2019 TMU-MIT Healthcare Datathon 醫療數據松- 9/28 Keynote Speeches

※已報名「2019 TMU-MIT Healthcare Datathon醫療數據松」之參賽者不需再另外報名此系列講座。

※Keynote Speech

Topic: AI in Healthcare: Enhancing the Efficacy and Efficiency of Interactions to the Rural World

Speaker: Rani Shifron

Global Medicine has turned the World Flat - Technologies are flattening the global provision and extend healthcare access and affordability. Today's Technologies enhance the efficacy and efficiency of interactions among people who are geographically dispersed.

Technologies that approximate Presence by enabling interaction in Mixed Reality between two parties, who are geographically apart, to allow for pin-pointing and finger-pointing on an object, a document or, in the case of Radiology, an X-ray Film for clarity in understanding and accuracy/precision in description and thereby avoiding errors.

Technologies that allow for doing routine checkups at home or in remote locations and those that can help monitor high risk patients in real time.

As an example VR (Virtual Reality) Streaming and MR (Mixed Reality) Streaming with the advent of 5G cellular coverage will be an industry disruptor. The rural world is getting 5G before the western world and that is why this is our chance to allow the rural world to play catch up with the rest of the world.

I will give some examples and discuss what I think the short and longer term future will bring.

※Keynote Speech

Topic: Time-Limited Trials for Critically Ill Cancer Patients

Speaker: Peggy Lai

There are many clinical questions that are difficult to answer, because conducting a trial would be unethical while observational studies cannot address unmeasured or unmeasurable confounders. Careful application of innovative approaches to "big data" collected through electronic health records may fill some of these gaps. When a patient with poor long-term prognosis (as in the case of advanced cancer) becomes critically ill, with uncertain short-term prognosis determined more by severity of organ failure than underlying disease, what should a clinician recommend? We applied decision-analytic methods to three large intensive care unit databases to ask the question, "If I have a poor-prognosis cancer and become critically ill, how long of a trial of intensive care would give me the same shot at surviving 30 days as time-unlimited care?" (PMID: 26469222).

※Keynote Speech

Topic: When Machine Learning Meets Healthcare

Speaker: Mengling 'Mornin' Feng

Machine Learning (ML), especially the application of Artificial Intelligence (AI), is no doubt one of the most popular research filed at the moment. Specifically for healthcare, many believe that the current advancement in ML and AL is going to augment and disrupt the current practices so to achieve better and more cost effective care. In my talk, I will share a number of real use cases that my group have been working on in the past years, where ML and AI technologies were applied to address healthcare problems. More importantly, I will also share our realisations on the major challenges and limitations while deploying ML and AI solutions for real clinical applications.

9/27-9/29: Taipei Medical University- Xing-Chun Auditorium 1F

No.250, Wuxing St., Xinyi Dist, Taipei City, Taiwan

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