Flatiron Health, Inc.

Joshua Daniel Haimson

Benjamin Edward Birnbaum

Nathan Coleman Nussbaum

Katharina Nicola Seidl-Rathkopf

Lucy Dao-Ke He

Paul Stephen Richardson

Monica Nayan Agrawal

Peter Daniel Larson

Melissa Hedberg

Evan Eino Estola

Geetu Ambwani

Neal Meropol

Daniel Backenroth

Chengsheng Jiang

Andrej Rosic

Shrujal Baxi

Mahesh Murali

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

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

A model-assisted system for predicting survivability of a patient may include at least one processor. The processor may be programmed to access a database storing a medical record for the patient. The medical record may include at least one of structured and unstructured information relative to the patient and may lack a structured patient ECOG score. The processor may be further programmed to analyze at least one of the structured and unstructured information relative to the patient; based on the analysis, and in the absence of a structured ECOG score, generate a performance status prediction for the patient; and provide an output indicative of the predicted performance status. The analysis of at least one of the structured and unstructured information and the generation of the predicted performance status may be performed by at least one of a trained machine learning model or a natural language processing algorithm.

Prognostic score based on health information

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Brooklyn, NY, United States of America

Joshua Daniel Haimson