Leveraging mobile NER for real-time capture of symptoms, diagnoses, and treatments from clinical dialogues

Abstract

In the dynamic world of healthcare technology, efficiently and accurately extracting medical data from physician-patient conversations is vital. This paper presents a new approach in healthcare technology, employing Natural Language Processing (NLP) to identify and extract critical information from doctor-patient conversations on mobile devices. Unlike traditional methods that rely on Electronic Health Records, our novel application enables the extraction of symptoms, diagnoses, and treatments directly on a mobile device during medical consultations, significantly enhancing patient privacy. We managed to integrate both Bidirectional Encoder Representations from Transformers (BERT) models and optimized Large Language Models (LLMs) on a mobile device without compromising performance significantly. Our findings reveal that the BERT model attained an F1-score of 85.1%, while FLERT and its compressed variant DistilFLERT showed superior performance. The FLAN-T5 model outperformed all models we tested with scores up to 92.7%. These results highlight the efficacy of leveraging advanced NLP and LLM technologies in healthcare environments on a mobile device, offering a promising direction for accessible and efficient patient care.