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Table 1 Descriptive table of ML-based predictive works on discharge disposition

From: Early prediction of patient discharge disposition in acute neurological care using machine learning

Author

Setting

Discharge Outcome

Summary

Models Used

Goto et al. [9]

Asthma/COPD patients in the emergency dept

Binary (ICU vs. non-ICU hospitalization)

Compares the four models’ predictive capability to a baseline logistic regression concluding that the ML models markedly improved prediction capability

Lasso regression (LR), Randon forest (RF), Boosted decision tree (BDT), Artificial Neural network (ANN)

Karhade et al. [10]

Elective inpatient surgery for lumbar degenerative disc disorders

Binary (routine vs non-routine postoperative discharge)

Created an open-access web application for healthcare professionals that showed promising results for preoperative prediction of non-routine discharge

ANN, Support vector machine (SVM), Bayes point machine, BDT

Greenstein et al. [11]

Post-operative discharge after total joint arthroplasty (TJA)

Binary (skilled nursing facility vs. elsewhere)

Developed an EMR-integrated prediction tool to predict discharge disposition after TJA

ANN

Ogink et al. [12]

Post-operative discharge after degenerative spondylolisthesis

Binary (home vs. non-home)

Similar to [5], compares a set of predictive model’s performance after elective spinal surgery

ANN, SVM, Bayes point machine, BDT

Cho et al. [13]

Post-stroke acute care

Binary (home vs. facility)

Compares the performance of four interpretable ML models on post-stroke discharge prediction

LR, RF, AdaBoost, multi-layer perceptron

Muhlestein et al. [14]

Post-craniotomy

Binary (home vs. non-home)

Uses 26 ML algorithms to combine the best performers into ensemble model investigate the impact of race on discharge disposition

Ensemble (various)

Muhlestein et al. [15]

Post-meningioma resection

Binary (home vs. non-home)

Similar to [10], creates an ensemble model showing significantly improved accuracy compared to traditional logistic regression

Ensemble (various)

Abad et al. [16]

ICU critical care

Multi-class (home, nursing facility, rehab, death)

Investigates the impacts of APACHE IV scores on patient discharge via an array of different ML models

LR, XGBC, RF

This research study

Post-stroke acute care

Binary (home vs. non-home) and Multi-class (home, nursing facility, rehab, death)

Compares the performance of 5 ML models in both a binary and multi-class experiment and investigates the explainability of the best-performing models

RF, XGBC, KNN, SVM, LR