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. 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 2020 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 2020 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 2020 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):e834 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. Barros A, Silva A, Zibordi M, Spagnolo J, Corrêa R, Belli C, Camargo M. Equine simplified acute physiology score: Personalised medicine for the equine emergency patient. Veterinary Record 2021 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
  119. Lu Y, Khazi Z, Agarwalla A, Forsythe B, Taunton M. Development of a Machine Learning Algorithm to Predict Nonroutine Discharge Following Unicompartmental Knee Arthroplasty. The Journal of Arthroplasty 2021;36(5):1568 View
  120. ZhuParris A, Kruizinga M, Gent M, Dessing E, Exadaktylos V, Doll R, Stuurman F, Driessen G, Cohen A. Development and Technical Validation of a Smartphone-Based Cry Detection Algorithm. Frontiers in Pediatrics 2021;9 View
  121. Zhao J, Zhang W, Fan C, Zhang J, Yuan F, Liu S, Li F, Song B. Development and validation of preoperative magnetic resonance imaging-based survival predictive nomograms for patients with perihilar cholangiocarcinoma after radical resection: A pilot study. European Journal of Radiology 2021;138:109631 View
  122. Saboonchi H, Blanchette D, Hayes K. Advancements in Radiographic Evaluation Through the Migration into NDE 4.0. Journal of Nondestructive Evaluation 2021;40(1) View
  123. Lu Y, Forlenza E, Wilbur R, Lavoie-Gagne O, Fu M, Yanke A, Cole B, Verma N, Forsythe B. Machine-learning model successfully predicts patients at risk for prolonged postoperative opioid use following elective knee arthroscopy. Knee Surgery, Sports Traumatology, Arthroscopy 2021 View
  124. Douville N, Douville C, Mentz G, Mathis M, Pancaro C, Tremper K, Engoren M. Clinically applicable approach for predicting mechanical ventilation in patients with COVID-19. British Journal of Anaesthesia 2021;126(3):578 View
  125. Kunze K, Polce E, Nwachukwu B, Chahla J, Nho S. Development and Internal Validation of Supervised Machine Learning Algorithms for Predicting Clinically Significant Functional Improvement in a Mixed Population of Primary Hip Arthroscopy. Arthroscopy: The Journal of Arthroscopic & Related Surgery 2021;37(5):1488 View
  126. Castaldo R, Cavaliere C, Soricelli A, Salvatore M, Pecchia L, Franzese M. Radiomic and Genomic Machine Learning Method Performance for Prostate Cancer Diagnosis: Systematic Literature Review. Journal of Medical Internet Research 2021;23(4):e22394 View
  127. Calderaro J, Kather J. Artificial intelligence-based pathology for gastrointestinal and hepatobiliary cancers. Gut 2021;70(6):1183 View
  128. Khan O, Badhiwala J, Akbar M, Fehlings M. Prediction of Worse Functional Status After Surgery for Degenerative Cervical Myelopathy: A Machine Learning Approach. Neurosurgery 2021;88(3):584 View
  129. Castillo-Sánchez G, Marques G, Dorronzoro E, Rivera-Romero O, Franco-Martín M, De la Torre-Díez I. Suicide Risk Assessment Using Machine Learning and Social Networks: a Scoping Review. Journal of Medical Systems 2020;44(12) View
  130. Calabrese F, Pezzuto F, Fortarezza F, Boscolo A, Lunardi F, Giraudo C, Cattelan A, Del Vecchio C, Lorenzoni G, Vedovelli L, Sella N, Rossato M, Rea F, Vettor R, Plebani M, Cozzi E, Crisanti A, Navalesi P, Gregori D. Machine learning‐based analysis of alveolar and vascular injury in SARS‐CoV ‐2 acute respiratory failure. The Journal of Pathology 2021;254(2):173 View
  131. Honoré H, Gade R, Nielsen J, Mechlenburg I. Developing and validating an accelerometer-based algorithm with machine learning to classify physical activity after acquired brain injury. Brain Injury 2021;35(4):460 View
  132. Yu D, Williams G, Aguilar D, Yamal J, Maroufy V, Wang X, Zhang C, Huang Y, Gu Y, Talebi Y, Wu H. Machine learning prediction of the adverse outcome for nontraumatic subarachnoid hemorrhage patients. Annals of Clinical and Translational Neurology 2020;7(11):2178 View
