Predicting and explaining inflammation in Crohn's disease patients using predictive analytics methods and electronic medical record data.

Crohn's disease is among the chronic inflammatory bowel diseases that impact the gastrointestinal tract. Understanding and predicting the severity of inflammation in real-time settings is critical to disease management. Extant literature has primarily focused on studies that are conducted in clinical trial settings to investigate the impact of a drug treatment on the remission status of the disease. This research proposes an analytics methodology where three different types of prediction models are developed to predict and to explain the severity of inflammation in patients diagnosed with Crohn's disease. The results show that machine-learning-based analytic methods such as gradient boosting machines can predict the inflammation severity with a very high accuracy (area under the curve = 92.82%), followed by regularized regression and logistic regression. According to the findings, a combination of baseline laboratory parameters, patient demographic characteristics, and disease location are among the strongest predictors of inflammation severity in Crohn's disease patients.

Predicting hospital readmission for lupus patients: An RNN-LSTM-based deep-learning methodology.

Hospital readmission is one of the critical metrics used for measuring the performance of hospitals. The HITECH Act imposes penalties when patients are readmitted to hospitals if they are diagnosed with one of the six conditions mentioned in the Act. However, patients diagnosed with lupus are the sixth highest in terms of rehospitalization. The heterogeneity in the disease and patient characteristics makes it very hard to predict rehospitalization. This research utilizes deep learning methods to predict rehospitalization within 30 days by extracting the temporal relationships in the longitudinal EHR clinical data. Prediction results from deep learning methods such as LSTM are evaluated and compared with traditional classification methods such as penalized logistic regression and artificial neural networks. The simple recurrent neural network method and its variant, gated recurrent unit network, are also developed and validated to compare their performance against the proposed LSTM model. The results indicated that the deep learning method RNN-LSTM has a significantly better performance (with an AUC of .70) compared to traditional classification methods such as ANN (with an AUC of 0.66) and penalized logistic regression (with an AUC of 0.63). The rationale for the better performance of the deep learning method may be due to its ability to leverage the temporal relationships of the disease state in patients over time and to capture the progression of the disease-relevant clinical information from patients' prior visits is carried forward in the memory, which may have enabled the higher predictability for the deep learning methods.