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  1. Book: Clinical Applications of Artificial Intelligence in Real-World Data

    Asselbergs, Folkert W. / Moore, Jason H. / Oberski, Daniel L. / Denaxas, Spiros

    2023  

    Author's details Dr Folkert Asselbergs is a clinical cardiologist at Amsterdam Heart Center, Prof of Precision medicine at the Institute of Health Informatics, University College London, director and founder of the BRC Clinical Research Informatics Unit and the recently initiated Nudging Unit at University College London Hospital, chair of the data infrastructure of the Dutch Cardiovascular Alliance, and associate editor of European Heart Journal for digital health and innovation. His research program focuses on translational data science using existing health data such as electronic health records and clinical registries enriched with novel modalities such as -omics and sensor data for knowledge discovery, drug target validation and precision medicine in cardiovascular disease. §Dr Spiros Denaxas is a Professor in Biomedical Informatics based at the Institute of Health Informatics at University College London and Associate Director leading phenomics at the British Heart Foundation Data Science Cent
    Keywords Bighealthdata ; ArtificialIntelligence ; machinelearning ; DeepLearning ; Biomedicalontologies ; electronichealthrecords ; Big health data ; Artificial intelligence ; Machine learning ; Deep learning ; Biomedical ontologies ; Electronic Health Records
    Language English
    Size 292 p.
    Edition 1
    Publisher Springer International Publishing
    Document type Book
    Note PDA Manuell_25
    Format 183 x 260 x 21
    ISBN 9783031366772 ; 3031366778
    Database PDA

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  2. Article ; Online: The CODE-EHR global framework: lifting the veil on health record data.

    Asselbergs, Folkert W / Kotecha, Dipak

    European heart journal

    2023  Volume 44, Issue 36, Page(s) 3398–3400

    MeSH term(s) Humans ; Electronic Health Records
    Language English
    Publishing date 2023-08-16
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 603098-1
    ISSN 1522-9645 ; 0195-668X
    ISSN (online) 1522-9645
    ISSN 0195-668X
    DOI 10.1093/eurheartj/ehad424
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  3. Article ; Online: Artificial intelligence in cardiology: the debate continues.

    Asselbergs, Folkert W / Fraser, Alan G

    European heart journal. Digital health

    2021  Volume 2, Issue 4, Page(s) 721–726

    Abstract: In 1955, when John McCarthy and his colleagues proposed their first study of artificial intelligence, they suggested that 'every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made ...

    Abstract In 1955, when John McCarthy and his colleagues proposed their first study of artificial intelligence, they suggested that 'every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it'. Whether that might ever be possible would depend on how we define intelligence, but what is indisputable is that new methods are needed to analyse and interpret the copious information provided by digital medical images, genomic databases, and biobanks. Technological advances have enabled applications of artificial intelligence (AI) including machine learning (ML) to be implemented into clinical practice, and their related scientific literature is exploding. Advocates argue enthusiastically that AI will transform many aspects of clinical cardiovascular medicine, while sceptics stress the importance of caution and the need for more evidence. This report summarizes the main opposing arguments that were presented in a debate at the 2021 Congress of the European Society of Cardiology. Artificial intelligence is an advanced analytical technique that should be considered when conventional statistical methods are insufficient, but testing a hypothesis or solving a clinical problem-not finding another application for AI-remains the most important objective. Artificial intelligence and ML methods should be transparent and interpretable, if they are to be approved by regulators and trusted to provide support for clinical decisions. Physicians need to understand AI methods and collaborate with engineers. Few applications have yet been shown to have a positive impact on clinical outcomes, so investment in research is essential.
    Language English
    Publishing date 2021-10-18
    Publishing country England
    Document type Journal Article
    ISSN 2634-3916
    ISSN (online) 2634-3916
    DOI 10.1093/ehjdh/ztab090
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  4. Article ; Online: Prioritising genetic findings for drug target identification and validation.

    Hukerikar, Nikita / Hingorani, Aroon D / Asselbergs, Folkert W / Finan, Chris / Schmidt, Amand F

    Atherosclerosis

    2024  Volume 390, Page(s) 117462

    Abstract: The decreasing costs of high-throughput genetic sequencing and increasing abundance of sequenced genome data have paved the way for the use of genetic data in identifying and validating potential drug targets. However, the number of identified potential ... ...

