Tempus Labs, Inc.

Daniel Neems

Martin Christian Stumpe

Jonathan Ozeran

Eric Lefkofsky

Ashraf Hafez

Caroline Epstein

Nike Tsiapera Beaubier

Louis Fernandes

Hunter Lane

Michael Lucas

Jason Taylor

Adrian William George Lange

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, method, and mobile device application are configured to capture, with a mobile device, a document such as a next generation sequencing (NGS) report that includes NGS medical information about a genetically sequenced patient. The method includes receiving, from a mobile device, an image of a medical document comprising NGS medical information of the patient, extracting a first region from the image, extracting NGS medical information of the patient from the first region into a structured dataset, the extracted NGS medical information including at least one RNA expression, correlating a portion of the extracted NGS medical information that includes the at least one RNA expression with summarized medical information from a cohort of patients similar to the patient, and generating, for display on the mobile device, a clinical decision support report comprising the summarized medical information.

Mobile supplementation, extraction, and analysis of health records

A system, method, and mobile device application are configured to capture, with a mobile device, a document such as a next generation sequencing (NGS) report that includes NGS medical information about a genetically sequenced patient. At least some of the information is extracted from the document using an entity linking engine, and the extracted information is provided into a structured data repository where it is accessible to provide information regarding the patient specifically as well as collectively as part of a cohort of patients with similar genetic variants, medical histories, or other commonalities. In one aspect, the document is matched to a template model, and the document is processed using one or more masks segregating the template model, and therefore the document, into a series of distinct subregions.

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Chicago, IL, United States of America

Daniel Neems