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A Multi-Model Approach In Developing An Intelligent Assistant For Diagnosis Recommendation In Clinical Health Systems, Christian E. Pulmano, Ma. Regina Justina E. Estuar

Department of Information Systems & Computer Science Faculty Publications

Clinical health information systems capture massive amounts of unstructured data from various health and medical facilities. This study utilizes unstructured patient clinical text data to develop an intelligent assistant that can identify possible related diagnoses based on a given text input. The approach applies a one-vs-rest binary classification technique wherein given an input text data, it is identified whether it can be positively or negatively classified for a given diagnosis. Multi-layer Feed-Forward Neural Network models were developed for each individual diagnosis case. The task of the intelligent assistant is to iterate over all the different models and return those that …