  133. Young C, Luo W, Gastin P, Dwyer D. Understanding the relative contribution of technical and tactical performance to match outcome in Australian Football. Journal of Sports Sciences 2020;38(6):676 View
  134. Howard F, Kochanny S, Koshy M, Spiotto M, Pearson A. Machine Learning–Guided Adjuvant Treatment of Head and Neck Cancer. JAMA Network Open 2020;3(11):e2025881 View
  135. Spence J, Mazer C. The Future Directions of Research in Cardiac Anesthesiology. Advances in Anesthesia 2020;38:269 View
  136. Brnabic A, Hess L. Systematic literature review of machine learning methods used in the analysis of real-world data for patient-provider decision making. BMC Medical Informatics and Decision Making 2021;21(1) View
  137. Dihge L, Vallon-Christersson J, Hegardt C, Saal L, Häkkinen J, Larsson C, Ehinger A, Loman N, Malmberg M, Bendahl P, Borg Å, Staaf J, Rydén L. Prediction of Lymph Node Metastasis in Breast Cancer by Gene Expression and Clinicopathological Models: Development and Validation within a Population-Based Cohort. Clinical Cancer Research 2019;25(21):6368 View
  138. Kennedy-Metz L, Mascagni P, Torralba A, Dias R, Perona P, Shah J, Padoy N, Zenati M. Computer Vision in the Operating Room: Opportunities and Caveats. IEEE Transactions on Medical Robotics and Bionics 2021;3(1):2 View
  139. Bhambhvani H, Zamora A, Velaer K, Greenberg D, Sheth K. Deep learning enabled prediction of 5-year survival in pediatric genitourinary rhabdomyosarcoma. Surgical Oncology 2021;36:23 View
  140. Groot O, Bindels B, Ogink P, Kapoor N, Twining P, Collins A, Bongers M, Lans A, Oosterhoff J, Karhade A, Verlaan J, Schwab J. Availability and reporting quality of external validations of machine-learning prediction models with orthopedic surgical outcomes: a systematic review. Acta Orthopaedica 2021:1 View
  141. Karhade A, Schwab J. CORR Synthesis: When Should We Be Skeptical of Clinical Prediction Models?. Clinical Orthopaedics & Related Research 2020;478(12):2722 View
  142. Stevens L, Linstead E, Hall J, Kao D. Association Between Coffee Intake and Incident Heart Failure Risk. Circulation: Heart Failure 2021;14(2) View
  143. Zhao X, Liao K, Wang W, Xu J, Meng L. Can a deep learning model based on intraoperative time-series monitoring data predict post-hysterectomy quality of recovery?. Perioperative Medicine 2021;10(1) View
  144. Young C, Luo W, Gastin P, Tran J, Dwyer D. Modelling Match Outcome in Australian Football: Improved accuracy with large databases. International Journal of Computer Science in Sport 2019;18(1):80 View
  145. Anteby R, Horesh N, Soffer S, Zager Y, Barash Y, Amiel I, Rosin D, Gutman M, Klang E. Deep learning visual analysis in laparoscopic surgery: a systematic review and diagnostic test accuracy meta-analysis. Surgical Endoscopy 2021;35(4):1521 View
  146. . A Machine Learning Algorithm to Identify Patients with Tibial Shaft Fractures at Risk for Infection After Operative Treatment. Journal of Bone and Joint Surgery 2021;103(6):532 View
  147. Morgenstern J, Buajitti E, O’Neill M, Piggott T, Goel V, Fridman D, Kornas K, Rosella L. Predicting population health with machine learning: a scoping review. BMJ Open 2020;10(10):e037860 View
  148. Hu M, Shu X, Yu G, Wu X, Välimäki M, Feng H. A Risk Prediction Model Based on Machine Learning for Cognitive Impairment Among Chinese Community-Dwelling Elderly People With Normal Cognition: Development and Validation Study. Journal of Medical Internet Research 2021;23(2):e20298 View
  149. Liu Y, Qu H, Wenocur A, Qu J, Chang X, Glessner J, Sleiman P, Tian L, Hakonarson H. Interpretation of Maturity-Onset Diabetes of the Young Genetic Variants Based on American College of Medical Genetics and Genomics Criteria: Machine-Learning Model Development. JMIR Biomedical Engineering 2020;5(1):e20506 View
  150. Sampa M, Hossain M, Hoque M, Islam R, Yokota F, Nishikitani M, Ahmed A. Blood Uric Acid Prediction With Machine Learning: Model Development and Performance Comparison. JMIR Medical Informatics 2020;8(10):e18331 View
