Flatiron Health, Inc.

John Ritten

Nisha Singh

Will Shapiro

Sharang Phadke

Guy Amster

Brett Wittmershaus

Kimberly Wiederkehr

Jeremy Canfield

Sankeerth Garapati

Stacie Sienicki

Aaron Cohen

Suganya Sridharma

Marc Knight

Melissa Estevez

Katherine Harrison

Andrea Ratzlaff

Ashley Allen

James Gippetti

Sarina Dass

Described herein are techniques of using machine learning to automatically extract clinical variable values for subjects from clinical record data. The techniques designate certain clinical variables as hybrid variables that can be assigned values by machine learning model prediction. The techniques process, using a machine learning model trained to predict a value of a hybrid variable, clinical record data associated with a subject to obtain a predicted hybrid variable value and an associated confidence score. The techniques set the value of the hybrid variable for the subject to the predicted hybrid variable value when the model prediction is of sufficiently high confidence.

Machine learning extraction of clinical variable values for subjects from clinical record data

A model-assisted system for extracting patient information. A processor may be programmed to access a database storing one or more medical records associated with a patient and determine, using a first machine learning model and based on unstructured information included in the one or more medical records, whether the patient is associated with a condition. The processor may further be programmed to identify a date associated with the patient and determine, using a second machine learning model and based on the unstructured information, whether the patient is associated with the condition relative to the date. The processor may 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.

Systems and methods for extracting dates associated with a patient condition

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Brooklyn, NY, United States of America

John Ritten