Published on in Vol 18, No 12 (2016): December

Guidelines for Developing and Reporting Machine Learning Predictive Models in Biomedical Research: A Multidisciplinary View

Guidelines for Developing and Reporting Machine Learning Predictive Models in Biomedical Research: A Multidisciplinary View

Guidelines for Developing and Reporting Machine Learning Predictive Models in Biomedical Research: A Multidisciplinary View

Journals

  1. Thomas Homescu A. Leveraging Big Data for Personalized Treatment of Anxiety and Depression: Review and Possible Future Directions. SSRN Electronic Journal 2018 View
  2. Park S, Kim Y, Lee J, Yoo S, Kim C. Ethical challenges regarding artificial intelligence in medicine from the perspective of scientific editing and peer review. Science Editing 2019;6(2):91 View
  3. Kim D, Jang H, Kim K, Shin Y, Park S. Design Characteristics of Studies Reporting the Performance of Artificial Intelligence Algorithms for Diagnostic Analysis of Medical Images: Results from Recently Published Papers. Korean Journal of Radiology 2019;20(3):405 View
  4. Khan O, Badhiwala J, Wilson J, Jiang F, Martin A, Fehlings M. Predictive Modeling of Outcomes After Traumatic and Nontraumatic Spinal Cord Injury Using Machine Learning: Review of Current Progress and Future Directions. Neurospine 2019;16(4):678 View
  5. Coleman B, Fodeh S, Lisi A, Goulet J, Corcoran K, Bathulapalli H, Brandt C. Exploring supervised machine learning approaches to predicting Veterans Health Administration chiropractic service utilization. Chiropractic & Manual Therapies 2020;28(1) View
  6. Tandon N, Tandon R. Using machine learning to explain the heterogeneity of schizophrenia. Realizing the promise and avoiding the hype. Schizophrenia Research 2019;214:70 View
  7. Mathis M, Engoren M, Joo H, Maile M, Aaronson K, Burns M, Sjoding M, Douville N, Janda A, Hu Y, Najarian K, Kheterpal S. Early Detection of Heart Failure With Reduced Ejection Fraction Using Perioperative Data Among Noncardiac Surgical Patients: A Machine-Learning Approach. Anesthesia & Analgesia 2020;130(5):1188 View
  8. Tandon N, Tandon R. Machine learning in psychiatry- standards and guidelines. Asian Journal of Psychiatry 2019;44:A1 View
  9. Karhade A, Shah A, Bono C, Ferrone M, Nelson S, Schoenfeld A, Harris M, Schwab J. Development of machine learning algorithms for prediction of mortality in spinal epidural abscess. The Spine Journal 2019;19(12):1950 View
  10. Bhambhvani H, Zamora A, Shkolyar E, Prado K, Greenberg D, Kasman A, Liao J, Shah S, Srinivas S, Skinner E, Shah J. Development of robust artificial neural networks for prediction of 5-year survival in bladder cancer. Urologic Oncology: Seminars and Original Investigations 2021;39(3):193.e7 View
  11. Calanna P, Lauriola M, Saggino A, Tommasi M, Furlan S. Using a supervised machine learning algorithm for detecting faking good in a personality self‐report. International Journal of Selection and Assessment 2020;28(2):176 View
  12. Mongan J, Moy L, Kahn C. Checklist for Artificial Intelligence in Medical Imaging (CLAIM): A Guide for Authors and Reviewers. Radiology: Artificial Intelligence 2020;2(2):e200029 View
  13. Kim D, Jang H, Ko Y, Son J, Kim P, Kim S, Lim J, Park S, Hong J. Inconsistency in the use of the term “validation” in studies reporting the performance of deep learning algorithms in providing diagnosis from medical imaging. PLOS ONE 2020;15(9):e0238908 View
  14. Verrusio W, Renzi A, Dellepiane U, Renzi S, Zaccone M, Gueli N, Cacciafesta M. A new tool for the evaluation of the rehabilitation outcomes in older persons: a machine learning model to predict functional status 1 year ahead. European Geriatric Medicine 2018;9(5):651 View
  15. Molina-García D, Vera-Ramírez L, Pérez-Beteta J, Arana E, Pérez-García V. Prognostic models based on imaging findings in glioblastoma: Human versus Machine. Scientific Reports 2019;9(1) View
  16. Kim H, Lee S, Lee S, Hong S, Kang H, Kim N. Depression Prediction by Using Ecological Momentary Assessment, Actiwatch Data, and Machine Learning: Observational Study on Older Adults Living Alone. JMIR mHealth and uHealth 2019;7(10):e14149 View
  17. Lonsdale H, Jalali A, Ahumada L, Matava C. Machine Learning and Artificial Intelligence in Pediatric Research: Current State, Future Prospects, and Examples in Perioperative and Critical Care. The Journal of Pediatrics 2020;221:S3 View
  18. Panchagnula U, Shanmugam M, Rao B. Digital future in perioperative medicine: Are we there yet?. Journal of Anaesthesiology Clinical Pharmacology 2019;35(3):292 View
