Unsupervised patient representations from clinical notes with interpretable classification decisions

We have two main contributions in this work: 1. We explore the usage of a stacked denoising autoencoder, and a paragraph vector model to learn task-independent dense patient representations directly from clinical notes. We evaluate these representations by using them as features in multiple supervised setups, and compare their performance with those of sparse representations. 2. To understand and interpret the representations, we explore the best encoded features within the patient representations obtained from the autoencoder model. Further, we calculate the significance of the input features of the trained classifiers when we use these pretrained representations as input.Comment: Accepted poster at NIPS 2017 Workshop on Machine Learning for Health (https://ml4health.github.io/2017/

KFU at CLEF eHealth 2017 Task 1: ICD-10 coding of English death certificates with recurrent neural networks

This paper describes the participation of the KFU team in the CLEF eHealth 2017 challenge. Specifically, we participated in Task 1, namely "Multilingual Information Extraction - ICD-10 coding" for which we implemented recurrent neural networks to automatically assign ICD-10 codes to fragments of death certificates written in English. Our system uses Long Short-Term Memory (LSTM) to map the input sequence into a vector representation, and then another LSTM to decode the target sequence from the vector. We initialize the input representations with word embeddings trained on user posts in social media. The encoderdecoder model obtained F-measure of 85.01% on a full test set with significant improvement as compared to the average score of 62.2% for all participants' approaches. We also obtained significant improvement from 26.1% to 44.33% on an external test set as compared to the average score of the submitted runs