SIMpat : A Synthetic Benchmark for Similarity Metrics on Patient Representations

Article Article scientifique Article SIMpat : A Synthetic Benchmark for Similarity Metrics on Patient Representations eng 2024-08-22 author Voegeli Jean-Virgile Division of Medical Information Sciences, Geneva University Hospitals, Geneva, Switzerland 837452 author Bjelogrlic Mina 0000-0002-6922-3283 Division of Medical Information Sciences, Geneva University Hospitals, Geneva, Switzerland 918489 Département de radiologie et informatique médicale (RADIO) author Gaudet-Blavignac Christophe 0000-0001-6527-5898 Division of Medical Information Sciences, Geneva University Hospitals, Geneva, Switzerland 645533 author Dubos Richard Division of Medical Information Sciences, Geneva University Hospitals, Geneva, Switzerland author Zimmermann Myriam Ophelia Division of Medical Information Sciences, Geneva University Hospitals, Geneva, Switzerland 839405 Section de psychologie (PSICO) author Bensalah Talet Adel Fouad Division of Medical Information Sciences, Geneva University Hospitals, Geneva, Switzerland 420891 author Zheng Yuanyuan Division of Medical Information Sciences, Geneva University Hospitals, Geneva, Switzerland 406172 Département de radiologie et informatique médicale (RADIO) author Ehrsam Julien Olivier Division of Medical Information Sciences, Geneva University Hospitals, Geneva, Switzerland 644990 author Lovis Christian 0000-0002-2681-8076 Division of Medical Information Sciences, Geneva University Hospitals, Geneva, Switzerland 29016 Département de radiologie et informatique médicale (RADIO) 1647-1651 10.3233/SHTI240739 39176526 0926-9630 https://ebooks.iospress.nl/doi/10.3233/SHTI240739 <p>Similarity and clustering tasks based on data extracted from electronic health records on the patient level suffer from the curse of dimensionality and the lack of inter-patient data comparability. Indeed, for many health institutions, there are many more variables, and ways of expressing those variables to represent patients than patients sharing the same set of data. To lower redundancy and increase interoperability one strategy is to map data to semantic-driven representations through medical knowledge graphs such as SNOMED-CT. However, patient similarity metrics based on this knowledge-graph information lack quantitative evaluation and comparisons with pure data-driven methods. The reasons are twofold, firstly, it is hard to conceptually assess and formalize a gold-standard similarity between patients resulting in poor inter-annotator agreement in qualitative evaluations. Secondly, the community has been lacking a clear benchmark to compare existing metrics developed by scientific communities coming from various fields such as ontology, data science, and medical informatics. This study proposes to leverage the known challenges of evaluating patient similarities by proposing SIMpat, a synthetic benchmark to quantitatively evaluate available metrics, based on controlled cohorts, which could later be used to assess their sensibility regarding aspects such as the sparsity of variables or specificities of patient disease patterns.</p> Benchmark Patient representations Similarity metrics Dewey 616.0757 62 Département de radiologie et informatique médicale 227 false 344 Interfaces Homme-machine en milieu clinique 610 Studies in health technology and informatics 316 e604dd91-8c2c-4b09-8550-9b94615fa34c Article (Published version) Public Article_scientifique_from_unpaywall.pdf 337413 application/pdf CC BY-NC-4.0 16febbd6e83718135d220b4e2db9a11fc5f411fb6a083be15e6e3176097e828a openAccess Faculté de médecine / Section de médecine clinique / Département de radiologie et informatique médicale

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SIMpat : A Synthetic Benchmark for Similarity Metrics on Patient Representations	Studies in health technology and informatics			2024	191	298

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Voegeli, Jean-Virgile