Tentative diagnosis prediction via deep understanding of patient narratives

A tentative diagnosis is a preliminary suspicion of patient status, which is usually made by physicians according to patient narrative right at admission. It largely depends on the experiences and professional knowledge of physicians. We explored a combination model for automatic tentative diagnosis prediction based on clinical narratives. Text features are extracted in two ways. Firstly, the context semantic features are extracted by attention-based bidirectional long-short term memory (BiLSTM) network. Secondly, the symptom concepts recognized from input texts by Metamap and are vectorized by TF-IDF. Two combination strategies are proposed to utilize both two features for one candidate international classification of diseases (ICD) code recommendation: feature vectors combination and prediction results combination. The experiments performed on MIMIC III dataset. Both of the two combination strategies achieved better performance, comparing with either of the model based on single type feature.