    Abstract The decreasing costs of high-throughput genetic sequencing and increasing abundance of sequenced genome data have paved the way for the use of genetic data in identifying and validating potential drug targets. However, the number of identified potential drug targets is often prohibitively large to experimentally evaluate in wet lab experiments, highlighting the need for systematic approaches for target prioritisation. In this review, we discuss principles of genetically guided drug development, specifically addressing loss-of-function analysis, colocalization and Mendelian randomisation (MR), and the contexts in which each may be most suitable. We subsequently present a range of biomedical resources which can be used to annotate and prioritise disease-associated proteins identified by these studies including 1) ontologies to map genes, proteins, and disease, 2) resources for determining the druggability of a potential target, 3) tissue and cell expression of the gene encoding the potential target, and 4) key biological pathways involving the potential target. We illustrate these concepts through a worked example, identifying a prioritised set of plasma proteins associated with non-alcoholic fatty liver disease (NAFLD). We identified five proteins with strong genetic support for involvement with NAFLD: CYB5A, NT5C, NCAN, TGFBI and DAPK2. All of the identified proteins were expressed in both liver and adipose tissues, with TGFBI and DAPK2 being potentially druggable. In conclusion, the current review provides an overview of genetic evidence for drug target identification, and how biomedical databases can be used to provide actionable prioritisation, fully informing downstream experimental validation.
    MeSH term(s) Humans ; Non-alcoholic Fatty Liver Disease/drug therapy ; Non-alcoholic Fatty Liver Disease/genetics ; Non-alcoholic Fatty Liver Disease/metabolism ; Death-Associated Protein Kinases/genetics ; Proteins/genetics ; Genome-Wide Association Study
    Chemical Substances Death-Associated Protein Kinases (EC 2.7.11.1) ; Proteins
    Language English
    Publishing date 2024-01-26
    Publishing country Ireland
    Document type Journal Article ; Review
    ZDB-ID 80061-2
    ISSN 1879-1484 ; 0021-9150
    ISSN (online) 1879-1484
    ISSN 0021-9150
    DOI 10.1016/j.atherosclerosis.2024.117462
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  5. Article: Improving Genetic Association Studies with a Novel Methodology that Unveils the Hidden Complexity of All-Cause Heart Failure.

    Gregg, John T / Himes, Blanca E / Asselbergs, Folkert W / Moore, Jason H

    medRxiv : the preprint server for health sciences

    2023  

    Abstract: Motivation: Genome-Wide Association Studies (GWAS) commonly assume phenotypic and genetic homogeneity that is not present in complex conditions. We designed Transformative Regression Analysis of Combined Effects (TRACE), a GWAS methodology that better ... ...

    Abstract Motivation: Genome-Wide Association Studies (GWAS) commonly assume phenotypic and genetic homogeneity that is not present in complex conditions. We designed Transformative Regression Analysis of Combined Effects (TRACE), a GWAS methodology that better accounts for clinical phenotype heterogeneity and identifies gene-by-environment (GxE) interactions. We demonstrated with UK Biobank (UKB) data that TRACE increased the variance explained in All-Cause Heart Failure (AHF) via the discovery of novel single nucleotide polymorphism (SNP) and SNP-by-environment (i.e. GxE) interaction associations. First, we transformed 312 AHF-related ICD10 codes (including AHF) into continuous low-dimensional features (i.e., latent phenotypes) for a more nuanced disease representation. Then, we ran a standard GWAS on our latent phenotypes to discover main effects and identified GxE interactions with target encoding. Genes near associated SNPs subsequently underwent enrichment analysis to explore potential functional mechanisms underlying associations. Latent phenotypes were regressed against their SNP hits and the estimated latent phenotype values were used to measure the amount of AHF variance explained.
    Results: Our method identified over 100 main GWAS effects that were consistent with prior studies and hundreds of novel gene-by-smoking interactions, which collectively accounted for approximately 10% of AHF variance. This represents an improvement over traditional GWAS whose results account for a negligible proportion of AHF variance. Enrichment analyses suggested that hundreds of miRNAs mediated the SNP effect on various AHF-related biological pathways. The TRACE framework can be applied to decode the genetics of other complex diseases.
    Availability: All code is available at https://github.com/EpistasisLab/latent_phenotype_project.
    Language English
    Publishing date 2023-08-04
    Publishing country United States
    Document type Preprint
    DOI 10.1101/2023.08.02.23293567
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  6. Article ; Online: Learning from individualised variation for evidence generation within a learning health system.

    Wilson, Matthew G / Asselbergs, Folkert W / Harris, Steve K

    British journal of anaesthesia

    2022  Volume 128, Issue 5, Page(s) e320–e322

    MeSH term(s) Delivery of Health Care ; Electronic Health Records ; Humans ; Learning Health System
    Language English
    Publishing date 2022-03-09
    Publishing country England
    Document type Letter ; Comment
    ZDB-ID 80074-0
    ISSN 1471-6771 ; 0007-0912
    ISSN (online) 1471-6771
    ISSN 0007-0912
    DOI 10.1016/j.bja.2022.02.008
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  7. Article ; Online: Fit for the future: empowering clinical trials with digital technology.

    Kotecha, Dipak / DeVore, Adam D / Asselbergs, Folkert W

    European heart journal

    2022  Volume 44, Issue 1, Page(s) 64–67

    MeSH term(s) Humans ; Digital Technology ; Clinical Trials as Topic ; Forecasting
    Language English
    Publishing date 2022-11-11
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 603098-1
    ISSN 1522-9645 ; 0195-668X
    ISSN (online) 1522-9645
    ISSN 0195-668X
    DOI 10.1093/eurheartj/ehac650
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  8. Article ; Online: CAPACITY-COVID: a European Registry to determine the role of cardiovascular disease in the COVID-19 pandemic.