  19. Behrend M, Basáñez M, Hamley J, Porco T, Stolk W, Walker M, de Vlas S, Blanton J. Modelling for policy: The five principles of the Neglected Tropical Diseases Modelling Consortium. PLOS Neglected Tropical Diseases 2020;14(4):e0008033 View
  20. Parisi L, RaviChandran N, Manaog M. A novel hybrid algorithm for aiding prediction of prognosis in patients with hepatitis. Neural Computing and Applications 2020;32(8):3839 View
  21. Liu X, Faes L, Kale A, Wagner S, Fu D, Bruynseels A, Mahendiran T, Moraes G, Shamdas M, Kern C, Ledsam J, Schmid M, Balaskas K, Topol E, Bachmann L, Keane P, Denniston A. A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis. The Lancet Digital Health 2019;1(6):e271 View
  22. Buchlak Q, Esmaili N, Leveque J, Bennett C, Piccardi M, Farrokhi F. Ethical thinking machines in surgery and the requirement for clinical leadership. The American Journal of Surgery 2020;220(5):1372 View
  23. Bey R, Goussault R, Grolleau F, Benchoufi M, Porcher R. Fold-stratified cross-validation for unbiased and privacy-preserving federated learning. Journal of the American Medical Informatics Association 2020;27(8):1244 View
  24. De la Garza-Salazar F, Romero-Ibarguengoitia M, Rodriguez-Diaz E, Azpiri-Lopez J, González-Cantu A, Ab Rahman N. Improvement of electrocardiographic diagnostic accuracy of left ventricular hypertrophy using a Machine Learning approach. PLOS ONE 2020;15(5):e0232657 View
  25. Panwar S, Joshi S, Gupta A, Agarwal P. Automated Epilepsy Diagnosis Using EEG With Test Set Evaluation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 2019;27(6):1106 View
  26. Dihge L, Ohlsson M, Edén P, Bendahl P, Rydén L. Artificial neural network models to predict nodal status in clinically node-negative breast cancer. BMC Cancer 2019;19(1) View
  27. Klimuntowski M, Alam M, Singh G, Howlader M. Electrochemical Sensing of Cannabinoids in Biofluids: A Noninvasive Tool for Drug Detection. ACS Sensors 2020;5(3):620 View
  28. Triantafyllidis A, Tsanas A. Applications of Machine Learning in Real-Life Digital Health Interventions: Review of the Literature. Journal of Medical Internet Research 2019;21(4):e12286 View
  29. Burns M, Mathis M, Vandervest J, Tan X, Lu B, Colquhoun D, Shah N, Kheterpal S, Saager L. Classification of Current Procedural Terminology Codes from Electronic Health Record Data Using Machine Learning. Anesthesiology 2020;132(4):738 View
  30. Karhade A, Cha T, Fogel H, Hershman S, Tobert D, Schoenfeld A, Bono C, Schwab J. Predicting prolonged opioid prescriptions in opioid-naïve lumbar spine surgery patients. The Spine Journal 2020;20(6):888 View
  31. Hatib F, Jian Z, Buddi S, Lee C, Settels J, Sibert K, Rinehart J, Cannesson M. Machine-learning Algorithm to Predict Hypotension Based on High-fidelity Arterial Pressure Waveform Analysis. Anesthesiology 2018;129(4):663 View
  32. Cherifa M, Blet A, Chambaz A, Gayat E, Resche-Rigon M, Pirracchio R. Prediction of an Acute Hypotensive Episode During an ICU Hospitalization With a Super Learner Machine-Learning Algorithm. Anesthesia & Analgesia 2020;130(5):1157 View
  33. Zhao R, Zhang W, Zhou L, Chen Y. Building a predictive model for successful vaginal delivery in nulliparas with term cephalic singleton pregnancies using decision tree analysis. Journal of Obstetrics and Gynaecology Research 2019;45(8):1536 View
  34. Schultebraucks K, Qian M, Abu-Amara D, Dean K, Laska E, Siegel C, Gautam A, Guffanti G, Hammamieh R, Misganaw B, Mellon S, Wolkowitz O, Blessing E, Etkin A, Ressler K, Doyle F, Jett M, Marmar C. Pre-deployment risk factors for PTSD in active-duty personnel deployed to Afghanistan: a machine-learning approach for analyzing multivariate predictors. Molecular Psychiatry 2021;26(9):5011 View
  35. Higaki A, Uetani T, Ikeda S, Yamaguchi O. Co-authorship network analysis in cardiovascular research utilizing machine learning (2009–2019). International Journal of Medical Informatics 2020;143:104274 View
  36. Kakarmath S, Golas S, Felsted J, Kvedar J, Jethwani K, Agboola S. Validating a Machine Learning Algorithm to Predict 30-Day Re-Admissions in Patients With Heart Failure: Protocol for a Prospective Cohort Study. JMIR Research Protocols 2018;7(9):e176 View
  37. Kendale S, Kulkarni P, Rosenberg A, Wang J. Supervised Machine-learning Predictive Analytics for Prediction of Postinduction Hypotension. Anesthesiology 2018;129(4):675 View
