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      New Horizons in artificial intelligence in the healthcare of older people

      research-article
      , , , , , , Ageing Data Research Collaborative (Geridata) AI group ,
      Age and Ageing
      Oxford University Press
      artificial intelligence, technology, health, ageing, older people

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          Abstract

          Artificial intelligence (AI) in healthcare describes algorithm-based computational techniques which manage and analyse large datasets to make inferences and predictions. There are many potential applications of AI in the care of older people, from clinical decision support systems that can support identification of delirium from clinical records to wearable devices that can predict the risk of a fall. We held four meetings of older people, clinicians and AI researchers. Three priority areas were identified for AI application in the care of older people. These included: monitoring and early diagnosis of disease, stratified care and care coordination between healthcare providers. However, the meetings also highlighted concerns that AI may exacerbate health inequity for older people through bias within AI models, lack of external validation amongst older people, infringements on privacy and autonomy, insufficient transparency of AI models and lack of safeguarding for errors. Creating effective interventions for older people requires a person-centred approach to account for the needs of older people, as well as sufficient clinical and technological governance to meet standards of generalisability, transparency and effectiveness. Education of clinicians and patients is also needed to ensure appropriate use of AI technologies, with investment in technological infrastructure required to ensure equity of access.

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          Most cited references51

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          Machine learning in medicine: a practical introduction

          Background Following visible successes on a wide range of predictive tasks, machine learning techniques are attracting substantial interest from medical researchers and clinicians. We address the need for capacity development in this area by providing a conceptual introduction to machine learning alongside a practical guide to developing and evaluating predictive algorithms using freely-available open source software and public domain data. Methods We demonstrate the use of machine learning techniques by developing three predictive models for cancer diagnosis using descriptions of nuclei sampled from breast masses. These algorithms include regularized General Linear Model regression (GLMs), Support Vector Machines (SVMs) with a radial basis function kernel, and single-layer Artificial Neural Networks. The publicly-available dataset describing the breast mass samples (N=683) was randomly split into evaluation (n=456) and validation (n=227) samples. We trained algorithms on data from the evaluation sample before they were used to predict the diagnostic outcome in the validation dataset. We compared the predictions made on the validation datasets with the real-world diagnostic decisions to calculate the accuracy, sensitivity, and specificity of the three models. We explored the use of averaging and voting ensembles to improve predictive performance. We provide a step-by-step guide to developing algorithms using the open-source R statistical programming environment. Results The trained algorithms were able to classify cell nuclei with high accuracy (.94 -.96), sensitivity (.97 -.99), and specificity (.85 -.94). Maximum accuracy (.96) and area under the curve (.97) was achieved using the SVM algorithm. Prediction performance increased marginally (accuracy =.97, sensitivity =.99, specificity =.95) when algorithms were arranged into a voting ensemble. Conclusions We use a straightforward example to demonstrate the theory and practice of machine learning for clinicians and medical researchers. The principals which we demonstrate here can be readily applied to other complex tasks including natural language processing and image recognition. Electronic supplementary material The online version of this article (10.1186/s12874-019-0681-4) contains supplementary material, which is available to authorized users.
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            Geriatric syndromes: clinical, research, and policy implications of a core geriatric concept.

            Geriatricians have embraced the term "geriatric syndrome," using it extensively to highlight the unique features of common health conditions in older people. Geriatric syndromes, such as delirium, falls, incontinence, and frailty, are highly prevalent, multifactorial, and associated with substantial morbidity and poor outcomes. Nevertheless, this central geriatric concept has remained poorly defined. This article reviews criteria for defining geriatric syndromes and proposes a balanced approach of developing preliminary criteria based on peer-reviewed evidence. Based on a review of the literature, four shared risk factors-older age, baseline cognitive impairment, baseline functional impairment, and impaired mobility-were identified across five common geriatric syndromes (pressure ulcers, incontinence, falls, functional decline, and delirium). Understanding basic mechanisms involved in geriatric syndromes will be critical to advancing research and developing targeted therapeutic options, although given the complexity of these multifactorial conditions, attempts to define relevant mechanisms will need to incorporate more-complex models, including a focus on synergistic interactions between different risk factors. Finally, major barriers have been identified in translating research advances, such as preventive strategies of proven effectiveness for delirium and falls, into clinical practice and policy initiatives. National strategic initiatives are required to overcome barriers and to achieve clinical, research, and policy advances that will improve quality of life for older persons.
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              Clinical information extraction applications: A literature review

              With the rapid adoption of electronic health records (EHRs), it is desirable to harvest information and knowledge from EHRs to support automated systems at the point of care and to enable secondary use of EHRs for clinical and translational research. One critical component used to facilitate the secondary use of EHR data is the information extraction (IE) task, which automatically extracts and encodes clinical information from text.
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                Author and article information

                Contributors
                Journal
                Age Ageing
                Age Ageing
                ageing
                Age and Ageing
                Oxford University Press
                0002-0729
                1468-2834
                December 2023
                19 December 2023
                19 December 2023
                : 52
                : 12
                : afad219
                Affiliations
                Academic Unit for Ageing & Stroke Research , Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust , Duckworth Lane, Bradford, West Yorkshire BD9 6RJ, UK
                Leeds Institute of Health Sciences, University of Leeds , Leeds, UK
                Leeds Institute of Health Sciences, University of Leeds , Leeds, UK
                Academic Unit of Ophthalmology, Institute of Inflammation & Ageing, College of Medical and Dental Sciences, University of Birmingham , Birmingham, UK
                Academic Unit for Ageing & Stroke Research , Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust , Duckworth Lane, Bradford, West Yorkshire BD9 6RJ, UK
                Academic Unit for Ageing & Stroke Research , Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust , Duckworth Lane, Bradford, West Yorkshire BD9 6RJ, UK
                Academic Unit for Ageing & Stroke Research , Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust , Duckworth Lane, Bradford, West Yorkshire BD9 6RJ, UK
                Author notes
                Address correspondence to: Taha Shiwani. Tel: 01274 383418. Email: t.shiwani@ 123456nhs.net

                The Ageing Data Research Collaborative(@Geridata) AI group members are: Aseel Abuzour, Joseph Alderman, Atul Anand, Cini Bhanu, Jonathan Bunn, Jemima Collins, Luisa Cutillo, Marlous Hall, Victoria Keevil, Lara Mitchell, Giulia Ogliari, Rose Penfold, James van Oppen, Emma Vardy, Katherine Walesby, Chris Wilkinson, Kieran Zucker.

                Author information
                https://orcid.org/0000-0003-1809-7077
                Article
                afad219
                10.1093/ageing/afad219
                10733173
                38124256
                e99bbce0-62ef-4cae-816d-7a25a015f4fc
                © The Author(s) 2023. Published by Oxford University Press on behalf of the British Geriatrics Society.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 12 May 2023
                : 4 October 2023
                Page count
                Pages: 11
                Funding
                Funded by: Alan Turing Institute, DOI 10.13039/100012338;
                Funded by: National Institute for Health Research Applied Research Collaboration Yorkshire & Humber;
                Funded by: NIHR Leeds Biomedical Research Centre, DOI 10.13039/501100018955;
                Funded by: Health Data Research UK, DOI 10.13039/501100023699;
                Funded by: Department of Health and Social Care, DOI 10.13039/501100000276;
                Categories
                New Horizons
                AcademicSubjects/MED00280
                ageing/15

                Geriatric medicine
                artificial intelligence,technology,health,ageing,older people
                Geriatric medicine
                artificial intelligence, technology, health, ageing, older people

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