  151. Maitín A, García-Tejedor A, Muñoz J. Machine Learning Approaches for Detecting Parkinson’s Disease from EEG Analysis: A Systematic Review. Applied Sciences 2020;10(23):8662 View
  152. Kocak B, Kus E, Kilickesmez O. How to read and review papers on machine learning and artificial intelligence in radiology: a survival guide to key methodological concepts. European Radiology 2021;31(4):1819 View
  153. Azad T, Ehresman J, Ahmed A, Staartjes V, Lubelski D, Stienen M, Veeravagu A, Ratliff J. Fostering reproducibility and generalizability in machine learning for clinical prediction modeling in spine surgery. The Spine Journal 2020 View
  154. Polce E, Kunze K, Paul K, Levine B. Machine Learning Predicts Femoral and Tibial Implant Size Mismatch for Total Knee Arthroplasty. Arthroplasty Today 2021;8:268 View
  155. Lu Y, Forlenza E, Cohn M, Lavoie-Gagne O, Wilbur R, Song B, Krych A, Forsythe B. Machine learning can reliably identify patients at risk of overnight hospital admission following anterior cruciate ligament reconstruction. Knee Surgery, Sports Traumatology, Arthroscopy 2020 View
  156. Alabi R, Youssef O, Pirinen M, Elmusrati M, Mäkitie A, Leivo I, Almangush A. Machine learning in oral squamous cell carcinoma: Current status, clinical concerns and prospects for future—A systematic review. Artificial Intelligence in Medicine 2021;115:102060 View
  157. Sax D, Mark D, Huang J, Sofrygin O, Rana J, Collins S, Storrow A, Liu D, Reed M. Use of Machine Learning to Develop a Risk-Stratification Tool for Emergency Department Patients With Acute Heart Failure. Annals of Emergency Medicine 2021;77(2):237 View
  158. Loher P, Karathanasis N. Machine Learning Approaches Identify Genes Containing Spatial Information From Single-Cell Transcriptomics Data. Frontiers in Genetics 2021;11 View
  159. Pethani F. Promises and perils of artificial intelligence in dentistry. Australian Dental Journal 2021;66(2):124 View
  160. Iorfino F, Ho N, Carpenter J, Cross S, Davenport T, Hermens D, Yee H, Nichles A, Zmicerevska N, Guastella A, Scott E, Hickie I, De Luca V. Predicting self-harm within six months after initial presentation to youth mental health services: A machine learning study. PLOS ONE 2020;15(12):e0243467 View
  161. Lee C, Samad M, Hofer I, Cannesson M, Baldi P. Development and validation of an interpretable neural network for prediction of postoperative in-hospital mortality. npj Digital Medicine 2021;4(1) View
  162. van der Ven W, Veelo D, Wijnberge M, van der Ster B, Vlaar A, Geerts B. One of the first validations of an artificial intelligence algorithm for clinical use: The impact on intraoperative hypotension prediction and clinical decision-making. Surgery 2021;169(6):1300 View
  163. Saw S, Biswas A, Mattar C, Lee H, Yap C. Machine learning improves early prediction of small‐for‐gestational‐age births and reveals nuchal fold thickness as unexpected predictor. Prenatal Diagnosis 2021;41(4):505 View
  164. Adil S, Elahi C, Gramer R, Spears C, Fuller A, Haglund M, Dunn T. Predicting the Individual Treatment Effect of Neurosurgery for Patients with Traumatic Brain Injury in the Low-Resource Setting: A Machine Learning Approach in Uganda. Journal of Neurotrauma 2021;38(7):928 View
  165. Polce E, Kunze K, Fu M, Garrigues G, Forsythe B, Nicholson G, Cole B, Verma N. Development of supervised machine learning algorithms for prediction of satisfaction at 2 years following total shoulder arthroplasty. Journal of Shoulder and Elbow Surgery 2021;30(6):e290 View
  166. O’Shea R, Sharkey A, Cook G, Goh V. Systematic review of research design and reporting of imaging studies applying convolutional neural networks for radiological cancer diagnosis. European Radiology 2021 View
  167. Kunze K, Polce E, Rasio J, Nho S. Machine Learning Algorithms Predict Clinically Significant Improvements in Satisfaction After Hip Arthroscopy. Arthroscopy: The Journal of Arthroscopic & Related Surgery 2021;37(4):1143 View
