Tempus Labs, Inc.

Tempus Ai, Inc.

Geisinger Clinic

Ashraf Hafez

Caroline Epstein

Hailey B Lefkofsky

Julian Habib

Martin Christian Stumpe

Nike Tsiapera Beaubier

Daniel Neems

Carin Fishel

Adrian William George Lange

Carin Fishel Queen

Brandon K Fornwalt

Christopher M Haggerty

Christopher Good

Arun Nemani

John Pfeifer

Sushravya Raghunath

Tanner Carbonati

Alvaro Ulloa-Cerna

An evaluation of a cancer treatment start date for a cancer medication identifies a first plurality of subjects and, for each, the treatment start date. A subject is selected for a second plurality of subjects by applying features for the subject to a model at a propensity value threshold. One subset of the features is associated with a time period and another subset is static. The applying obtains anchor point predictions, each associated with a time in the time period and including a probability that the time is the start date. The time of the anchor point prediction having the greatest probability is assigned the anchor point of the subject. The start date is evaluated with a survival objective based on the start date for each subject in the first plurality and the assigned anchor point for each subject in the second plurality.

Methods for evaluating the effect of the start date for cancer treatment with a cancer medication using propensity scoring

Systems and methods are provided for predicting metastasis of a cancer in a subject. A plurality of data elements for the subject's cancer is obtained, including sequence features comprising relative abundance values for gene expression in a cancer biopsy of the subject, optional personal characteristics about the subject, and optional clinical features related to the stage, histopathological grade, diagnosis, symptom, comorbidity, and/or treatment of the cancer in the subject, and/or a temporal element associated therewith. One or more models are applied to the plurality of data elements, determining one or more indications of whether the cancer will metastasize. A clinical report comprising the one or more indications is generated.

Predicting likelihood and site of metastasis from patient records

A system and method for analyzing a data store of de-identified patient data to generate one or more dynamic user interfaces usable to predict an expected response of a particular patient population or cohort when provided with a certain treatment. The automated analysis of patterns occurring in patient clinical, molecular, phenotypic, and response data, as facilitated by the various user interfaces, provides an efficient, intuitive way for clinicians to evaluate large data sets to aid in the potential discovery of insights of therapeutic significance.

Method and process for predicting and analyzing patient cohort response, progression, and survival

Systems and methods are provided for implementing a tool for evaluating an effect on an event, such as a medication or treatment, on a subject's condition, using a propensity model that identifies matched treatment and control cohorts within a base population of subjects. A propensity value threshold, which can be obtained based on user input, can be used to adjust the selection of subjects for treatment and control cohorts. The tool allows analyzing features of the subjects in the treatment and control groups, and further allows for evaluation and comparison of survival objectives of subjects in the treatment and control groups.

Evaluating effect of event on condition using propensity scoring

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Wheaton, IL, United States of America

Ashraf Hafez