Aligning Subjective Ratings in Clinical Decision Making

Pick, Annika, Ginzel, Sebastian, Rüping, Stefan, Sander, Jil, Foldenauer, Ann Christina, Köhm, Michaela

arXiv.org Machine Learning 

While objective indicators are more transparent and robust, the subjective evaluation contains a wealth of expert knowledge and intuition. In this work, we demonstrate the potential of pairwise ranking methods to align the subjective evaluation with objective indicators, creating a new score that combines their advantages and facilitates diagnosis. In a case study on patients at risk for developing Psoriatic Arthritis, we illustrate that the resulting score (1) increases classification accuracy when detecting disease presence/absence, (2) is sparse and (3) provides a nuanced assessment of severity for subsequent analysis.

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