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Health Care Science 2023年第4期2卷 255-263页

作者： Rui Yang Ting Fang Tan Wei Lu Arun James Thirunavukarasu Daniel Shu Wei Ting Nan Liu Department of Biomedical Informatics Yong Loo Lin School of MedicineNational University of SingaporeSingaporeSingapore Singapore National Eye Center Singapore Eye Research InstituteSingapore Health ServiceSingaporeSingapore StatNLP Research Group Singapore University of Technology and DesignSingapore University of Cambridge School of Clinical Medicine CambridgeUK Duke-NUS Medical School Centre for Quantitative MedicineSingaporeSingapore Duke-NUS Medical School Programme in Health Services and Systems ResearchSingaporeSingapore

Recently,the emergence of ChatGPT,an artificial intelligence chatbot developed by OpenAI,has attracted significant attention due to its exceptional language comprehension and content generation capabilities,highlighting the immense potential of large language models(LLMs).LLMs have become a burgeoning hotspot across many fields,including health *** health care,LLMs may be classified into LLMs for the biomedical domain and LLMs for the clinical domain based on the corpora used for *** the last 3 years,these domain-specific LLMs have demonstrated exceptional perform-ance on multiple natural language processing tasks,surpassing the perform-ance of general LLMs as *** not only emphasizes the significance of developing dedicated LLMs for the specific domains,but also raises expectations for their applications in health *** believe that LLMs may be used widely in preconsultation,diagnosis,and management,with appropriate development and ***,LLMs hold tremen-dous promise in assisting with medical education,medical writing and other related ***,health care systems must recognize and address the challenges posed by LLMs.

关键词： Large language model AI Health care

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P05-09 NAMASTOX: a computational tool supporting the use of NGRA workflows

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Toxicology Letters 2024年 399卷 S135-S135页

作者： M. Pastor K. Kopańska A. Cabrera Universitat Pompeu Fabra Department of Medicine and Life Sciences Research Programme on Biomedical Informatics (GRIB) Barcelona Spain

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P05-08 Bridging uncertainty analysis with NGRA workflows through computational tools

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Toxicology Letters 2024年 399卷 S135-S135页

作者： K. Kopańska A. Cabrera M. Pastor Universitat Pompeu Fabra Department of Medicine and Life Sciences Research Programme on Biomedical Informatics (GRIB) Barcelona Spain

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A needle for Alzheimer's in a haystack of claims data (Vol 2, Pg 90, 2022)

NATURE AGING

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NATURE AGING 2022年第1期2卷 90-90页

作者： Guney, Emre Athie, Alejandro Research Programme on Biomedical Informatics Hospital del Mar Medical Research Institute (IMIM) Pompeu Fabra University (UPF) Barcelona Spain Discovery and Data Science (DDS) Unit STALICLA R&D SL Barcelona Spain Accenture Barcelona Spain

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Ethical Considerations for Participatory Health through Social Media: Healthcare Workforce and Policy Maker Perspectives

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Yearbook of Medical informatics 2020年第1期29卷 71-76页

作者： Rivera-Romero, Octavio Konstantinidis, Stathis Denecke, Kerstin Gabarrón, Elia Petersen, Carolyn Househ, Mowafa Merolli, Mark Mayer, Miguel Ángel Computer Engineering School Universidad de Sevilla Seville Spain University of Nottingham Nottingham United Kingdom Bern University of Applied Sciences Bern Switzerland Norwegian Centre of E-Health Research University Hospital North Norway Norway Division of Biomedical Statistics and Informatics Mayo Clinic MN Rochester United States College of Science and Engineering Hamad Bin Khalifa University Doha Qatar Health and Biomedical Informatics Centre Melbourne Medical School University of Melbourne Melbourne Australia Research Programme on Biomedical Informatics Hospital del Mar Medical Research Institute Universitat Pompeu Fabra Barcelona Spain

