ArticleBMC medical informatics and decision making2016
Diagnostic support for selected neuromuscular diseases using answer-pattern recognition and data mining techniques: a proof of concept multicenter prospective trial.
Article in BMC medical informatics and decision making, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it, 34 citations in OpenAlex.
- [Short paths to diagnosis with artificial intelligence: systematic literature review on diagnostic decision support systems].Schmerz (Berlin, Germany) · 2024Pooled it
- Characterization of Dystrophin-Related Syndromes: Carriers, DMD, and BMD.Muscles (Basel, Switzerland) · 2026Review
- Patient-Oriented Questionnaires and Machine Learning for Rare Disease Diagnosis: A Systematic Review.Journal of clinical medicine · 2024Review
- The value of knowing: preferences for genetic testing to diagnose rare muscle diseases.Orphanet journal of rare diseases · 2024Article
- Effects of machine learning-based clinical decision support systems on decision-making, care delivery, and patient outcomes: a scoping review.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- A diagnostic support system based on pain drawings: binary and k-disease classification of EDS, GBS, FSHD, PROMM, and a control group with Pain2D.Orphanet journal of rare diseases · 2023Article
- Improving Recognition of Treatable Rare Neuromuscular Disorders in Primary Care: A Pilot Feasibility Study.Children (Basel, Switzerland) · 2022Article
- Pain drawings as a diagnostic tool for the differentiation between two pain-associated rare diseases (Ehlers-Danlos-Syndrome, Guillain-Barré-Syndrome).Orphanet journal of rare diseases · 2020Observational
- Diagnosis of Rare Diseases: a scoping review of clinical decision support systems.Orphanet journal of rare diseases · 2020Article
- Diagnosis support systems for rare diseases: a scoping review.Orphanet journal of rare diseases · 2020Article
- Model-Based and Model-Free Techniques for Amyotrophic Lateral Sclerosis Diagnostic Prediction and Patient Clustering.Neuroinformatics · 2019Article
- Article
- [Computer-assisted diagnosis of rare diseases].Der Internist · 2018Article
- Patient's Experience in Pediatric Primary Immunodeficiency Disorders: Computerized Classification of Questionnaires.Frontiers in immunology · 2017Article
Corrections and comments
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Authors and funding
13 authors at 6 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDiagnosis of neuromuscular diseases in primary care is often challenging. Rare diseases such as Pompe disease are easily overlooked by the general practitioner. We therefore aimed to develop a diagnostic support tool using patient-oriented questions and combined data mining algorithms recognizing answer patterns in individuals with selected neuromuscular diseases. A multicenter prospective study for the proof of concept was conducted thereafter.
methodsFirst, 16 interviews with patients were conducted focusing on their pre-diagnostic observations and experiences. From these interviews, we developed a questionnaire with 46 items. Then, patients with diagnosed neuromuscular diseases as well as patients without such a disease answered the questionnaire to establish a database for data mining. For proof of concept, initially only six diagnoses were chosen (myotonic dystrophy and myotonia (MdMy), Pompe disease (MP), amyotrophic lateral sclerosis (ALS), polyneuropathy (PNP), spinal muscular atrophy (SMA), other neuromuscular diseases, and no neuromuscular disease (NND). A prospective study was performed to validate the automated malleable system, which included six different classification methods combined in a fusion algorithm proposing a final diagnosis. Finally, new diagnoses were incorporated into the system.
resultsIn total, questionnaires from 210 individuals were used to train the system. 89.5 % correct diagnoses were achieved during cross-validation. The sensitivity of the system was 93-97 % for individuals with MP, with MdMy and without neuromuscular diseases, but only 69 % in SMA and 81 % in ALS patients. In the prospective trial, 57/64 (89 %) diagnoses were predicted correctly by the computerized system. All questions, or rather all answers, increased the diagnostic accuracy of the system, with the best results reached by the fusion of different classifier methods. Receiver operating curve (ROC) and p-value analyses confirmed the results.
conclusionA questionnaire-based diagnostic support tool using data mining methods exhibited good results in predicting selected neuromuscular diseases. Due to the variety of neuromuscular diseases, additional studies are required to measure beneficial effects in the clinical setting.
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