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Gill SK, Karwath A, Uh HW, Cardoso VR, Gu Z, Barsky A, Slater L, Acharjee A, Duan J, Dall'Olio L, el Bouhaddani S, Chernbumroong S, Stanbury M, Haynes S, Asselbergs FW, Grobbee DE, Eijkemans MJC, Gkoutos GV, Kotecha D. Artificial intelligence to enhance clinical value across the spectrum of cardiovascular healthcare. Eur Heart J 2023; 44:713-725. [PMID: 36629285 PMCID: PMC9976986 DOI: 10.1093/eurheartj/ehac758] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Revised: 11/22/2022] [Accepted: 12/05/2022] [Indexed: 01/12/2023] Open
Abstract
Artificial intelligence (AI) is increasingly being utilized in healthcare. This article provides clinicians and researchers with a step-wise foundation for high-value AI that can be applied to a variety of different data modalities. The aim is to improve the transparency and application of AI methods, with the potential to benefit patients in routine cardiovascular care. Following a clear research hypothesis, an AI-based workflow begins with data selection and pre-processing prior to analysis, with the type of data (structured, semi-structured, or unstructured) determining what type of pre-processing steps and machine-learning algorithms are required. Algorithmic and data validation should be performed to ensure the robustness of the chosen methodology, followed by an objective evaluation of performance. Seven case studies are provided to highlight the wide variety of data modalities and clinical questions that can benefit from modern AI techniques, with a focus on applying them to cardiovascular disease management. Despite the growing use of AI, further education for healthcare workers, researchers, and the public are needed to aid understanding of how AI works and to close the existing gap in knowledge. In addition, issues regarding data access, sharing, and security must be addressed to ensure full engagement by patients and the public. The application of AI within healthcare provides an opportunity for clinicians to deliver a more personalized approach to medical care by accounting for confounders, interactions, and the rising prevalence of multi-morbidity.
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Affiliation(s)
- Simrat K Gill
- Institute of Cardiovascular Sciences, University of Birmingham, Vincent Drive, B15 2TT Birmingham, UK
- Health Data Research UK Midlands, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
| | - Andreas Karwath
- Health Data Research UK Midlands, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
- Institute of Cancer and Genomic Sciences, University of Birmingham, Vincent Drive, B15 2TT Birmingham, UK
| | - Hae-Won Uh
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht, The Netherlands
| | - Victor Roth Cardoso
- Institute of Cardiovascular Sciences, University of Birmingham, Vincent Drive, B15 2TT Birmingham, UK
- Health Data Research UK Midlands, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
- Institute of Cancer and Genomic Sciences, University of Birmingham, Vincent Drive, B15 2TT Birmingham, UK
| | - Zhujie Gu
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht, The Netherlands
| | - Andrey Barsky
- Health Data Research UK Midlands, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
- Institute of Cancer and Genomic Sciences, University of Birmingham, Vincent Drive, B15 2TT Birmingham, UK
| | - Luke Slater
- Health Data Research UK Midlands, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
- Institute of Cancer and Genomic Sciences, University of Birmingham, Vincent Drive, B15 2TT Birmingham, UK
| | - Animesh Acharjee
- Health Data Research UK Midlands, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
- Institute of Cancer and Genomic Sciences, University of Birmingham, Vincent Drive, B15 2TT Birmingham, UK
| | - Jinming Duan
- School of Computer Science, University of Birmingham, Birmingham, UK
- Alan Turing Institute, London, UK
| | - Lorenzo Dall'Olio
- Department of Physics and Astronomy, University of Bologna, Bologna, Italy
| | - Said el Bouhaddani
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht, The Netherlands
| | - Saisakul Chernbumroong
- Health Data Research UK Midlands, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
- Institute of Cancer and Genomic Sciences, University of Birmingham, Vincent Drive, B15 2TT Birmingham, UK
| | | | | | - Folkert W Asselbergs
- Amsterdam University Medical Center, Department of Cardiology, University of Amsterdam, Amsterdam, The Netherlands
- Health Data Research UK and Institute of Health Informatics, University College London, London, UK
| | - Diederick E Grobbee
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht, The Netherlands
| | - Marinus J C Eijkemans
