THNN - A Neural Network Model for Telehealth Data Incompleteness Prediction

In modern-day medical practices, practitioners and physicians are adapting to new technologies and utilizing new methods of communication with patients. Telemedicine, or telehealth, is one of the newest innovations in medical technology, enabling practitioners to communicate with their patients over the phone, video conferencing, or chat. However, clinical data and sentiments/attitudes are often not reflected in the practitioner’s analysis and diagnosis of the patients they serve. As a solution to the problem of data incompleteness in telehealth, THNN allows medical practices to accommodate for possible missing or incomplete data and provide a greater quality of care overall. Through an ensemble of Natural Language Processing (NLP) and AI-enabled systems, THNN produces sentiment and incompleteness mapping to provide seamless results.Clinical relevance— The method presented utilizes telehealth natural language data to process the sentiments of patients and the incompleteness found in the conversations, increasing the possibility of improved healthcare outcomes.