Objectives: To identify the different ethical issues that should be considered in participatory health through social media from different stakeholder perspectives (i.e., patients/service users, health professionals, health information technology (IT) professionals, and policy makers) in any healthcare context. Methods: We implemented a two-round survey composed of open ended questions in the first round, aggregated into a list of ethical issues rated for importance by participants in the second round, to generate a ranked list of possible ethical issues in participatory health based on healthcare professionals’ and policy makers’ opinions on both their own point of view and their beliefs for other stakeholders’ perspectives. Results: Twenty-six individuals responded in the first round of the survey. Multiple ethical issues were identified for each perspective. Data privacy, data security, and digital literacy were common themes in all perspectives. Thirty-three individuals completed the second round of the survey. Data privacy and data security were ranked among the three most important ethical issues in all perspectives. Quality assurance was the most important issue from the healthcare professionals’ perspective and the second most important issue from the patients’ perspective. Data privacy was the most important consideration for patients/service users. Digital literacy was ranked as the fourth most important issue, except for policy makers’ perspective. Conclusions: Different stakeholders’ opinions fairly agreed that there are common ethical issues that should be considered across the four groups (patients, healthcare professionals, health IT professionals, policy makers) such as data privacy, security, and quality assurance. © 2020 IMIA and Georg Thieme Verlag KG.

关键词： clinical ethics ethical issues patient participation patient-centered care Social media

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Age at menarche by birth cohort: A pooled analysis of half a million women in Asia

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Public Health 2024年 237卷 130-134页

作者： Abe, Sarah K. Nishio, Marisa Huang, Hsi-Lan Leung, Chi Yan Islam, Md Rashedul Rahman, Md Shafiur Saito, Eiko Shin, Aesun Merritt, Melissa A. Choi, Ji-Yeob Katagiri, Ryoko Mohammadi, Zahra Shu, Xiao-Ou Wakai, Kenji Sawada, Norie Ideno, Yuki Tamakoshi, Akiko Seow, Wei Jie Koh, Woon-Puay Sakata, Ritsu Hozawa, Atsushi Kim, Jeongseon Nagata, Chisato Sugawara, Yumi Park, Sue K. Kweon, Sun-Seog Azizi, Fereidoun Malekzadeh, Reza Moy, Foong-Ming Pourfarzi, Farhad Gao, Yu-Tang Kubo, Yoko Hirabayashi, Mayo Nagai, Kazue Kimura, Takashi Yuan, Jian-Min Kanemura, Seiki Wada, Keiko Kang, Daehee Shin, Min-Ho Khalili, Davood Poustchi, Hossein Rezaianzadeh, Abbas Mansour-Ghanaei, Fariborz Najafi, Farid Mohebbi, Iraj Boffetta, Paolo Lee, Jung Eun Matsuo, Keitaro Rothman, Nathaniel Qiao, You-Lin Zheng, Wei Inoue, Manami Division of Prevention National Cancer Center Institute for Cancer Control Japan Hitotsubashi Institute for Advanced Study Hitotsubashi University Japan Research Center for Child Mental Development Hamamatsu University School of Medicine Japan Sustainable Society Design Center Graduate School of Frontier Sciences the University of Tokyo Japan Department of Preventive Medicine Seoul National University College of Medicine Seoul South Korea Cancer Research Institute Seoul National University Seoul South Korea The Daffodil Centre The University of Sydney a joint venture with Cancer Council NSW Sydney NSW Australia Department of Biomedical Sciences Seoul National University Graduate School Seoul South Korea Graduate School of Informatics Faculty of Informatics Chiba University Japan Liver and Pancreatobiliary Diseases Research Center Digestive Diseases Research Institute Tehran University of Medical Sciences Tehran Iran Division of Epidemiology Department of Medicine Vanderbilt Epidemiology Center Vanderbilt-Ingram Cancer Center Vanderbilt University Medical Center Nashville TN United States Department of Preventive Medicine Nagoya University School of Medicine Nagoya Japan Division of Cohort Research National Cancer Center Institute for Cancer Control Japan Gunma University Center for Food Science and Wellness Maebashi Japan Department of Public Health Hokkaido University Faculty of Medicine Sapporo Japan Saw Swee Hock School of Public Health and Department of Medicine National University of Singapore and National University Health System Singapore Healthy Longevity Translational Research Programme Yong Loo Lin School of Medicine National University of Singapore Singapore Singapore Institute for Clinical Sciences Agency for Science Technology and Research (A∗STAR) Singapore Department of Epidemiology Radiation Effects Research Foundation Hiroshima Japan Division of Epidemiology Department of Public Health and Forensic Medicine Tohoku University Graduate School