  38. Tosado J, Zdilar L, Elhalawani H, Elgohari B, Vock D, Marai G, Fuller C, Mohamed A, Canahuate G. Clustering of Largely Right-Censored Oropharyngeal Head and Neck Cancer Patients for Discriminative Groupings to Improve Outcome Prediction. Scientific Reports 2020;10(1) View
  39. Bracher-Smith M, Crawford K, Escott-Price V. Machine learning for genetic prediction of psychiatric disorders: a systematic review. Molecular Psychiatry 2021;26(1):70 View
  40. Wu G, Woodruff H, Chatterjee A, Lambin P. Reply to “COVID-19 prediction models should adhere to methodological and reporting standards”. European Respiratory Journal 2020;56(3):2002918 View
  41. Doupe P, Faghmous J, Basu S. Machine Learning for Health Services Researchers. Value in Health 2019;22(7):808 View
  42. Spence J, Mazer C. The Future Directions of Research in Cardiac Anesthesiology. Anesthesiology Clinics 2019;37(4):801 View
  43. Kopitar L, Kocbek P, Cilar L, Sheikh A, Stiglic G. Early detection of type 2 diabetes mellitus using machine learning-based prediction models. Scientific Reports 2020;10(1) View
  44. Chancellor S, De Choudhury M. Methods in predictive techniques for mental health status on social media: a critical review. npj Digital Medicine 2020;3(1) View
  45. de Keijzer I, Vos J, Scheeren T. Hypotension Prediction Index: from proof-of-concept to proof-of-feasibility. Journal of Clinical Monitoring and Computing 2020;34(6):1135 View
  46. Mathis M, Kheterpal S, Najarian K. Artificial Intelligence for Anesthesia: What the Practicing Clinician Needs to Know. Anesthesiology 2018;129(4):619 View
  47. Koprowski R, Foster K. Machine learning and medicine: book review and commentary. BioMedical Engineering OnLine 2018;17(1) View
  48. Grados D, García S, Schrevens E. Assessing the potato yield gap in the Peruvian Central Andes. Agricultural Systems 2020;181:102817 View
  49. Curchoe C, Bormann C. Artificial intelligence and machine learning for human reproduction and embryology presented at ASRM and ESHRE 2018. Journal of Assisted Reproduction and Genetics 2019;36(4):591 View
  50. Danielsen A, Fenger M, Østergaard S, Nielbo K, Mors O. Predicting mechanical restraint of psychiatric inpatients by applying machine learning on electronic health data. Acta Psychiatrica Scandinavica 2019;140(2):147 View
  51. Ming C, Viassolo V, Probst-Hensch N, Chappuis P, Dinov I, Katapodi M. Letter to the editor: Response to Giardiello D, Antoniou AC, Mariani L, Easton DF, Steyerberg EW. Breast Cancer Research 2020;22(1) View
  52. Chi T, Zhu H, Zhang M. Risk factors associated with nonsteroidal anti-inflammatory drugs (NSAIDs)-induced gastrointestinal bleeding resulting on people over 60 years old in Beijing. Medicine 2018;97(18):e0665 View
  53. Karhade A, Ogink P, Thio Q, Cha T, Gormley W, Hershman S, Smith T, Mao J, Schoenfeld A, Bono C, Schwab J. Development of machine learning algorithms for prediction of prolonged opioid prescription after surgery for lumbar disc herniation. The Spine Journal 2019;19(11):1764 View
  54. Lee C, Hofer I, Gabel E, Baldi P, Cannesson M. Development and Validation of a Deep Neural Network Model for Prediction of Postoperative In-hospital Mortality. Anesthesiology 2018;129(4):649 View
  55. Rahimian F, Salimi-Khorshidi G, Payberah A, Tran J, Ayala Solares R, Raimondi F, Nazarzadeh M, Canoy D, Rahimi K, Sheikh A. Predicting the risk of emergency admission with machine learning: Development and validation using linked electronic health records. PLOS Medicine 2018;15(11):e1002695 View
  56. Zhang X, Bellolio M, Medrano-Gracia P, Werys K, Yang S, Mahajan P. Use of natural language processing to improve predictive models for imaging utilization in children presenting to the emergency department. BMC Medical Informatics and Decision Making 2019;19(1) View
  57. Jethanandani A, Lin T, Volpe S, Elhalawani H, Mohamed A, Yang P, Fuller C. Exploring Applications of Radiomics in Magnetic Resonance Imaging of Head and Neck Cancer: A Systematic Review. Frontiers in Oncology 2018;8 View
  58. Smith M, Dietrich B, Bai E, Bockholt H. Vocal pattern detection of depression among older adults. International Journal of Mental Health Nursing 2020;29(3):440 View
  59. Shirole U, Joshi M, Bagul P. Cardiac, diabetic and normal subjects classification using decision tree and result confirmation through orthostatic stress index. Informatics in Medicine Unlocked 2019;17:100252 View
  60. Zhang B, Yu K, Ning Z, Wang K, Dong Y, Liu X, Liu S, Wang J, Zhu C, Yu Q, Duan Y, Lv S, Zhang X, Chen Y, Wang X, Shen J, Peng J, Chen Q, Zhang Y, Zhang X, Zhang S. Deep learning of lumbar spine X-ray for osteopenia and osteoporosis screening: A multicenter retrospective cohort study. Bone 2020;140:115561 View
