AI Article Synopsis
- Asthma contributes to numerous hospital visits each year, prompting the need for effective predictive models to identify high-risk patients for proactive care and reduce these encounters.
- Researchers developed a new machine learning model using data from Intermountain Healthcare to predict asthma-related hospitalizations, achieving better accuracy than previous models.
- The study tested the model's effectiveness at Kaiser Permanente Southern California, resulting in a model with strong accuracy (90.08%) and validated its generalizability across different healthcare systems.
Article Abstract
Background: Asthma causes numerous hospital encounters annually, including emergency department visits and hospitalizations. To improve patient outcomes and reduce the number of these encounters, predictive models are widely used to prospectively pinpoint high-risk patients with asthma for preventive care via care management. However, previous models do not have adequate accuracy to achieve this goal well. Adopting the modeling guideline for checking extensive candidate features, we recently constructed a machine learning model on Intermountain Healthcare data to predict asthma-related hospital encounters in patients with asthma. Although this model is more accurate than the previous models, whether our modeling guideline is generalizable to other health care systems remains unknown.
Objective: This study aims to assess the generalizability of our modeling guideline to Kaiser Permanente Southern California (KPSC).
Methods: The patient cohort included a random sample of 70.00% (397,858/568,369) of patients with asthma who were enrolled in a KPSC health plan for any duration between 2015 and 2018. We produced a machine learning model via a secondary analysis of 987,506 KPSC data instances from 2012 to 2017 and by checking 337 candidate features to project asthma-related hospital encounters in the following 12-month period in patients with asthma.
Results: Our model reached an area under the receiver operating characteristic curve of 0.820. When the cutoff point for binary classification was placed at the top 10.00% (20,474/204,744) of patients with asthma having the largest predicted risk, our model achieved an accuracy of 90.08% (184,435/204,744), a sensitivity of 51.90% (2259/4353), and a specificity of 90.91% (182,176/200,391).
Conclusions: Our modeling guideline exhibited acceptable generalizability to KPSC and resulted in a model that is more accurate than those formerly built by others. After further enhancement, our model could be used to guide asthma care management.
International Registered Report Identifier (irrid): RR2-10.2196/resprot.5039.
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Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7683251 | PMC |
| http://dx.doi.org/10.2196/22689 | DOI Listing |
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