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Garbage In, Garbage Out? Negative Impact of Physiological Waveform Artifacts in a Hospital Clinical Data Warehouse. 垃圾进,垃圾出?医院临床数据仓库中生理波形伪影的负面影响。
IF 3.5 3区 医学 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2024-11-25 DOI: 10.1007/s10916-024-02128-1
Frederick H Kuo, Mohamed A Rehman, Luis M Ahumada

Hospitals around the world are deploying increasingly advanced systems to collect and store high-resolution physiological patient data for quality improvement and research. However, data accuracy, completeness, consistency, and contextual validity remain issues. This report highlights a data artifact known as waveform clipping in our hospital's physiological data capture system that went unnoticed for years, limiting data analysis and delaying several research projects. We aim to raise awareness in the medical informatics community about the importance of careful system setup, ongoing data validation, and close cooperation between clinicians and data scientists.

世界各地的医院正在部署越来越先进的系统,以收集和存储高分辨率的病人生理数据,用于质量改进和研究。然而,数据的准确性、完整性、一致性和上下文的有效性仍然是个问题。本报告重点介绍了本医院生理数据采集系统中的一种被称为 "波形剪切 "的数据假象,该假象多年来一直未被注意到,限制了数据分析并延误了多个研究项目。我们旨在提高医疗信息学界对谨慎设置系统、持续验证数据以及临床医生和数据科学家密切合作重要性的认识。
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引用次数: 0
21st Century Cures Act and Information Blocking: How Have Different Specialties Responded? 21 世纪治愈法案与信息封锁:不同专科如何应对?
IF 3.5 3区 医学 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2024-11-23 DOI: 10.1007/s10916-024-02130-7
Amy Xiong, James Xie
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引用次数: 0
Self-Supervised Learning for Near-Wild Cognitive Workload Estimation. 近乎野生认知工作量估算的自我监督学习
IF 3.5 3区 医学 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2024-11-22 DOI: 10.1007/s10916-024-02122-7
Mohammad H Rafiei, Lynne V Gauthier, Hojjat Adeli, Daniel Takabi

Feedback on cognitive workload may reduce decision-making mistakes. Machine learning-based models can produce feedback from physiological data such as electroencephalography (EEG) and electrocardiography (ECG). Supervised machine learning requires large training data sets that are (1) relevant and decontaminated and (2) carefully labeled for accurate approximation, a costly and tedious procedure. Commercial over-the-counter devices are low-cost resolutions for the real-time collection of physiological modalities. However, they produce significant artifacts when employed outside of laboratory settings, compromising machine learning accuracies. Additionally, the physiological modalities that most successfully machine-approximate cognitive workload in everyday settings are unknown. To address these challenges, a first-ever hybrid implementation of feature selection and self-supervised machine learning techniques is introduced. This model is employed on data collected outside controlled laboratory settings to (1) identify relevant physiological modalities to machine approximate six levels of cognitive-physical workloads from a seven-modality repository and (2) postulate limited labeling experiments and machine approximate mental-physical workloads using self-supervised learning techniques.

认知工作量反馈可减少决策失误。基于机器学习的模型可以从脑电图(EEG)和心电图(ECG)等生理数据中产生反馈。有监督的机器学习需要大量的训练数据集,这些数据集(1)具有相关性并经过净化,(2)经过仔细标注以实现准确的近似,这是一个昂贵而繁琐的过程。商用非处方设备是实时收集生理模式的低成本解决方案。然而,在实验室以外的环境中使用时,它们会产生明显的伪影,影响机器学习的准确性。此外,能够最成功地通过机器估算日常认知工作量的生理模式尚不清楚。为了应对这些挑战,我们首次引入了特征选择和自监督机器学习技术的混合实施方法。该模型应用于在受控实验室环境外收集的数据,以便:(1)识别相关的生理模态,从七个模态库中机器近似六个级别的认知-物理工作量;(2)假设有限的标记实验,并使用自我监督学习技术机器近似心理-物理工作量。
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引用次数: 0
Electronic Health Records Sharing Based on Consortium Blockchain. 基于联盟区块链的电子健康记录共享。
IF 3.5 3区 医学 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2024-11-18 DOI: 10.1007/s10916-024-02120-9
Guangfu Wu, Haiping Wang, Zi Yang, Daojing He, Sammy Chan

