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.

利用保险数据，通过构建双向 LSTM 神经网络，本研究成功预测了医院的所有诊断病例中患者 30 天再次入院的风险。与随机森林分类器的结果相比，LSTM 模型的性能显著提高，证明了序列预测在该问题中的重要性。同时，加入了 30 天的历史数据后，模型的性能也有显著提高，提示历史诊疗记录的重要性。