Systems and methods for extracting dates associated with a patient condition

A computer-assisted method for extracting patient information, the method comprising:

• accessing a database storing a plurality of medical records associated with a plurality of patients;
• inputting 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;
• identifying, 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;
• identifying, 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;
• identifying, 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;
• inputting 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;
• generating 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;
• determining, based on a second output from the second machine learning model, whether the patient is associated with the condition relative to the date;
• and
• generating an output indicating whether the patient is associated with the condition and whether the patient is associated with the condition relative to the date.

A non-transitory computer-readable medium storing instructions executable by at least one processor to perform a method, the method comprising:

• accessing a database storing a plurality of medical records associated with a plurality of patients;
• inputting 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;
• identifying, 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;
• identifying, 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;
• identifying, 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;
• inputting 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;
• generating 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;
• determining, based on a second output from the second machine learning model, whether the patient is associated with the condition relative to the date;
• and
• generating an output indicating whether the patient is associated with the condition and whether the patient is associated with the condition relative to the date.