System and Method for Digital Therapeutics Implementing a Digital Deep Layer Patient Profile

Abstract

A system, method, and computer-readable medium are disclosed for digital therapeutics directed to patient care specific to a disease for digital therapeutics that implement digital deep layer patient profile. Patient related information is presented by receiving data that includes patient data, lab result data, machine learning calculation data related to the patient, and physician result data. The data is mapped as to intensities, multiple dimensions and time. The mapping is converted to create an unstructured binary data with binary correlations as a digital deep layer patient profile. The digital deep layer patient profile can be processed with machine learning and image processing algorithms.

What is claimed is: 
     
         1 . A computer-implementable method of presenting patient related information comprising:
 receiving data that includes patient data, lab result data, machine learning calculation data related to the patient, and physician result data;   mapping the data as to intensities, multiple dimensions and time;   converting the mapping to create an unstructured binary data with binary correlations as a digital deep layer patient profile; and   processing the digital deep layer patient profile with machine learning and image processing algorithms.   
     
     
         2 . The computer-implementable method of  claim 1 , wherein the binary correlations are used to obtain data insights, recommendations and predictions. 
     
     
         3 . The computer-implementable method of  claim 1 , wherein the digital deep layer patient profile is a binary multiple dimension image deidentified as to patient, disease, time or entity. 
     
     
         4 . The computer-implementable method of  claim 1 , wherein the digital deep layer patient profile is multi-sliced into portions of the digital deep layer patient profile and is used to perform algorithms as to areas of interest. 
     
     
         5 . The computer-implementable method of  claim 1 , wherein the digital deep layer patient profile is searched as to edge detection, rate of change, contour identification, color enhancing, color reduction, dilation, moments and masking, to deliver non intuitive trends and correlations in the data. 
     
     
         6 . The computer-implementable method of  claim 1 , wherein the digital deep layer patient profile is compared, using machine language trained patient profile sets, using sliced and transformed portions of the digital deep layer patient profile to portions of other digital deep layer patient profiles to determine similar patient profiles. 
     
     
         7 . The computer-implementable method of  claim 1 , wherein the digital deep layer patient profile is implemented with machine learning algorithms to present a future view of the digital deep layer patient profile that shows predicted treatment changes, predicted medicine changes, adverse event expected results, and expected patient reactions. 
     
     
         8 . A system comprising:
 a processor;   a data bus coupled to the processor; and   a computer-usable medium embodying computer program code, the computer-usable medium being coupled to the data bus, the computer program code used presenting patient related information and comprising instructions executable by the processor and configured for:
 receiving data that includes patient data, lab result data, machine learning calculation data related to the patient, and physician result data; 
 mapping the data as to intensities, multiple dimensions and time; 
 converting the mapping to create an unstructured binary data with binary correlations as a digital deep layer patient profile; and 
 processing the digital deep layer patient profile with machine learning and image processing algorithms. 
   
     
     
         9 . The system of  claim 8 , wherein the binary correlations are used to obtain data insights, recommendations and predictions. 
     
     
         10 . The system of  claim 8 , wherein the digital deep layer patient profile is a binary multiple dimension image deidentified as to patient, disease, time or entity 
     
     
         11 . The system of  claim 8 , wherein the digital deep layer patient profile is multi-sliced into portions of the digital deep layer patient profile and is used to perform algorithms as to areas of interest. 
     
     
         12 . The system of  claim 8 , wherein the digital deep layer patient profile is searched as to edge detection, rate of change, contour identification, color enhancing, color reduction, dilation, moments and masking, to deliver non intuitive trends and correlations in the data. 
     
     
         13 . The system of  claim 8 , wherein the digital deep layer patient profile is compared, using machine language trained patient profile sets, using sliced and transformed portions of the digital deep layer patient profile to portions of other digital deep layer patient profiles to determine similar patient profiles. 
     
     
         14 . The system of  claim 8 , wherein the digital deep layer patient profile is implemented with machine learning algorithms to present a future view of the digital deep layer patient profile that shows predicted treatment changes, predicted medicine changes, adverse event expected results, and expected patient reactions. 
     
     
         15 . A non-transitory, computer-readable storage medium embodying computer program code, the computer program code comprising computer executable instructions configured for:
 receiving data that includes patient data, lab result data, machine learning calculation data related to the patient, and physician result data;   mapping the data as to intensities, multiple dimensions and time;   converting the mapping to create an unstructured binary data with binary correlations as a digital deep layer patient profile; and   processing the digital deep layer patient profile with machine learning and image processing algorithms.   
     
     
         16 . The non-transitory, computer-readable storage medium of  claim 15 , wherein the binary correlations are used to obtain data insights, recommendations and predictions. 
     
     
         17 . The non-transitory, computer-readable storage medium of  claim 15 , wherein the digital deep layer patient profile is a binary multiple dimension image deidentified as to patient, disease, time or entity. 
     
     
         18 . The non-transitory, computer-readable storage medium of  claim 15 , wherein the digital deep layer patient profile is multi-sliced into portions of the digital deep layer patient profile and is used to perform algorithms as to areas of interest. 
     
     
         19 . The non-transitory, computer-readable storage medium of  claim 15 , wherein the digital deep layer patient profile is searched as to edge detection, rate of change, contour identification, color enhancing, color reduction, dilation, moments and masking, to deliver non intuitive trends and correlations in the data. 
     
     
         20 . The non-transitory, computer-readable storage medium of  claim 15 , wherein the digital deep layer patient profile is implemented with machine learning algorithms to present a future view of the digital deep layer patient profile that shows predicted treatment changes, predicted medicine changes, adverse event expected results, and expected patient reactions.