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

BACKGROUND AND AIMS
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 an Natural Language Processing (NLP) algorithm to perform such a search in an automated manner, in order to improve early diagnosis and treatment.


METHODS
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.


RESULTS
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 0.88-0.98. A relative increase of 48.6% in genetic testing, in order to identify patients with a rare disease earlier, was demonstrated.


INTERPRETATION
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. This article is protected by copyright. All rights reserved.