Objectives: To evaluate changes in the age at menarche in Asian populations. Study design: Retrospective cohort study. Methods: We included 548,830 women from six countries in Asia. The data were sourced from 20 cohorts participating in the Asia Cohort Consortium (ACC) and two additional cohort studies: Japan Multi-institutional Collaborative Cohorts (J-MICC), and Japan Nurse Health Study (JNHS) with data on age at menarche. Joinpoint regression was used to evaluate changes in age at menarche by birth year and by country. Results: The study includes data from cohorts in six Asian countries namely, China, Iran, Japan, Korea, Malaysia and Singapore. Birth cohorts ranged from 1873 to 1995. The mean age of menarche was 14.0 years with a standard deviation (SD) of 1.4 years, ranged from 12.6 to 15.5 years. Over 100 years age at menarche showed an overall decrease in all six countries. China showed a mixed pattern of decrease, increase, and subsequent decrease from 1926 to 1960. Iran and Malaysia experienced a sharp decline between about 1985 and 1990, with APC values of −4.48 and −1.24, respectively, while Japan, South Korea, and Singapore exhibited a nearly linear decline since the 1980s, notably with an APC of −3.41 in Singapore from 1993 to 1995. Conclusions: Overall, we observed a declining age at menarche, while the pace of the change differed by country. Additional long-term observation is needed to examine the contributing factors of differences in trend across Asian countries. The study could serve as a tool to strengthen global health campaigns. © 2024 The Royal Society for Public Health

关键词： Age Asia Birth cohort Menarche

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Personalised risk-prediction tools for cryptococcal meningitis mortality to guide treatment stratification in sub-Saharan Africa: a prognostic modelling study based on pooled analysis of two randomised controlled trials

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The Lancet Global Health 2025年第5期13卷 e920-e930页

作者： Samuels, Thomas H A Molloy, Sile F Lawrence, David S Loyse, Angela Kanyama, Cecilia Heyderman, Robert S Lai, Wai Shing Mfinanga, Sayoki Lesikari, Sokoine Chanda, Duncan Kouanfack, Charles Temfack, Elvis Lortholary, Olivier Hosseinipour, Mina C Chan, Adrienne K Meya, David B Boulware, David R Mwandumba, Henry C Meintjes, Graeme Muzoora, Conrad Mosepele, Mosepele Ndhlovu, Chiratidzo E Youssouf, Nabila Harrison, Thomas S Jarvis, Joseph N Gupta, Rishi K Division of Infection and Immunity University College London London United Kingdom UCL Respiratory Research Department Division of Medicine University College London London United Kingdom Centre for Global Health Institute for Infection and Immunity City St George's University of London London United Kingdom Department of Clinical Research Faculty of Infectious and Tropical Diseases London School of Hygiene & Tropical Medicine London United Kingdom Botswana Harvard Health Partnership Gaborone Botswana School of Pathology Faculty of Health Sciences University of the Witwatersrand Johannesburg South Africa University of North Carolina Project–Malawi Lilongwe Malawi Department of Medicine Kamuzu Central Hospital Lilongwe Malawi Malawi–Liverpool–Wellcome Trust Clinical Research Programme Blantyre Malawi National Institute for Health and Care Research Biomedical Research Centre Clinical and Research Informatics Unit University College London Hospitals NHS Foundation Trust London United Kingdom National Institute for Medical Research Muhimbili Medical Research Centre Dar es Salaam Tanzania Institute for Medical Research and Training Lusaka Zambia Department of International Medicine University Teaching Hospital Lusaka Zambia Department of Public Health Faculty of Medicine and Pharmaceutical Sciences University of Dschang Dschang Cameroon Research Center on Emerging and Re-emerging diseases Yaoundé Cameroon Site Agence Nationale de Recherche sur le Sida Cameroun Hôpital Central Yaoundé Yaoundé Cameroon Molecular Mycology Unit Institut Pasteur Paris France Department of Internal Medicine Douala General Hospital Douala Cameroon Department of Infectious Disease and Tropical Medicine University of Paris Paris France Department of Mycology National Reference Center for Invasive Mycoses and Antifungals Institut Pasteur Paris France Institut Imagine Necker Enfant Malades University Hospital Assistance Publique–Hôpitaux de Paris Paris France Department of Medicine

