GRANULAR DICHOTOMOUS SCORING METHOD FOR MACHINE LEARNING IN HEALTHCARE

摘要

The invention pertains to the fields of healthcare services and machine learning. More particularly, the invention pertains to a computer-implemented system for and method of gathering patient attitudes, values, opinions, traits, indicators of health, and symptoms of distress via a user interface that includes a moveable element with high granularity that presents a series of dichotomous choices allowing a patient or other person to move the element across the range of available values. The disclosure also describes a method of transforming legacy psychometric items into a form that modifies the kind of data produced through their usage. This continuous, high-granularity, data generated by the patient interaction can be used to generate training and test sets in machine learning and to facilitate the AI-optimized assessment, diagnosis, and treatment of mental and emotional health and distress at a distance. The present invention is unlimited with regard to the type of patient entity or healthcare professional entity.