Title

Communicating Deep Learning Results For Healthcare

Presenter Information

Luke Andrews

Department

Engineering Management and Systems Engineering

Major

Engineering Management with an industrial emphasis

Research Advisor

Canfield, Casey I.

Advisor's Department

Engineering Management and Systems Engineering

Abstract

There is a rising interest in the use of artificial intelligence (AI), such as “deep learning,” in the healthcare industry. AI techniques aim to identify patterns in healthcare data to improve diagnosis and prognosis. This proposal focuses on a large data set that includes health outcomes, treatments, medications, and Medicare/Medicaid costs for patients who have received a liver or kidney transplant. In this project, we will (1) perform a literature review on the use of AI in healthcare and health communications, (2) collect qualitative data on the implications of using data from an AI to make health decisions, and (3) develop an experiment to test approaches for communicating the uncertainty associated with AI findings. Ultimately, this work may lead to the development of a decision tool, such as an app. or web-page, for transplant patients and/ or doctors.

Biography

Luke Andrews was adopted at the age of three and a half from Mogilovo, Belarus and then moved to the United States where he grew up in St. Charles Missouri. Luke is now a junior at the Missouri University of Science and Technology where he is pursuing a B.S in Engineering Management. When Luke is not busy with school work or activities he enjoys spending his time outdoor hiking, cycling, and hunting.

Research Category

Research Proposals

Presentation Type

Poster Presentation

Document Type

Poster

Location

Upper Atrium

Presentation Date

16 Apr 2019, 9:00 am - 3:00 pm

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Apr 16th, 9:00 AM Apr 16th, 3:00 PM

Communicating Deep Learning Results For Healthcare

Upper Atrium

There is a rising interest in the use of artificial intelligence (AI), such as “deep learning,” in the healthcare industry. AI techniques aim to identify patterns in healthcare data to improve diagnosis and prognosis. This proposal focuses on a large data set that includes health outcomes, treatments, medications, and Medicare/Medicaid costs for patients who have received a liver or kidney transplant. In this project, we will (1) perform a literature review on the use of AI in healthcare and health communications, (2) collect qualitative data on the implications of using data from an AI to make health decisions, and (3) develop an experiment to test approaches for communicating the uncertainty associated with AI findings. Ultimately, this work may lead to the development of a decision tool, such as an app. or web-page, for transplant patients and/ or doctors.