Systems and method for medical platform employing artificial intellegence and wearable devices

Abstract

A system, method and computer program product for a medical platform, including a wearable device configured to collect implicit patient information; a database configured to receive the implicit and explicit patient information from the wearable device and generate aggregated patient information; a machine learning system configured to receive the aggregated patient information from the database and generate personalized patient intervention information; and a patient user interface configured to receive patient intervention information.

What is claimed is: 
     
         1 . A computer implemented system for a medical platform for use with at least one user in accordance with treatment for a patient, the system comprising:
 a database configured to receive implicit and explicit patient information and generate aggregated patient information;   a wearable device configured to collect and send the implicit and explicit patient information to the database, the wearable device including:
 an interface configured to receive implicit patient information on subjective factors including at least one of pain, user behavior, demographic and medical background from the aggregate information from the database; 
 at least one near-field communication sensor and biosensor, the near-field communication sensor and biosensor being configured to measure health condition factors of the user and record data of the communication between the patient and the users, wherein the measured health condition factors of the users are included in the explicit patient information; 
   a machine learning system configured to:
 receive the aggregated patient information from the database, 
 identify sentiment of the patient and the user based on the recorded data of the communication between the patient and the user; and 
 generate and transmit to the user a recommendation related to treatment based on the identified sentiment and the aggregated patient information, and 
   a user interface display configured to display the generated recommendation to the user.   
     
     
         2 . The system of  claim 1 , further comprising a wearable device configured to receive the explicit and implicit patient information and the data from the communication between the patient and the user. 
     
     
         3 . The system of  claim 2 , wherein the wearable device transmits the patient information to the database to aggregate available patient information. 
     
     
         4 . The system of  claim 1 , wherein the user is a healthcare professional engaged in treating the patient, and wherein the aggregated patient information from the database is received by the machine learning system and the recorded data of the communication between the patient and the user. 
     
     
         5 . The system of  claim 4 , wherein the user is a supporting member of the patient, and wherein the aggregated patient information from the database is received by the machine learning system and the recorded data of the communication between the patient and the user. 
     
     
         6 . The system of  claim 5 , wherein the machine learning system determines the sentiment of the patient and the user based on the communication via the aggregated patient database. 
     
     
         7 . The system of  claim 1 , wherein the user is a healthcare professional engaged in treating the patient, and wherein the step of generating a recommendation to the user based on the identified sentiment is repeated after each single communication between the patient and the user. 
     
     
         8 . The system of  claim 7 , the user is a supporting member of the patient, and wherein the step of generating a recommendation to the user based on the identified sentiment is repeated after each single communication between the patient and the user. 
     
     
         9 . The system of  claim 8 , wherein the step of identifying sentiment of the patient and the user based on the recorded data of the communication between the patient and the user, further includes identifying sentiment from recorded text-based communication between the patient and the user. 
     
     
         10 . The system of  claim 9 , wherein the step of identifying sentiment of the patient and the user based on the recorded data of the communication between the patient and the user, further includes identifying bilateral sentiment of both the patient and the user. 
     
     
         11 . The system of  claim 10 , wherein the step of generating a recommendation to the user based on the identified bilateral sentiment, further includes recommending at least one of diction, tone, emoji and punctuation to manage the patient's sentiment. 
     
     
         12 . The system of  claim 1 , wherein the user interface display is configured to alert the user of changes in the bilateral sentiment of the recorded communication between the user and the patient. 
     
     
         13 . The system of  claim 11 , wherein the user interface display is configured to collect feedback from the user and the patient to track the effect of the recommendation generated by the machine learning system. 
     
     
         14 . The system of  claim 13 , wherein the machine learning system includes an artificial intelligence (AI) engine configured to train based on the feedback collected by the user interface display of the users and the patient. 
     
     
         15 . The system of  claim 1 , wherein the machine learning system includes an artificial intelligence (AI) engine configured to predict the overall sentiment of the recorded communication between the user and the patient based on the aggregated patient information. 
     
     
         16 . The system of  claim 15 , wherein the machine learning system includes an artificial intelligence (AI) engine configured to estimate the effectiveness level of the generated recommendation. 
     
     
         17 . The system of  claim 1 , wherein the machine learning system is configured to perform the identify and generate steps for multiple users.