    Linschoten, Marijke / Asselbergs, Folkert W

    European heart journal

    2020  Volume 41, Issue 19, Page(s) 1795–1796

    MeSH term(s) Betacoronavirus ; COVID-19 ; Cardiovascular Diseases/epidemiology ; Cardiovascular Diseases/virology ; Coronavirus Infections/complications ; Coronavirus Infections/epidemiology ; Europe ; Humans ; Pandemics ; Pneumonia, Viral/complications ; Pneumonia, Viral/epidemiology ; Registries ; SARS-CoV-2
    Keywords covid19
    Language English
    Publishing date 2020-04-03
    Publishing country England
    Document type Journal Article
    ZDB-ID 603098-1
    ISSN 1522-9645 ; 0195-668X
    ISSN (online) 1522-9645
    ISSN 0195-668X
    DOI 10.1093/eurheartj/ehaa280
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  9. Article ; Online: COVID-19 related thrombi in ascending and descending thoracic aorta with peripheral embolization: a case report.

    Buikema, Jan W / Asselbergs, Folkert W / Tekstra, Janneke

    European heart journal. Case reports

    2021  Volume 5, Issue 2, Page(s) ytaa525

    Abstract: Background: COVID-19 (severe acute respiratory syndrome coronavirus 2) infected patients have increased risk for thrombotic events, which initially may have been under recognized. The existence of cardiovascular emboli can be directly life threatening ... ...

    Abstract Background: COVID-19 (severe acute respiratory syndrome coronavirus 2) infected patients have increased risk for thrombotic events, which initially may have been under recognized. The existence of cardiovascular emboli can be directly life threatening when obstructing the blood flow to vital organs such as the brain or other parts of the body. The exact mechanism for this hypercoagulable state in COVID-19 patients yet remains to be elucidated.
    Case summary: A 72-year-old man critically ill with COVID-19 was diagnosed with a free-floating and mural thrombus in the thoracic aorta. Subsequent distal embolization to the limbs led to ischaemia and necrosis of the right foot. Treatment with heparin and anticoagulants reduced thrombus load in the ascending and thoracic aorta.
    Discussion: One-third of COVID-19 patients show major thrombotic events, mostly pulmonary emboli. The endothelial expression of angiotensin-converting enzyme-2 receptors makes it feasible that in patients with viraemia direct viral-toxicity to the endothelium of also the large arteries results in local thrombus formation. Up to date, prophylactic anticoagulants are recommended in all patients that are hospitalized with COVID-19 infections to prevent venous and arterial thrombotic complications.
    Language English
    Publishing date 2021-02-04
    Publishing country England
    Document type Case Reports
    ISSN 2514-2119
    ISSN (online) 2514-2119
    DOI 10.1093/ehjcr/ytaa525
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  10. Article ; Online: Digital technology and patient and public involvement (PPI) in routine care and clinical research-A pilot study.

    Chen, Yang / Hosin, Ali A / George, Marc J / Asselbergs, Folkert W / Shah, Anoop D

    PloS one

    2023  Volume 18, Issue 2, Page(s) e0278260

    Abstract: Background: Patient and public involvement (PPI) has growing impact on the design of clinical care and research studies. There remains underreporting of formal PPI events including views related to using digital tools. This study aimed to assess the ... ...

    Abstract Background: Patient and public involvement (PPI) has growing impact on the design of clinical care and research studies. There remains underreporting of formal PPI events including views related to using digital tools. This study aimed to assess the feasibility of hosting a hybrid PPI event to gather views on the use of digital tools in clinical care and research.
    Methods: A PPI focus day was held following local procedures and published recommendations related to advertisement, communication and delivery. Two exemplar projects were used as the basis for discussions and qualitative and quantitative data was collected.
    Results: 32 individuals expressed interest in the PPI day and 9 were selected to attend. 3 participated in person and 6 via an online video-calling platform. Selected written and verbal feedback was collected on two digitally themed projects and on the event itself. The overall quality and interactivity for the event was rated as 4/5 for those who attended in person and 4.5/5 and 4.8/5 respectively, for those who attended remotely.
    Conclusions: A hybrid PPI event is feasible and offers a flexible format to capture the views of patients. The overall enthusiasm for digital tools amongst patients in routine care and clinical research is high, though further work and standardised, systematic reporting of PPI events is required.
    MeSH term(s) Humans ; Pilot Projects ; Digital Technology ; Patient Participation ; Research Design
    Language English
    Publishing date 2023-02-03
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2267670-3
    ISSN 1932-6203 ; 1932-6203
    ISSN (online) 1932-6203
    ISSN 1932-6203
    DOI 10.1371/journal.pone.0278260
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