NDLI: An audit method suited for decision support systems for clinical environment

Content Provider	IEEE Xplore Digital Library
Author	Vicente, J. Tortajada, S. Fuster-Garcia, E. Garcia-Gomez, J.M. Robles, M.
Copyright Year	2012
Description	Author affiliation: Universitat Politècnica de València, Valencia, Spain (Vicente, J.; Tortajada, S.; Fuster-Garcia, E.; Garcia-Gomez, J.M.; Robles, M.)
Abstract	We present a novel on-line method to audit predictive models using a Bayesian perspective. The auditing model has been specifically designed for decision support systems (DSSs) suited for clinical environments. Taking as starting point the proposed diagnosis supplied by the clinician, our method compares and evaluates the predictive skills of those models able to answer to that diagnosis. Our approach consists in calculating the posterior odds of a model through the composition of three different odds: prior, static and dynamic. To do so, our method estimates the posterior odds from the cases that the comparing models had in common during the design stage and from the cases already viewed by the DSS after deployment in the clinical site. In addition, if an ontology of the classes is available, our method can audit models answering related questions, which offers a reinforcement to the decisions the user already took and gives orientation on further diagnostic steps. The main technical novelty of this approach lies in the design of an audit model adapted to suit the decision workflow of a clinical environment. The audit model allows deciding which is the classifier that best suits each particular case under evaluation and allows the detection of possible misbehaviours due to population differences or systematic errors in the clinical site. We show the efficacy of our method for the problem of brain tumour diagnosis with magnetic resonance spectroscopy.
Starting Page	281
Ending Page	288
File Size	747894
Page Count	8
File Format	PDF
ISBN	9781467322768
e-ISBN	9781467322782
DOI	10.1109/IS.2012.6335149
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-09-06
Publisher Place	Bulgaria
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Predictive models Decision support systems Tumors Mathematical model Brain models Data models Bioengineering Decision Support Systems Machine Learning Health Medicine
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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