ArticleThe journal of headache and pain2015
A clinical decision support system for the diagnosis of probable migraine and probable tension-type headache based on case-based reasoning.
Article in The journal of headache and pain, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 2 of them syntheses that pooled it.
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Who cites it
21 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Designing Clinical Decision Support Systems (CDSS)-A User-Centered Lens of the Design Characteristics, Challenges, and Implications: Systematic Review.Journal of medical Internet research · 2025Pooled it
- Biomarkers in headaches as a potential solution to simplify differential diagnosis of primary headache disorders: a systematic review.The journal of headache and pain · 2025Pooled it
- Personalized machine learning prediction of next-day migraine persistence using digital headache diary data.Headache · 2026Article
- Article
- Methodology and baseline characteristics of the China Migraine registry (CHIME): a nationwide, multi-center, prospective cohort study of 11,814 patients.The journal of headache and pain · 2026Observational
- Integrated analysis of single-cell and bulk RNA-sequence data reveal plasma dendritic cell-related diagnostic model for Sjögren's syndrome based on machine learning.Journal of dental sciences · 2026Article
- Preliminary External Validation Results of the Artificial Intelligence-Based Headache Diagnostic Model: A Multicenter Prospective Observational Study.Life (Basel, Switzerland) · 2024Article
- Decision Support for Managing Common Musculoskeletal Pain Disorders: Development of a Case-Based Reasoning Application.JMIR formative research · 2024Article
- Clinical decision support system using hierarchical fuzzy diagnosis model for migraine and tension-type headache based on International Classification of Headache Disorders, 3rd edition.Frontiers in neurology · 2024Article
- Developing an Artificial Intelligence-Based Pediatric and Adolescent Migraine Diagnostic Model.Cureus · 2023Article
- A New Hybrid Approach Based on Time Frequency Images and Deep Learning Methods for Diagnosis of Migraine Disease and Investigation of Stimulus Effect.Diagnostics (Basel, Switzerland) · 2023Article
- A case-based reasoning system for neonatal survival and LOS prediction in neonatal intensive care units: a development and validation study.Scientific reports · 2023Article
- Verification of a clinical decision support system for the diagnosis of headache disorders based on patient-computer interactions: a multi-center study.The journal of headache and pain · 2023Article
- Artificial Intelligence-Aided Headache Classification Based on a Set of Questionnaires: A Short Review.Cureus · 2022Review
- Gradually shifting clinical phenomics in migraine spectrum: a cross-sectional, multicenter study of 5438 patients.The journal of headache and pain · 2022Article
- Computerized migraine diagnostic tools: a systematic review.Therapeutic advances in chronic disease · 2022Article
- Headache and Neuroimaging: Why We Continue to Do It.AJNR. American journal of neuroradiology · 2020Review
- Automatic migraine classification using artificial neural networks.F1000Research · 2020Article
- Clinical Features of Probable Cluster Headache: A Prospective, Cross-Sectional Multicenter Study.Frontiers in neurology · 2018Article
- Lifting the burden of headache in China: managing migraine in a SMART way.The journal of headache and pain · 2017Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe overlap between probable migraine (PM) and probable tension-type headache (PTTH) often confuses physicians in clinical practice. Although clinical decision support systems (CDSSs) have been proven to be helpful in the diagnosis of primary headaches, the existing guideline-based headache disorder CDSSs do not perform adequately due to this overlapping issue. Thus, in this study, a CDSS based on case-based reasoning (CBR) was developed in order to solve this problem.
methodsFirst, a case library consisting of 676 clinical cases, 56.95% of which had been diagnosed with PM and 43.05% of which had been diagnosed with PTTH, was constructed, screened by a three-member panel, and weighted by engineers. Next, the resulting case library was used to diagnose current cases based on their similarities to the previous cases. The test dataset was composed of an additional 222 historical cases, 76.1% of which had been diagnosed with PM and 23.9% of which had been diagnosed with PTTH. The cases that comprised the case library as well as the test dataset were actual clinical cases obtained from the International Headache Center in Chinese PLA General Hospital.
resultsThe results indicated that the PM and PTTH recall rates were equal to 97.02% and 77.78%, which were 34.31% and 16.91% higher than that of the guideline-based CDSS, respectively. Furthermore, the PM and PTTH precision rates were equal to 93.14% and 89.36%, which were7.09% and 15.68% higher than that of the guideline-based CDSS, respectively. Comparing CBR CDSS and guideline-based CDSS, the p-value of PM diagnoses was equal to 0.019, while that of PTTH diagnoses was equal to 0.002, which indicated that there was a significant difference between the two approaches.
conclusionsThe experimental results indicated that the CBR CDSS developed in this study diagnosed PM and PTTH with a high degree of accuracy and performed better than the guideline-based CDSS. This system could be used as a diagnostic tool to assist general practitioners in distinguishing PM from PTTH.
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