Machine Learning–Based Prediction Models for Different Clinical Risks in Different Hospitals: Evaluation of Live Performance

Journals

1. Fliegenschmidt J, Hulde N, Gedinha Preising M, Ruggeri S, Szymanowsky R, Meesseman L, Sun H, Dahlweid M, von Dossow V. Leveraging artificial intelligence for the management of postoperative delirium following cardiac surgery. European Journal of Anaesthesiology Intensive Care 2023;2(1):e0010 View
2. Matsumoto K, Nohara Y, Sakaguchi M, Takayama Y, Fukushige S, Soejima H, Nakashima N. Delirium Prediction Using Machine Learning Interpretation Method and Its Incorporation into a Clinical Workflow. Applied Sciences 2023;13(3):1564 View
3. Wehkamp K, Krawczak M, Schreiber S. The quality and utility of artificial intelligence in patient care. Deutsches Ärzteblatt international 2023 View
4. Maheshwari A, Motta M, Lui K. We Need New Tools to Evaluate Neurological Development in Utero and after Birth. Newborn 2023;2(2):iv View
5. Bhat J, Feng X, Mir Z, Raina A, Siddique K. Recent advances in artificial intelligence, mechanistic models, and speed breeding offer exciting opportunities for precise and accelerated genomics‐assisted breeding. Physiologia Plantarum 2023;175(4) View
6. Jankowska A, Ngai J. I, Robot: Healthcare Decisions Made With Artificial Intelligence. Journal of Cardiothoracic and Vascular Anesthesia 2023;37(10):1852 View
7. Krumm A, Ötleş E, Marcotte K, Spencer B, Izadi S, George B, Zendejas B. Strategies for evaluating predictive models: examples and implications based on a natural language processing model used to assess operative performance feedback. Global Surgical Education - Journal of the Association for Surgical Education 2023;3(1) View
8. Strating T, Shafiee Hanjani L, Tornvall I, Hubbard R, Scott I. Navigating the machine learning pipeline: a scoping review of inpatient delirium prediction models. BMJ Health & Care Informatics 2023;30(1):e100767 View
9. Giddings R, Joseph A, Callender T, Janes S, van der Schaar M, Sheringham J, Navani N. Factors influencing clinician and patient interaction with machine learning-based risk prediction models: a systematic review. The Lancet Digital Health 2024;6(2):e131 View
10. Marques L, Costa B, Pereira M, Silva A, Santos J, Saldanha L, Silva I, Magalhães P, Schmidt S, Vale N. Advancing Precision Medicine: A Review of Innovative In Silico Approaches for Drug Development, Clinical Pharmacology and Personalized Healthcare. Pharmaceutics 2024;16(3):332 View
11. Li M, Han S, Liang F, Hu C, Zhang B, Hou Q, Zhao S. Machine Learning for Predicting Risk and Prognosis of Acute Kidney Disease in Critically Ill Elderly Patients During Hospitalization: Internet-Based and Interpretable Model Study. Journal of Medical Internet Research 2024;26:e51354 View
12. Liou L, Scott E, Parchure P, Ouyang Y, Egorova N, Freeman R, Hofer I, Nadkarni G, Timsina P, Kia A, Levin M. Assessing calibration and bias of a deployed machine learning malnutrition prediction model within a large healthcare system. npj Digital Medicine 2024;7(1) View
13. Cabanillas Silva P, Sun H, Rodriguez-Brazzarola P, Rezk M, Zhang X, Fliegenschmidt J, Hulde N, von Dossow V, Meesseman L, Depraetere K, Szymanowsky R, Stieg J, Dahlweid F. Evaluating gender bias in ML-based clinical risk prediction models: A study on multiple use cases at different hospitals. Journal of Biomedical Informatics 2024;157:104692 View
14. Jauk S, Kramer D, Sumerauer S, Veeranki S, Schrempf M, Puchwein P. Machine learning-based delirium prediction in surgical in-patients: a prospective validation study. JAMIA Open 2024;7(3) View