Validation of an Artificial Intelligence driven framework to automatically detect red flag symptoms in screening for rare diseases in electronic health records: hereditary transthyretin amyloidosis polyneuropathy as a key example. – ScienceOpen
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      Validation of an Artificial Intelligence driven framework to automatically detect red flag symptoms in screening for rare diseases in electronic health records: hereditary transthyretin amyloidosis polyneuropathy as a key example.

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          Abstract

          Rare life-threatening conditions, such as multisystemic hereditary transthyretin amyloidosis (ATTRv) polyneuropathy, are often underdiagnosed or diagnosed late in the disease course, although early diagnosis is crucial for treatment success. Red flag symptoms have been identified, but manual screening of multidisciplinary medical records on this set of symptoms is time-consuming. This study aimed to validate a Natural Language Processing (NLP) algorithm to perform such a search in an automated manner, in order to improve early diagnosis and treatment. A novel state-of-the-art NLP procedure was applied to extract red flag symptoms from patients' electronic medical records and to select patients at risk for ATTRv polyneuropathy for further clinical review. Accuracy of the algorithm was assessed through comparison with a manual standard on a random sample of 300 patients. Out of a retrospective sample of 1015 patients, the NLP algorithm yielded 128 patients with three or more red flag symptoms of which 69 patients were considered eligible for genetic testing after clinical review. High accuracy was found in the detection of red flag symptoms, with F1 scores between 0.88 and 0.98. A relative increase of 48.6% in genetic testing, to identify patients with a rare disease earlier, was demonstrated. An NLP algorithm, after clinical validation, offers a valid and accurate tool to detect red flag symptoms in medical records across multiple disciplines, supporting better screening for patients with rare diseases. This opens the door to further NLP applications, facilitating rapid diagnosis and early treatment of rare diseases.

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          Author and article information

          Journal
          J Peripher Nerv Syst
          Journal of the peripheral nervous system : JPNS
          Wiley
          1529-8027
          1085-9489
          Mar 2023
          : 28
          : 1
          Affiliations
          [1 ] LynxCare, Leuven, Belgium.
          [2 ] Department of Neurology, University Hospitals Leuven, Leuven, Belgium.
          [3 ] Laboratory for Muscle Diseases and Neuropathies, Department of Neurosciences, KU Leuven, and Leuven Brain Institute (LBI), Leuven, Belgium.
          Article
          10.1111/jns.12523
          36468607
          88a6e480-84ec-4e29-a440-d1a9c743d970
          History

          hereditary transthyretin amyloidosis polyneuropathy,rare diseases,natural language processing,early diagnosis and treatment,data mining

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