In recent years, Electronic health records (EHR) has gradually become the mainstream in the healthcare field. However, due to the fact that EHR systems are provided by different vendors, data is dispersed and stored, which leads to the phenomenon of data silos, making medical information too fragmented and bringing some challenges to current medical services. Therefore, in view of the difficulties in sharing EHR between medical institutions, the risk of privacy leakage, and the lack of EHR usage control by patients, an EHR sharing model based on consortium blockchain is proposed in this paper. Firstly, the Interplanetary File System is combined with consortium blockchain, which forms a hybrid storage scheme of EHR, this technology effectively improves data security, privacy protection, and operational efficiency. Secondly, the model combines unidirectional multi-hop conditional proxy re-encryption based on type and identity with distributed key generation technology to achieve secure EHR sharing with fine grained control. At the same time, users are required to link the operation records of EHR, so as to realize the traceability of EHR usage. A dynamic Byzantine fault-tolerant algorithm based on reputation and clustering is then proposed to solve the problems of arbitrary master node selection, high latency and low throughput of PBFT, enabling the nodes to reach consensus more efficiently. Finally, the model is analyzed in terms of security and user control, showing that the model is less energy intensive in terms of communication overhead and time consumption, and can effectively achieve secure sharing between medical data.

近年来,电子病历(EHR)逐渐成为医疗领域的主流。然而,由于电子病历系统由不同厂商提供,数据分散存储,导致数据孤岛现象,使得医疗信息过于分散,给当前的医疗服务带来了一定的挑战。因此,针对医疗机构间电子病历共享困难、隐私泄露风险大、患者缺乏电子病历使用控制等问题,本文提出了一种基于联盟区块链的电子病历共享模式。首先,将星际文件系统与联盟区块链相结合,形成电子病历的混合存储方案,该技术有效提高了数据安全性、隐私保护和运行效率。其次,该模型将基于类型和身份的单向多跳条件代理重加密技术与分布式密钥生成技术相结合,实现了细粒度控制的电子病历安全共享。同时,要求用户链接电子病历的操作记录,实现电子病历使用的可追溯性。然后提出了一种基于声誉和聚类的动态拜占庭容错算法,以解决 PBFT 的主节点任意选择、高延迟和低吞吐量等问题,使节点更高效地达成共识。最后,从安全性和用户控制方面对该模型进行了分析,结果表明该模型在通信开销和时间消耗方面能耗较低,能有效实现医疗数据之间的安全共享。
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引用次数: 0
Large Language Models in Healthcare: An Urgent Call for Ongoing, Rigorous Validation. 医疗保健领域的大型语言模型:紧急呼吁持续、严格的验证。
IF 3.5 3区 医学 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2024-11-16 DOI: 10.1007/s10916-024-02126-3
Gerson Hiroshi Yoshinari Júnior, Luciano Magalhães Vitorino
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引用次数: 0
Why Clinicians should Care about YouCare and Other Wearable Health Devices. 为什么临床医生应该关注 YouCare 和其他可穿戴健康设备?
IF 3.5 3区 医学 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2024-11-15 DOI: 10.1007/s10916-024-02123-6
Asif Padiyath, J Nick Pratap, Allan F Simpao

In this issue of Journal of Medical Systems, Neri et al. share results from their study in which they compared the YouCare device to a standard Holter monitor. The wearable used in the study incorporates a single electrocardiogram lead in a crop top garment that is customized for each patient. This editorial discusses the YouCare device, the study findings, and their clinical relevance and impact in the context of wearable technology.

在本期《医疗系统杂志》上,Neri 等人分享了他们将 YouCare 设备与标准 Holter 监护仪进行比较的研究结果。研究中使用的可穿戴设备在为每位患者量身定制的上衣中集成了单个心电图导联。这篇社论讨论了 YouCare 设备、研究结果及其在可穿戴技术方面的临床意义和影响。
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引用次数: 0
A Joint Message from the Outgoing and Incoming Editors-in-Chief. 即将离任和即将上任的主编的联合致辞。
IF 3.5 3区 医学 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2024-11-14 DOI: 10.1007/s10916-024-02124-5
Allan F Simpao, Jesse M Ehrenfeld
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引用次数: 0
How Useful are Current Chatbots Regarding Urology Patient Information? Comparison of the Ten Most Popular Chatbots' Responses About Female Urinary Incontinence. 当前有关泌尿科患者信息的聊天机器人有多有用?比较十大最受欢迎聊天机器人关于女性尿失禁的回答。
IF 3.5 3区 医学 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2024-11-13 DOI: 10.1007/s10916-024-02125-4
Arzu Malak, Mehmet Fatih Şahin

