Predictive AI Models - Data Standards In Action

On May 10, 2024, Travis Osterman, PhD. presented a talk on Predictive AI Models and Data Standards In Action. Dr. Osterman is Associate VP for Research Informatics at Vanderbilt University Medical Center and Director of Cancer Clinical Informatics at the Vanderbilt-Ingram Cancer Center. In this talk Dr. Osterman presented an overview of several recent data standards including mCODE and the HL7 Genomics Reporting standard with a special focus on implementation and future directions of interoperability. This talk was hosted by the AI Precision Health Institute at the University of Hawai'i Cancer Center as part of a popular seminar series featuring leading AI researchers from around the world.

Building Predictive AI Models

“We focused primarily on routinely collected structured data to build predictive models with the goal that these models would be able to be implemented in any clinical setting."

Travis Osterman, Director of Cancer Clinical Informatics at Vanderbilt-Ingram Cancer Center

Dr. Osterman and his colleagues at Vanderbilt University Medical Center and Vanderbilt-Ingram Cancer Center are developing AI models to help predict cancer patients’ responses to immunotherapies by finding patterns within routinely collected clinical data. They have developed AI models that parse EMRs, diagnosis codes, and patient history, and analyze demographic data, genomic data, tumor data, cellular data, proteomic data, and imaging data. In a recent study, these AI algorithms demonstrated 70% to 80% accuracy in forecasting the likelihood of the most common severe adverse events (hepatitis, colitis, pneumonitis) and one-year overall survival. This study was published in the Journal of Clinical Oncology Clinical Cancer Informatics.

Highlights and Slides

About Dr. Travis Osterman

Dr. Travis Osterman is a practicing medical oncologist, informatician, Associate VP for Research Informatics at Vanderbilt University Medical Center (VUMC), and Director of Cancer Clinical Informatics at the Vanderbilt-Ingram Cancer Center. He is board certified in internal medicine, medical oncology, and clinical informatics. At VUMC he leads the Clinical Genomics Workstream and has led an effort to make genomic data more accessible for patient care and research. Nationally, Dr. Osterman is involved in several efforts to improve the availability of oncology-specific electronic health record data to support quality improvement across oncology practices. He serves as Chair of the Minimum Common Oncology Data Elements (mCODE™) Executive Committee which is implemented at more than 70 institutions across six countries. mCODE was recently announced as the only method of submitting data to the new Enhancing Oncology Model from CMS.

AI Precision Health Institute Seminar Series

In 2022 we formed the AI Precision Health Institute Affinity Group Seminar Series to discuss current trends and applications of AI in cancer research and clinical practice. The group brings together AI researchers in a variety of fields including computer science, engineering, nutrition, epidemiology, and radiology with clinicians and advocates. The goal is to foster collaborative interactions to solve problems in cancer that were thought to be unsolvable a decade ago before the broad use of deep learning and AI in medicine.

Past Seminars In This Seminar Series

New Applications of AI in Cancer Research & Clinical Practice (2023 Recap)

AI Powered Dermatology Tools and Consumer Decision Making, April 2024

AI Decodes Waveforms To Help Prevent Sudden Cardiac Death, March 2024

Mitigating Unintended Consequences of AI in Biomedicine, February 2024

How To Build Responsible, Safe, Trusted AI For Precision Health, January 2024

Robust Interpretability Methods For Large Language Models, December 2023

Machine Learning Captures Insights Into Brain Tumor Biology, November 2023

Comparing AI Algorithms To Predict 5 Year Breast Cancer Risk, October 2023

Disrupting the Indigenous DNA SupplyChain, September 2023

AI Based Lab Test Approved To Phenotype, Grade Breast Cancer, July 2023

Trustworthy AI and Clinical Validation In Breast Cancer Imaging, June 2023

AI For Ultrasound For Real-Time Breast Cancer Decision Support, May 2023

Deep Learning To Diagnose Breast Cancer With High Accuracy, April 2023

Precision Oncology: Empowering Radiologists With AI, January 2023

Machine Learning For Personalized Cancer Screening, December 2022

AI Driven Surgical Robots To Diagnose/Treat Prostate Cancer, November 2022

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Copyright © 2024 Margaretta Colangelo. All Rights Reserved.

This article was written by Margaretta Colangelo. Margaretta is a leading AI analyst who tracks significant milestones in AI in healthcare. She consults with AI healthcare companies and writes about some of the companies she consults with. Margaretta serves on the advisory board of the AI Precision Health Institute at the University of Hawaiʻi Cancer Center @realmargaretta