Previously submitted to: JMIR Medical Informatics (no longer under consideration since Oct 26, 2018)
Date Submitted: Aug 19, 2018
Open Peer Review Period: Aug 22, 2018 - Oct 14, 2018
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Common pre-diagnostic features in individuals with different rare diseases represent a key for diagnostic support with computerized pattern recognition?
Background:
Different rare diseases (RD) obviously result in substantial clinical appearances and diagnostic challenges for health professionals. However, we hypothesized that there are consistencies and shared phenomena among all individuals affected by (different) RD during the time before the diagnosis could be established.
Objective:
We aimed to identify communalities between different RD and developed a machine learning diagnostic support tool for RD.
Methods:
20 interviews with affected individuals with different RD focusing on the time before their final diagnosis was established were performed and qualitatively analyzed. Out of these pre-diagnostic experiences we distilled key phenomena and created a questionnaire which was then distributed among individuals with the established diagnosis of i.) RD, ii.) common non-rare diseases (NRO) iii.) common chronic diseases (CD), iv.), psychosomatic or somatoform diseases (PSY), individuals. Finally, four combined single data mining methods and a fusion algorithm were trained to distinguish the different answer patterns of the questionnaires.
Results:
The questionnaire contained 53 questions. At the end of the campaign a total sum of 1763 questionnaires (758 RD, 149 CD, 48 PSY, 200 NRO, 34 healthy individuals and 574 not evaluable questionnaires) were collected. Based on 3 independent data sets the 10-fold stratified cross-validation method for the answer-pattern recognition resulted in sensitivity values of 88.9% to detect the answer pattern of a RD, 86.6% for NRO, 87.7% for CD and 84.2% for PSY.
Conclusions:
Despite of being so different, patients with RD share surprisingly similar pre-diagnosis experiences. These communalities were qualitatively explored and successfully used to develop a questionnaire. Mathematical algorithms learned to distinguish these different answer-patterns. Such a questionnaire-based diagnostic support tool might aid professional medical users to raise suspicion for a RD and it could help to shorten the way to the correct diagnosis. Our questionnaire- and data-mining based approach was successfully able to detect unique patterns in individuals affected by a broad range of different rare diseases. Therefore, this approach may shorten the often observed diagnostic delay in RD.
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