Published on in Vol 4, No 4 (2017): Oct-Dec

Mental and Emotional Self-Help Technology Apps: Cross-Sectional Study of Theory, Technology, and Mental Health Behaviors

Mental and Emotional Self-Help Technology Apps: Cross-Sectional Study of Theory, Technology, and Mental Health Behaviors

Mental and Emotional Self-Help Technology Apps: Cross-Sectional Study of Theory, Technology, and Mental Health Behaviors

Journals

  1. Keim-Malpass J, Clark M, Lake D, Moorman J. Towards development of alert thresholds for clinical deterioration using continuous predictive analytics monitoring. Journal of Clinical Monitoring and Computing 2020;34(4):797 View
  2. Raita Y, Camargo C, Macias C, Mansbach J, Piedra P, Porter S, Teach S, Hasegawa K. Machine learning-based prediction of acute severity in infants hospitalized for bronchiolitis: a multicenter prospective study. Scientific Reports 2020;10(1) View
  3. Goto T, Camargo C, Faridi M, Freishtat R, Hasegawa K. Machine Learning–Based Prediction of Clinical Outcomes for Children During Emergency Department Triage. JAMA Network Open 2019;2(1):e186937 View
  4. Kirkendall E, Ni Y, Lingren T, Leonard M, Hall E, Melton K. Data Challenges With Real-Time Safety Event Detection And Clinical Decision Support. Journal of Medical Internet Research 2019;21(5):e13047 View
  5. Goto T, Jo T, Matsui H, Fushimi K, Hayashi H, Yasunaga H. Machine Learning-Based Prediction Models for 30-Day Readmission after Hospitalization for Chronic Obstructive Pulmonary Disease. COPD: Journal of Chronic Obstructive Pulmonary Disease 2019;16(5-6):338 View
  6. Tarekegn A, Ricceri F, Costa G, Ferracin E, Giacobini M. Predictive Modeling for Frailty Conditions in Elderly People: Machine Learning Approaches. JMIR Medical Informatics 2020;8(6):e16678 View
  7. Al-Mamun M, Brothers T, Newsome A. Development of Machine Learning Models to Validate a Medication Regimen Complexity Scoring Tool for Critically Ill Patients. Annals of Pharmacotherapy 2021;55(4):421 View
  8. Mayampurath A, Jani P, Dai Y, Gibbons R, Edelson D, Churpek M. A Vital Sign-Based Model to Predict Clinical Deterioration in Hospitalized Children*. Pediatric Critical Care Medicine 2020;21(9):820 View
  9. Lin Y, Zhou Y, Faghri F, Shaw M, Campbell R, Moskovitch R. Analysis and prediction of unplanned intensive care unit readmission using recurrent neural networks with long short-term memory. PLOS ONE 2019;14(7):e0218942 View
  10. Inoue T, Ichikawa D, Ueno T, Cheong M, Inoue T, Whetstone W, Endo T, Nizuma K, Tominaga T. XGBoost, a Machine Learning Method, Predicts Neurological Recovery in Patients with Cervical Spinal Cord Injury. Neurotrauma Reports 2020;1(1):8 View
  11. Giannini H, Ginestra J, Chivers C, Draugelis M, Hanish A, Schweickert W, Fuchs B, Meadows L, Lynch M, Donnelly P, Pavan K, Fishman N, Hanson C, Umscheid C. A Machine Learning Algorithm to Predict Severe Sepsis and Septic Shock. Critical Care Medicine 2019;47(11):1485 View
  12. Sosa T, Dewan M, Tegtmeyer K. Back to the Basics or Back to the Future? The Art and Science of Predicting Clinical Deterioration in Hospitalized Children*. Pediatric Critical Care Medicine 2020;21(9):839 View
  13. Raita Y, Goto T, Faridi M, Brown D, Camargo C, Hasegawa K. Emergency department triage prediction of clinical outcomes using machine learning models. Critical Care 2019;23(1) View
  14. Peine A, Hallawa A, Schöffski O, Dartmann G, Fazlic L, Schmeink A, Marx G, Martin L. A Deep Learning Approach for Managing Medical Consumable Materials in Intensive Care Units via Convolutional Neural Networks: Technical Proof-of-Concept Study. JMIR Medical Informatics 2019;7(4):e14806 View
  15. Hu Z, Du D, Kaderali L. A new analytical framework for missing data imputation and classification with uncertainty: Missing data imputation and heart failure readmission prediction. PLOS ONE 2020;15(9):e0237724 View
  16. Sakib N, Ahamed S, Khan R, Griffin P, Haque M. Unpacking Prevalence and Dichotomy in Quick Sequential Organ Failure Assessment and Systemic Inflammatory Response Syndrome Parameters: Observational Data–Driven Approach Backed by Sepsis Pathophysiology. JMIR Medical Informatics 2020;8(12):e18352 View
  17. Schwartz J, Moy A, Rossetti S, Elhadad N, Cato K. Clinician involvement in research on machine learning–based predictive clinical decision support for the hospital setting: A scoping review. Journal of the American Medical Informatics Association 2021;28(3):653 View
  18. Alshwaheen T, Hau Y, Ass'Ad N, Abualsamen M. A Novel and Reliable Framework of Patient Deterioration Prediction in Intensive Care Unit Based on Long Short-Term Memory-Recurrent Neural Network. IEEE Access 2021;9:3894 View
  19. Alexander J, Romito B, Çobanoğlu M. The present and future role of artificial intelligence and machine learning in anesthesiology. International Anesthesiology Clinics 2020;Publish Ahead of Print View
  20. Wu Z, Wang X, Pan R, Huang X, Li Y, Jiang L. Study of the Relationship between ICU Patient Recovery and TCM Treatment in Acute Phase: A Retrospective Study Based on Python Data Mining Technology. Evidence-Based Complementary and Alternative Medicine 2021;2021:1 View
  21. Romero-Brufau S, Whitford D, Johnson M, Hickman J, Morlan B, Therneau T, Naessens J, Huddleston J. Using machine learning to improve the accuracy of patient deterioration predictions: Mayo Clinic Early Warning Score (MC-EWS). Journal of the American Medical Informatics Association 2021 View
  22. Kareemi H, Vaillancourt C, Rosenberg H, Fournier K, Yadav K, Mitchell A. Machine Learning Versus Usual Care for Diagnostic and Prognostic Prediction in the Emergency Department: A Systematic Review. Academic Emergency Medicine 2021;28(2):184 View
  23. Al-Shwaheen T, Moghbel M, Hau Y, Ooi C. Use of learning approaches to predict clinical deterioration in patients based on various variables: a review of the literature. Artificial Intelligence Review 2021 View

Books/Policy Documents

  1. Chang A. Intelligence-Based Medicine. View
  2. Yao J, Liu Y, Li B, Gou S, Pou-Prom C, Murray J, Verma A, Mamdani M, Ghassemi M. Explainable AI in Healthcare and Medicine. View