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht, The Netherlands
| | - Georgios V Gkoutos
- Health Data Research UK Midlands, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
- Institute of Cancer and Genomic Sciences, University of Birmingham, Vincent Drive, B15 2TT Birmingham, UK
| | - Dipak Kotecha
- Institute of Cardiovascular Sciences, University of Birmingham, Vincent Drive, B15 2TT Birmingham, UK
- Health Data Research UK Midlands, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
- Department of Cardiology, Division Heart and Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
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Fang Z, Peltz G. An automated multi-modal graph-based pipeline for mouse genetic discovery. Bioinformatics 2022; 38:3385-3394. [PMID: 35608290 PMCID: PMC9992076 DOI: 10.1093/bioinformatics/btac356] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Revised: 04/18/2022] [Accepted: 05/19/2022] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION Our ability to identify causative genetic factors for mouse genetic models of human diseases and biomedical traits has been limited by the difficulties associated with identifying true causative factors, which are often obscured by the many false positive genetic associations produced by a GWAS. RESULTS To accelerate the pace of genetic discovery, we developed a graph neural network (GNN)-based automated pipeline (GNNHap) that could rapidly analyze mouse genetic model data and identify high probability causal genetic factors for analyzed traits. After assessing the strength of allelic associations with the strain response pattern; this pipeline analyzes 29M published papers to assess candidate gene-phenotype relationships; and incorporates the information obtained from a protein-protein interaction network and protein sequence features into the analysis. The GNN model produces markedly improved results relative to that of a simple linear neural network. We demonstrate that GNNHap can identify novel causative genetic factors for murine models of diabetes/obesity and for cataract formation, which were validated by the phenotypes appearing in previously analyzed gene knockout mice. The diabetes/obesity results indicate how characterization of the underlying genetic architecture enables new therapies to be discovered and tested by applying 'precision medicine' principles to murine models. AVAILABILITY AND IMPLEMENTATION The GNNHap source code is freely available at https://github.com/zqfang/gnnhap, and the new version of the HBCGM program is available at https://github.com/zqfang/haplomap. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Zhuoqing Fang
- Department of Anesthesia, Pain and Perioperative Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Gary Peltz
- Department of Anesthesia, Pain and Perioperative Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
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Elevated All-Cause Mortality among Overweight Older People: AI Predicts a High Normal Weight Is Optimal. Geriatrics (Basel) 2022; 7:geriatrics7030068. [PMID: 35735773 PMCID: PMC9222635 DOI: 10.3390/geriatrics7030068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2022] [Revised: 06/12/2022] [Accepted: 06/14/2022] [Indexed: 11/19/2022] Open
Abstract
It has been proposed that being overweight may provide an advantage with respect to mortality in older people, although this has not been investigated fully. Therefore, to confirm that and elucidate the underlying mechanism, we investigated mortality in older people using explainable artificial intelligence (AI) with the gradient-boosting algorithm XGboost. Baseline body mass indexes (BMIs) of 5699 people (79.3 ± 3.9 years) were evaluated to determine the relationship with all-cause mortality over eight years. In the unadjusted model, the first negative (protective) BMI range for mortality was 25.9−28.4 kg/m2. However, in the adjusted cross-validation model, this range was 22.7−23.6 kg/m2; the second and third negative BMI ranges were then 25.8−28.2 and 24.6−25.8 kg/m2, respectively. Conversely, the first advancing BMI range was 12.8−18.7 kg/m2, which did not vary across conditions with high feature importance. Actual and predicted mortality rates in participants aged <90 years showed a negative-linear or L-shaped relationship with BMI, whereas predicted mortality rates in men aged ≥90 years showed a blunt U-shaped relationship. In conclusion, AI predicted that being overweight may not be an optimal condition with regard to all-cause mortality in older adults. Instead, it may be that a high normal weight is optimal, though this may vary according to the age and sex.
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