Predicting readmission after bariatric surgery using machine learning

Butler, Logan R.; Chen, Kevin A; Hsu, Justin; Kapadia, Muneera R.; Gomez, Shawn M.; Farrell, Timothy M.

doi:10.1016/j.soard.2023.05.025

Surgery for Obesity and Related Diseases

2023

DOI: 10.1016/j.soard.2023.05.025

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Predicting readmission after bariatric surgery using machine learning

Logan R. Butler

Kevin A Chen

Justin Hsu

et al.

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Cited by 3 publications

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“…인공지능기법을 보건의료에 적용한 연구에서는 응급실이나 중환자실의 환자 [9,10], 재원일수 [11,12], 재입원 [13,14], 진료비 [2] 등을 예측한 사례가 있다. 환자 수 예측에는 병원의 외래환자 수 예측을 위한 시계열 데이터처리 딥러닝 시스템 [10], 기계 적 학습방법을 이용한 응급실 환자 수 [9]가 있으며, 병원의 입원 재원기간 예측에 관한 연구들도 다수 존재한다 [15,16].…”

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Performance Evaluation of Artificial Intelligence Methods Predicting Annual Number of Patients in Hospitals

Park,

Lee,

Lee

et al. 2024

HIRA Research

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Background: Multiple artificial intelligence (AI) methods are being applied to estimate health care sectors' capacities. This study evaluated AI techniques' predictive performance for determining the annual number of patients in hospitals. Methods:The units of analysis were individual hospitals. This study used a dataset consisting data from 708 hospitals in 2021. Training and test datasets were divided by a ratio of 8:2. The two dependent variables were total length of inpatient stay (TLOS) and total number of outpatient visits (TNOV). Four machine learning techniques were used: linear regressor (LR), random forest (RF), gradient boosting model, and extreme gradient boost. Model performance was evaluated with coefficient of determination (R 2 ) in addition to mean squared error (MSE), mean absolute error (MAE), and root mean square error (RMSE). The study used Python version 3.7.0. Results:The best-fit model for predicting TLOS (R 2 =0.730) and TNOV (R 2 =0.707) was RF and LR, respectively. Both RF and LR also had the lowest MSE, MAE, and RMSE scores for inpatient and outpatient predictions. The most remarkable factor associated with a good prediction of the inpatient sector was number of beds followed by number of nurses. In contrast, the best predictive factor for the outpatient sector was number of doctors followed by number of local households. Conclusion:This study confirmed that AI methods are successful at predicting hospitals' annual patient loads. Among them, the RF and LR models showed the best performance in predicting both inpatient and outpatient annual loads, respectively. This study proposes that the AI tools utilized in this study can accurately predict future medical demand.

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Performance Evaluation of Artificial Intelligence Methods Predicting Annual Number of Patients in Hospitals

Park,

Lee,

Lee

et al. 2024

HIRA Research

View full text Add to dashboard Cite

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Comment on: Predicting readmission after bariatric surgery using machine learning

Rogers

2023

Surgery for Obesity and Related Diseases

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Machine learning analysis of lab tests to predict bariatric readmissions

Zhang,

Chen,

Yang

et al. 2024

Sci Rep

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The purpose of this study was to develop a machine learning model for predicting 30-day readmission after bariatric surgery based on laboratory tests. Data were collected from patients who underwent bariatric surgery between 2018 and 2023. Laboratory test indicators from the preoperative stage, one day postoperatively, and three days postoperatively were analyzed. Least absolute shrinkage and selection operator regression was used to select the most relevant features. Models constructed included support vector machine (SVM), generalized linear model, multi-layer perceptron, random forest, and extreme gradient boosting. Model performance was evaluated and compared using the area under the receiver operating characteristic curve (AUROC). A total of 1262 patients were included, of which 7.69% of cases were readmitted. The SVM model achieved the highest AUROC (0.784; 95% CI 0.696–0.872), outperforming other models. This suggests that machine learning models based on laboratory test data can effectively identify patients at high risk of readmission after bariatric surgery.

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Predicting readmission after bariatric surgery using machine learning

Cited by 3 publications

References 28 publications

Performance Evaluation of Artificial Intelligence Methods Predicting Annual Number of Patients in Hospitals

Performance Evaluation of Artificial Intelligence Methods Predicting Annual Number of Patients in Hospitals

Comment on: Predicting readmission after bariatric surgery using machine learning

Machine learning analysis of lab tests to predict bariatric readmissions

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