Multi-Modal Data drives our Differentiation
Tempus has launched data and computational partnerships with the majority of the largest oncology companies today and can augment your portfolio strategies.
Access a Diverse network
Tempus works with over 50 National Cancer Institute (NCI)-designated cancer centers, as well as hundreds of other U.S. hospitals.
Structured Clinical Data
Tempus organizes clinical data from unstructured documents and structured EMR fields, digitizes whole slide pathology images, and generates genomic and transcriptomic data with its clinical NGS lab.
DATA AND ANALYTICS PLATFORM
Our platform organizes millions of oncology cases into a common format across hundreds of hospitals and a variety of data types, adding new de-identified data every day based on clinical records from across the U.S.
With our platform, build external control arms using retrospective or prospective real-world data programs, compare clinical trial results with real-world observations and endpoints, and gain real-time market understanding through real-world patient outcomes.
POWERED BY TEMPUS
Tempus structures clinical data from medical documents using Natural Language Processing and machine learning techniques combined with expert review to ensure our data is to the highest standards. In addition, the structured clinical data model is connected to the deep molecular data from our NGS lab, creating one of the largest real-world clinical and molecular datasets in the market.
Tempus enhances real-world data with key data collection methods to capture missing information that is rarely found in EMRs to calculate critical end-points for your study.
With Tempus’ network of hospital relationships, Tempus builds contemporaneous cohorts for your study to collect information that reflects the most current and evolving definition of standard-of-care.
Regulatory support with our in-house experts to prepare and analyze real-world evidence to aid in regulatory review meetings.
ANALYTICS AND COMPUTATIONAL EXPERTISE
The LENS application dives into multi-modal data to derive meaningful insights on patient populations, treatment pathways and outcomes. With expansive filtering capabilities, populations can be segmented based on key phenotypic, molecular, and diagnostic features.
With Tempus’ real-world data organized with a centralized data model, we can make AI more accessible by enabling researchers to spend more time building algorithms and less time cleaning datasets.
Tempus Publication: Shah, L et al. Multi-field-of-view deep learning model predicts nonsmall cell lung cancer programmed death-ligand 1 status from whole-slide hematoxylin and eosin images. November 20, 2019.
Tempus partners with researchers to develop machine learning models that can predict and interrogate features across a broad range of inputs and data modalities.
With Tempus’ data and technology, collaborators are launching pragmatic AI applications, accelerating the pace of development and commercialization.
Our collaborators can analyze multi-modal datasets to explore and predict known and novel molecular signatures that can uncover new clinical insights. This type of computational research was historically limited by small sample sizes and was not applicable with real-world data, until now.
Tempus Publication (Nature Biotechnology) from Beaubier, N. et al. Integrated genomic profiling expands clinical options for patients with cancer. Comparing known somatic signatures to Tumor Mutational Burden and Microsatellite Instability