US 12,170,148 B2
Learning platform for patient journey mapping
Ye Jin Eun, Princeton, NJ (US); Wei Wang, Berwyn, PA (US); Xiaoying Wu, Upper Holland, PA (US); Jun Morimura, Tokyo (JP); and Geoffrey Townsend Red, Devon, PA (US)
Assigned to Janssen Pharmaceuticals, Inc., Titusville, NJ (US)
Filed by Janssen Pharmaceuticals, Inc., Titusville, NJ (US)
Filed on Jul. 23, 2020, as Appl. No. 16/937,441.
Claims priority of provisional application 62/878,174, filed on Jul. 24, 2019.
Prior Publication US 2021/0027896 A1, Jan. 28, 2021
Int. Cl. G16H 50/70 (2018.01); A61B 5/00 (2006.01); G16H 10/60 (2018.01); G16H 15/00 (2018.01); G16H 50/20 (2018.01); G16H 50/50 (2018.01); G16H 70/20 (2018.01); G06Q 50/26 (2012.01); G16H 10/20 (2018.01); G16H 20/10 (2018.01)
CPC G16H 50/70 (2018.01) [A61B 5/7267 (2013.01); G16H 10/60 (2018.01); G16H 15/00 (2018.01); G16H 50/20 (2018.01); G16H 50/50 (2018.01); G16H 70/20 (2018.01); G06Q 50/26 (2013.01); G16H 10/20 (2018.01); G16H 20/10 (2018.01)] 12 Claims
OG exemplary drawing
 
1. A computer-implemented method, comprising:
defining, by a processor of a mapping server, a pre-identified cohort of patients by identifying a reduced set of patients having a common medical event in a medical record of each patient, the set of patients reduced (i) based on a sequence of events in the medical record of each patient, and (ii) by excluding a patient based on a diagnosis in the medical record of the patient;
randomizing, by the processor of the mapping server, an order of two or more medical events from the medical record of each patient in the pre-identified cohort, the medical record including two time-separated medical events in at least two overlapping timelines including past diagnoses, medications, lab tests and procedures for the pre-identified cohort of patients to a medical-event embedding engine of the mapping server;
providing, by the processor of the mapping server, the two or more medical events from the medical record of each patient in the pre-identified cohort to the medical-event embedding engine of the mapping server, the medical-event embedding engine having parameters trained by a neural network model executed by the processor of the mapping server to cause the medical-event embedding engine to generate an output vector corresponding to a medical event in an input medical record, the processor of the mapping server configured to perform computations in parallel using the neural network model;
generating, by the processor of the mapping server, a vector with the medical-event embedding engine by operating on the two or more medical events for each of the patients in the pre-identified cohort, each vector corresponding to a medical event in the medical record of one of the patients in the pre-identified cohort;
generating, by the processor of the mapping server, a weight for each vector using a timestamp for the medical event corresponding to the vector, wherein a vector with a more recent timestamp has a greater weight than a vector with a less recent timestamp;
combining, with the processor of the mapping server, the vector for each patient in the pre-identified cohort to form a single vector representation of a medical history for each patient in the pre-identified cohort by generating a weighted average of the vectors using the weight for each vector;
reducing, by the processor of the mapping server, a number of dimensions of each single vector representation to reduce a computational complexity of a clustering operation performed by the processor of the mapping server;
identifying, by the processor of the mapping server, with a clustering engine of the mapping server, multiple clusters of the patients in the pre-identified cohort that have similar patient journeys by performing the clustering operation on the single vector representations;
identifying, by the processor of the mapping server, with a cluster profiling engine of the mapping server, a differentiating medical event of each of the clusters by performing a cluster-profiling operation using an output of the clustering engine and the medical records of the patients in the clusters; and
providing, by the processor and for display, at least relative numbers of the differentiating medical event in at least one of the clusters.