Predicting Mortality with Applied Machine Learning: Can We Get There?. Issue 1 (September 2019)

• Record Type: Journal Article
• Authors: Patterson, Emily S.; Hansen, C.J.; Allen, Theodore T.; Yang, Qiwei; Moffatt-Bruce, Susan D.
• Is Part Of: Proceedings of the International Symposium of Human Factors and Ergonomics in Healthcare. Volume 8:Issue 1(2019)
• Page Start: 115
• Page End: 119
• Publication Date: 2019-09
• Subjects: Human engineering -- Periodicals; Medical care -- Periodicals; 620.8
• DOI: 10.1177/2327857919081026
• Languages: English
• ISSNs: 2327-8579

Abstract

There is growing interest in using AI-based algorithms to support clinician decision-making. An important consideration is how transparent complex algorithms can be for predictions, particularly with respect to imminent mortality in a hospital environment. Understanding the basis of predictions, the process used to generate models and recommendations, how to generalize models based on one patient population to another, and the role of oversight organizations such as the Food and Drug Administration are important topics. In this paper, we debate opposing positions regarding whether these algorithms are 'ready yet' for use today in clinical settings for physicians, patients and caregivers. We report voting results from participating audience members in attendance at the conference debate for each of these positions obtained real-time from a smartphone-based platform.