title: Diagnostic support for selected neuromuscular diseases using answer-pattern recognition and data mining techniques: a proof of concept multicenter prospective trial.
abstract: 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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Department of Pediatric Hematology and Oncology, Hannover Medical School, Carl-Neuberg Str. 1, D-30623, Hannover, Germany. Grigull.lorenz@mh-hannover.de.
authors.forename: Lorenz
authors.initials: L
authors.lastname: Grigull
authors.affiliation: Improved Medical Diagnostics, IMD GmbH, Hannover, Germany. werner.lechner@improvedmedicaldiagnostics.com.
authors.forename: Werner
authors.initials: W
authors.lastname: Lechner
authors.affiliation: Department of Neurology, Hannover Medical School, Hannover, Germany. petri.susanne@mh-hannover.de.
authors.forename: Susanne
authors.initials: S
authors.lastname: Petri
authors.affiliation: Department of Neurology, Hannover Medical School, Hannover, Germany. kollewe.katja@mh-hannover.de.
authors.forename: Katja
authors.initials: K
authors.lastname: Kollewe
authors.affiliation: Department of Neurology, Hannover Medical School, Hannover, Germany. dengler.reinhard@mh-hannover.de.
authors.forename: Reinhard
authors.initials: R
authors.lastname: Dengler
authors.affiliation: Department of Neurology, Hannover Medical School, Hannover, Germany. mehmecke.sandra@mh-hannover.de.
authors.forename: Sandra
authors.initials: S
authors.lastname: Mehmecke
authors.affiliation: DRK Clementinenkrankenhaus, Hannover, Germany. uschumacher313@gmx.de.
authors.forename: Ulrike
authors.initials: U
authors.lastname: Schumacher
authors.affiliation: Klinik für Kinder- und Jugendmedizin im St. Josef Hospital, Ruhr- Universität Bochum, Bochum, Germany. luecke.thomas@ruhr-uni-bochum.de.
authors.forename: Thomas
authors.initials: T
authors.lastname: Lücke
authors.affiliation: Department of Neurology, Heimer-Institute at the BG University-Hospital Bergmannsheil GmbH, Ruhr- University Bochum, Bochum, Germany. chrisschneigold@icloud.com.
authors.forename: Christiane
authors.initials: C
authors.lastname: Schneider-Gold
authors.affiliation: Klinik für Kinder- und Jugendmedizin im St. Josef Hospital, Ruhr- Universität Bochum, Bochum, Germany. c.koehler@klinikum-bochum.de.
authors.forename: Cornelia
authors.initials: C
authors.lastname: Köhler
authors.affiliation: Department of Neurology, Heimer-Institute at the BG University-Hospital Bergmannsheil GmbH, Ruhr- University Bochum, Bochum, Germany. Anne.Guettsches@ruhr-uni-bochum.de.
authors.forename: Anne-Katrin
authors.initials: AK
authors.lastname: Güttsches
authors.affiliation: Ostfalia University of Applied Sciences, Wolfenbuettel, Germany. X.Kortum@ostfalia.de.
authors.forename: Xiaowei
authors.initials: X
authors.lastname: Kortum
authors.affiliation: Ostfalia University of Applied Sciences, Wolfenbuettel, Germany. Frank.Klawonn@helmholtz-hzi.de.|Helmholtz Centre for Infection Research, Biostatistics Group, Braunschweig, Germany. Frank.Klawonn@helmholtz-hzi.de.
authors.forename: Frank
authors.initials: F
authors.lastname: Klawonn
mesh_terms: D057225:Data Mining / Q000379:methods*; D020000:Decision Support Systems, Clinical; D006801:Humans; D009468:Neuromuscular Diseases / Q000175:diagnosis*; D010363:Pattern Recognition, Automated / Q000379:methods*; D010865:Pilot Projects; D011446:Prospective Studies; D011795:Surveys and Questionnaires