Background: Cryptococcal meningitis is a major driver of global HIV-related mortality, and validated approaches to stratify mortality risk could help to target effective treatment strategies. We aimed to develop and validate models to predict risk of all-cause mortality in people with HIV-associated cryptococcal meningitis in sub-Saharan African countries. Methods: For this prediction modelling study, we pooled individual-level data from the ACTA (ISRCTN45035509) and AMBITION-cm (ISRCTN72509687) randomised controlled trials. Data in ACTA were collected between Feb 12, 2013, and Jan 10, 2017, and data in AMBITION-cm were collected between Jan 31, 2018, and June 11, 2021. Adults aged 18 years or older with a first episode of HIV-associated cryptococcal meningitis were recruited to both trials. Exclusion criteria included pregnancy or lactation;receipt of high-dose anti-fungal treatment doses before screening;and contraindications to trial medication. Participants were recruited from nine hospitals across Cameroon, Malawi, Tanzania, and Zambia in ACTA and eight hospitals across Botswana, Malawi, South Africa, Uganda, and Zimbabwe in AMBITION-cm. We developed two primary multivariable logistic-regression models for the primary outcome of 2-week mortality: a basic model for use in a resource-limited setting that contained only candidate predictors that are routinely, programmatically obtained at hospital admission and a research model for which all predefined candidate predictors were considered for inclusion. We used internal–external cross-validation to evaluate model performance across countries within the development cohort (ie, data from all countries except Malawi participants in AMBITION-cm), before validation of discrimination, calibration, and net benefit in held-out data from Malawi. Findings: We included 674 eligible participants from ACTA and 814 from AMBITION-cm in the pooled analysis (total sample size 1488). 1263 participants were included in model develop

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Insights on Obesity Prevalence and Management Using Adipoview, a Real-Time Clinical Obesity Landscape Indicator: A Population-Based Cross-Sectional Study with Practical Recommendations

SSRN

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SSRN 2025年

作者： Chew, Han Shi Jocelyn Wan, Su Wei Liu, Xueling Chia, Daryl Kai Ann Ngooi, Jia Wen Koh, Sky Wei Chee Pang, Ning Qi Chew, Nicholas W.S. Aung, Myint Oo Tham, Kwang Wei Foo, Roger S.Y. So, Jimmy Bok-Yan Shabbir, Asim Lee, James Wai Kit Sevdalis, Nick Ngiam, Kee Yuan Alice Lee Centre for Nursing Studies Yong Loo Lin School of Medicine National University of Singapore Singapore Cardiovascular Metabolic Disease Translational Research Programme Yong Loo Lin School of Medicine National University of Singapore Singapore Department of Biomedical Informatics Yong Loo Lin School of Medicine National University of Singapore Singapore Yong Loo Lin School of Medicine National University of Singapore Singapore Singapore Association for the Study of Obesity Singapore Department of Surgery National University Hospital Singapore National University Health System Singapore National University Polyclinics National University Health System Singapore Department of Cardiology National University Heart Centre Singapore Department of Surgery Tan Tock Seng Hospital Singapore Department of Endocrinology Woodlands Health Singapore Singapore Genome Institute of Singapore Singapore Group Chief Technology Office Singapore

Background: Obesity, usually perceived as a lifestyle problem rather than a chronic disease, is often inadequately measured, monitored, diagnosed or managed. Therefore, we aimed to study the prevalence and management of obesity within the Western healthcare cluster of Singapore using a novel AI-driven dashboard called Adipoview. Methods: A population-based cross-sectional study was conducted on a cohort of adult patients (≥18 years old) captured within the electronic health records (EHR) from 23 August 2023 to 2024. Deidentified patient-level data was derived from Adipoview, a dashboard that uses data analytics and visualisation methods to display the obesity management landscape in real-time. Sociodemographic and clinical characteristics were extracted. Findings: Our study analysed 313,369 patients with a mean age of 58.3 years, and 53% being female. The most common co-morbid chronic diseases were hyperlipidaemia (40.0%), hypertension (36.7%), and diabetes (22.1%). While the prevalence of excess adiposity was 88.2% (based on Asian standards), only 10.5% of patients had a diagnosis of excess adiposity. Diagnosis of patients with excess adiposity was slightly higher in primary compared to tertiary care (89.8% versus 86.1%), where patients from primary care tended to be older with a higher prevalence of DHLs. Interpretation: The high prevalence of excess adiposity paired with its underdiagnosis as a chronic disease limits the potential to curb cardiometabolic implications of obesity. Greater efforts are needed to increase weight management awareness, refine obesity assessment measures, ensure proper diagnosis using clinical decision support aids, review existing weight-loss programme efficacies and promote screening of comorbidities through real-time visualisation tools such as Adipoview. Funding: No funding received. Declaration of Interest: The authors declare no conflict of interest. Ethical Approval: Institutional approval to conduct the research was obtained from