  168. Wang Q, Zhu H. Letter to the Editor: Can Predictive Modeling Tools Identify Patients at High Risk of Prolonged Opioid Use After ACL Reconstruction?. Clinical Orthopaedics & Related Research 2021;479(3):634 View
  169. Brisk R, Bond R, Finlay D, McLaughlin J, Piadlo A, Leslie S, Gossman D, Menown I, McEneaney D, Warren S. The effect of confounding data features on a deep learning algorithm to predict complete coronary occlusion in a retrospective observational setting. European Heart Journal - Digital Health 2021;2(1):127 View
  170. Mordaunt D. On Clinical Utility and Systematic Reporting in Case Studies of Healthcare Process Mining. Comment on: 10.3390/ijerph17041348 “Towards the Use of Standardised Terms in Clinical Case Studies for Process Mining in Healthcare”. International Journal of Environmental Research and Public Health 2020;17(22):8298 View
  171. Kunze K, Polce E, Clapp I, Nwachukwu B, Chahla J, Nho S. Machine Learning Algorithms Predict Functional Improvement After Hip Arthroscopy for Femoroacetabular Impingement Syndrome in Athletes. Journal of Bone and Joint Surgery 2021;103(12):1055 View
  172. Ghaednia H, Lans A, Sauder N, Shin D, Grant W, Chopra R, Oosterhoff J, Fourman M, Schwab J, Tobert D. Deep learning in spine surgery. Seminars in Spine Surgery 2021;33(2):100876 View
  173. Carnero-Pardo C, López-Alcalde S, Florido-Santiago M, Espinosa-García M, Rego-García I, Calle-Calle R, Carrera-Muñoz I, de la Vega-Cotarelo R. Utilidad diagnóstica y validez predictiva del uso conjunto de Fototest y Mini-Cog en deterioro cognitivo. Neurología 2021 View
  174. Karhade A, Shin D, Florissi I, Schwab J. Development of predictive algorithms for length of stay greater than one day after one- or two-level anterior cervical discectomy and fusion. Seminars in Spine Surgery 2021;33(2):100874 View
  175. Adlung L, Cohen Y, Mor U, Elinav E. Machine learning in clinical decision making. Med 2021;2(6):642 View
  176. He B, Chen W, Liu L, Hou Z, Zhu H, Cheng H, Zhang Y, Zhan S, Wang S. Prediction Models for Prognosis of Cervical Cancer: Systematic Review and Critical Appraisal. Frontiers in Public Health 2021;9 View
  177. Zhong J, Si L, Zhang G, Huo J, Xing Y, Hu Y, Zhang H, Yao W. Prognostic models for knee osteoarthritis: a protocol for systematic review, critical appraisal, and meta-analysis. Systematic Reviews 2021;10(1) View
  178. Kunze K, Polce E, Alter T, Nho S. Machine Learning Algorithms Predict Prolonged Opioid Use in Opioid-Naïve Primary Hip Arthroscopy Patients. JAAOS: Global Research and Reviews 2021;5(5):e21.00093 View
  179. Kino S, Hsu Y, Shiba K, Chien Y, Mita C, Kawachi I, Daoud A. A scoping review on the use of machine learning in research on social determinants of health: Trends and research prospects. SSM - Population Health 2021;15:100836 View
  180. Grzenda A, Kraguljac N, McDonald W, Nemeroff C, Torous J, Alpert J, Rodriguez C, Widge A. Evaluating the Machine Learning Literature: A Primer and User’s Guide for Psychiatrists. American Journal of Psychiatry 2021;178(8):715 View
  181. Anteby R, Klang E, Horesh N, Nachmany I, Shimon O, Barash Y, Kopylov U, Soffer S, Valenti L. Deep learning for noninvasive liver fibrosis classification: A systematic review. Liver International 2021 View
  182. Barrachina-Fernández M, Maitín A, Sánchez-Ávila C, Romero J. Wearable Technology to Detect Motor Fluctuations in Parkinson’s Disease Patients: Current State and Challenges. Sensors 2021;21(12):4188 View
  183. Verdonck M, Carvalho H, Berghmans J, Forget P, Poelaert J. Exploratory Outlier Detection for Acceleromyographic Neuromuscular Monitoring: Machine Learning Approach. Journal of Medical Internet Research 2021;23(6):e25913 View
  184. Arabi Belaghi R, Beyene J, McDonald S, Szecsi P. Prediction of preterm birth in nulliparous women using logistic regression and machine learning. PLOS ONE 2021;16(6):e0252025 View
  185. George N, Moseley E, Eber R, Siu J, Samuel M, Yam J, Huang K, Celi L, Lindvall C, Kou Y. Deep learning to predict long-term mortality in patients requiring 7 days of mechanical ventilation. PLOS ONE 2021;16(6):e0253443 View
