On average, measures that did not make use of an external information content measure performed slightly better, however the best-performing configurations when measured by area under receiver operating characteristic curve and Top Ten Accuracy used term-specificity and annotation-frequency measures. Resultsģ00 semantic similarity configurations were evaluated, as well as one embedding-based approach.

Using the platform, we evaluate the task of ranking shared primary diagnosis from uncurated phenotype profiles derived from all text narrative associated with admissions in the medical information mart for intensive care (MIMIC-III). We develop a platform for reproducible benchmarking and comparison of experimental conditions for patient phentoype similarity. Moreover, there are no experimental explorations of optimal parameters or better methods in the area. While a large body of work exists exploring the use of semantic similarity for multiple tasks, including protein interaction prediction, and rare disease differential diagnosis, there is less work exploring comparison of patient phenotype profiles for clinical tasks. When applied to phenotype profiles derived from clinical text, they have the capacity to enable and enhance ‘patient-like me’ analyses, automated coding, differential diagnosis, and outcome prediction. Semantic similarity is a valuable tool for analysis in biomedicine.