Based on Enhanced CT to Predict the Response of Metastatic Lymph Nodes to Neoadjuvant Chemotherapy in Locally Advanced Gastric Cancer

Zhong, H., Wang, T., Hou, M., Liu, X., Tian, Y., Cao, S., Li, Z., Han, Z., Liu, G., Sun, Y., et al. (2023) · Annals of Surgical Oncology · doi:10.1245/s10434-023-14424-0 · Cited by 25

Development and validation of a combined nomogram model based on deep learning contrast-enhanced ultrasound and clinical factors to predict preoperative aggressiveness in pancreatic neuroendocrine neoplasms

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