  186. Dhiman P, Ma J, Navarro C, Speich B, Bullock G, Damen J, Kirtley S, Hooft L, Riley R, Van Calster B, Moons K, Collins G. Reporting of prognostic clinical prediction models based on machine learning methods in oncology needs to be improved. Journal of Clinical Epidemiology 2021;138:60 View
  187. Soffer S, Morgenthau A, Shimon O, Barash Y, Konen E, Glicksberg B, Klang E. Artificial Intelligence for Interstitial Lung Disease Analysis on Chest Computed Tomography: A Systematic Review. Academic Radiology 2021 View
  188. Ahmadi A, Noetel M, Schellekens M, Parker P, Antczak D, Beauchamp M, Dicke T, Diezmann C, Maeder A, Ntoumanis N, Yeung A, Lonsdale C. A Systematic Review of Machine Learning for Assessment and Feedback of Treatment Fidelity. Psychosocial Intervention 2021;30(3):139 View
  189. Zhao H, You J, Peng Y, Feng Y. Machine Learning Algorithm Using Electronic Chart-Derived Data to Predict Delirium After Elderly Hip Fracture Surgeries: A Retrospective Case-Control Study. Frontiers in Surgery 2021;8 View
  190. Kunze K, Polce E, Patel A, Courtney P, Levine B. Validation and performance of a machine-learning derived prediction guide for total knee arthroplasty component sizing. Archives of Orthopaedic and Trauma Surgery 2021 View
  191. Katakam A, Karhade A, Collins A, Shin D, Bragdon C, Chen A, Melnic C, Schwab J, Bedair H. Development of machine learning algorithms to predict achievement of minimal clinically important difference for the KOOS‐PS following total knee arthroplasty. Journal of Orthopaedic Research 2021 View
  192. Zhao J, Zhang W, Zhu Y, Zheng H, Xu L, Zhang J, Liu S, Li F, Song B. Development and Validation of Noninvasive MRI ‐Based Signature for Preoperative Prediction of Early Recurrence in Perihilar Cholangiocarcinoma. Journal of Magnetic Resonance Imaging 2021 View
  193. Forte C, Voinea A, Chichirau M, Yeshmagambetova G, Albrecht L, Erfurt C, Freundt L, Carmo L, Henning R, Horst I, Sundelin T, Wiering M, Axelsson J, Epema A. Deep Learning for Identification of Acute Illness and Facial Cues of Illness. Frontiers in Medicine 2021;8 View
  194. Walsh I, Fishman D, Garcia-Gasulla D, Titma T, Pollastri G, Capriotti E, Casadio R, Capella-Gutierrez S, Cirillo D, Del Conte A, Dimopoulos A, Del Angel V, Dopazo J, Fariselli P, Fernández J, Huber F, Kreshuk A, Lenaerts T, Martelli P, Navarro A, Broin P, Piñero J, Piovesan D, Reczko M, Ronzano F, Satagopam V, Savojardo C, Spiwok V, Tangaro M, Tartari G, Salgado D, Valencia A, Zambelli F, Harrow J, Psomopoulos F, Tosatto S. DOME: recommendations for supervised machine learning validation in biology. Nature Methods 2021 View
  195. Gräßer F, Tesch F, Schmitt J, Abraham S, Malberg H, Zaunseder S. A pharmaceutical therapy recommender system enabling shared decision-making. User Modeling and User-Adapted Interaction 2021 View
  196. Hill B, Rakocz N, Rudas Á, Chiang J, Wang S, Hofer I, Cannesson M, Halperin E. Imputation of the continuous arterial line blood pressure waveform from non-invasive measurements using deep learning. Scientific Reports 2021;11(1) View
  197. Kwong J, McLoughlin L, Haider M, Goldenberg M, Erdman L, Rickard M, Lorenzo A, Hung A, Farcas M, Goldenberg L, Nguan C, Braga L, Mamdani M, Goldenberg A, Kulkarni G. Standardized Reporting of Machine Learning Applications in Urology: The STREAM-URO Framework. European Urology Focus 2021 View
  198. Harrison C, Sidey-Gibbons C. Machine learning in medicine: a practical introduction to natural language processing. BMC Medical Research Methodology 2021;21(1) View
  199. Haymond S, McCudden C. Rise of the Machines: Artificial Intelligence and the Clinical Laboratory. The Journal of Applied Laboratory Medicine 2021 View
  200. Grazal C, Anderson A, Booth G, Geiger P, Forsberg J, Balazs G. A Machine Learning Algorithm to Predict the Likelihood of Prolonged Opioid Use Following Arthroscopic Hip Surgery. Arthroscopy: The Journal of Arthroscopic & Related Surgery 2021 View

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