Patient-Oriented Questionnaires and Machine Learning for Rare Disease Diagnosis: A Systematic Review
Abstract
:1. Introduction
2. Materials and Methods
2.1. Research Design
2.2. Study Selection
Research Question To what extent has the systematic use of structured history-taking questions and questionnaires for diagnostic support been explored in medical research, and what evidence exists regarding the effectiveness and precision of these methods in supporting the diagnosis of RDs?
2.3. Data Extraction
2.4. Risk of Bias
- Selection bias: a lack of comprehensive search strategy or inclusion criteria, leading to the exclusion of relevant studies. This can result in an incomplete synthesis of evidence and bias the conclusions drawn from the review.
- Publication and reporting bias: in the majority of publications, positive results are often emphasised, potentially overshadowing undisclosed negative results and rendering us unaware of their existence due to non-publication. This selective reporting may lead to an overestimation of treatment effects and intervention outcomes.
- Recall and knowledge bias: in certain studies, patients were requested to complete questionnaires post-diagnosis or post-treatment, potentially resulting in patients possessing enhanced knowledge about the disease. Additionally, participants may be tasked with recalling information from memory, which can lead to distorted responses if past events or experiences are inaccurately remembered.
- Methodological bias: when assessing ML algorithms, it is uncommon to evaluate quality based on a single measure. Certain studies solely mentioned accuracy (without specifying class distribution), while others exclusively presented recall (without precision or -score). The upcoming Section 3.3.5 will delve into a more detailed explanation of performance measurement.
3. Results
3.1. Literature Search
3.2. Literature Review
3.3. Statistical Facts
3.3.1. Chronological Trend
3.3.2. Number of Questions
3.3.3. Answer Scales
3.3.4. ML Techniques
3.3.5. Performance
4. Discussion
4.1. Potentials
4.2. Scope and Limitations
4.3. Research Question Review
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Conflicts of Interest
References
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questionnaire |
AND |
(patient oriented questionnaire OR patient reported outcome OR patient experience OR patient perspective) |
AND |
(machine learning OR artificial intelligence OR data mining) |
AND |
(diagnostic support OR clinical decision making) |
AND |
(rare disease OR orphan disease) |
Inclusion Criteria | Exclusion Criteria |
---|---|
|
|
Model | Abs. Feq | Rel. Freq | Publications |
---|---|---|---|
Naive Bayes | 4 | 25% | [24,26,27,32] |
LDA | 6 | 38% | [24,25,26,27,28,32] |
QDA | 1 | 6% | [32] |
Decision Tree | 1 | 6% | [33,34] |
Random Forest | 5 | 31% | [24,25,26,28,31] |
Logistic Regression | 8 | 50% | [24,25,26,27,28,30,33,37] |
SVM | 6 | 38% | [24,25,26,27,28,34] |
KNN | 4 | 25% | [24,26,27,32] |
Fuzzy RB | 3 | 6% | [27] |
MLP | 1 | 19% | [27,33,34] |
Fusion | 5 | 31% | [24,25,26,27,28] |
Threshold | 1 | 6% | [36] |
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Brauner, L.E.; Yao, Y.; Grigull, L.; Klawonn, F. Patient-Oriented Questionnaires and Machine Learning for Rare Disease Diagnosis: A Systematic Review. J. Clin. Med. 2024, 13, 5132. https://doi.org/10.3390/jcm13175132
Brauner LE, Yao Y, Grigull L, Klawonn F. Patient-Oriented Questionnaires and Machine Learning for Rare Disease Diagnosis: A Systematic Review. Journal of Clinical Medicine. 2024; 13(17):5132. https://doi.org/10.3390/jcm13175132
Chicago/Turabian StyleBrauner, Lea Eileen, Yao Yao, Lorenz Grigull, and Frank Klawonn. 2024. "Patient-Oriented Questionnaires and Machine Learning for Rare Disease Diagnosis: A Systematic Review" Journal of Clinical Medicine 13, no. 17: 5132. https://doi.org/10.3390/jcm13175132