  61. Hofer I, Lee C, Gabel E, Baldi P, Cannesson M. Development and validation of a deep neural network model to predict postoperative mortality, acute kidney injury, and reintubation using a single feature set. npj Digital Medicine 2020;3(1) View
  62. Sapir-Pichhadze R, Kaplan B. Seeing the Forest for the Trees: Random Forest Models for Predicting Survival in Kidney Transplant Recipients. Transplantation 2020;104(5):905 View
  63. Schultebraucks K, Galatzer‐Levy I. Machine Learning for Prediction of Posttraumatic Stress and Resilience Following Trauma: An Overview of Basic Concepts and Recent Advances. Journal of Traumatic Stress 2019;32(2):215 View
  64. op den Buijs J, Simons M, Golas S, Fischer N, Felsted J, Schertzer L, Agboola S, Kvedar J, Jethwani K. Predictive Modeling of 30-Day Emergency Hospital Transport of Patients Using a Personal Emergency Response System: Prognostic Retrospective Study. JMIR Medical Informatics 2018;6(4):e49 View
  65. Speiser J, Callahan K, Houston D, Fanning J, Gill T, Guralnik J, Newman A, Pahor M, Rejeski W, Miller M, Melzer D. Machine Learning in Aging: An Example of Developing Prediction Models for Serious Fall Injury in Older Adults. The Journals of Gerontology: Series A 2021;76(4):647 View
  66. Neubauer N, Liu L. Development and validation of a conceptual model and strategy adoption guidelines for persons with dementia at risk of getting lost. Dementia 2021;20(2):534 View
  67. Groezinger M, Huppert D, Strobl R, Grill E. Development and validation of a classification algorithm to diagnose and differentiate spontaneous episodic vertigo syndromes: results from the DizzyReg patient registry. Journal of Neurology 2020;267(S1):160 View
  68. Ershoff B, Lee C, Wray C, Agopian V, Urban G, Baldi P, Cannesson M. Training and Validation of Deep Neural Networks for the Prediction of 90-Day Post-Liver Transplant Mortality Using UNOS Registry Data. Transplantation Proceedings 2020;52(1):246 View
  69. Karhade A, Ogink P, Thio Q, Broekman M, Cha T, Hershman S, Mao J, Peul W, Schoenfeld A, Bono C, Schwab J. Machine learning for prediction of sustained opioid prescription after anterior cervical discectomy and fusion. The Spine Journal 2019;19(6):976 View
  70. Park S, Do K, Choi J, Sim J, Yang D, Eo H, Woo H, Lee J, Jung S, Oh J. Principles for evaluating the clinical implementation of novel digital healthcare devices. Journal of the Korean Medical Association 2018;61(12):765 View
  71. Wei W, Wang K, Liu Z, Tian K, Wang L, Du J, Ma J, Wang S, Li L, Zhao R, Cui L, Wu Z, Tian J. Radiomic signature: A novel magnetic resonance imaging-based prognostic biomarker in patients with skull base chordoma. Radiotherapy and Oncology 2019;141:239 View
  72. Anderson A, Grazal C, Balazs G, Potter B, Dickens J, Forsberg J. Can Predictive Modeling Tools Identify Patients at High Risk of Prolonged Opioid Use After ACL Reconstruction?. Clinical Orthopaedics & Related Research 2020;478(7):00 View
  73. Bahl M. Artificial Intelligence: A Primer for Breast Imaging Radiologists. Journal of Breast Imaging 2020;2(4):304 View
  74. Thomsen K, Iversen L, Titlestad T, Winther O. Systematic review of machine learning for diagnosis and prognosis in dermatology. Journal of Dermatological Treatment 2020;31(5):496 View
  75. Christodoulou E, Ma J, Collins G, Steyerberg E, Verbakel J, Van Calster B. A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models. Journal of Clinical Epidemiology 2019;110:12 View
  76. Karhade A, Thio Q, Ogink P, Bono C, Ferrone M, Oh K, Saylor P, Schoenfeld A, Shin J, Harris M, Schwab J. Predicting 90-Day and 1-Year Mortality in Spinal Metastatic Disease: Development and Internal Validation. Neurosurgery 2019;85(4):E671 View
  77. Moon S, Hwang J, Kana R, Torous J, Kim J. Accuracy of Machine Learning Algorithms for the Diagnosis of Autism Spectrum Disorder: Systematic Review and Meta-Analysis of Brain Magnetic Resonance Imaging Studies. JMIR Mental Health 2019;6(12):e14108 View
  78. Futoma J, Simons M, Panch T, Doshi-Velez F, Celi L. The myth of generalisability in clinical research and machine learning in health care. The Lancet Digital Health 2020;2(9):e489 View
  79. Young C, Luo W, Gastin P, Tran J, Dwyer D. The relationship between match performance indicators and outcome in Australian Football. Journal of Science and Medicine in Sport 2019;22(4):467 View
  80. Park S, Kressel H. Connecting Technological Innovation in Artificial Intelligence to Real-world Medical Practice through Rigorous Clinical Validation: What Peer-reviewed Medical Journals Could Do. Journal of Korean Medical Science 2018;33(22) View
