Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Digital Triage Tool Using Artificial Intelligence and Patient History for Detecting Selected Neurological Diseases and Sensing the Bottleneck between Symptoms, Diagnosis, and Therapy

Version 1 : Received: 12 March 2022 / Approved: 14 March 2022 / Online: 14 March 2022 (08:58:29 CET)

How to cite: Grigull, L.; Lechner, W.; Klawonn, F. Digital Triage Tool Using Artificial Intelligence and Patient History for Detecting Selected Neurological Diseases and Sensing the Bottleneck between Symptoms, Diagnosis, and Therapy. Preprints 2022, 2022030178 (doi: 10.20944/preprints202203.0178.v1). Grigull, L.; Lechner, W.; Klawonn, F. Digital Triage Tool Using Artificial Intelligence and Patient History for Detecting Selected Neurological Diseases and Sensing the Bottleneck between Symptoms, Diagnosis, and Therapy. Preprints 2022, 2022030178 (doi: 10.20944/preprints202203.0178.v1).

Abstract

During the COVID-19 pandemic, individuals with symptoms other than cough or fever have refrained from seeking medical advice. However, a delay in treatment might lead to serious consequences. At the same time, digital health initiatives have emerged to overcome this bottleneck of healthcare. Herein, we report the results of a multi-center initiative using a combination of patient history and artificial intelligence (AI) to identify individuals with rare neuromuscular diseases. First, a questionnaire with 46 items was developed by interviewing patients with muscular dystrophies, amyotrophic lateral sclerosis, Morbus Pompe, neuropathies, and myasthenia gravis. Second, patients with proven neurological diseases answered the questionnaire. Third, a combination of classifiers (artificial neural network, support vector, and random forest) was trained and, finally, the system was challenged with new questionnaires. Users with an abnormal questionnaire pattern received a unique code for data privacy and contact details for a neurologist for further advice. The neurologists confirmed or refuted the AI-based diagnosis. The questionnaire was accessed 3122 times, leading to 853 unique codes. Only for a few patients the computer-based diagnoses and the confirmed final diagnoses were reported to us. However, for these few patients, the genetic testing and high CK levels finally ended their long-lasting diagnostic odyssey.

Keywords

artificial intelligence; data mining; diagnostic decision support; rare diseases; questionnaire anamnesis; neuromuscular diseases; high latencies

Subject

MATHEMATICS & COMPUTER SCIENCE, Computational Mathematics

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