Mr. Larson works in Oak Park Heights, MN and 2 other locations and specializes in Orthopaedic Surgery. Mr. Larson is affiliated with Fairview Lakes Medical Center, Hudson Hospital & Clinic, Lakeview Hospital, Osceola Medical Center, Saint Johns Hospital, Westfields Hospital & Clinic and Woodwinds Health Campus.
- New York NY, US Angel Leung - Brooklyn NY, US Caroline Nightingale - Brooklyn NY, US Zexi Chen - Forest Hills NY, US Janet Donegan - Park City UT, US Peter Larson - New York NY, US Lauren Sutton - New York NY, US
Assignee:
Flatiron Health, Inc. - New York NY
International Classification:
G16H 10/20 G16H 10/60 G16H 50/20
Abstract:
A computer-implemented system for determining trials using a metastatic condition of a patient may include at least one processor programmed to receive a selection of a patient; access, in response to the selection of the patient, a patient dataset associated with the patient; receive a predicted metastatic condition associated with the patient; cause display of at least a first portion of the patient dataset and the predicted metastatic condition; determine, based on at least a second portion of the patient dataset or the predicted metastatic condition, a subset of trials for the patient, wherein the subset of trials for the patient is determined from a plurality of trials; and cause display of at least the subset of the trials for the patient.
Systems And Methods For Automatic Bias Monitoring Of Cohort Models And Un-Deployment Of Biased Models
- New York NY, US Joshua Daniel Haimson - Brooklyn NY, US Lucy Dao-Ke He - New York NY, US Melissa Hedberg - New York NY, US Nathan Coleman Nussbaum - South Orange NJ, US Paul Stephen Richardson - Brooklyn NY, US Evan Eino Estola - Brooklyn NY, US Peter Daniel Larson - Brooklyn NY, US
Assignee:
Flatiron Health, Inc. - New York NY
International Classification:
G16H 10/60 G06N 99/00
Abstract:
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
- New York NY, US Joshua Daniel Haimson - Brooklyn NY, US Lucy Dao-Ke He - New York NY, US Melissa Hedberg - New York NY, US Nathan Coleman Nussbaum - New York NY, US Paul Stephen Richardson - Brooklyn NY, US Evan Eino Estola - Brooklyn NY, US Peter Daniel Larson - Brooklyn NY, US
Assignee:
Flatiron Health, Inc. - New York NY
International Classification:
G16H 10/60 G06N 20/00
Abstract:
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.