  81. Saugel B, Kouz K, Hoppe P, Maheshwari K, Scheeren T. Predicting hypotension in perioperative and intensive care medicine. Best Practice & Research Clinical Anaesthesiology 2019;33(2):189 View
  82. Ortiz A, Costa C, Silva R, Biazevic M, Michel-Crosato E. Sex estimation: Anatomical references on panoramic radiographs using Machine Learning. Forensic Imaging 2020;20:200356 View
  83. Weenk M, van Goor H, Frietman B, Engelen L, van Laarhoven C, Smit J, Bredie S, van de Belt T. Continuous Monitoring of Vital Signs Using Wearable Devices on the General Ward: Pilot Study. JMIR mHealth and uHealth 2017;5(7):e91 View
  84. Yusuf M, Atal I, Li J, Smith P, Ravaud P, Fergie M, Callaghan M, Selfe J. Reporting quality of studies using machine learning models for medical diagnosis: a systematic review. BMJ Open 2020;10(3):e034568 View
  85. Khan O, Badhiwala J, Grasso G, Fehlings M. Use of Machine Learning and Artificial Intelligence to Drive Personalized Medicine Approaches for Spine Care. World Neurosurgery 2020;140:512 View
  86. Pickhardt P, Graffy P, Zea R, Lee S, Liu J, Sandfort V, Summers R. Automated CT biomarkers for opportunistic prediction of future cardiovascular events and mortality in an asymptomatic screening population: a retrospective cohort study. The Lancet Digital Health 2020;2(4):e192 View
  87. Unberath P, Prokosch H, Gründner J, Erpenbeck M, Maier C, Christoph J. EHR-Independent Predictive Decision Support Architecture Based on OMOP. Applied Clinical Informatics 2020;11(03):399 View
  88. Weenk M, Bredie S, Koeneman M, Hesselink G, van Goor H, van de Belt T. Continuous Monitoring of Vital Signs in the General Ward Using Wearable Devices: Randomized Controlled Trial. Journal of Medical Internet Research 2020;22(6):e15471 View
  89. Sullivan S, Hewner S, Chandola V, Westra B. Mortality Risk in Homebound Older Adults Predicted From Routinely Collected Nursing Data. Nursing Research 2019;68(2):156 View
  90. Khan O, Badhiwala J, Witiw C, Wilson J, Fehlings M. Machine learning algorithms for prediction of health-related quality-of-life after surgery for mild degenerative cervical myelopathy. The Spine Journal 2021;21(10):1659 View
  91. Montagnon E, Cerny M, Cadrin-Chênevert A, Hamilton V, Derennes T, Ilinca A, Vandenbroucke-Menu F, Turcotte S, Kadoury S, Tang A. Deep learning workflow in radiology: a primer. Insights into Imaging 2020;11(1) View
  92. Salagre E, Dodd S, Aedo A, Rosa A, Amoretti S, Pinzon J, Reinares M, Berk M, Kapczinski F, Vieta E, Grande I. Toward Precision Psychiatry in Bipolar Disorder: Staging 2.0. Frontiers in Psychiatry 2018;9 View
  93. Sheyn D, Ju M, Zhang S, Anyaeche C, Hijaz A, Mangel J, Mahajan S, Conroy B, El-Nashar S, Ray S. Development and Validation of a Machine Learning Algorithm for Predicting Response to Anticholinergic Medications for Overactive Bladder Syndrome. Obstetrics & Gynecology 2019;134(5):946 View
  94. Karhade A, Ogink P, Thio Q, Broekman M, Cha T, Gormley W, Hershman S, Peul W, Bono C, Schwab J. Development of machine learning algorithms for prediction of discharge disposition after elective inpatient surgery for lumbar degenerative disc disorders. Neurosurgical Focus 2018;45(5):E6 View
  95. Ordovas K, Seo Y. Artificial Intelligence Pipeline for Risk Prediction in Cardiovascular Imaging. Circulation: Cardiovascular Imaging 2020;13(2) View
  96. Fritz B, Cui Z, Zhang M, He Y, Chen Y, Kronzer A, Ben Abdallah A, King C, Avidan M. Deep-learning model for predicting 30-day postoperative mortality. British Journal of Anaesthesia 2019;123(5):688 View
  97. Flechet M, Falini S, Bonetti C, Güiza F, Schetz M, Van den Berghe G, Meyfroidt G. Machine learning versus physicians’ prediction of acute kidney injury in critically ill adults: a prospective evaluation of the AKIpredictor. Critical Care 2019;23(1) View
  98. Roth J, Radevski G, Marzolini C, Rauch A, Günthard H, Kouyos R, Fux C, Scherrer A, Calmy A, Cavassini M, Kahlert C, Bernasconi E, Bogojeska J, Battegay M. Cohort-Derived Machine Learning Models for Individual Prediction of Chronic Kidney Disease in People Living With Human Immunodeficiency Virus: A Prospective Multicenter Cohort Study. The Journal of Infectious Diseases 2021;224(7):1198 View
