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The urgent need to accelerate synthetic data privacy frameworks for medical research 迫切需要加快医学研究的合成数据隐私框架。
IF 23.8 1区 医学 Q1 MEDICAL INFORMATICS Pub Date : 2025-02-01 DOI: 10.1016/S2589-7500(24)00196-1
Anmol Arora MBBChir MA , Siegfried Karl Wagner PhD FRCOphth , Robin Carpenter BSc , Rajesh Jena MD , Pearse A Keane MD FRCOphth
Synthetic data, generated through artificial intelligence technologies such as generative adversarial networks and latent diffusion models, maintain aggregate patterns and relationships present in the real data the technologies were trained on without exposing individual identities, thereby mitigating re-identification risks. This approach has been gaining traction in biomedical research because of its ability to preserve privacy and enable dataset sharing between organisations. Although the use of synthetic data has become widespread in other domains, such as finance and high-energy physics, use in medical research raises novel issues. The use of synthetic data as a method of preserving the privacy of data used to train models requires that the data are high fidelity with the original data to preserve utility, but must be sufficiently different as to protect against adversarial or accidental re-identification. There is a need for the development of standards for synthetic data generation and consensus standards for its evaluation. As synthetic data applications expand, ongoing legal and ethical evaluations are crucial to ensure that they remain a secure and effective tool for advancing medical research without compromising individual privacy.
通过生成式对抗网络和潜在扩散模型等人工智能技术生成的合成数据,可以在不暴露个人身份的情况下,保持这些技术所训练的真实数据中存在的总体模式和关系,从而降低重新识别风险。由于这种方法能够保护隐私并实现组织间的数据集共享,因此在生物医学研究中越来越受到重视。虽然合成数据的使用在金融和高能物理等其他领域已经非常普遍,但在医学研究中的使用却带来了新的问题。使用合成数据作为保护用于训练模型的数据隐私的一种方法,要求数据与原始数据具有高保真性,以保持实用性,但必须有足够的差异,以防止对抗性或意外的重新识别。有必要制定合成数据生成标准和评估标准。随着合成数据应用的不断扩大,持续的法律和伦理评估对于确保合成数据在不损害个人隐私的情况下继续成为推进医学研究的安全有效工具至关重要。
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引用次数: 0
Artificial intelligence-guided detection of under-recognised cardiomyopathies on point-of-care cardiac ultrasonography: a multicentre study
IF 23.8 1区 医学 Q1 MEDICAL INFORMATICS Pub Date : 2025-02-01 DOI: 10.1016/S2589-7500(24)00249-8
Evangelos K Oikonomou MD , Akhil Vaid MD , Gregory Holste BA , Andreas Coppi PhD , Robert L McNamara MD , Cristiana Baloescu MD , Harlan M Krumholz MD , Zhangyang Wang PhD , Donald J Apakama MD , Girish N Nadkarni MD , Rohan Khera MD

Background

Point-of-care ultrasonography (POCUS) enables cardiac imaging at the bedside and in communities but is limited by abbreviated protocols and variation in quality. We aimed to develop and test artificial intelligence (AI) models to screen for under-diagnosed cardiomyopathies from cardiac POCUS.

Methods

In a development set of 290 245 transthoracic echocardiographic videos across the Yale–New Haven Health System (YNHHS), we used augmentation approaches, and a customised loss function weighted for view quality to derive a POCUS-adapted, multi-label, video-based convolutional neural network that discriminates hypertrophic cardiomyopathy and transthyretin amyloid cardiomyopathy from controls without known disease. We evaluated the model across independent, internal, and external, retrospective cohorts of individuals undergoing cardiac POCUS across YNHHS and the Mount Sinai Health System (MSHS) emergency departments (between 2012 and 2024) to prioritise key views and validate the diagnostic and prognostic performance of single-view screening protocols.

