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.

PC-LSTM: Ontology-based Long Short-Term Memory State Model for Data Incompleteness Prediction.

Medical practices are engaged and motivated by new technologies and methods to enhance patient care as efficiently as possible. These new methods and technologies give way for medical practices and clinicians to have the insight, comprehension, and projections to develop better decisions and overall levels of care. In this paper, we propose a model, PatientCentered-LSTM (or PC-LSTM), using the states of the LSTM model to produce a novel, ontology-based state system for data incompleteness. The overall architecture and system design are based around utilizing the hidden and cell states of the LSTM model to produce a network of states for each of the corresponding hierarchies in an Electronic Health Record (EHR) system. The resulting methodology allows for an accurate and precise approach to predicting data incompleteness in electronic health records. Clinical relevance- The method presented uses the hierarchical nature of electronic health record systems to positively influence the analysis of its data completeness; thereby, increasing the possibility of improved healthcare outcomes.