Artificial intelligence and computer simulation models in critical illness
Open Access
- 5 June 2020
- journal article
- review article
- Published by Baishideng Publishing Group Inc. in World Journal of Critical Care Medicine
- Vol. 9 (2), 13-19
- https://doi.org/10.5492/wjccm.v9.i2.13
Abstract
Widespread implementation of electronic health records has led to the increased use of artificial intelligence (AI) and computer modeling in clinical medicine. The early recognition and treatment of critical illness are central to good outcomes but are made difficult by, among other things, the complexity of the environment and the often non-specific nature of the clinical presentation. Increasingly, AI applications are being proposed as decision supports for busy or distracted clinicians, to address this challenge. Data driven “associative” AI models are built from retrospective data registries with missing data and imprecise timing. Associative AI models lack transparency, often ignore causal mechanisms, and, while potentially useful in improved prognostication, have thus far had limited clinical applicability. To be clinically useful, AI tools need to provide bedside clinicians with actionable knowledge. Explicitly addressing causal mechanisms not only increases validity and replicability of the model, but also adds transparency and helps gain trust from the bedside clinicians for real world use of AI models in teaching and patient care.Keywords
This publication has 40 references indexed in Scilit:
- Tele-ICU TechnologiesCritical Care Clinics, 2019
- Impact of Intensive Care Unit Telemedicine on OutcomesCritical Care Clinics, 2019
- Machine learning in medicine: a practical introductionBMC Medical Research Methodology, 2019
- Artificial Intelligence in Critical CareInternational Anesthesiology Clinics, 2019
- Encouraging Physical Activity in Patients With Diabetes: Intervention Using a Reinforcement Learning SystemJournal of Medical Internet Research, 2017
- A Semi-Supervised Learning Approach to Enhance Health Care Community–Based Question Answering: A Case Study in AlcoholismJMIR Public Health and Surveillance, 2016
- Machine Learning in MedicineCirculation, 2015
- What Is Machine Learning?Published by Springer Science and Business Media LLC ,2015
- Deep Learning: Methods and ApplicationsFoundations and Trends® in Signal Processing, 2014
- Artificial intelligence in medicineThe Annals of The Royal College of Surgeons of England, 2004