ArticleExpert systems with applications2016
Cardiac ScoreCard: A Diagnostic Multivariate Index Assay System for Predicting a Spectrum of Cardiovascular Disease.
Article in Expert systems with applications, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it, 39 citations in OpenAlex.
- Interpretability of Clinical Decision Support Systems Based on Artificial Intelligence from Technological and Medical Perspective: A Systematic Review.Journal of healthcare engineering · 2023Pooled it
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- Simultaneous Quantitative SARS-CoV-2 Antigen and Host Antibody Detection and Pre-Screening Strategy at the Point of Care.Bioengineering (Basel, Switzerland) · 2023Article
- Smart Diagnostics: Combining Artificial Intelligence and In Vitro Diagnostics.Sensors (Basel, Switzerland) · 2022Article
- Article
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- Clinical decision support tool and rapid point-of-care platform for determining disease severity in patients with COVID-19.Lab on a chip · 2020Article
- Clinical Decision Support Tool and Rapid Point-of-Care Platform for Determining Disease Severity in Patients with COVID-19.medRxiv : the preprint server for health sciences · 2020Article
- Sensors that Learn: The Evolution from Taste Fingerprints to Patterns of Early Disease Detection.Micromachines · 2019Review
- Innovative Programmable Bio-Nano-Chip Digitizes Biology Using Sensors That Learn Bridging Biomarker Discovery and Clinical Implementation.Frontiers in public health · 2017Review
- A Hybrid Classification System for Heart Disease Diagnosis Based on the RFRS Method.Computational and mathematical methods in medicine · 2017Article
- Programmable Bio-nanochip Platform: A Point-of-Care Biosensor System with the Capacity To Learn.Accounts of chemical research · 2016Article
- Challenges and opportunities for translating medical microdevices: insights from the programmable bio-nano-chip.Bioanalysis · 2016Article
Corrections and comments
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Authors and funding
12 authors at 7 institutions in 1 country.
Funding
Abstract
Clinical decision support systems (CDSSs) have the potential to save lives and reduce unnecessary costs through early detection and frequent monitoring of both traditional risk factors and novel biomarkers for cardiovascular disease (CVD). However, the widespread adoption of CDSSs for the identification of heart diseases has been limited, likely due to the poor interpretability of clinically relevant results and the lack of seamless integration between measurements and disease predictions. In this paper we present the Cardiac ScoreCard-a multivariate index assay system with the potential to assist in the diagnosis and prognosis of a spectrum of CVD. The Cardiac ScoreCard system is based on lasso logistic regression techniques which utilize both patient demographics and novel biomarker data for the prediction of heart failure (HF) and cardiac wellness. Lasso logistic regression models were trained on a merged clinical dataset comprising 579 patients with 6 traditional risk factors and 14 biomarker measurements. The prediction performance of the Cardiac ScoreCard was assessed with 5-fold cross-validation and compared with reference methods. The experimental results reveal that the ScoreCard models improved performance in discriminating disease versus non-case (AUC = 0.8403 and 0.9412 for cardiac wellness and HF, respectively), and the models exhibit good calibration. Clinical insights to the prediction of HF and cardiac wellness are provided in the form of logistic regression coefficients which suggest that augmenting the traditional risk factors with a multimarker panel spanning a diverse cardiovascular pathophysiology provides improved performance over reference methods. Additionally, a framework is provided for seamless integration with biomarker measurements from point-of-care medical microdevices, and a lasso-based feature selection process is described for the down-selection of biomarkers in multimarker panels.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.