Company: TEM
Filing Date: 2025-02-24
Form Type: 10-K
Source: 0000950170-25-025603
Chunk: 14

Company: Tempus AI, Inc.
Filing Date: 2025-02-24
Form: 10-K
Item: Item 1
Chunk 14
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 such as HLA type, immune infiltrates and neoantigens. Finally, Time on Therapy provides physicians a view into the Tempus Precision Medicine Library, which includes the treatment paths of patients within our de-identified database who display similar molecular or phenotypic profiles to their own patients. These tools enable new patients to potentially benefit from the experience of those that came before.

One example of an AI model whose results are available within Hub, and which illustrates a typical development and validation process for our AI models, is our tumor origin, or TO, algorithm. Our TO algorithm predicts the site of origin for cancer patients whose primary tumor site is unknown using machine learning models trained on tumor RNA expression results from our de-identified multimodal database. We began developing our TO algorithm in 2019, and it was first deployed in a clinical setting in 2021. We developed and trained the TO algorithm, like other machine learning models, by adopting current best practices for AI model development. For example, in developing the TO algorithm, we explored distinct model architectures (logistic regression, random forests and neural networks) and feature selection methods, and we utilized multiple cross validation techniques using both our own and independent third-party datasets. After its launch, we continue to monitor the performance of the TO algorithm by using advanced statistical methods to detect potential model drift or degradation over time. Each TO prediction is reviewed by our board certified pathologists for consistency with underlying data, and the distribution of expected cases is reviewed and assessed against the expected distribution of diagnoses.

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We include below some illustrations depicting some of Hub’s capabilities:

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Lens

Lens is our software application for life sciences and advanced precision research. We designed Lens to expose our multimodal, de-identified dataset to two main constituencies: (i) clinicians interested in exploring data related both to their own patients and to similarly situated patients from the broader Tempus dataset, and (ii) pharmaceutical and biotechnology clients that are focused on drug discovery and development and want to explore our dataset and/or supplement their own analytics with our tools and data.

For clinicians, Lens helps users filter our multimodal database to identify groups of patients that meet their research requirements. It allows browsing, segmenting, selecting, and analyzing cohorts of patients using a variety of clinical, molecular, and demographic characteristics. We generally make these aspects of Lens available to our customers without charge because such access helps our customers identify data cohorts of interest and facilitates data licensing opportunities.

In addition to this basic functionality, Lens allows advanced computational