A Mathematical Model for Interpretable Clinical Decision Support with Applications in Gynecology

By V. Van Belle, B. Van calster, Dirk Timmerman, T. Bourne, C. Bottomley, L. Valentin, Patrick Neven, S. Van Huffel, Johan A. K. Suykens, Stephen P. Boyd. 2012

39 citations recorded by the source corpus.

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. Methods and Findings 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.

Subfields: Artificial Intelligence, Health Information Management

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Health Information Management

Citations in the corpus

Interval Coded Scoring: a toolbox for interpretable scoring systems (2018, background): “The Interval Coded Scoring (ICS) system discussed in this paper resides in this category as well (Van Belle et al., 2012).”
Manipulating and Measuring Model Interpretability (2018, background): “[105] Vanya MCA Van Belle, Ben Van Calster, Dirk Timmerman, Tom Bourne, Cecilia Bottomley, Lil Valentin, Patrick Neven, Sabine Van Huffel, Johan AK Suykens, and Stephen Boyd.”
Methods and Models for Interpretable Linear Classification (2014, background): “These domains include credit scoring [76], crime prediction [102, 2, 93], national defense [27], marketing [49, 114], medical diagnosis [106, 112], and scientific discovery [104, 36, 48].”
Interval Coded Scoring extensions for larger problems (2017, methodology): “For details of its implementation, see [6].”
Simple rules to guide expert classifications (2020, methodology): “For example, Van Belle et al. (2012) used convex optimization to build interval-coded scoring models for binary outcomes.”
Multicenter Comparison of Machine Learning Methods and Conventional Regression for Predicting Clinical Deterioration on the Wards (2016, methodology): “org 373 developed, as we illustrated in this study (10, 16, 25).”