Findings

Between Nov 1, 2023, and March 28, 2024, we identified 33 127 patients (mean age 58·9 [SD 20·5] years, 17 276 [52·2%] were female, 14 923 [45·0%] were male, and for 928 [2·8%] sex was recorded as unknown) at YNHHS and 5624 patients (mean age 56·0 [20·5] years, 1953 [34·7%] were female, 2470 [43·9%] were male, and for 1201 [21·4%] sex was recorded as unknown) at MSHS with 78 054 and 13 796 eligible cardiac POCUS videos, respectively. AI deployed to single-view POCUS videos successfully discriminated hypertrophic cardiomyopathy (eg, area under the receiver operating characteristic curve 0·903 [95% CI 0·795–0·981] in YNHHS; 0·890 [0·839–0·938] in MSHS for apical-4-chamber acquisitions) and transthyretin amyloid cardiomyopathy (0·907 [0·874–0·932] in YNHHS; 0·972 [0·959–0·983] in MSHS for parasternal acquisitions). In YNHHS, 40 (58%) of 69 hypertrophic cardiomyopathy cases and 22 (46%) of 48 transthyretin amyloid cardiomyopathy cases would have had a positive screen by AI-POCUS at a median of 2·1 (IQR 0·9–4·5) years and 1·9 (0·6–3·5) years before diagnosis. Moreover, among 25 261 participants without known cardiomyopathy followed up over a median of 2·8 (1·2–6·4) years, AI-POCUS probabilities in the highest (vs lowest) quintile for hypertrophic cardiomyopathy and transthyretin amyloid cardiomyopathy conferred a 17% (adjusted hazard ratio 1·17, 95% CI 1·06–1·29; p=0·0022) and 32% (1·39, 1·19–1·46; p<0·0001) higher adjusted mortality risk, respectively.

Interpretation

We developed and validated an AI framework that enables scalable, opportunistic screening of under-recognised cardiomyopathies through simple POCUS acquisitions.

