09/2020 – 02/2021
Inflammatory rheumatic diseases (IRD) often go along with unspecific symptoms that make the correct diagnosis difficult . The European League Against Rheumatism (EULAR) passed clear recommendations that patients should be seen during 6 weeks after symptom onset  as early
treatment start is likely to improve the patient outcome significantly . Various strategies have been identified [1, 4] to implement these recommendations, however diagnostic delays are high [5, 6]. There are different reasons for this delay, one of them is that the time until a patient is seeking medical advice is too long, due to lack of awareness of the disease . Symptom checkers (SCs) foster patient-empowerment and could be used to optimise triage decisions in a personalised manner (the right appointment, for the right patient, at the right time). 90%
of patients with IRDs regularly use smartphones, 65% believe that using medical apps could be beneficial to improve their health  and 4% consulted the internet to check their symptoms  previous to a rheumatologist appointment. SCs like artificial-intelligence-driven Ada (www.ada.com) have been used to complete more than 15 million health assessments in 130 countries  and a recent publication suggests that the diagnostic delay could significantly be reduced even for rare diseases when using digital SCs . Another SC is Rheport (www.rheport.de) which has been specifically designed for IRD identification. Current research (data not published) shows that sensitivity and specificity of Rheport regarding IRDs is limited, however the majority of patients would recommend the use of SC and their usability was well perceived.
The current Rheport algorithm is rule-based and builds on subjective expert knowledge. Machine learning (ML), a way of mathematically modelling patterns in available training data and making predictions on new data could help to improve the algorithm. There are a number of general (not specifically designed for IRD) SCs that use ML techniques. These SCs include buoy (www.buoyhealth.com) and Isabel (www.isabelhealthcare.com), among others. SCs for IRD, however, are mostly simple questionnaires as they can be found on Rheuma-Check (www.rheumacheck.rheumanet.org/questionnaire.aspx) or Arthritis (www.arthritis.ca). It has recently been shown that the use of ML can outperform conventional techniques, which has not been shown for IRD yet [11, 12].
For a classification problem like distinguishing between patients with and without IRD, a number of ML algorithms could be used including Naive Bayes Classifier, Decision Trees, Support Vector Machines or Neural Networks in order to improve sensitivity and specificity of Rheport. Based
on structure, quality and quantity of data, the best algorithm for performing the required task needs to be determined. The results will be compared to previously determined performances. As Rheport is currently being used by several German rheumatologists, the results can directly
improve the quality of life of patients with IRD. The aim of this project is to improve Rheport’s algorithm using ML based on self-reported multicentric real-world data.
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