MACHINE LEARNING & AI FOR HEALTHCARE: A HIMSS EVENT

Boston, MA
Westin Copley Place
June 13-14, 2019

Subha Madhaven

Founding Director, Innovation Center for Biomedical Informatics
Georgetown University

Subha Madhavan is the founding director of the Innovation Center for Biomedical Informatics (ICBI) at the Georgetown University Medical Center and is associate professor in the department of oncology. She leads many programs in data science, clinical informatics and health IT with responsibility for several biomedical research efforts, including the software development of Georgetown Database of Cancer (G-DOC) a resource for both researchers and clinicians to realize the goals of personalized medicine, leadership of Lombardi Cancer Center’s Biostatistics and Bioinformatics shared resource and the biomedical informatics component of the Georgetown-Howard Universities Clinical and Translational Science Award (CTSA). In her role as the CTSA biomedical informatics director, she has enabled access to over 4 million patient records from 10 MedStar Health hospitals, Howard University Hospital and the VA to clinical and translational researchers.

June 14, 2019
2:00pm - 2:40pm
Grand Ballroom

Suicide is the 10th leading cause of death in the United States and the second leading cause of death among young people. Mental health professionals fare poorly at predicting suicide risk, mainly because patients hide these thoughts during counselling.

Machine learning and AI hold promise for analyzing patterns in data (EMR data and social media posts, for example) to help identify patients with suicidal thoughts and tendencies. These efforts toward an early warning system could help alert physicians, mental health professionals and family members when someone in their care needs help.

In this session, two healthcare experts discuss their efforts to use machine learning to detect suicidal thinking in two at-risk populations: convicts and veterans.

Key takeaways:

  • The challenges of using behavioral prediction data
  • How machine learning might help clinicians identify high-risk patients in clinical settings
  • Speech analysis is a promising approach and can be useful in suicide ideation detection

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