What is an Optimal Diagnosis?

TL;DR

This paper critiques existing definitions of diagnosis in diagnostic reasoning, emphasizing the importance of utility and purpose in determining an optimal diagnosis rather than solely relying on probability-based approaches.

Contribution

It introduces the idea that diagnosis should incorporate utility and intended use, challenging the assumption that the most probable hypothesis is always optimal.

Findings

Different definitions of diagnosis have distinct qualitative meanings.

Current approaches often ignore the utility of outcomes in diagnosis.

Optimal diagnosis should consider utility and purpose, not just probability.

Abstract

Within diagnostic reasoning there have been a number of proposed definitions of a diagnosis, and thus of the most likely diagnosis, including most probable posterior hypothesis, most probable interpretation, most probable covering hypothesis, etc. Most of these approaches assume that the most likely diagnosis must be computed, and that a definition of what should be computed can be made a priori, independent of what the diagnosis is used for. We argue that the diagnostic problem, as currently posed, is incomplete: it does not consider how the diagnosis is to be used, or the utility associated with the treatment of the abnormalities. In this paper we analyze several well-known definitions of diagnosis, showing that the different definitions of the most likely diagnosis have different qualitative meanings, even given the same input data. We argue that the most appropriate definition of (optimal) diagnosis needs to take into account the utility of outcomes and what the diagnosis is used for.