Abstract
BackgroundHeadache disorders are an important health burden, having a large health-economic impact worldwide. Current treatment & follow-up processes are often archaic, creating opportunities for computer-aided and decision support systems to increase their efficiency. Existing systems are mostly completely data-driven, and the underlying models are a black-box, deteriorating interpretability and transparency, which are key factors in order to be deployed in a clinical setting.MethodsIn this paper, a decision support system is proposed, composed of three components: (i) a cross-platform mobile application to capture the required data from patients to formulate a diagnosis, (ii) an automated diagnosis support module that generates an interpretable decision tree, based on data semantically annotated with expert knowledge, in order to support physicians in formulating the correct diagnosis and (iii) a web application such that the physician can efficiently interpret captured data and learned insights by means of visualizations.ResultsWe show that decision tree induction techniques achieve competitive accuracy rates, compared to other black- and white-box techniques, on a publicly available dataset, referred to as migbase. Migbase contains aggregated information of headache attacks from 849 patients. Each sample is labeled with one of three possible primary headache disorders. We demonstrate that we are able to reduce the classification error, statistically significant (ρ≤0.05), with more than 10% by balancing the dataset using prior expert knowledge. Furthermore, we achieve high accuracy rates by using features extracted using the Weisfeiler-Lehman kernel, which is completely unsupervised. This makes it an ideal approach to solve a potential cold start problem.ConclusionDecision trees are the perfect candidate for the automated diagnosis support module. They achieve predictive performances competitive to other techniques on the migbase dataset and are, foremost, completely interpretable. Moreover, the incorporation of prior knowledge increases both predictive performance as well as transparency of the resulting predictive model on the studied dataset.
Highlights
Headache disorders are an important health burden, having a large health-economic impact worldwide
A cell is marked as .+ or .− if the result is a statistically significant (ρ ≤ 0.05) improvement or detriment respectively compared to the baseline (None), according to a bootstrap test of the International Classification of Headache Disorders (ICHD) classification)
The decision support system consists of three large components and a shared back-end: a mobile application for the patients, a web application to visualize the collected data to the physicians and an automated diagnosis module
Summary
Headache disorders are an important health burden, having a large health-economic impact worldwide. Introduction Headache disorders are an increasingly recognized health issue in modern society, causing a substantial burden both at personal and societal level [1, 2]. The fact that headache disorders have been underestimated and undertreated globally has been acknowledged by the World Health Organization [3]. Three main classes of headache disorders are recognized. The first class are the primary headache disorders, in which no underlying pathology can be identified, such as trauma or infection. The main subdivisions of the primary headaches disorders are migraine, tension-type headache and trigeminal autonomic cephalalgias (TAC). Especially migraine, account for the vast majority of headache burden [7]. According to the 2016 Global Burden of Disease Study migraine is the second leading cause of Years Lived with Disability, and ranks 16th on Disability Adjusted Life Years, which measures health loss due to both fatal and non-fatal disease burden [8, 9]
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.