US 11,908,586 B2
Systems and methods for extracting dates associated with a patient condition
James Gippetti, Bay Shore, NY (US); Sharang Phadke, Plainsboro, NJ (US); Guy Amster, Hoboken, NJ (US); Nisha Singh, South Richmond Hill, NY (US); Suganya Sridharma, Ashburn, VA (US); Melissa Estevez, Raleigh, NC (US); John Ritten, Brooklyn, NY (US); Sankeerth Garapati, New York, NY (US); and Aaron Cohen, South Orange, NJ (US)
Assigned to Flatiron Health, Inc., New York, NY (US)
Filed by Flatiron Health, Inc., New York, NY (US)
Filed on Jun. 11, 2021, as Appl. No. 17/345,448.
Claims priority of provisional application 63/038,397, filed on Jun. 12, 2020.
Prior Publication US 2021/0391087 A1, Dec. 16, 2021
Int. Cl. G16H 50/70 (2018.01); G16H 40/20 (2018.01); G16H 10/60 (2018.01); G16H 50/20 (2018.01); G06F 16/33 (2019.01); G06N 20/00 (2019.01)
CPC G16H 50/70 (2018.01) [G06F 16/33 (2019.01); G06N 20/00 (2019.01); G16H 10/60 (2018.01); G16H 40/20 (2018.01); G16H 50/20 (2018.01)] 16 Claims
OG exemplary drawing
 
1. A model-assisted system for extracting patient information, the system comprising:
at least one processor programmed to:
access a database storing a plurality of medical records associated with a plurality of patients;
input unstructured information included in the plurality of medical records into a first machine learning model, the first machine learning model being trained using first training data to identify patients associated with a condition;
identify, based on a first output from the first machine learning model, a subset of the plurality of patients, the subset of the plurality of patients being associated with the condition;
identify, based on an input by a user through a user interface, a date associated with a patient of the subset of the plurality of patients;
identify, within the plurality of medical records, one or more documents associated with the patient and having a timestamp prior to a cutoff date such that documents including one or more dates expressed in relative terms are excluded from the identified one or more documents, the cutoff date being based on a predetermined buffer period before or after the date;
input unstructured information included in the one or more documents into a second machine learning model, the second machine learning model being trained using second training data to indicate dates associated with the condition;
generate one or more pseudo-documents for input into the second machine learning model, the one or more pseudo-documents accounting for one or more dates within the unstructured information included in the one or more documents;
determine, based on a second output from the second machine learning model, whether the patient is associated with the condition relative to the date; and
generate an output indicating whether the patient is associated with the condition and whether the patient is associated with the condition relative to the date.