Bayesian causal relationship network models for healthcare diagnosis and treatment based on patient data

Patent metadata

Field	Value
Publication number	US-10482385-B2
Application number	US-201514851846-A
Country	US
Kind code	B2
Filing date	Sep 11, 2015
Priority date	Sep 11, 2014
Publication date	Nov 19, 2019
Grant date	Nov 19, 2019

Abstract

Systems, methods, and computer-readable medium are provided for healthcare analysis. Data corresponding to a plurality of patients is received. The data is parsed to generate normalized data for a plurality of variables, with normalized data generated for more than one variable for each patient. A causal relationship network model is generated relating the plurality of variables based on the generated normalized data using a Bayesian network algorithm. The causal relationship network model includes variables related to a plurality of medical conditions or medical drugs. In another aspect, a selection of a medical condition or drug is received. A sub-network is determined from a causal relationship network model. The sub-network includes one or more variables associated with the selected medical condition or drug. One or more predictors for the selected medical condition or drug are identified.

First claim

The invention claimed is:

1. A computer-implemented method for generating a causal relationship network model based on patient data, the method comprising:
receiving data corresponding to a plurality of patients including between 50 and 1,000,000 patients, the data including diagnostic information and/or treatment information for each patient;
parsing the data to generate normalized data for a plurality of variables including at least one variable related to diagnosis or treatment for each patient, wherein, for each patient, the normalized data is generated for more than one variable;
generating a causal relationship network model relating the plurality of variables based on the generated normalized data, the generating including creating and evolving an ensemble of Bayesian networks based on the generalized normalized data from between 50 patients and 1,000,000 patients, the causal relationship network model including variables related to a plurality of medical conditions; and
the causal relationship network generated using a programmed computing system including storage holding network model building code and one or more processors configured to execute the network model building code.

2. The method of claim 1 , wherein the causal relationship network model includes relationships indicating one or more predictors for each of the plurality of medical conditions.

3. The method of claim 1 , wherein the data received is not pre-selected as being relevant to one or more of the plurality of medical conditions.

4. The method of claim 1 , wherein the plurality of patients includes a first subset of patients each having data indicating a diagnosis of a medical condition in the patient and includes a second subset of patients each having data that does not indicate a diagnosis of a medical condition in the patient.

5. The method of claim 1 , further comprising:
receiving additional data corresponding to one or more additional patients; and
updating the causal relationship network model based on the additional data.

6. The method of claim 1 , further comprising:
receiving updated or additional data corresponding to one or more of the plurality of patients; and
updating the causal relationship network model based on the updated or additional data.

7. The method of claim 1 , wherein the causal relationship network model is generated based solely on the generated normalized data.

8. The method of claim 1 , further comprising:
determining a sub-network from the causal relationship network model, one or more variables in the sub-network associated with a selected medical condition; and
probing relationships in the sub-network to determine one or more predictors for the selected medical condition.

9. The method of claim 8 , wherein the one or more predictors for the selected medical condition indicate a medical condition co-occurring with the selected medical condition.

10. The method of claim 8 , wherein the extent of the sub-network is determined based on the one or more variables associated with the selected medical condition and the strength of the relationships between the one or more variables and other variables in the causal relationship network model.

11. The method of claim 8 , wherein the sub-network includes the one or more variables associated with the selected medical condition, a first set of additional variables each having a first degree relationship with the one or more variables, and a second set of additional variables each having second degree relationship with the one or more variables.

12. The method of claim 8 , wherein at least one of the one or more predictors is previously unknown as a predictor for the selected medical condition.

13. The method of claim 8 , wherein at least one of the one or more predictors is newly identified as a predictor for the selected medical condition.

14. The method of claim 8 , wherein the number of predictors is less than the number of variables.

15. The method of claim 8 , further comprising:
displaying the one or more predictors in a user interface, the displaying including a graphical representation of the one or more variables, the one or more predictors, and relationships among the one or more variables and the one or more predictors.

16. The method of claim 8 , further comprising displaying a graphical representation of the sub-network in a user interface.

17. The method of claim 8 , further comprising ranking the one or more predictors based on strength of relationships between the one or more variables and the one or more predictors.

18. The method of claim 1 , further comprising:
determining a sub-network from the causal relationship network model, one or more variables in the sub-network associated with a selected drug; and
probing the sub-network to determine one or more predictors relevant to the selected drug.

19. The method of claim 18 , wherein the one or more predictors relevant to the selected drug indicates a drug administered in conjunction with the selected drug.

20. The method of claim 18 , wherein the one or more predictors indicate an adverse drug interaction between the selected drug and one or more other drugs.

21. The method of claim 20 , wherein at least one of the one or more predictors is newly identified as a predictor for an adverse drug interaction between the selected drug and the one or more other drugs.

22. The method of claim 18 , wherein the extent of the sub-network is determined based on the one or more variables associated with the selected drug and the strength of the relationships between the one or more variables and other variables in the causal relationship network model.

23. The method of claim 18 , wherein the sub-network includes the one or more variables associated with the selected drug, a first set of additional variables each having a first degree relationship with the one or more variables, and a second set of additional variables each having second degree relationship with the one or more variables.

24. The method of claim 18 , wherein at least one of the one or more predictors is previously unknown as a predictor for the selected drug.

25. The method of claim 18 , wherein the number of predictors is less than the number of variables.

26. The method of claim 1 , wherein the causal relationship network model is generated based on at least 50 variables.

27. The method of claim 1 , wherein the data includes information from patient electronic health records.

28. The method of claim 1 , wherein the received data further includes at least one of: patient demographics, medical history, patient family medical history, active medication information, inactive past medication information, allergy information, immunization status information, laboratory test results, radiology images, vital sign information, patient weight, billing information, life style information, habits information, insurance claims information, and pharmacy information, for at least some of the plurality of patients.

29. The method of claim 28 , wherein the patient demographics includes at least one of: patient age, patient race, and patient ethnicity.

30. The method of claim 1 , wherein the received data includes information from a patient chart.

31. The method of claim 30 , wherein information from the patient chart includes at

Assignees

• Berg Llc

Inventors

• Narain Niven Rajin
• Akmaev Viatcheslav R
• Vemulapalli Vijetha

Classifications

• G16H50/50 Primary

  for simulation or modelling of medical disorders · CPC title

• G16H50/70

  for mining of medical data, e.g. analysing previous cases of other patients · CPC title

• G06N7/01 Primary

  Probabilistic graphical models, e.g. probabilistic networks · CPC title

• G06N7/005 Primary

  Physics · mapped topic