Advances in Prediction of Readmission Rates Using Long Term Short Term
Memory Networks on Healthcare Insurance Data
- URL: http://arxiv.org/abs/2207.00066v1
- Date: Thu, 30 Jun 2022 19:07:10 GMT
- Title: Advances in Prediction of Readmission Rates Using Long Term Short Term
Memory Networks on Healthcare Insurance Data
- Authors: Shuja Khalid, Francisco Matos, Ayman Abunimer, Joel Bartlett, Richard
Duszak, Michal Horny, Judy Gichoya, Imon Banerjee, Hari Trivedi
- Abstract summary: 30-day hospital readmission is a long standing medical problem that affects patients' morbidity and mortality and costs billions of dollars annually.
We developed a bi-directional Long Short Term Memory (LSTM) Network that is able to use readily available insurance data.
Our results demonstrate that a machine learning model is able to predict risk of inpatient readmission with reasonable accuracy for all patients.
- Score: 1.454498931674109
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 30-day hospital readmission is a long standing medical problem that affects
patients' morbidity and mortality and costs billions of dollars annually.
Recently, machine learning models have been created to predict risk of
inpatient readmission for patients with specific diseases, however no model
exists to predict this risk across all patients. We developed a bi-directional
Long Short Term Memory (LSTM) Network that is able to use readily available
insurance data (inpatient visits, outpatient visits, and drug prescriptions) to
predict 30 day re-admission for any admitted patient, regardless of reason. The
top-performing model achieved an ROC AUC of 0.763 (0.011) when using
historical, inpatient, and post-discharge data. The LSTM model significantly
outperformed a baseline random forest classifier, indicating that understanding
the sequence of events is important for model prediction. Incorporation of
30-days of historical data also significantly improved model performance
compared to inpatient data alone, indicating that a patients clinical history
prior to admission, including outpatient visits and pharmacy data is a strong
contributor to readmission. Our results demonstrate that a machine learning
model is able to predict risk of inpatient readmission with reasonable accuracy
for all patients using structured insurance billing data. Because billing data
or equivalent surrogates can be extracted from sites, such a model could be
deployed to identify patients at risk for readmission before they are
discharged, or to assign more robust follow up (closer follow up, home health,
mailed medications) to at-risk patients after discharge.
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