Funding

National Heart, Lung, and Blood Institute, Doris Duke Charitable Foundation, and BridgeBio.
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引用次数: 0
Exploring electronic health records to study rare diseases
IF 23.8 1区 医学 Q1 MEDICAL INFORMATICS Pub Date : 2025-02-01 DOI: 10.1016/j.landig.2025.01.008
The Lancet Digital Health
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引用次数: 0
AI for medical diagnosis: does a single negative trial mean it is ineffective?
IF 23.8 1区 医学 Q1 MEDICAL INFORMATICS Pub Date : 2025-02-01 DOI: 10.1016/j.landig.2025.01.005
Olga Kostopoulou , Brendan Delaney
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引用次数: 0
Prediction of emergency admissions: trade-offs between model simplicity and performance
IF 23.8 1区 医学 Q1 MEDICAL INFORMATICS Pub Date : 2025-02-01 DOI: 10.1016/j.landig.2024.12.008
Shishir Rao , Kazem Rahimi
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引用次数: 0
From the 100 Day Mission to 100 lines of software development: how to improve early outbreak analytics 从100天任务到100行软件开发:如何改进早期爆发分析。
IF 23.8 1区 医学 Q1 MEDICAL INFORMATICS Pub Date : 2025-02-01 DOI: 10.1016/S2589-7500(24)00218-8
Carmen Tamayo Cuartero DVM PhD , Anna C Carnegie MPP , Zulma M Cucunuba MD PhD , Anne Cori PhD , Sara M Hollis MSc , Rolina D Van Gaalen PhD , Amrish Y Baidjoe , Alexander F Spina MPH , John A Lees PhD , Simon Cauchemez PhD , Mauricio Santos PhD , Juan D Umaña MSc , Chaoran Chen PhD , Hugo Gruson PhD , Pratik Gupte PhD , Joseph Tsui MSc , Anita A Shah MPH , Geraldine Gomez Millan SEP , David Santiago Quevedo MSc , Neale Batra MSc , Prof Adam J Kucharski
Since the COVID-19 pandemic, considerable advances have been made to improve epidemic preparedness by accelerating diagnostics, therapeutics, and vaccine development. However, we argue that it is crucial to make equivalent efforts in the field of outbreak analytics to help ensure reliable, evidence-based decision making. To explore the challenges and key priorities in the field of outbreak analytics, the Epiverse-TRACE initiative brought together a multidisciplinary group of experts, including field epidemiologists, data scientists, academics, and software engineers from public health institutions across multiple countries. During a 3-day workshop, 40 participants discussed what the first 100 lines of code written during an outbreak should look like. The main findings from this workshop are summarised in this Viewpoint. We provide an overview of the current outbreak analytic landscape by highlighting current key challenges that should be addressed to improve the response to future public health crises. Furthermore, we propose actionable solutions to these challenges that are achievable in the short term, and longer-term strategic recommendations. This Viewpoint constitutes a call to action for experts involved in epidemic response to develop modern and robust data analytic approaches at the heart of epidemic preparedness and response.
自2019冠状病毒病大流行以来,通过加快诊断、治疗和疫苗开发,在加强流行病防范方面取得了相当大的进展。然而,我们认为,在疫情分析领域做出同样的努力,以帮助确保可靠的、基于证据的决策是至关重要的。为了探索疫情分析领域的挑战和关键优先事项,Epiverse-TRACE计划汇集了一个多学科专家组,包括来自多个国家公共卫生机构的现场流行病学家、数据科学家、学者和软件工程师。在为期3天的研讨会中,40名参与者讨论了在爆发期间编写的前100行代码应该是什么样子。本观点总结了本次研讨会的主要发现。我们通过强调当前应解决的主要挑战,以改善对未来公共卫生危机的反应,概述当前疫情分析形势。此外,我们为这些挑战提出了短期内可实现的可行解决方案,并提出了长期战略建议。这一观点呼吁参与流行病应对的专家采取行动,在流行病防范和应对的核心制定现代和可靠的数据分析方法。
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引用次数: 0
Diagnoses supported by a computerised diagnostic decision support system versus conventional diagnoses in emergency patients (DDX-BRO): a multicentre, multiple-period, double-blind, cluster-randomised, crossover superiority trial
IF 23.8 1区 医学 Q1 MEDICAL INFORMATICS Pub Date : 2025-02-01 DOI: 10.1016/S2589-7500(24)00250-4
Wolf E Hautz MD , Thimo Marcin PhD , Stefanie C Hautz PhD , Stefan K Schauber PhD , Prof Gert Krummrey MD , Martin Müller MD , Thomas C Sauter MD , Cornelia Lambrigger RN , David Schwappach PhD , Prof Mathieu Nendaz MD , Gregor Lindner MD , Simon Bosbach MD , Ines Griesshammer MD , Philipp Schönberg MD , Emanuel Plüss MD , Valerie Romann MD , Svenja Ravioli MD , Nadine Werthmüller MD , Fabian Kölbener MD , Prof Aristomenis K Exadaktylos MD , Laura Zwaan PhD
<div><h3>Background</h3><div>Diagnostic error is a frequent and clinically relevant health-care problem. Whether computerised diagnostic decision support systems (CDDSSs) improve diagnoses is controversial, and prospective randomised trials investigating their effectiveness in routine clinical practice are scarce. We hypothesised that diagnoses made with a CDDSS in the emergency department setting would be superior to unsupported diagnoses.</div></div><div><h3>Methods</h3><div>This multicentre, multiple-period, double-blind, cluster-randomised, crossover superiority trial was done in four emergency departments in Switzerland. Eligible patients were adults (aged ≥18 years) presenting with abdominal pain, fever of unknown origin, syncope, or non-specific symptoms. Emergency departments were randomly assigned (1:1) to one of two predefined sequences of six alternating periods of intervention or control. Patients presenting during an intervention period were diagnosed with the aid of a CDDSS, whereas patients presenting during a control period were diagnosed without a CDDSS (usual care). Patients and personnel assessing outcomes were masked to group allocation; treating physicians were not. The primary binary outcome (false or true) was a composite score indicating a risk of reduced diagnostic quality, which was deemed to be present if any of the following occurred within 14 days: unscheduled medical care, a change in diagnosis, an unexpected intensive care unit admission within 24 h if initially admitted to hospital, or death. We assessed superiority of supported versus unsupported diagnoses in all consenting patients using a generalised linear mixed effects model. All participants who received any study treatment (including control) and completed the study were included in the safety analysis. This trial is registered with <span><span>ClinicalTrials.gov</span><svg><path></path></svg></span> (<span><span>NCT05346523</span><svg><path></path></svg></span>) and is closed to accrual.</div></div><div><h3>Findings</h3><div>Between June 9, 2022, and June 23, 2023, 15 845 patients were screened and 1204 (591 [49·1%] female and 613 [50·9%] male) were included in the primary efficacy analysis. The median age of participants was 53 years (IQR 34–69). Diagnostic quality risk was observed in 100 (18%) of 559 patients with CDDSS-supported diagnoses and 119 (18%) of 645 with unsupported diagnoses (adjusted odds ratio 0·96 [95% CI 0·71–1·3]). 94 (7·8%) patients suffered a serious adverse event, none related to the study.</div></div><div><h3>Interpretation</h3><div>Use of a CDDSS did not reduce the occurrence of diagnostic quality risk compared with the usual diagnostic process in adults presenting to emergency departments. Future research should aim to identify specific contexts in which CDDSSs are effective and how existing CDDSSs can be adapted to improve patient outcomes.</div></div><div><h3>Funding</h3><div>Swiss National Science Foundation and University Hospi
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引用次数: 0
Thank you to The Lancet Digital Health's statistical and peer reviewers in 2024 感谢《柳叶刀数字健康》在 2024 年的统计和同行评审人员。
IF 23.8 1区 医学 Q1 MEDICAL INFORMATICS Pub Date : 2025-02-01 DOI: 10.1016/j.landig.2025.01.009
The Lancet Digital Health Editors
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引用次数: 0
Using artificial intelligence to switch from accident to sagacity in the serendipitous detection of uncommon diseases
IF 23.8 1区 医学 Q1 MEDICAL INFORMATICS Pub Date : 2025-02-01 DOI: 10.1016/j.landig.2024.12.006
Roberto Sciagrà
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引用次数: 0
Harnessing temporal patterns in administrative patient data to predict risk of emergency hospital admission
IF 23.8 1区 医学 Q1 MEDICAL INFORMATICS Pub Date : 2025-02-01 DOI: 10.1016/S2589-7500(24)00254-1
Benjamin Post MBChB , Roman Klapaukh PhD , Prof Stephen J Brett MD , Prof A Aldo Faisal PhD

