Next best action based on mental predictive model of patient mental health

Abstract

A method may include collecting sensor data related to a physical or mental health of a patient. The method may include determining a series of mental evidence nodes. The method may include determining a series of mental states of the patient. Each mental state is associated with a single mental evidence node. The method may include determining an expected value for each mental evidence node based on a mental baseline of the patient. The method may include determining, after each day, a value for each corresponding mental evidence node, wherein the value for each mental evidence node includes a value from each associated mental state. The method may include determining, after each day, a deviation of the patient from the expected value. The method may include generating a mental predictive model of the mental health of the patient based on the deviation of the patient from the expected value.

What is claimed is: 
     
         1 . A system to generate a mental predictive model of a mental health of a patient, the system comprising:
 one or more sensors configured to collect sensor data related to a physical or mental health of a patient that pertains to at least one of a diet pattern, a sleep pattern, an exercise pattern, an activity level, a heart rate, a posture, a stress, a blood pressure variation, a blood glucose, a heart rhythm, a smoking status, a pain level, and a GPS data;   a memory configured to store the sensor data; and   a processor coupled to the one or more sensors and the memory, wherein the processor is configured to perform or control performance of executable operations comprising:
 determining a series of mental evidence nodes, wherein the mental evidence nodes correspond with at least one of a schedule and one or more goals for the patient during a single day within a period of time; 
 determining a series of mental states of the patient, wherein each mental state is associated with a single mental evidence node; 
 determining an expected value for each mental evidence node based on a mental baseline of the patient; 
 determining, after each day, a value for each corresponding mental evidence node, wherein the value for each mental evidence node includes a value from each mental state associated with the mental evidence node; 
 determining, after each day, a deviation of the patient from the expected value for each corresponding mental evidence node; and 
 generating a mental predictive model of the mental health of the patient based on the deviation of the patient from the expected value for each corresponding mental evidence node. 
   
     
     
         2 . The system of  claim 1 , wherein generating the mental predictive model of the mental health of the patient based on the deviation of the patient from the expected value for each corresponding mental evidence node comprises determining a probability of the patient being in a particular mental state according to P(πt|E day1 , E day2 , . . . , E dayn ), and wherein E day1  through E dayn  represent the mental evidence nodes for each day of the period of time and π t  represents the particular mental state. 
     
     
         3 . The system of  claim 2 , wherein generating the mental predictive model of the mental health of the patient based on the deviation of the patient from the expected value for each corresponding mental evidence node further comprises marginalizing a probability of the patient being in a particular mental state on a particular day according to E Day i ⊥{π x ≠i}, and wherein x represents a day number in a series of days, E day1  represent the mental evidence node associated with the particular day, and π x  represents the particular mental state. 
     
     
         4 . The system of  claim 1 , wherein generating the mental predictive model of the mental health of the patient based on the deviation of the patient from the expected value for each corresponding mental evidence node comprises determining a significance level of a particular mental state according to:
   α t (π t )= P (π t   ,E   day 1:t )=Σ π     t−1     P (π t ,π t-1   ,E   day 1:t );
     α t (π t )= P ( E   day t |π t )Σ π     t−1     P ( E   Day t |π t ,π t-1   ,E   day 1:t−1 ) P (π t   |t   −1   ,E   day 1:t−1 ) P (η t-1   ,E   day 1:t−1 );
     and     α t (π t )= P ( E   day t |π t )Σ π     t−1     P (π t |π t-1 )α t-1 (π t-1 );
   
       and wherein E day1:t  represent the mental evidence nodes for each day of the period of time, π t  represents the particular mental state, and at represents the significance level of the particular mental state. 
     
     
         5 . The system of  claim 4 , wherein generating the mental predictive model of the mental health of the patient based on the deviation of the patient from the expected value for each corresponding mental evidence node further comprises determining a probability of the patient being in a particular mental state on a particular day according to 
       
         
           
             
               
                 
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       and Ω represents the series of mental states of the patient corresponding with the particular day. 
     
     
         6 . The system of  claim 1 , wherein generating the mental predictive model of the mental health of the patient based on the deviation of the patient from the expected value for each corresponding mental evidence node comprises determining a probability of the patient being in a particular mental state according to P(π t |E day1 , E day2 , . . . , E dayN )=[P πt *(Π n   i=1 P(E Day(i) |E Day(i−1) , E Day(i−2) , . . . , E Day1 , π t ))]/P(E Day1 , . . . , E DayN ), and wherein E day1  through E dayN  represent the mental evidence nodes for each day of the period of time and π t  represents the particular mental state. 
     
     
         7 . The system of  claim 1 , the system further comprising a chronic disease database and the executable operations further comprising determining a health risk score of the patient according to HS=f(SHC, CCB, LCC), wherein HS represents the health risk score, SHC represents a short term health score, CCB represents a chronic burden score, and LCC represents a lifestyle choice score, and wherein a quantification is based on a statistical distribution of the mental predictive model and of data included in the chronic disease database with respect to at least one of the CCB and the LCC of the patient. 
     