关键词： Electronic health record

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P01-72 EUROTOX Early Career Forum (ECF): The voice for early career scientists in EUROTOX

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Toxicology Letters 2024年 399卷 S99-S100页

作者： J. Dietrich I. Ora M. Burbank K. Kopańska M. Jobst E. Kuchovska D. Basili C. Voss Wageningen Food Safety Research Center BU Contaminants & Toxicology Wageningen Netherlands Wageningen University Department of Bioinformatics Wageningen Netherlands University of Eastern Finland Kuopio Finland L'Oreal Research Paris France Universitat Pompeu Fabra Department of Medicine and Life Sciences Research Programme on Biomedical Informatics (GRIB) Barcelona Spain University of Vienna Department of Food Chemistry and Toxicology Vienna Austria IUF Leibniz Research Institute for Environmental Medicine Düsseldorf Germany Nestlè Research Institute of Food Safety and Analytical Sciences Lausanne Switzerland Helmholtz Center Munich Munich Germany

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Towards Clinical AI Fairness: Filling Gaps in the Puzzle

arXiv

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arXiv 2024年

作者： Liu, Mingxuan Ning, Yilin Teixayavong, Salinelat Liu, Xiaoxuan Mertens, Mayli Shang, Yuqing Li, Xin Miao, Di Xu, Jie Ting, Daniel Shu Wei Cheng, Lionel Tim-Ee Ong, Jasmine Chiat Ling Teo, Zhen Ling Tan, Ting Fang RaviChandran, Narrendar Wang, Fei Celi, Leo Anthony Ong, Marcus Eng Hock Liu, Nan Centre for Quantitative Medicine Duke-NUS Medical School Singapore Singapore College of Medical and Dental Sciences University of Birmingham Birmingham United Kingdom University Hospitals Birmingham NHS Foundation Trust Birmingham United Kingdom National Institute for Health and Care Research Birmingham Biomedical Research Centre University of Birmingham Birmingham United Kingdom Centre for Ethics Department of Philosophy University of Antwerp Antwerp Belgium Antwerp Center on Responsible AI University of Antwerp Antwerp Belgium Department of Health Outcomes and Biomedical Informatics University of Florida GainesvilleFL United States Singapore Eye Research Institute Singapore National Eye Centre Singapore Singapore Department of Diagnostic Radiology Singapore General Hospital Singapore Singapore Department of Pharmacy Singapore General Hospital Singapore Singapore Department of Population Health Sciences Weill Cornell Medicine New YorkNY United States Laboratory for Computational Physiology Massachusetts Institute of Technology CambridgeMA United States Division of Pulmonary Critical Care and Sleep Medicine Beth Israel Deaconess Medical Center BostonMA United States Department of Biostatistics Harvard T.H. Chan School of Public Health BostonMA United States Programme in Health Services and Systems Research Duke-NUS Medical School Singapore Singapore Department of Emergency Medicine Singapore General Hospital Singapore Singapore Institute of Data Science National University of Singapore Singapore Singapore

The ethical integration of Artificial Intelligence (AI) in healthcare necessitates addressing fairness—a concept that is highly context-specific across medical fields. Extensive studies have been conducted to expand the technical components of AI fairness, while tremendous calls for AI fairness have been raised from healthcare. Despite this, a significant disconnect persists between technical advancements and their practical clinical applications, resulting in a lack of contextualized discussion of AI fairness in clinical settings. Through a detailed evidence gap analysis, our review systematically pinpoints several deficiencies concerning both healthcare data and the provided AI fairness solutions. We highlight the scarcity of research on AI fairness in many medical domains where AI technology is increasingly utilized. Additionally, our analysis highlights a substantial reliance on group fairness, aiming to ensure equality among demographic groups from a macro healthcare system perspective;in contrast, individual fairness, focusing on equity at a more granular level, is frequently overlooked. To bridge these gaps, our review advances actionable strategies for both the healthcare and AI research communities. Beyond applying existing AI fairness methods in healthcare, we further emphasize the importance of involving healthcare professionals to refine AI fairness concepts and methods to ensure contextually relevant and ethically sound AI applications in healthcare. © 2024, CC BY.

关键词： Health care

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