A Mathematical Model for Interpretable Clinical Decision Support with Applications in Gynecology
Van Belle, Vanya M. C. A. ; Van Calster, Ben ; Timmerman, Dirk ; Bourne, Tom ; Bottomley, Cecilia ; Valentin, Lil LU
; Neven, Patrick
; Van Huffel, Sabine
; Suykens, Johan A. K.
and Boyd, Stephen
(2012)
In PLoS ONE
7(3).
- 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... (More)
- 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. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/2911709
- author
- Van Belle, Vanya M. C. A.
; Van Calster, Ben
; Timmerman, Dirk
; Bourne, Tom
; Bottomley, Cecilia
; Valentin, Lil
LU
; Neven, Patrick
; Van Huffel, Sabine
; Suykens, Johan A. K.
and Boyd, Stephen
- organization
- publishing date
- 2012
- type
- Contribution to journal
- publication status
- published
- subject
- in
- PLoS ONE
- volume
- 7
- issue
- 3
- publisher
- Public Library of Science (PLoS)
- external identifiers
-
- wos:000304523400072
- scopus:84859069072
- pmid:22479598
- ISSN
- 1932-6203
- DOI
- 10.1371/journal.pone.0034312
- language
- English
- LU publication?
- yes
- id
- 949cbfbb-09f9-4b50-9086-161d3b305880 (old id 2911709)
- date added to LUP
- 2016-04-01 14:08:19
- date last changed
- 2022-04-22 01:28:39
@article{949cbfbb-09f9-4b50-9086-161d3b305880,
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. 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.}},
author = {{Van Belle, Vanya M. C. A. and Van Calster, Ben and Timmerman, Dirk and Bourne, Tom and Bottomley, Cecilia and Valentin, Lil and Neven, Patrick and Van Huffel, Sabine and Suykens, Johan A. K. and Boyd, Stephen}},
issn = {{1932-6203}},
language = {{eng}},
number = {{3}},
publisher = {{Public Library of Science (PLoS)}},
series = {{PLoS ONE}},
title = {{A Mathematical Model for Interpretable Clinical Decision Support with Applications in Gynecology}},
url = {{https://lup.lub.lu.se/search/files/3810985/3350510.pdf}},
doi = {{10.1371/journal.pone.0034312}},
volume = {{7}},
year = {{2012}},
}