Deep Multi-Instance Learning for Concept Annotation from Medical Time Series Data

Published in NIPS Workshop on Machine Learning for Health (NIPS-ML4H), 2017

Authors: Sanjay Purushotham, Zhengping Che, Bo Jiang and Yan Liu


Recent advances in computing technology and sensor design have made it easier to collect longitudinal or time series data from patients, resulting in a gigantic amount of available medical data. Most of the medical time series lack annotations or even when the annotations are available they could be subjective and prone to human errors. Earlier works have developed natural language processing techniques to extract concept annotations and/or clinical narratives from doctor notes. However, these approaches are slow and do not use the accompanying medical time series data. To address this issue, we introduce the problem of concept annotation for the medical time series data, i.e., the task of predicting and localizing medical concepts by using the time series data as input. We propose a deep Multi Instance Learning framework based on recurrent neural networks, which uses pooling functions for the concept annotation tasks. Empirical results on medical datasets show that our proposed models outperform popular multi-instance learning models.