Robust diagnosis recommendation system for Primary Care Telemedicine using long short-term memory multi-class sequence classification

Background: Telemedicine offers opportunity for robust diagnoses recommendations to support healthcare providers intra-consultation in a way that does not limit providers ability to explore diagnostic codes and make the most appropriate selection for each consultation. Objective: The objective of this work was to develop a recommendation system for ICD-10 coding using multiclass sequence classification and deep learning. The recommendations are intended to support telemedicine clinicians in making timely and appropriate diagnosis selections. The recommendations allow clinicians to find and select the best diagnosis code much quicker and without leaving the telemedicine platform to search codes and code descriptions. Methods: We developed an LSTM model for multi-class text sequence classification to make diagnosis recommendations. The LSTM recommender used text-based symptoms, complaints, and consultation request reasons as model inputs. Data were extracted from a live telemedicine platform which spans general medicine, dermatology, and mental health clinical specialties. A popularity-based model was used for baseline comparison. Results: Using over 2.8 MM telemedicine consultations during 2021 and 2022, our LSTM recommender average accuracy was 31.7%. LSTM recommender average coverage in the top 20 recommended diagnoses was 85.8% with an average personalization score of 0.87. Conclusions: LSTM multi-class sequence classification recommends diagnoses specific to individual consultations, is retrainable on regular intervals, and could improve diagnoses recommendations such that providers require less time and resources searching for diagnosis codes. In addition, the LSTM recommender is robust enough to make recommendations across clinical specialties such as general medicine, dermatology, and mental health.