A Systematic Approach to Prioritise Diagnostically Useful Findings for Inclusion in Electronic Health Records as Discrete Data to Improve Clinical Artificial Intelligence Tools and Genomic Research.

Journal: Clinical oncology (Royal College of Radiologists (Great Britain))
PMID: 39837110

Abstract

AIMS: The recent widespread use of electronic health records (EHRs) has opened the possibility for innumerable artificial intelligence (AI) tools to aid in genomics, phenomics, and other research, as well as disease prevention, diagnosis, and therapy. Unfortunately, much of the data contained in EHRs are not optimally structured for even the most sophisticated AI approaches. There are very few published efforts investigating methods for recording discrete data in EHRs that would not slow current clinical workflows or ways to prioritise patient characteristics worth recording. Here, we propose an approach to identify and prioritise findings (phenotypes) useful for differentiating diseases, with an initial focus on relatively common small B-cell lymphomas.

Authors

  • P Guillod
    Yale School of Medicine, Yale University, 333 Cedar Street, New Haven, CT 06510, USA.
  • A Savvas
    Department of Computer Sciences, University of Wisconsin Madison, 1210 W Dayton St, Madison, WI 53706, USA.
  • P N Robinson
    Roux Family Center for Genomics and Computational Biology, The Jackson Laboratory for Genomic Medicine, 10 Discovery Dr., Farmington, CT 06032, USA; Berlin Institute of Health at Charité (BIH), Rahel Hirsch Center for Translational Medicine, Luisenstraße 65, 10117 Berlin, Germany.
  • D Nai
    Department of Pathology and Laboratory Medicine, Medical College of Wisconsin, 9200 W Wisconsin Ave, Milwaukee, WI 53226, USA.
  • K N Naresh
    Pathology Department, Fred Hutchison Cancer Center, 1100 Fairview Ave. N., Mailstop D2-194, Seattle, WA 98109, USA; Department of Laboratory Medicine and Pathology, University of Washington, 1959 NE Pacific St, BOX 357470, Seattle, WA, 98195, USA.
  • G Ott
    Department of Clinical Pathology, Robert-Bosch-Krankenhaus, and Dr. Margarete Fischer-Bosch Institute of Clinical Pharmacology, Stuttgart, Germany.
  • A Schuh
    Oxford Molecular Diagnostic Centre, Department of Oncology, University of Oxford, Oxford, UK.
  • W A Sewell
    Immunology Division, Garvan Institute of Medical Research, Sydney, Australia.
  • M Anderson
    Department of Pathology and Laboratory Medicine, Medical College of Wisconsin, 9200 W Wisconsin Ave, Milwaukee, WI 53226, USA; Wisconsin Diagnostic Laboratories, 8777 Connell Ave., Milwaukee, WI 53226, USA.
  • N Matentzoglu
    Independent Consultant, Athens, Greece. Electronic address: nico@semanticly.ai.
  • D Durgavarjhula
    Department of Computer Sciences, University of Wisconsin Madison, 1210 W Dayton St, Madison, WI 53706, USA.
  • M L Xu
    Department of Foot and Ankle Surgery, Xuzhou Renci Hospital, Xuzhou 221004, China.
  • M J Druzdzel
    Department of Computer Science, Bialystok University of Technology, Wiejska 45A, Bialystok, 15-351, Poland.
  • J M Astle
    Department of Pathology and Laboratory Medicine, Medical College of Wisconsin, 9200 W Wisconsin Ave, Milwaukee, WI 53226, USA; Department of Pathology, Yale School of Medicine, 20 York Street, Ste East Pavilion 2-631, New Haven, CT 06510, USA. Electronic address: john.m.astle@questdiagnostics.com.

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