Cedars-Sinai Advances Knowledge of Machine Learning and Big Data in Medicine
Investigators in Two Studies Develop New Ways to Identify Impact of Medications on Blood Sugar and Combine Genetic and Clinical Data From Multiple Hospitals
Two new studies from the Department of Computational Biomedicine at Cedars-Sinai are advancing what we know about using machine learning and big data to improve healthcare and medical research. Both studies were published in the peer-reviewed journal Patterns.
In the first study, Cedars-Sinai investigators applied advanced statistical techniques to analyze electronic health records from nearly 100,000 hospital stays. This approach identified drugs that were unexpectedly associated with raising or lowering blood sugar levels of hospitalized patients.
“Our findings offer practical insights to help clinicians anticipate and manage medication-related blood sugar changes, ultimately improving glycemic safety for patients in hospitals,” said Jesse G. Meyer, PhD, assistant professor of Computational Biomedicine at Cedars-Sinai and corresponding author of the study.
In the second study, co-led by Cedars-Sinai, investigators developed a secure method to pool patient data from multiple hospitals for research studies. This method allows hospitals to send statistical summaries of their patients’ characteristics, rather than the healthcare data of individuals, to a central location for analysis by investigators, reducing the risk of inadvertent disclosure of sensitive patient information.
“Our innovative approach opens the door for larger, more diverse studies that better protect patient privacy, improve research quality and support the development of more effective treatments,” said Ruowang Li, PhD, assistant professor of Computational Biomedicine at Cedars-Sinai and co-corresponding author of the study.
“Both studies emphasize our unique approach to using machine learning and big data in academic medicine,” said Jason Moore, PhD, professor and chair of the Department of Computational Biomedicine at Cedars-Sinai and co-corresponding author of the study. “These studies foster collaboration, ultimately leading to patient care and research that are driven by data, overcoming gaps in outcomes and creating healthier lives.”
First study:
Other Cedars-Sinai authors include: Amanda Momenzadeh, PharmD, Caleb Cranney, MS, So Yung Choi, MS, Catherine Bresee, MS, Mourad Tighiouart, PhD, Roma Gianchandani, MD, Joshua Pevnick, MD, MSHS, and Jason H. Moore, PhD.
Acknowledgments: The authors thank Edward Kowalewski, Kevin Japardi and the Honest Enterprise Research Broker for EHR data extraction services. This research was supported by NIH National Center for Advancing Translational Science (NCATS), UCLA CTSI Grant Number UL1TR001881.
Declaration of interests: Amanda Momenzadeh and Jesse G. Meyer have a provisional patent related to this work. Jason H. Moore is a member of Patterns Advisory Board.
Second study:
The other Cedars-Sinai author was Jason H. Moore. Other authors include Luke Benz, Rui Duan, Joshua C. Denny, Hakon Hakonarson, Jonathan D. Mosley, Jordan W. Smoller, Wei-Qi Wei, Thomas Lumley, Marylyn D. Ritchie and Yong Chen (co-corresponding author).
Acknowledgment: Funding sources included NIH R01 LM010098, AG066833, GM148494, LM014344, LM012607, LM013519, AI130460, AG073435, RF1AG077820, R56AG069880, R56AG074604, U01TR003709, R21AI167418 and R21EY034179. MDR was funded by R01HG010067 and R01HL169458.
eMERGE Network (Phase III). This phase of the eMERGE Network was initiated and funded by the NHGRI through the following grants: U01HG8657 (Group Health Cooperative/University of Washington); U01HG8685 (Brigham and Women’s Hospital); U01HG8672 (Vanderbilt University Medical Center); U01HG8666 (Cincinnati Children’s Hospital Medical Center); U01HG6379 (Mayo Clinic); U01HG8679 (Geisinger Clinic); U01HG8680 (Columbia University Health Sciences); U01HG8684 (Children’s Hospital of Philadelphia); U01HG8673 (Northwestern University); U01HG8701 (Vanderbilt University Medical Center serving as the Coordinating Center); U01HG8676 (Partners Healthcare/Broad Institute); and U01HG8664 (Baylor College of Medicine).
UK Biobank. All data for this cohort pertained to project 32133 – “Integration of multi-organ imaging phenotypes, clinical phenotypes, and genomic data.”
Declaration of interests: Jason H. Moore serves as a current member of the Patterns advisory board. The other authors report no competing interests.
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