Pneumonia Outcome Prediction Using Structured And Unstructured Data From EHR

In Intensive Care Unit (ICU), it is important to anticipate interventions for patients at a high-risk of death. This requires identifying those patients ideally at the time of their admission in ICU, and update their initial risk rate every time new data is available. This predictive task can be performed by analyzing structured and unstructured routine data to make sure that we can initiate a prediction for every patient. Traditional statistical tools have been used to assess disease like pneumonia and predict the outcome of a patient. Recently, machine learning models emerged and have shown better performances on such tasks. Although authors have published various results, their works rely on a single datatype, either structured or unstructured data. Using the Medical Information Mart for intensive Care data-set, we are proposing an ensemble model, that aggregates different data-types to predict the outcome of a pneumonia patient admitted in ICU using limited data that can be available at the very early stage of his stay. To demonstrate the importance of this approach, we compared it with 2 different models, one based only on structured data, and another one based on narratives text from caregivers, and we were able to show that our ensemble model can perform way better with an accuracy of 0.98 of F1-score(0.97 MCC), while a model using only structured data had 0.79 of F1-score and where text notes predicted the outcome with an accuracy of 0.89 of Matthews Correlation Coefficient. In addition to showing how ensemble learning models can outperform other models on this task, we demonstrated the importance and usefulness of interpreting the predictions pointing out the leading factors that are determining the global and individual outcome predictions.