A simulation-based evaluation of machine learning models for clinical decision support: application and analysis using hospital readmission
| Autor: | Kumar Rajaram, Eilon Gabel, Velibor V. Mišić |
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| Rok vydání: | 2021 |
| Předmět: |
Schedule
Computer science Computer applications to medicine. Medical informatics R858-859.7 Medicine (miscellaneous) Health Informatics 030204 cardiovascular system & hematology Machine learning computer.software_genre Clinical decision support system Article 03 medical and health sciences 0302 clinical medicine Health Information Management 030212 general & internal medicine Implementation Statistic Selection (genetic algorithm) business.industry Statistics Emergency department Predictive analytics Health policy Health services Computer Science Applications Workflow Artificial intelligence business computer |
| Zdroj: | NPJ Digital Medicine npj Digital Medicine, Vol 4, Iss 1, Pp 1-11 (2021) |
| ISSN: | 2398-6352 |
| DOI: | 10.1038/s41746-021-00468-7 |
| Popis: | The interest in applying machine learning in healthcare has grown rapidly in recent years. Most predictive algorithms requiring pathway implementations are evaluated using metrics focused on predictive performance, such as the c statistic. However, these metrics are of limited clinical value, for two reasons: (1) they do not account for the algorithm’s role within a provider workflow; and (2) they do not quantify the algorithm’s value in terms of patient outcomes and cost savings. We propose a model for simulating the selection of patients over time by a clinician using a machine learning algorithm, and quantifying the expected patient outcomes and cost savings. Using data on unplanned emergency department surgical readmissions, we show that factors such as the provider’s schedule and postoperative prediction timing can have major effects on the pathway cohort size and potential cost reductions from preventing hospital readmissions. |
| Databáze: | OpenAIRE |
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