Predicting Hospital Readmission Within Thirty-Days

Ghamdi, Huda Al; Alshammari, Riyad

doi:10.1166/jmihi.2017.2107

Cited by 1 publication

(1 citation statement)

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“…Furthermore, the study suggested that SVM was the best model. Of all the variables used in the training set, the visit history, lab tests, and patient age had more weight in the prediction than other variables in addition to the length of stay before discharge [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Model for Predicting the Risk of Readmission in Community-Acquired Pneumonia

Aldhoayan¹,

Alghamdi²,

Khayat³

et al. 2022

Cureus

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BackgroundPneumonia is a common respiratory infection that affects all ages, with a higher rate anticipated as age increases. It is a disease that impacts patient health and the economy of the healthcare institution. Therefore, machine learning methods have been used to guide clinical judgment in disease conditions and can recognize patterns based on patient data. This study aims to develop a prediction model for the readmission risk within 30 days of patient discharge after the management of community-acquired pneumonia (CAP). MethodologyUnivariate and multivariate logistic regression were used to identify the statistically significant factors that are associated with the readmission of patients with CAP. Multiple machine learning models were used to predict the readmission of CAP patients within 30 days by conducting a retrospective observational study on patient data. The dataset was obtained from the Hospital Information System of a tertiary healthcare organization across Saudi Arabia. The study included all patients diagnosed with CAP from 2016 until the end of 2018. ResultsThe collected data included 8,690 admission records related to CAP for 5,776 patients (2,965 males, 2,811 females). The results of the analysis showed that patient age, heart rate, respiratory rate, medication count, and the number of comorbidities were significantly associated with the odds of being readmitted. All other variables showed no significant effect. We ran four algorithms to create the model on our data. The decision tree gave high accuracy of 83%, while support vector machine (SVM), random forest (RF), and logistic regression provided better accuracy of 90%. However, because the dataset was unbalanced, the precision and recall for readmission were zero for all models except the decision tree with 16% and 18%, respectively. By applying the Synthetic Minority Oversampling TEchnique technique to balance the training dataset, the results did not change significantly; the highest precision achieved was 16% in the SVM model. RF achieved the highest recall with 45%, but without any advantage to this model because the accuracy was reduced to 65%. ConclusionsPneumonia is an infectious disease with major health and economic complications. We identified that less than 10% of patients were readmitted for CAP after discharge; in addition, we identified significant predictors. However, our study did not have enough data to develop a proper machine learning prediction model for the risk of readmission.

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Section: Introductionmentioning

confidence: 99%