Abstract
Objective: Laryngopharyngeal Reflux Disease (LPRD) is prevalent and has a range of symptoms. However, diagnosing LPRD is difficult because of the lack of specific symptoms or clinical gold standard. An objective and reliable test, which does not rely on a perfect clinical gold standard, is required in clinical practice. Methods: 60 normal volunteers and 74 confirmed Laryngopharyngeal Reflux (LPR) patients were labelled based on the combined consideration of the reflux symptom index, reflux finding score, 24h oropharyngeal pH monitoring and results of anti-reflux treatment. 72 candidate features were extracted from pH recordings and the most efficient feature combination was detected using a stepwise wrapper method. The labelled data were combined with 1552 unlabelled data for feature selection and model training using semi-supervised learning. A latent class model method was used to assess the proposed model based on 64 additional validation data and an imperfect clinical reference test. Results: A new score (named W score), which significantly improved the sensitivity of the LPRD test (82.67% vs. 24.09%) and has a relatively high specificity (80.19%), was proposed. W score concurs with the complicated clinical test. Conclusion: W score which significantly improves LPRD diagnostic efficiency, can aid the clinical diagnosis of LPRD. W score provides an objective, efficient, and reliable indicator for the application of anti-reflux treatment in clinical practice. Significance: More LPRD patients can benefit from anti-reflux treatment in clinical practice and fewer patients may suffer from, for example, the side effects of unnecessary long-term Proton Pump Inhibitors (PPI) treatments.
Highlights
In clinical studies, a gold standard is often an imperfect gold standard test is worth studying
To give a complete characterization of the profile of pH recordings, a total number of 72 features were extracted from the pH time series based on 14 different thresholds. These 14 thresholds were selected based on the distribution of 60 normal and 74 Laryngopharyngeal Reflux Disease (LPRD) subjects
The Restech Dx-pH monitoring technique has become a useful tool for diagnosing LPRD
Summary
A gold standard is often an imperfect gold standard test is worth studying. Machine learning has been proven powerful in exploratory data analysis and can diagnostic test and cannot achieve 100% sensitivity and speci- extract useful information that cannot be directly observed. Ficity [1], [2]. Machine learning has been used in pattern recognition and outcomes due to the lack of accurately labelled informa- with partially known information, for example, the one-class tion and reliable performance evaluation of new tests. There- supporter vector machine [3], classification using only posfore, the identification of disease without or with an imperfect itive and unlabelled examples in retrieval applications [4], learning with noisy labels [5], and so on.
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