Extracting Medical Information From Free-Text and Unstructured Patient-Generated Health Data Using Natural Language Processing Methods: Feasibility Study With Real-world Data (Preprint)

Abstract

BACKGROUND Patient generated health data (PGHD) is important to understand a patient's health condition out of the clinic and communicate timely. It plays a supplementary role in preventive medicine, self-care, remote patient monitoring and patient-reported outcomes. In addition to standard measures and structured data (sensors, biometric data), unstructured PGHD (free-text data) can provide a broader view of a patient's journey and health condition. OBJECTIVE Our aim to evaluate feasibility of an NLP pipeline with real-world patient and caregiver data. METHODS Using a zero-shot approach which is adaptive to low-resource settings, the NLP pipeline is built upon named entity recognition (NER) to identify medication and symptoms using the standard ontologies (RXNorm and SNOMED CT). Sentence level dependency parse trees and part-of-speech tags were included to extract additional entity information using the syntactic properties of a note. We tested the model with the patient notes (text-based or transcribed audio notes) collected from 24 parents of children with special healthcare needs during a 2-weeks use of a voice-interactive app. In total, 87 patient notes were used. RESULTS In total, 87 patient notes are included (voice entry transcriptions (n=78) and text entries (n=9)). 30 of the notes are drug and medication-related, and 57 of the notes are symptom-related. We are able to capture medication instances (medication, unit, quantity, and date) and symptoms satisfactorily (Precision >0.65, Recall >0.77, F1>0.72). These results indicate the potential when using NER and dependency parsing through an NLP pipeline on information extraction from unstructured PGHD. CONCLUSIONS Unstructured PGHD provides a new and untapped layer in patient health records which can inform decision making and support remote monitoring and self-care. In this paper, we share the new research findings and preliminary results for a customizable information extraction (IE) NLP model focused on extracting a broad-range of clinical information from unstructured PGHD in low-resource settings, especially as it relates to chronic disease management.