Method and system to optimize therapy efficacy

Abstract

A method of optimizing treatment of a condition includes monitoring one or more characteristics of a patient associated with a condition of the patient, predicting a severity of the condition of the patient, recommending a selected therapy to the patient based on a therapy-severity map, evaluating an actual severity of the condition of the patient, evaluating an efficacy of the selected therapy, and updating the therapy-severity map based on the evaluated efficacy of the therapy.

What is claimed is: 
     
         1 . A method of optimizing treatment of a condition, the method comprising:
 monitoring one or more characteristics of a patient associated with a condition of the patient;   predicting a severity of the condition of the patient;   recommending a selected therapy to the patient based on a therapy-severity map;   evaluating an actual severity of the condition of the patient;   evaluating an efficacy of the selected therapy; and   updating the therapy-severity map based on the evaluated efficacy of the selected therapy.   
     
     
         2 . The method of  claim 1 , wherein the therapy-severity map corresponds one or more therapies with one or more levels of severity of the condition and includes efficacies of the one or more therapies, and wherein recommending the selected therapy includes selecting a therapy from the therapy-severity map corresponding to the severity of the condition of the patient with the highest efficacy among the therapies corresponding to the severity of the condition of the patient. 
     
     
         3 . The method of  claim 2 , wherein updating the severity-therapy includes replacing the efficacy associated with the selected therapy with the evaluated efficacy. 
     
     
         4 . The method of  claim 2 , wherein the therapy-severity map is a default map based on population data, and wherein updating the severity-therapy map includes replacing the efficacy associated in the default severity-therapy map with the selected therapy with the evaluated efficacy. 
     
     
         5 . The method of  claim 1 , wherein evaluating the efficacy of the therapy is based on one or more of a comparison of the predicted severity of the condition to the actual severity of the condition, a sleep quality score, and an alertness score. 
     
     
         6 . The method of  claim 5 , wherein evaluating the efficacy of the therapy is based on a comparison of the predicted severity of the condition to the actual severity of the condition, a sleep quality score, and an alertness score, and wherein each of the comparison of the predicted severity of the condition to the actual severity of the condition, the sleep quality score, and the alertness score has an associated weighting. 
     
     
         7 . The method of  claim 1 , wherein monitoring one or more of the characteristics of the patient includes monitoring one or more objective physiological characteristics associated with Restless Legs Syndrome (RLS), and wherein evaluating the actual severity of the condition is based on the one or more objective physiological characteristics. 
     
     
         8 . The method of  claim 1 , wherein monitoring one or more characteristics of the patient includes monitoring one or more of sleep metrics, activity metrics, and behaviors related to RLS, and wherein predicting the severity of the condition is based on one or more of the monitored sleep metrics, activity metrics, and behaviors related to RLS. 
     
     
         9 . The method of  claim 1 , further comprising:
 building a severity prediction engine based on the monitored one or more characteristics of the patient, and   wherein predicting the severity of the condition of the patient uses the severity prediction engine.   
     
     
         10 . The method of  claim 9 , further comprising:
 updating the severity prediction engine based on the monitored one or more characteristics of the patient.   
     
     
         11 . The method of  claim 9 , wherein the monitoring one or more characteristics of the patient includes monitoring one or more of sleep metrics, activity metrics, and behaviors related to RLS, and wherein the severity prediction engine is a machine learning model that learns to predict the severity of the condition based on one or more of sleep metrics, activity metrics, and behaviors related to RLS. 
     
     
         12 . The method of  claim 1 , wherein the selected therapy includes at least one of usage of a device for treatment of RLS and medication for treatment of RLS. 
     
     
         13 . The method of  claim 1 , wherein the condition is RLS. 
     
     
         14 . A system for optimizing treatment of a condition, the system comprising:
 one or more sensing modules structured to monitor one or more characteristics of a patient associated with a condition of the patient;   a severity prediction engine structured to predict a severity of the condition of the patient;   a therapy recommendation module structured to recommend a selected therapy to the patient based on a therapy-severity map;   an actual severity evaluation module structured to evaluate an actual severity of the condition of the patient;   a therapy efficacy evaluation module structured to evaluate an efficacy of the selected therapy; and   a personalized therapy-severity map update module structured to update the therapy-severity map based on the evaluated efficacy of the selected therapy.   
     
     
         15 . A non-transitory computer readable medium storing one or more programs, including instructions, which when executed by a computer, causes the computer to perform a method of optimizing treatment of a condition, the method comprising:
 monitoring one or more characteristics of a patient associated with a condition of the patient;   predicting a severity of the condition of the patient;   recommending a selected therapy to the patient based on a therapy-severity map;   evaluating an actual severity of the condition of the patient;   evaluating an efficacy of the selected therapy; and   updating the therapy-severity map based on the evaluated efficacy of the selected therapy.