Flatiron Health, Inc.

Benjamin Edward Birnbaum

Nathan Coleman Nussbaum

Joshua Daniel Haimson

Lucy Dao-Ke He

Katharina Nicola Seidl-Rathkopf

Paul Stephen Richardson

Peter Daniel Larson

Monica Nayan Agrawal

Evan Eino Estola

Melissa Hedberg

Dennis Lee

Nisha Singh

Gil Shklarski

Alexander Rich

Joseph Delgado

Geetu Ambwani

Melisa Tucker

Dominique Connolly

Catherine Miller

James Dura

Joseph Mou

Barry Leybovich

Amy Abernethy

Daniel Eisenberg

Maayan Roth

Nathaniel Turner

Lauren Ellsworth

Zachary Weinberg

Geoffrey Calkins

Ross Feinstein

Paul You

Jeremy Feinstein

Caitlin Keenan

Benjamin Irvine

Cynthia Revol

Jeremy Kohansimeh

Joseph DiLallo

Elijah Meerson

A model-assisted system for identifying candidates for a cohort based on a biomarker may include at least one processor. The processor may be programmed to access a database from which information associated with a population of individuals can be derived; 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 using the information, wherein the first biomarker is different from the one or more second biomarkers; receive, from the generalized biomarker model, a first output indicating a first group of the population of individuals exceeding a first likelihood threshold of having been tested for the first biomarker; and determine, based on the first output, whether an individual from among the first group of the population of individuals is a candidate for the cohort.

Generalized biomarker model

Systems and methods are disclosed for monitoring models for bias. In one implementation, a system for automatically assessing a deployed model for selection of a cohort may include a processing device programmed to: apply the deployed model to data representing a first plurality of individuals, the data including at least one characteristic of the first plurality of individuals; based on the application, select a subset of the first plurality of individuals as a cohort; receive data representing a second plurality of individuals labeled as within the cohort, the data including the at least one characteristic of the second plurality of individuals; compare the selected subset and the second plurality of individuals along the at least one characteristic; and determine whether the comparison results in a difference between the selected subset and the second plurality of individuals greater than a threshold.

Systems and methods for automatic bias monitoring of cohort models and un-deployment of biased models

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

Systems and methods are disclosed for selecting cohorts. In one implementation, a model-assisted selection system for identifying candidates for placement into a cohort includes a data interface and at least one processing device. The at least one processing device is programmed to access, via the data interface, a database from which feature vectors associated with an individual from among a population of individuals can be derived; derive, for the individual, one or more feature vectors from the database; provide the one or more feature vectors to a model; receive an output from the model; and determine whether the individual from among the population of individuals is a candidate for the cohort based on the output received from the model.

Systems and methods for model-assisted cohort selection

Methods, systems, and apparatus, including computer programs encoded on computer storage media, to present a video. One of the methods includes obtaining one or more unstructured documents. The method includes obtaining, by a computer system, a data model, the data model identifying a type of fact that can be determined from the one or more unstructured documents. The method includes determining, by the computer system, a channel to extract facts from the document based on the type of fact. The method includes distributing, by the computer system, the one or more unstructured documents to the channel. The method includes extracting, by the channel, facts from the one or more unstructured documents. The method also includes storing the facts in a data model.

Extracting facts from unstructured data

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Brooklyn, NY, United States of America

Benjamin Edward Birnbaum