A virtual second opinion: Acceptability of a computer-based decision tool to assess older drivers with dementia

Rapoport, Mark J.; Sarracini, Carla Zucchero; Mulsant, Benoit M; Seitz, Dallas; Molnar, Frank; Naglie, Gary; Herrmann, Nathan; Rozmovits, Linda

doi:10.1177/1460458219852870

Cited by 2 publications

(2 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…8,9 By using medical informatics, medical data can be analysed and used for deriving meaningful information which in turn aids clinicians to make better decisions for treating AD. 10,11 The genes causing AD are obtained 8,9 and from the genes identified, the gene sequences and SNP sequences have been gathered. 12 The gene sequences can best identify the cause of the disease and SNPs can be best used in the prediction phase.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Clinical decision support system for early detection of Alzheimer’s disease using an enhanced gradient boosted decision tree classifier

Jayashree

Srinivasan

et al. 2022

Health Informatics J

View full text Add to dashboard Cite

Alzheimer’s disease (AD) is one of the most common forms of dementia contributing to more than 70% of the cases. The factors accounting for the cause and progression of neurodegenerative diseases like AD are primarily genetic, in addition to life style and environmental factors. Early and accurate diagnoses of AD empower practitioners to take timely clinical decisions and preventive actions. This being the motivation, the work proposes a novel pattern matching and scoring method on genetic material towards devising an effective classifier. We propose a distinctive disease causing gene sequence pattern identification using suffix trees as a base detection model with an accuracy of 91.5% in linear time complexity. A scoring mechanism is implemented to assign scores to genes based on the severity of the disease causing and disease resistant Single Nucleotide Polymorphisms associated with the genes. These scores are then used as a remarkable feature in the gradient boosted decision tree classifier to enhance the classification of AD versus healthy control. The efficiency of the proposed gene powered EGBDT classifier is evaluated on ADNI benchmark data set with the prediction accuracy of 94.16% and is found to be efficient compared to the recent works in the literature.

show abstract

Section: Related Workmentioning

confidence: 99%

“…8,9 By using medical informatics, medical data can be analysed and used for deriving meaningful information which in turn aids clinicians to make better decisions for treating AD. 10,11…”

Section: Related Workmentioning

confidence: 99%

Clinical decision support system for early detection of Alzheimer’s disease using an enhanced gradient boosted decision tree classifier

Jayashree

Srinivasan

et al. 2022

Health Informatics J

View full text Add to dashboard Cite

show abstract

Stakeholder Perspectives of Clinical Artificial Intelligence Implementation: Systematic Review of Qualitative Evidence

Hogg¹,

Al-Zubaidy²,

Talks³

et al. 2023

J Med Internet Res

View full text Add to dashboard Cite

Background The rhetoric surrounding clinical artificial intelligence (AI) often exaggerates its effect on real-world care. Limited understanding of the factors that influence its implementation can perpetuate this. Objective In this qualitative systematic review, we aimed to identify key stakeholders, consolidate their perspectives on clinical AI implementation, and characterize the evidence gaps that future qualitative research should target. Methods Ovid-MEDLINE, EBSCO-CINAHL, ACM Digital Library, Science Citation Index-Web of Science, and Scopus were searched for primary qualitative studies on individuals’ perspectives on any application of clinical AI worldwide (January 2014-April 2021). The definition of clinical AI includes both rule-based and machine learning–enabled or non–rule-based decision support tools. The language of the reports was not an exclusion criterion. Two independent reviewers performed title, abstract, and full-text screening with a third arbiter of disagreement. Two reviewers assigned the Joanna Briggs Institute 10-point checklist for qualitative research scores for each study. A single reviewer extracted free-text data relevant to clinical AI implementation, noting the stakeholders contributing to each excerpt. The best-fit framework synthesis used the Nonadoption, Abandonment, Scale-up, Spread, and Sustainability (NASSS) framework. To validate the data and improve accessibility, coauthors representing each emergent stakeholder group codeveloped summaries of the factors most relevant to their respective groups. Results The initial search yielded 4437 deduplicated articles, with 111 (2.5%) eligible for inclusion (median Joanna Briggs Institute 10-point checklist for qualitative research score, 8/10). Five distinct stakeholder groups emerged from the data: health care professionals (HCPs), patients, carers and other members of the public, developers, health care managers and leaders, and regulators or policy makers, contributing 1204 (70%), 196 (11.4%), 133 (7.7%), 129 (7.5%), and 59 (3.4%) of 1721 eligible excerpts, respectively. All stakeholder groups independently identified a breadth of implementation factors, with each producing data that were mapped between 17 and 24 of the 27 adapted Nonadoption, Abandonment, Scale-up, Spread, and Sustainability subdomains. Most of the factors that stakeholders found influential in the implementation of rule-based clinical AI also applied to non–rule-based clinical AI, with the exception of intellectual property, regulation, and sociocultural attitudes. Conclusions Clinical AI implementation is influenced by many interdependent factors, which are in turn influenced by at least 5 distinct stakeholder groups. This implies that effective research and practice of clinical AI implementation should consider multiple stakeholder perspectives. The current underrepresentation of perspectives from stakeholders other than HCPs in the literature may limit the anticipation and management of the factors that influence successful clinical AI implementation. Future research should not only widen the representation of tools and contexts in qualitative research but also specifically investigate the perspectives of all stakeholder HCPs and emerging aspects of non–rule-based clinical AI implementation. Trial Registration PROSPERO (International Prospective Register of Systematic Reviews) CRD42021256005; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=256005 International Registered Report Identifier (IRRID) RR2-10.2196/33145

show abstract

A virtual second opinion: Acceptability of a computer-based decision tool to assess older drivers with dementia

Cited by 2 publications

References 33 publications

Clinical decision support system for early detection of Alzheimer’s disease using an enhanced gradient boosted decision tree classifier

Clinical decision support system for early detection of Alzheimer’s disease using an enhanced gradient boosted decision tree classifier

Stakeholder Perspectives of Clinical Artificial Intelligence Implementation: Systematic Review of Qualitative Evidence

Contact Info

Product

Resources

About