  99. López Seguí F, Ander Egg Aguilar R, de Maeztu G, García-Altés A, García Cuyàs F, Walsh S, Sagarra Castro M, Vidal-Alaball J. Teleconsultations between Patients and Healthcare Professionals in Primary Care in Catalonia: The Evaluation of Text Classification Algorithms Using Supervised Machine Learning. International Journal of Environmental Research and Public Health 2020;17(3):1093 View
  100. Sufriyana H, Wu Y, Su E. Artificial intelligence-assisted prediction of preeclampsia: Development and external validation of a nationwide health insurance dataset of the BPJS Kesehatan in Indonesia. EBioMedicine 2020;54:102710 View
  101. Weenk M, Koeneman M, van de Belt T, Engelen L, van Goor H, Bredie S. Wireless and continuous monitoring of vital signs in patients at the general ward. Resuscitation 2019;136:47 View
  102. Hendrickx L, Sobol G, Langerhuizen D, Bulstra A, Hreha J, Sprague S, Sirkin M, Ring D, Kerkhoffs G, Jaarsma R, Doornberg J. A Machine Learning Algorithm to Predict the Probability of (Occult) Posterior Malleolar Fractures Associated With Tibial Shaft Fractures to Guide “Malleolus First” Fixation. Journal of Orthopaedic Trauma 2020;34(3):131 View
  103. Zhong J, Hu Y, Si L, Jia G, Xing Y, Zhang H, Yao W. A systematic review of radiomics in osteosarcoma: utilizing radiomics quality score as a tool promoting clinical translation. European Radiology 2021;31(3):1526 View
  104. Fatima N, Zheng H, Massaad E, Hadzipasic M, Shankar G, Shin J. Development and Validation of Machine Learning Algorithms for Predicting Adverse Events After Surgery for Lumbar Degenerative Spondylolisthesis. World Neurosurgery 2020;140:627 View
  105. Sufriyana H, Wu Y, Su E. Prediction of Preeclampsia and Intrauterine Growth Restriction: Development of Machine Learning Models on a Prospective Cohort. JMIR Medical Informatics 2020;8(5):e15411 View
  106. Fransquet P, Ryan J. Micro RNA as a potential blood-based epigenetic biomarker for Alzheimer's disease. Clinical Biochemistry 2018;58:5 View
  107. Farran B, AlWotayan R, Alkandari H, Al-Abdulrazzaq D, Channanath A, Thanaraj T. Use of Non-invasive Parameters and Machine-Learning Algorithms for Predicting Future Risk of Type 2 Diabetes: A Retrospective Cohort Study of Health Data From Kuwait. Frontiers in Endocrinology 2019;10 View
  108. Skrede O, De Raedt S, Kleppe A, Hveem T, Liestøl K, Maddison J, Askautrud H, Pradhan M, Nesheim J, Albregtsen F, Farstad I, Domingo E, Church D, Nesbakken A, Shepherd N, Tomlinson I, Kerr R, Novelli M, Kerr D, Danielsen H. Deep learning for prediction of colorectal cancer outcome: a discovery and validation study. The Lancet 2020;395(10221):350 View
  109. Schultebraucks K, Shalev A, Michopoulos V, Grudzen C, Shin S, Stevens J, Maples-Keller J, Jovanovic T, Bonanno G, Rothbaum B, Marmar C, Nemeroff C, Ressler K, Galatzer-Levy I. A validated predictive algorithm of post-traumatic stress course following emergency department admission after a traumatic stressor. Nature Medicine 2020;26(7):1084 View
  110. Silva K, Lee W, Forbes A, Demmer R, Barton C, Enticott J. Use and performance of machine learning models for type 2 diabetes prediction in community settings: A systematic review and meta-analysis. International Journal of Medical Informatics 2020;143:104268 View
  111. Zarinabad N, Meeus E, Manias K, Foster K, Peet A. Automated Modular Magnetic Resonance Imaging Clinical Decision Support System (MIROR): An Application in Pediatric Cancer Diagnosis. JMIR Medical Informatics 2018;6(2):e30 View
  112. Thomsen K, Christensen A, Iversen L, Lomholt H, Winther O. Deep Learning for Diagnostic Binary Classification of Multiple-Lesion Skin Diseases. Frontiers in Medicine 2020;7 View
  113. El Naqa I, Ruan D, Valdes G, Dekker A, McNutt T, Ge Y, Wu Q, Oh J, Thor M, Smith W, Rao A, Fuller C, Xiao Y, Manion F, Schipper M, Mayo C, Moran J, Ten Haken R. Machine learning and modeling: Data, validation, communication challenges. Medical Physics 2018;45(10) View
  114. Carson N, Mullin B, Sanchez M, Lu F, Yang K, Menezes M, Cook B, Fiorini N. Identification of suicidal behavior among psychiatrically hospitalized adolescents using natural language processing and machine learning of electronic health records. PLOS ONE 2019;14(2):e0211116 View
  115. Karhade A, Schwab J, Bedair H. Development of Machine Learning Algorithms for Prediction of Sustained Postoperative Opioid Prescriptions After Total Hip Arthroplasty. The Journal of Arthroplasty 2019;34(10):2272 View
  116. de Barros A, Silva A, Zibordi M, Spagnolo J, Corrêa R, Belli C, de Camargo M. Equine simplified acute physiology score: Personalised medicine for the equine emergency patient. Veterinary Record 2021;189(5) View
