Predicting health patterns using sensor sequence similarity and NLP

Health information technology has been used in long-term care to improve outcomes and reduce cost. In Tiger Pace, an aging in place facility from Columbia, MO, we deployed sensor networks together with an electronic health record (EHR) system to provide early illness recognition. In this paper, we describe a methodology for early illness based on non-wearable sensor data and concepts extracted from nursing notes using Natural Language Processing (NLP). The methodology is inspired from genomie sequence annotation using BLAST. First, we extract a set of Unified Medical Language System (UMLS) concepts from each nursing note using Metamap, a NLP tool provided by UMLS. Then, we associate each daily sensor sequence with the medical concepts related to the nursing notes issued that day for that patient. Finally, to infer the health concepts for an unknown day, we compute the similarity between its sensor sequence and those available in the database. The challenges presented by this method are finding the most suitable multi-attribute time sequence similarity and aggregation of the retrieved concepts. On a pilot dataset from three Tiger Place residents, with a total of 1685 sensor days and 358 nursing records, we obtained an average precision of 0.34 and a recall of 0.52.