Diagnostic support for selected neuromuscular diseases using answer-pattern recognition and data mining techniques: a proof of concept multicenter prospective trial.
Background: Diagnosis 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 re...
| Publicado en: | BMC Medical Informatics & Decision Making Vol. 16; pp. 1 - 10 |
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| Autores principales: | , , , , , , , , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
BioMed Central
3/8/2016
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=113741719&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 113741719 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14726947 1CI0 jtl: BMC Medical Informatics & Decision Making issn: 14726947 maglogo: N pubinfo: dt: 3/8/2016 vid: 16 pid: 24147 pub: BioMed Central artinfo: ui: 113741719 113741719 NLM26957320 113741719 10.1186/s12911-016-0268-5 NLM26957320 PMC4782522 113741719 ppf: 1 ppct: 9 formats: tig: atl: Diagnostic support for selected neuromuscular diseases using answer-pattern recognition and data mining techniques: a proof of concept multicenter prospective trial. aug: au: Grigull, Lorenz Lechner, Werner Petri, Susanne Kollewe, Katja Dengler, Reinhard Mehmecke, Sandra Schumacher, Ulrike Lücke, Thomas Schneider-Gold, Christiane Köhler, Cornelia Güttsches, Anne-Katrin Kortum, Xiaowei Klawonn, Frank affil: Department of Pediatric Hematology and Oncology, Hannover Medical School, Carl-Neuberg Str. 1, D-30623 Hannover, Germany sug: subj: Data Mining Methods Information Science Methods Decision Support Systems, Clinical Neuromuscular Diseases Diagnosis Prospective Studies Human Pilot Studies Validation Studies Comparative Studies Evaluation Research Multicenter Studies ab: Background: Diagnosis 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.Methods: First, 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.Results: In 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.Conclusion: A 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. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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