  117. Sufriyana H, Husnayain A, Chen Y, Kuo C, Singh O, Yeh T, Wu Y, Su E. Comparison of Multivariable Logistic Regression and Other Machine Learning Algorithms for Prognostic Prediction Studies in Pregnancy Care: Systematic Review and Meta-Analysis. JMIR Medical Informatics 2020;8(11):e16503 View
  118. Feng C, Zhou S, Qu Y, Wang Q, Bao S, Li Y, Yang T, Si W. Overview of Artificial Intelligence Applications in Chinese Medicine Therapy. Evidence-Based Complementary and Alternative Medicine 2021;2021:1 View
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  550. Ghasemi A, Hashtarkhani S, Schwartz D, Shaban‐Nejad A. Explainable artificial intelligence in breast cancer detection and risk prediction: A systematic scoping review. Cancer Innovation 2024;3(5) View
  551. Tahhan Z, Hatem G, Abouelmaty A, Rafei Z, Awada S. Design and validation of an artificial intelligence-powered instrument for the assessment of migraine risk in university students in Lebanon. Computers in Human Behavior Reports 2024;15:100453 View
  552. Cho Y, Yoon M, Kim J, Lee J, Oh I, Lee C, Kang S, Choi D. Artificial Intelligence–Based Electrocardiographic Biomarker for Outcome Prediction in Patients With Acute Heart Failure: Prospective Cohort Study. Journal of Medical Internet Research 2024;26:e52139 View
  553. Yu D, Kane M, Koay E, Wistuba I, Hobbs B. Machine learning identifies prognostic subtypes of the tumor microenvironment of NSCLC. Scientific Reports 2024;14(1) View
  554. Deo N, Nawaz F, du Toit C, Tran T, Mamillapalli C, Mathur P, Reddy S, Visweswaran S, Prabhu T, Moidu K, Padmanabhan S, Kashyap R. HUMANE: Harmonious Understanding of Machine Learning Analytics Network—global consensus for research on artificial intelligence in medicine. Exploration of Digital Health Technologies 2024;2(3):157 View
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  557. Zhao S, Zhou H, He M. Considerations regarding a prediction model of surgical site infection after gastrointestinal surgery. International Journal of Surgery 2024 View
  558. Vieta E, Salagre E, Grande I, Carvalho A, Fernandes B, Berk M, Birmaher B, Tohen M, Suppes T. Early Intervention in Bipolar Disorder. American Journal of Psychiatry 2018;175(5):411 View
  559. Cao S, Yang S, Chen B, Chen X, Fu X, Tang S. Establishing a differential diagnosis model between primary membranous nephropathy and non-primary membranous nephropathy by machine learning algorithms. Renal Failure 2024;46(2) View
  560. Endo Y, Tsilimigras D, Munir M, Woldesenbet S, Guglielmi A, Ratti F, Marques H, Cauchy F, Lam V, Poultsides G, Kitago M, Alexandrescu S, Popescu I, Martel G, Gleisner A, Hugh T, Aldrighetti L, Shen F, Endo I, Pawlik T. Machine learning models including preoperative and postoperative albumin-bilirubin score: short-term outcomes among patients with hepatocellular carcinoma. HPB 2024 View
  561. Cai Y, Gong D, Tang L, Cai Y, Li H, Jing T, Gong M, Hu W, Zhang Z, Zhang X, Zhang G. Pitfalls in Developing Machine Learning Models for Predicting Cardiovascular Diseases: Challenge and Solutions. Journal of Medical Internet Research 2024;26:e47645 View
  562. Li Q, Li P, Chen J, Ren R, Ren N, Xia Y. Machine Learning for Predicting Stillbirth: A Systematic Review. Reproductive Sciences 2024 View
  563. Boubekri A, Murphy M, Scheidt M, Shivdasani K, Anderson J, Garbis N, Salazar D. Artificial Intelligence Machine Learning Algorithms Versus Standard Linear Demographic Analysis in Predicting Implant Size of Anatomic and Reverse Total Shoulder Arthroplasty. JAAOS: Global Research and Reviews 2024;8(8) View
  564. Vachon J, Kerckhoffs J, Buteau S, Smargiassi A. Do machine learning methods improve prediction of ambient air pollutants with high spatial contrast? A systematic review. Environmental Research 2024;262:119751 View
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  567. Kong F, Zou Y, Li Z, Deng Y. Advances in Portable and Wearable Acoustic Sensing Devices for Human Health Monitoring. Sensors 2024;24(16):5354 View
  568. Iwagami M, Inokuchi R, Kawakami E, Yamada T, Goto A, Kuno T, Hashimoto Y, Michihata N, Goto T, Shinozaki T, Sun Y, Taniguchi Y, Komiyama J, Uda K, Abe T, Tamiya N, Penzel T. Comparison of machine-learning and logistic regression models for prediction of 30-day unplanned readmission in electronic health records: A development and validation study. PLOS Digital Health 2024;3(8):e0000578 View
  569. Szumilas D, Ochmann A, Zięba K, Bartoszewicz B, Kubrak A, Makuch S, Agrawal S, Mazur G, Chudek J. Evaluation of AI-Driven LabTest Checker for Diagnostic Accuracy and Safety: Prospective Cohort Study. JMIR Medical Informatics 2024;12:e57162 View
