Medical expert knowledge meets AI: Can expert knowledge improve symptom assessment apps - a new approach to rare diseases (Preprint)

Abstract

Although each individual disease in itself is rare, there are approximately 4 million people affected by rare diseases in Germany alone. Due to the limited knowledge, the time to diagnosis is often long and poses many challenges. Artificial intelligence (AI) approaches, like those used in symptom assessment applications (SAAs), can potentially help to detect rare diseases and thus shorten the time to diagnosis. Generally, SAAs use reasoning approaches applied to databases of both structured and unstructured medical knowledge from the medical literature. For rare diseases, however, there is only limited information available in medical literature. In this study we developed a new approach: abstracting medical expert knowledge from guided interviews and transforming them into clinical vignettes of the disease for the SAA knowledge base.

Our goal was to integrate expert knowledge on the lysosomal storage diseases (LSDs) Fabry, Gaucher and Pompe disease into the Ada SAA, and to investigate whether the novel approach of guided interviews in combination with literature review leads to a better performance in symptom assessment than literature data sourcing alone.

Clinical vignettes were created on the basis of interviews with 4 clinical experts from Hannover Medical School which gathered knowledge about typical symptom constellations. Structured case vignettes can easily be transcribed into the SAA’s knowledge base. Patients diagnosed with Fabry disease carried out an assessment using SAA versions generated from both the original disease modeling approach of data literature data sourcing alone and the novel approach. SAA performance and patient satisfaction were assessed using questionnaires.

With the help of the guided interviews 11 clinical vignettes were created to optimize the SAA’s LSD disease models. In the Fabry disease model comparison, participants generally favored the optimized SAA. With regard to diagnostic accuracy, there was a small difference between versions, favoring the optimized Fabry model.

The proposed novel approach for gaining medical knowledge about rare diseases for SAAs is a promising addition to the existing method of using literature research alone. Especially in case of rare diseases, knowledge is limited and medical literature is often incomplete – which often leads to diagnostic odysseys for the patients affected. Extending the traditional approach of disease modeling by extracting medical expert knowledge through clinical vignettes is a complementary method to fill and improve the knowledge base of SAAs, which may help to shorten the time to diagnosis for patients, including those suffering from rare diseases.