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A Roadmap for Boosting Model Generalizability for Predicting Hospital Encounters for Asthma

In the United States, ~9% of people have asthma. Each year, asthma incurs high health care cost and many hospital encounters covering 1.8 million emergency room visits and 439,000 hospitalizations. A small percentage of patients with asthma use most health care resources. To improve outcomes and cut...

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Detalles Bibliográficos
Autor principal: Luo, Gang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8924785/
https://www.ncbi.nlm.nih.gov/pubmed/35230246
http://dx.doi.org/10.2196/33044
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author Luo, Gang
author_facet Luo, Gang
author_sort Luo, Gang
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description In the United States, ~9% of people have asthma. Each year, asthma incurs high health care cost and many hospital encounters covering 1.8 million emergency room visits and 439,000 hospitalizations. A small percentage of patients with asthma use most health care resources. To improve outcomes and cut resource use, many health care systems use predictive models to prospectively find high-risk patients and enroll them in care management for preventive care. For maximal benefit from costly care management with limited service capacity, only patients at the highest risk should be enrolled. However, prior models built by others miss >50% of true highest-risk patients and mislabel many low-risk patients as high risk, leading to suboptimal care and wasted resources. To address this issue, 3 site-specific models were recently built to predict hospital encounters for asthma, gaining up to >11% better performance. However, these models do not generalize well across sites and patient subgroups, creating 2 gaps before translating these models into clinical use. This paper points out these 2 gaps and outlines 2 corresponding solutions: (1) a new machine learning technique to create cross-site generalizable predictive models to accurately find high-risk patients and (2) a new machine learning technique to automatically raise model performance for poorly performing subgroups while maintaining model performance on other subgroups. This gives a roadmap for future research.
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spelling pubmed-89247852022-03-17 A Roadmap for Boosting Model Generalizability for Predicting Hospital Encounters for Asthma Luo, Gang JMIR Med Inform Viewpoint In the United States, ~9% of people have asthma. Each year, asthma incurs high health care cost and many hospital encounters covering 1.8 million emergency room visits and 439,000 hospitalizations. A small percentage of patients with asthma use most health care resources. To improve outcomes and cut resource use, many health care systems use predictive models to prospectively find high-risk patients and enroll them in care management for preventive care. For maximal benefit from costly care management with limited service capacity, only patients at the highest risk should be enrolled. However, prior models built by others miss >50% of true highest-risk patients and mislabel many low-risk patients as high risk, leading to suboptimal care and wasted resources. To address this issue, 3 site-specific models were recently built to predict hospital encounters for asthma, gaining up to >11% better performance. However, these models do not generalize well across sites and patient subgroups, creating 2 gaps before translating these models into clinical use. This paper points out these 2 gaps and outlines 2 corresponding solutions: (1) a new machine learning technique to create cross-site generalizable predictive models to accurately find high-risk patients and (2) a new machine learning technique to automatically raise model performance for poorly performing subgroups while maintaining model performance on other subgroups. This gives a roadmap for future research. JMIR Publications 2022-03-01 /pmc/articles/PMC8924785/ /pubmed/35230246 http://dx.doi.org/10.2196/33044 Text en ©Gang Luo. Originally published in JMIR Medical Informatics (https://medinform.jmir.org), 01.03.2022. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Medical Informatics, is properly cited. The complete bibliographic information, a link to the original publication on https://medinform.jmir.org/, as well as this copyright and license information must be included.
spellingShingle Viewpoint
Luo, Gang
A Roadmap for Boosting Model Generalizability for Predicting Hospital Encounters for Asthma
title A Roadmap for Boosting Model Generalizability for Predicting Hospital Encounters for Asthma
title_full A Roadmap for Boosting Model Generalizability for Predicting Hospital Encounters for Asthma
title_fullStr A Roadmap for Boosting Model Generalizability for Predicting Hospital Encounters for Asthma
title_full_unstemmed A Roadmap for Boosting Model Generalizability for Predicting Hospital Encounters for Asthma
title_short A Roadmap for Boosting Model Generalizability for Predicting Hospital Encounters for Asthma
title_sort roadmap for boosting model generalizability for predicting hospital encounters for asthma
topic Viewpoint
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8924785/
https://www.ncbi.nlm.nih.gov/pubmed/35230246
http://dx.doi.org/10.2196/33044
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