  570. Zhang T, Ye Z, Cai J, Chen J, Zheng T, Xu J, Zhao J. Ensemble Algorithm for Risk Prediction of Clinical Failure After Anterior Cruciate Ligament Reconstruction. Orthopaedic Journal of Sports Medicine 2024;12(8) View
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  572. Oeding J, Boos A, Kalk J, Sorenson D, Verhooven F, Moatshe G, Camp C. Pitch-Tracking Metrics as a Predictor of Future Shoulder and Elbow Injuries in Major League Baseball Pitchers: A Machine-Learning and Game-Theory Based Analysis. Orthopaedic Journal of Sports Medicine 2024;12(8) View
  573. Karabacak M, Schupper A, Carr M, Margetis K. A machine learning-based approach for individualized prediction of short-term outcomes after anterior cervical corpectomy. Asian Spine Journal 2024;18(4):541 View
  574. Lisik D, Milani G, Salisu M, Özuygur Ermis S, Goksör E, Basna R, Wennergren G, Kankaanranta H, Nwaru B. Machine learning-derived phenotypic trajectories of asthma and allergy in children and adolescents: protocol for a systematic review. BMJ Open 2024;14(8):e080263 View
  575. Cai Z, Sun Q, Li C, Xu J, Jiang B. Machine-learning-based prediction by stacking ensemble strategy for surgical outcomes in patients with degenerative cervical myelopathy. Journal of Orthopaedic Surgery and Research 2024;19(1) View
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  577. Kim Y, Seo W, Lee S, Koo J, Kim G, Song H, Lee M. Early Prediction of Cardiac Arrest in the Intensive Care Unit Using Explainable Machine Learning: Retrospective Study. Journal of Medical Internet Research 2024;26:e62890 View
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  579. Turrisi R, Verri A, Barla A. Deep learning-based Alzheimer's disease detection: reproducibility and the effect of modeling choices. Frontiers in Computational Neuroscience 2024;18 View
  580. Stahl D. New horizons in prediction modelling using machine learning in older people’s healthcare research. Age and Ageing 2024;53(9) View
  581. Cata J, Soni B, Bhavsar S, Pillai P, Rypinski T, Deva A, Siewerdsen J, Soliz J. Forecasting intraoperative hypotension during hepatobiliary surgery. Journal of Clinical Monitoring and Computing 2024 View
  582. Benlaharche K, Benlaharche H. Machine learning for HELLP syndrome prediction: algorithms, case study and challenges. STUDIES IN ENGINEERING AND EXACT SCIENCES 2024;5(2):e8237 View
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  584. Zhou Z, Wang D, Sun J, Zhu M, Teng L. A Machine Learning–Based Prediction Model for the Probability of Fall Risk Among Chinese Community-Dwelling Older Adults. CIN: Computers, Informatics, Nursing 2024 View
  585. Marzano L. Predicting the resolution of hypertension following adrenalectomy in primary aldosteronism: Controversies and unresolved issues a narrative review. Langenbeck's Archives of Surgery 2024;409(1) View
  586. Araújo D, de Macedo A, Veloso A, Alpoim P, Gomes K, Carvalho M, Dusse L. Complete blood count as a biomarker for preeclampsia with severe features diagnosis: a machine learning approach. BMC Pregnancy and Childbirth 2024;24(1) View
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Books/Policy Documents

  1. F.I. Osman A. Artificial Intelligence - Applications in Medicine and Biology. View
  2. Dankers F, Traverso A, Wee L, van Kuijk S. Fundamentals of Clinical Data Science. View
  3. Allen B, Gish R, Dreyer K. Artificial Intelligence in Medical Imaging. View
  4. Haymond S, Julian R, Gill E, Master S. Biochemical and Molecular Basis of Pediatric Disease. View
  5. Cychnerski J, Dziubich T. New Trends in Database and Information Systems. View
  6. Schwarzerova J, Kostoval A, Bajger A, Jakubikova L, Pierides I, Popelinsky L, Sedlar K, Weckwerth W. Information Technology in Biomedicine. View
  7. Dee E, Yu R, Celi L, Nehal U. Artificial Intelligence in Medicine. View
  8. Kalpana , Srivastava A, Jha S. Predictive Modeling in Biomedical Data Mining and Analysis. View
  9. Dee E, Yu R, Celi L, Nehal U. Artificial Intelligence in Medicine. View
  10. Lopez-Ramos L. Intelligent Technologies and Applications. View
  11. Urbanowicz R, Zhang R, Cui Y, Suri P. Genetic Programming Theory and Practice XIX. View
  12. McMahon J, Craig A, Cameron I. Service-Oriented Computing – ICSOC 2023 Workshops. View
  13. Glavaški M, Velicki L. In Silico Clinical Trials for Cardiovascular Disease. View
  14. Ong Y, Kee S, Chai K, Lim T, Tan C. Advances in Intelligent Healthcare Delivery and Management. View