title: Clinical data miner: an electronic case report form system with integrated data preprocessing and machine-learning libraries supporting clinical diagnostic model research.
abstract: BACKGROUND
  Using machine-learning techniques, clinical diagnostic model research extracts diagnostic models from patient data. Traditionally, patient data are often collected using electronic Case Report Form (eCRF) systems, while mathematical software is used for analyzing these data using machine-learning techniques. Due to the lack of integration between eCRF systems and mathematical software, extracting diagnostic models is a complex, error-prone process. Moreover, due to the complexity of this process, it is usually only performed once, after a predetermined number of data points have been collected, without insight into the predictive performance of the resulting models.
  
  
  OBJECTIVE
  The objective of the study of Clinical Data Miner (CDM) software framework is to offer an eCRF system with integrated data preprocessing and machine-learning libraries, improving efficiency of the clinical diagnostic model research workflow, and to enable optimization of patient inclusion numbers through study performance monitoring.
  
  
  METHODS
  The CDM software framework was developed using a test-driven development (TDD) approach, to ensure high software quality. Architecturally, CDM's design is split over a number of modules, to ensure future extendability.
  
  
  RESULTS
  The TDD approach has enabled us to deliver high software quality. CDM's eCRF Web interface is in active use by the studies of the International Endometrial Tumor Analysis consortium, with over 4000 enrolled patients, and more studies planned. Additionally, a derived user interface has been used in six separate interrater agreement studies. CDM's integrated data preprocessing and machine-learning libraries simplify some otherwise manual and error-prone steps in the clinical diagnostic model research workflow. Furthermore, CDM's libraries provide study coordinators with a method to monitor a study's predictive performance as patient inclusions increase.
  
  
  CONCLUSIONS
  To our knowledge, CDM is the only eCRF system integrating data preprocessing and machine-learning libraries. This integration improves the efficiency of the clinical diagnostic model research workflow. Moreover, by simplifying the generation of learning curves, CDM enables study coordinators to assess more accurately when data collection can be terminated, resulting in better models or lower patient recruitment costs.
authors.affiliation: Department of Electrical Engineering ESAT, STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, Leuven, Belgium. arnaud.installe@esat.kuleuven.be.
authors.forename: Arnaud Jf
authors.identifier: http://orcid.org/0000-0001-6837-3979
authors.initials: AJ
authors.lastname: Installé
authors.forename: Thierry
authors.identifier: http://orcid.org/0000-0001-6777-5036
authors.initials: T
authors.lastname: Van den Bosch
authors.forename: Bart
authors.identifier: http://orcid.org/0000-0002-1154-5028
authors.initials: B
authors.lastname: De Moor
authors.forename: Dirk
authors.identifier: http://orcid.org/0000-0002-3707-6645
authors.initials: D
authors.lastname: Timmerman
keywords: clinical decision support systems; data analysis; data collection; machine-learning