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Full-Text Articles in Physical Sciences and Mathematics
Artificial Intelligence And The Situational Rationality Of Diagnosis: Human Problem-Solving And The Artifacts Of Health And Medicine, Michael W. Raphael
Artificial Intelligence And The Situational Rationality Of Diagnosis: Human Problem-Solving And The Artifacts Of Health And Medicine, Michael W. Raphael
Publications and Research
What is the problem-solving capacity of artificial intelligence (AI) for health and medicine? This paper draws out the cognitive sociological context of diagnostic problem-solving for medical sociology regarding the limits of automation for decision-based medical tasks. Specifically, it presents a practical way of evaluating the artificiality of symptoms and signs in medical encounters, with an emphasis on the visualization of the problem-solving process in doctor-patient relationships. In doing so, the paper details the logical differences underlying diagnostic task performance between man and machine problem-solving: its principle of rationality, the priorities of its means of adaptation to abstraction, and the effects …
A Novel Tropical Geometry-Based Interpretable Machine Learning Method: Pilot Application To Delivery Of Advanced Heart Failure Therapies, Heming Yao, Harm Derkson, Jessica R. Golbus, Justin Zhang, Keith D. Aaronson, Jonathan Gryak, Kayvan Najarian
A Novel Tropical Geometry-Based Interpretable Machine Learning Method: Pilot Application To Delivery Of Advanced Heart Failure Therapies, Heming Yao, Harm Derkson, Jessica R. Golbus, Justin Zhang, Keith D. Aaronson, Jonathan Gryak, Kayvan Najarian
Publications and Research
Abstract—A model’s interpretability is essential to many practical applications such as clinical decision support systems. In this paper, a novel interpretable machine learning method is presented, which can model the relationship between input variables and responses in humanly understandable rules. The method is built by applying tropical geometry to fuzzy inference systems, wherein variable encoding functions and salient rules can be discovered by supervised learning. Experiments using synthetic datasets were conducted to demonstrate the performance and capacity of the proposed algorithm in classification and rule discovery. Furthermore, we present a pilot application in identifying heart failure patients that are eligible …