     
         8 . The system of  claim 1 , the system further comprising a questionnaire module configured to receive patient input in response to one or more questions directed to a quality of health of the patient, wherein the mental evidence nodes are further determined based on the patient input. 
     
     
         9 . The system of  claim 1 , wherein each day represents a period of time between a start node that relates to the patient waking up and an end node that relates to the patient falling asleep. 
     
     
         10 . The system of  claim 1 , wherein the one or more sensors comprise at least one of a smartphone, a biosensor, a smartphone application, and a facial action coding systems (FACS). 
     
     
         11 . The system of  claim 1 , wherein the one or more sensors comprise a smartphone that detects at least one of a speed at which the patient types on the smartphone, how one or more buttons on the smartphone are being pressed, and how much the smartphone shakes during use. 
     
     
         12 . The system of  claim 1 , the system further comprising an electronic health record (EHR) database, wherein the mental predictive model of the mental health of the patient is further based on the EHR data included in the EHR database. 
     
     
         13 . The system of  claim 1 , wherein the mental state of the patient represents at least one of a particular day, a mood of the patient, an expected biomarker, and an external marker. 
     
     
         14 . A method to generate a mental predictive model of a mental health of a patient, the method comprising:
 collecting sensor data related to a physical or mental health of a patient that pertains to at least one of a diet pattern, a sleep pattern, an exercise pattern, an activity level, a heart rate, a posture, a stress, a blood pressure variation, a blood glucose, a heart rhythm, a smoking status, a pain level, and a GPS data;   determining a series of mental evidence nodes, wherein the mental evidence nodes correspond with at least one of a schedule and one or more goals for the patient during an activity on a particular day within a period of time;   determining a series of mental states of the patient, wherein each mental state is associated with a single mental evidence node;   determining an expected value for each mental evidence node based on a mental baseline of the patient;   determining, after each day, a value for each corresponding mental evidence node, wherein the value for each mental evidence node includes a value from each mental state associated with the mental evidence node;   determining, after each day, a deviation of the patient from the expected value for each corresponding mental evidence node; and   generating a mental predictive model of the mental health of the patient based on the deviation of the patient from the expected value for each corresponding mental evidence node.   
     
     
         15 . The method of  claim 14 , wherein generating the mental predictive model of the mental health of the patient based on the deviation of the patient from the expected value for each corresponding mental evidence node comprises determining a probability of the patient being in a particular mental state according to P(πt|E day1 , E day2 , . . . , E dayn ), and wherein E day1  through E dayn  represent the mental evidence nodes for each day of the period of time and π t  represents the particular mental state. 
     
     
         16 . The method of  claim 15 , wherein generating the mental predictive model of the mental health of the patient based on the deviation of the patient from the expected value for each corresponding mental evidence node further comprises marginalizing a probability of the patient being in a particular mental state on a particular day according to E Day i ⊥{π x : x≠i}, and wherein x represents a day number in a series of days, E dayi  represent the mental evidence node associated with the particular day, and π x  represents the particular mental state. 
     
     
         17 . The method of  claim 14 , wherein generating a mental predictive model of a mental health of a patient based on the deviation of the patient from the expected value for each corresponding mental evidence node comprises determining a significance level of a particular mental state according to:
   α t (π t )= P (π t   ,E   day 1:t )=Σ π     t−1     P (π t ,π t-1   ,E   day 1:t );
     α t (π t )= P ( E   day t |π t )Σ π     t−1     P ( E   Day t |π t ,π t-1   ,E   day 1:t−1 ) P (π t   |t   −1   ,E   day 1:t−1 ) P (η t-1   ,E   day 1:t−1 );
     and     α t (π t )= P ( E   day t |π t )Σ π     t−1     P (π t |π t-1 )α t-1 (π t-1 );
   and wherein E day1:t  represent the mental evidence nodes for each day of the period of time, π t  represents the particular mental state, and at represents the significance level of the particular mental state.   
     
     
         18 . The method of  claim 14 , the method further comprising:
 receiving chronic disease data; and   determining a health risk score of the patient according to HS=f(SHC, CCB, LCC), wherein HS represents the health risk score, SHC represents a short term health score, CCB represents a chronic burden score, and LCC represents a lifestyle choice score, and wherein a quantification is based on a statistical distribution of the mental predictive model and of the chronic disease data with respect to at least one of the CCB and the LCC of the patient.   
     
     
         19 . The method of  claim 14 , the method further comprising receiving patient input indicating a number of healthy mental days in a period time, wherein the mental evidence nodes are further determined based on the patient input. 
     
     
         20 . The method of  claim 14 , the method further comprising collecting EHR data, wherein the mental predictive model of the mental health of the patient is further based on the EHR data.