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Machine Learning-Based Hospital Discharge Prediction for Patients With Cardiovascular Diseases: Development and Usability Study

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Abstract
Background: Effective resource management in hospitals can improve the quality of medical services by reducing labor-intensive burdens on staff, decreasing inpatient waiting time, and securing the optimal treatment time. The use of hospital processes requires effective bed management; a stay in the hospital that is longer than the optimal treatment time hinders bed management. Therefore,predicting a patient’s hospitalization period may support the making of judicious decisions regarding bed management.

Objective: First, this study aims to develop a machine learning (ML)?based predictive model for predicting the discharge
probability of inpatients with cardiovascular diseases (CVDs). Second, we aim to assess the outcome of the predictive model and explain the primary risk factors of inpatients for patient-specific care. Finally, we aim to evaluate whether our ML-based predictive model helps manage bed scheduling efficiently and detects long-term inpatients in advance to improve the use of hospital processes and enhance the quality of medical services.

Methods: We set up the cohort criteria and extracted the data from CardioNet, a manually curated database that specializes in CVDs. We processed the data to create a suitable data set by reindexing the date-index, integrating the present features with past features from the previous 3 years, and imputing missing values. Subsequently, we trained the ML-based predictive models and evaluated them to find an elaborate model. Finally, we predicted the discharge probability within 3 days and explained the outcomes of the model by identifying, quantifying, and visualizing its features.

Results: We experimented with 5 ML-based models using 5 cross-validations. Extreme gradient boosting, which was selected
as the final model, accomplished an average area under the receiver operating characteristic curve score that was 0.865 higher than that of the other models (ie, logistic regression, random forest, support vector machine, and multilayer perceptron). Furthermore, we performed feature reduction, represented the feature importance, and assessed prediction outcomes. One of the outcomes, the individual explainer, provides a discharge score during hospitalization and a daily feature influence score to the medical team and patients. Finally, we visualized simulated bed management to use the outcomes.

Conclusions: In this study, we propose an individual explainer based on an ML-based predictive model, which provides the
discharge probability and relative contributions of individual features. Our model can assist medical teams and patients in identifying individual and common risk factors in CVDs and can support hospital administrators in improving the management of hospital beds and other resources.
Author(s)
강희준권한슬김민경김영학김윤하서혜람안임진전태준조하나최희정
Issued Date
2021
Type
Article
Keyword
electronic health recordscardiovascular diseasesdischarge predictionbed managementexplainable artificial intelligence
DOI
10.2196/32662
URI
https://oak.ulsan.ac.kr/handle/2021.oak/8338
https://ulsan-primo.hosted.exlibrisgroup.com/primo-explore/fulldisplay?docid=TN_cdi_proquest_miscellaneous_2598535365&context=PC&vid=ULSAN&lang=ko_KR&search_scope=default_scope&adaptor=primo_central_multiple_fe&tab=default_tab&query=any,contains,Machine%20Learning-Based%20Hospital%20Discharge%20Prediction%20for%20Patients%20With%20Cardiovascular%20Diseases:%20Development%20and%20Usability%20Study&offset=0&pcAvailability=true
Publisher
JMIR Medical Informatics
Location
캐나다
Language
영어
ISSN
2291-9694
Citation Volume
9
Citation Number
11
Citation Start Page
32662
Citation End Page
32662
Appears in Collections:
Medicine > Medicine
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