Background

Unplanned hospital admissions are associated with worse patient outcomes and cause strain on health systems worldwide. Primary care electronic health records (EHRs) have successfully been used to create prediction models for emergency hospitalisation, but these approaches require a broad range of diagnostic, physiological, and laboratory values. In this study, we aimed to capture temporal patterns of patient activity from EHR data and evaluate their effectiveness in predicting emergency hospital admissions compared with conventional methods.

Methods

In this retrospective observational study, we used the Secure Anonymised Information Linkage databank to extract temporal patterns of primary care activity from undifferentiated electronic health record timestamp data for 1·37 million patients in Wales aged 18–80 years with at least one recorded Read code between the years 2016 and 2018. Using Gaussian mixture modelling we grouped patients into distinct temporal clusters, performed a three-stage validation of our approach and calculated the risk of emergency hospital admission for each temporal cluster group. Finally, these temporal clusters were combined with five administrative variables and incorporated into four emergency hospital admission prediction models (logistic regression, naive Bayes, XGBoost, and multilayer perceptron [MLP]) and compared with a more traditional, but data-intensive, modelling technique. The primary outcome was emergency hospital admission as the next health-care event.

Findings

Six distinct temporal cluster patterns of primary care EHR activity were identified, associated with varying risks of future emergency hospital admission risk. These patterns were visually interpretable, repeatable at a population-level, and clinically plausible. The best emergency hospital admission prediction model (MLP) achieved an area under the receiver operating characteristic (AUROC) of 0·82 and precision of 0·94 in regional cohorts. In external validation in regional cohorts, similar model performance was observed (AUROC 0·82 and precision 0·92). This model also matched the performance of a more complex model (extended feature model) requiring 33 clinical parameters (AUROC 0·82 vs 0·83; precision 0·94 vs 0·90) for the same task on the same dataset.

Interpretation

We developed a novel machine learning pipeline that extracts interpretable temporal patterns from simple representations of EHR data and can be incorporated into emergency hospital admission predictors. This framework might enable more rapid development of parsimonious clinical prediction models.

Funding

UKRI CDT in AI for Healthcare, UKRI Turing AI Fellowship, NIHR Imperial Biomedical Research Centre, and Research Capability Funding.
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引用次数: 0
期刊
Lancet Digital Health
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