Flatiron Health, Inc.

Nisha Singh

Will Shapiro

Guy Amster

Sharang Phadke

Brett Wittmershaus

Alexander Rich

John Ritten

Michael Waskom

Natalie Roher

Lauren Sutton

Benjamin Edward Birnbaum

Edward Liu

David Light

Frank Zexi Chen

Kimberly Wiederkehr

Prakrit Baruah

Forrest Xiao

Benjamin Irvine

James Gippetti

Barry Leybovich

Melissa Estevez

Sankeerth Garapati

Aaron Cohen

Suganya Sridharma

Katherine Harrison

Andrea Ratzlaff

Ashley Allen

Sarina Dass

Claire Saint-Donat

Marc Knight

Jeremy Canfield

Stacie Sienicki

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 identifying a group of patients for a cohort using a generalized biomarker model may include a processor programmed to provide, to a generalized biomarker model, a first biomarker associated with a cohort, the generalized biomarker model being trained based on one or more second biomarkers; receive, from the generalized biomarker model, an output indicating a plurality of individuals with associated likelihoods of at least one of: having an attribute associated with the third biomarker or having been tested for the attribute associated with the first biomarker; determine a likelihood threshold based on a predetermined cohort size associated with the first biomarker and identify, based on the output, a group of the plurality of individuals for inclusion in a cohort, each individual in the group of the plurality of individuals being associated with a likelihood received from the generalized biomarker model that satisfies the likelihood threshold.

Clinical trial matching system using inferred biomarker status

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

A model-assisted system for determining a patient event date may include a processor. The processor may be programmed to access a database storing a medical record associated with a patient, the medical record comprising unstructured data; analyze the unstructured data to identify a plurality of snippets of information in the medical record associated with a patient event; determine a date associated with each of the plurality of snippets, identify a plurality of query periods associated with the patient event; and generate, for each of the query periods, a probability of whether the patient event occurred during the query period based on the plurality of snippets and the associated dates.

Machine learning model for extracting diagnoses, treatments, and key dates

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South Richmond Hill, NY, United States of America

Nisha Singh