Risk Factors Identification for Heart Disease in Unstructured Dataset using Deep Learning Approach An automatic identification of the heart disease status can give timely support to medical decisionmaking process. The identified key factors can expedite the prevention actions in right direction. Existing solutions to identify disease factor or current disease status is based on hybrid approach which requires significant amount of human efforts. In addition to that an information extraction and deidentification on clinical dataset performed manually is error prone, expensive, prohibitively and time consuming 1. These drawbacks can be overcome by using deep learning approach, in this paper we have used LSTM, BiLSTM and Googleu0027s Sentence encoder for automatic disease status identification. We have used i2b2 dataset with the proposed deep learning models that have led to promising results.

Domain Knowledge Guided Deep Learning with Electronic Health Records Due to their promising performance in clinical risk prediction with Electronic Health Records (EHRs), deep learning methods have attracted significant interest from healthcare researchers. However, there are 4 challenges: (i) Data insufficiency. Many methods require large amounts of training data to achieve satisfactory results. (ii) Interpretability. Results from many methods are hard to explain to clinicians (e.g., why the models make particular predictions and which events cause clinical outcomes). (iii) Domain knowledge integration. No existing method dynamically exploits complicated medical knowledge (e.g., relations such as cause and iscausedby between clinical events). (iv) Time interval information. Most existing methods only consider the relative order of visits from EHRs, but ignore the irregular time intervals between neighboring visits. In the study, we propose a new model, Domain Knowledge Guided Recurrent Neural Networks (DGRNN), by directly introducing domain knowledge from the medical knowledge graph into an RNN architecture, as well as taking the irregular time intervals into account. Experimental results on heart failure risk prediction tasks show that our model not only outperforms stateoftheart deeplearning based risk prediction models, but also associates individual medical events with heart failure onset, thus paving the way for interpretable accurate clinical risk predictions.