Detecting Early Symptoms And Providing Preventative Healthcare Using Minimally Required But Sufficient Data
A preventative healthcare system calibrates a risk model by assigning weights to attributes for the freshness, completeness and uncertainty of a user's medical information. A risk predictive model is implemented based on the medical information. The risk of a specific health outcome of the user is determined using the risk predictive model, which is calibrated by computing attribute scores for freshness, completeness and uncertainty of the medical information and by assigning weights to the attribute scores. A need-for-data (ND) score is computed using the weighted attribute scores. A need-for-checkup (NC) score is computed using traits of the user. The method determines that new medical information related to the user is needed or that the user needs a checkup based on the ND and NC scores. A prompt is delivered to the user indicating that new medical information related to the user is needed or that the user needs a checkup.
1 - 15 . (canceled)
16 . A method for early symptom detection and preventative healthcare, comprising:
receiving medical information related to a user;
receiving medical information related to individuals similar to the user;
implementing a risk predictive model using the medical information related to the user;
assessing a risk of a specific health outcome of the user using the risk predictive model; and
calibrating the risk predictive model using a decision control module by performing the steps of:
computing attribute scores for freshness, completeness and uncertainty of the medical information related to the user, wherein the attribute scores are computed using the medical information related to the user and the medical information related to the individuals similar to the user; and
assigning weights to the attribute scores;
computing a need-for-data (ND) score using the weighted attribute scores;
computing a need-for-checkup (NC) score using characteristics and traits of the user;
determining that new medical information related to the user is needed or that the user needs a checkup based on the ND score and the NC score; and
delivering a prompt to the user indicating that new medical information related to the user is needed or that the user needs a checkup.
17 . The method of claim 16 , wherein the medical information related to the user, the attribute scores, the ND score, and the NC score are stored in a database.
18 . The method of claim 16 , wherein the decision control module performs the further step of:
determining a usefulness parameter for the prompt by using a machine learning model on the medical information related to the user and on the medical information related to the individuals similar to the user.
19 . The method of claim 16 , wherein the decision control module performs the further step of:
determining a usefulness parameter for the prompt by using a deterministic algorithm on the medical information related to the user and on the medical information related to the individuals similar to the user.
20 . The method of claim 18 , further comprising:
comparing the ND score to a first threshold; and
comparing the usefulness parameter to a second threshold, wherein the prompt to the user indicating that new medical information related to the user is needed is delivered if the ND score is higher than the first threshold and the usefulness parameter is higher than the second threshold.
21 . The method of claim 18 , further comprising:
comparing the NC score to a first threshold; and
comparing the usefulness parameter to a second threshold, wherein the prompt to the user indicating that the user needs a checkup is delivered if the NC score is higher than the first threshold and the usefulness parameter is higher than the second threshold.
22 . The method of claim 16 , wherein the prompt to the user is delivered to a computing device of the user.
23 . The method of claim 16 , wherein the prompt to the user is delivered to a computing device of a relative or partner of the user.
24 . The method of claim 16 , wherein the weights of the attribute scores have magnitudes assigned using a machine learning model on the medical information related to the user and the medical information related to the individuals similar to the user.
25 . The method of claim 16 , wherein the medical information related to the user is obtained from a source selected from the group consisting of: a medical device, an electronic health record, a mobile phone, a wearable device, a biochemical test of the user, a genetic test of the user, a diary of the user, and a questionnaire answered by the user.
26 . An electronic system for early symptom detection and preventative healthcare, comprising:
a database in which medical information related to a user is stored and in which medical information related to individuals similar to the user is stored; and
a processor that implements a risk predictive model and a decision control model, wherein the decision control model uses a machine learning model that is built on the database, and wherein the processor is configured for:
assessing a risk of a specific health outcome of the user using the risk predictive model based on the medical information related to the user and the medical information related to individuals similar to the user;
calibrating the risk predictive model by computing attribute scores for freshness, completeness and uncertainty of the medical information related to the user, and by assigning weights to the attribute scores;
computing a need-for-data (ND) score using the weighted attribute scores;
computing a need-for-checkup (NC) score using characteristics and traits of the user;
determining that new medical information related to the user is needed or that the user needs a checkup based on the ND score and the NC score; and
delivering a prompt to the user indicating that new medical information related to the user is needed or that the user needs a checkup.
27 . The electronic system of claim 26 , wherein the attribute scores for freshness, completeness and uncertainty are computed using the medical information related to the user and the medical information related to the individuals similar to the user.
28 . The electronic system of claim 26 , wherein the processor is further configured for:
determining a usefulness parameter for the prompt by using the machine learning model on the medical information related to the user and on the medical information related to the individuals similar to the user.
29 . The electronic system of claim 26 , wherein the processor is further configured for:
determining a usefulness parameter for the prompt by using a deterministic algorithm on the medical information related to the user and on the medical information related to the individuals similar to the user.
30 . The electronic system of claim 28 , wherein the processor is further configured for:
comparing the ND score to a first threshold; and
comparing the usefulness parameter to a second threshold, wherein the prompt to the user indicating that new medical information related to the user is needed is delivered if the ND score is higher than the first threshold and the usefulness parameter is higher than the second threshold.
31 . The electronic system of claim 28 , wherein the processor is further configured for:
comparing the NC score to a first threshold; and
comparing the usefulness parameter to a second threshold, wherein the prompt to the user indicating that the user needs a checkup is delivered if the NC score is higher than the first threshold and the usefulness parameter is higher than the second threshold.
32 . The electronic system of claim 26 , wherein the prompt to the user is delivered to a computing device of the user.
33 . A method performed using a preventative healthcare system, comprising:
accessing from a database wellness information related to a user of the system, wherein the database contains the wellness information related to the user and characteristics and traits of the user;
implementing a risk predictive model based on the wellness information related to the user;
determining a probability of a specific health outcome of the user using the risk predictive model;
calibrating the risk predictive model by computing attribute scores for freshness, completeness and uncertainty of the wellness information related to the user, and by assigning weights to the attribute scores;
computing a need-for-data (ND) score using the weighted attribute scores;
computing a need-for-checkup (NC) score using the characteristics and traits of the user;
determining that new wellness information related to the user is needed or that the user needs a checkup based on the ND score and the NC score; and
delivering a prompt to the user indicating that new wellness information related to the user is needed or that the user needs a checkup.
34 . The method of claim 33 , further comprising:
determining a usefulness parameter for the prompt by using a machine learning model on the wellness information related to the user and on wellness information related to individuals similar to the user.
35 . The method of claim 34 , further comprising:
comparing the ND score to a first threshold; and
comparing the usefulness parameter to a second threshold, wherein the prompt to the user indicating that new wellness information related to the user is needed is delivered if the ND score is higher than the first threshold and the usefulness parameter is higher than the second threshold.