US 11,734,593 B2
Bayesian causal relationship network models for healthcare diagnosis and treatment based on patient data
Niven Rajin Narain, Cambridge, MA (US); Viatcheslav R. Akmaev, Sudbury, MA (US); and Vijetha Vemulapalli, Westborough, MA (US)
Assigned to BPGBio, Inc., Framingham, MA (US)
Filed by BPGBio, Inc., Framingham, MA (US)
Filed on Oct. 3, 2019, as Appl. No. 16/592,069.
Application 16/592,069 is a continuation of application No. 14/851,846, filed on Sep. 11, 2015, granted, now 10,482,385.
Claims priority of provisional application 62/049,148, filed on Sep. 11, 2014.
Prior Publication US 2020/0143278 A1, May 7, 2020
This patent is subject to a terminal disclaimer.
Int. Cl. G06N 7/01 (2023.01); G16H 50/50 (2018.01); G16H 50/70 (2018.01)
CPC G06N 7/01 (2023.01) [G16H 50/50 (2018.01); G16H 50/70 (2018.01)] 35 Claims
OG exemplary drawing
 
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, 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; and
generating a causal relationship network model relating the plurality of variables based on the generated normalized data using a programmed computing system including storage holding network model building code and a plurality of processors configured to execute the network model building code, the generating including creating and evolving an ensemble of probabilistic networks based on the generalized normalized data from the plurality of patients, the causal relationship network model including variables related to a plurality of medical conditions, the ensemble of probabilistic networks created and evolved in parallel on the plurality of processors.