title: A mathematical model for interpretable clinical decision support with applications in gynecology.
abstract: BACKGROUND
  Over time, methods for the development of clinical decision support (CDS) systems have evolved from interpretable and easy-to-use scoring systems to very complex and non-interpretable mathematical models. In order to accomplish effective decision support, CDS systems should provide information on how the model arrives at a certain decision. To address the issue of incompatibility between performance, interpretability and applicability of CDS systems, this paper proposes an innovative model structure, automatically leading to interpretable and easily applicable models. The resulting models can be used to guide clinicians when deciding upon the appropriate treatment, estimating patient-specific risks and to improve communication with patients.
  
  
  RESULTS
  We propose the interval coded scoring (ICS) system, which imposes that the effect of each variable on the estimated risk is constant within consecutive intervals. The number and position of the intervals are automatically obtained by solving an optimization problem, which additionally performs variable selection. The resulting model can be visualised by means of appealing scoring tables and color bars. ICS models can be used within software packages, in smartphone applications, or on paper, which is particularly useful for bedside medicine and home-monitoring. The ICS approach is illustrated on two gynecological problems: diagnosis of malignancy of ovarian tumors using a dataset containing 3,511 patients, and prediction of first trimester viability of pregnancies using a dataset of 1,435 women. Comparison of the performance of the ICS approach with a range of prediction models proposed in the literature illustrates the ability of ICS to combine optimal performance with the interpretability of simple scoring systems.
  
  
  CONCLUSIONS
  The ICS approach can improve patient-clinician communication and will provide additional insights in the importance and influence of available variables. Future challenges include extensions of the proposed methodology towards automated detection of interaction effects, multi-class decision support systems, prognosis and high-dimensional data.
authors.affiliation: Department of Electrical Engineering (ESAT-SCD), Katholieke Universiteit Leuven, Leuven, Belgium. vanya.vanbelle@esat.kuleuven.be
authors.forename: Vanya M C A
authors.initials: VM
authors.lastname: Van Belle
authors.forename: Ben
authors.initials: B
authors.lastname: Van Calster
authors.forename: Dirk
authors.initials: D
authors.lastname: Timmerman
authors.forename: Tom
authors.initials: T
authors.lastname: Bourne
authors.forename: Cecilia
authors.initials: C
authors.lastname: Bottomley
authors.forename: Lil
authors.initials: L
authors.lastname: Valentin
authors.forename: Patrick
authors.initials: P
authors.lastname: Neven
authors.forename: Sabine
authors.initials: S
authors.lastname: Van Huffel
authors.forename: Johan A K
authors.initials: JA
authors.lastname: Suykens
authors.forename: Stephen
authors.initials: S
authors.lastname: Boyd
mesh_terms: D000291:Adnexal Diseases / Q000175:diagnosis; D000465:Algorithms; D003142:Communication; D003198:Computer Simulation; D020000:Decision Support Systems, Clinical; D003661:Decision Support Techniques; D005103:Expert Systems; D005260:Female; D006176:Gynecology / Q000379:methods*; D006801:Humans; D008962:Models, Theoretical; D009774:Obstetrics / Q000379:methods; D010817:Physician-Patient Relations; D011247:Pregnancy; D011256:Pregnancy Outcome; D011379:Prognosis; D015203:Reproducibility of Results; D012306:Risk