The effective management of patient hospital stays is one of the most challenging yet paramount priorities of modern healthcare systems For this analysis, data extracted from the MIMIC database is used to build two models in order to generate both predictive and exploratory insights regarding patient hospital stays. First is a machine learning classification model to predict the categorical length of a patient’s hospital stay, given a patient’s observable characteristics at time of admission. Then, used unsupervised learning techniques to cluster patients based on the number of various patient-caretaker interactions — such as procedures, inputs taken, and drugs prescribed — which can quantify the amount of human or physical resources used by a patient during their stay. LOS is divided into three classes with a comparable number of observations in each class: Short stays: 0–5 days, Medium stays: 6–10 days, Long stays: greater than 10 days. To predict the length of a patient’s stay, three different classification models are used:Multinomial Logistic Regression (MLR), Random Forest (RF), Gradient Boosting Machine (GBM). Gradient Boosting Machine marginally outperformed the other two models tested. To complement the classification task, K-Means Clustering Model is used to explore human or physical resources utilized on patients during their stay
Input variables : Gender, age, admit type, location, diagnosis, insurance
Output Variables : Length of stay (Short stays: 0–5 days,
Medium stays: 6–10 days,
Long stays: greater than 10 days)
| Statistical | : | Somers D | Accuracy | Precision and Recall | Confusion Matrix | F1 Score | Roc and Auc | Prevalence | Detection Rate | Balanced Accuracy | Cohen's Kappa | Concordance | Gini Coefficent | KS Statistic | Youden's J Index |
| Business | : | Bed Occupancy Rate | Medical Equipment Utilization | Optimized Hospital Resource Utilization |
| Infrastructure | : | Log Bytes | Logging/User/IAMPolicy | Logging/User/VPN | CPU Utilization | Memory Usage | Error Count | Prediction Count | Prediction Latencies | Private Endpoint Prediction Latencies | Private Endpoint Response Count |
Visit Model : github.com
Additional links : towardsdatascience.com
| Model Category | : | Public |
| Date Published | : | March, 2021 |
| Healthcare Domain | : |
Payer
Provider |
| Code | : | github.com |
| Utilization Management |
| Hospital Resource Utilization |