Abstract: Disclosed herein are methods and systems for a training a model for real-time patient diagnosis. A system may include a computer configured to receive audio data and video data of a clinical encounter, the audio data comprising spoken words by an entity and the video data depicting the entity; retrieve clinical data regarding the entity; execute a model using the words of the audio data and the retrieved clinical data regarding the entity as input, the execution causing the model to output a plurality of clinical diagnoses for the entity; concurrently render the corresponding video data and audio data and the plurality of clinical diagnoses via a computing device associated with a user; and store an indication of a selected clinical diagnosis of the plurality of clinical diagnoses responsive to receiving a selection of the clinical diagnosis at the computing device.

Abstract: A method of assessing the psychological or physiological state by analyzing language cues captured from a patient. The language cues may be semantic cues (speech or written text) or visual cues (expression or body language). Key features are extracted from the language cues and compiled into a data file which is submitted to one or more pre-taught machine learning algorithms. The output of the machine learning algorithms are combined to determine the psychological or physiological state of the patient. The method of teaching the machine learning algorithms is also described. In the preferred form there are three machine learning algorithms including a support vector machine, a decision tree learning algorithm and a neural network.