This research evaluates the readability and quality of patient information material about female urinary incontinence (fUI) in ten popular artificial intelligence (AI) supported chatbots. We used the most recent versions of 10 widely-used chatbots, including OpenAI's GPT-4, Claude-3 Sonnet, Grok 1.5, Mistral Large 2, Google Palm 2, Meta's Llama 3, HuggingChat v0.8.4, Microsoft's Copilot, Gemini Advanced, and Perplexity. Prompts were created to generate texts about UI, stress type UI, urge type UI, and mix type UI. The modified Ensuring Quality Information for Patients (EQIP) technique and QUEST (Quality Evaluating Scoring Tool) were used to assess the quality, and the average of 8 well-known readability formulas, which is Average Reading Level Consensus (ARLC), were used to evaluate readability. When comparing the average scores, there were significant differences in the mean mQEIP and QUEST scores across ten chatbots (p = 0.049 and p = 0.018). Gemini received the greatest mean scores for mEQIP and QUEST, whereas Grok had the lowest values. The chatbots exhibited significant differences in mean ARLC, word count, and sentence count (p = 0.047, p = 0.001, and p = 0.001, respectively). For readability, Grok is the easiest to read, while Mistral is highly complex to understand. AI-supported chatbot technology needs to be improved in terms of readability and quality of patient information regarding female UI.

本研究评估了十种流行的人工智能(AI)支持聊天机器人中有关女性尿失禁(fUI)的患者信息资料的可读性和质量。我们使用了 10 个广泛使用的聊天机器人的最新版本,包括 OpenAI 的 GPT-4、Claude-3 Sonnet、Grok 1.5、Mistral Large 2、Google Palm 2、Meta's Llama 3、HuggingChat v0.8.4、Microsoft's Copilot、Gemini Advanced 和 Perplexity。我们创建了提示来生成有关用户界面、压力型用户界面、冲动型用户界面和混合型用户界面的文本。使用修改后的 "确保患者信息质量(EQIP)"技术和 QUEST(质量评估评分工具)来评估质量,并使用 8 个著名的可读性公式的平均值,即平均阅读水平共识(ARLC)来评估可读性。在比较平均得分时,十个聊天机器人的 mQEIP 和 QUEST 平均得分存在显著差异(p = 0.049 和 p = 0.018)。Gemini 的 mEQIP 和 QUEST 平均得分最高,而 Grok 的得分最低。聊天机器人在平均 ARLC、字数和句数方面表现出显著差异(分别为 p = 0.047、p = 0.001 和 p = 0.001)。就可读性而言,Grok 最容易阅读,而 Mistral 则非常复杂难懂。在女性用户界面方面,人工智能支持的聊天机器人技术需要在可读性和患者信息质量方面加以改进。
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引用次数: 0
Maximising the Quality of Stroke Care: Reporting of Data Collection Methods and Resourcing in National Stroke Registries: A Systematic Review. 最大限度地提高卒中治疗质量:报告国家卒中登记处的数据收集方法和资源配置:系统回顾。
IF 3.5 3区 医学 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2024-10-28 DOI: 10.1007/s10916-024-02119-2
Agnes Jonsson, Nicole Cosgrave, Anna Healy, Lisa Mellon, David J Williams, Anne Hickey

Stroke registries are tools for improving care and advancing research. We aim to describe the methodology and resourcing of existing national stroke registries. We conducted a systematic search of the published, peer-reviewed literature and grey literature examining descriptions of data collection methods and resourcing of national stroke registries published from 2012 to 2023. The systematic review was registered in PROSPERO (CRD42023393841). 101 records relating to 21 registries in 19 countries were identified. They universally employed web-based platforms for data collection. The principal profession of data collectors was nursing. All included the acute phase of care, 28% (6) registered the pre-hospital (ambulance) phase and 14% (3) included rehabilitation. 80% (17) collected outcome data. The registries varied in their approach to outcome data collection; in 9 registries it was collected by hospitals, in 2 it was collected by the registry, and 7 had linkage to national administrative databases allowing follow-up of a limited number of end points. Coverage of the total number of strokes varies from 6 to 95%. Despite widespread use of Electronic Health Records (EHRs) the ability to automatically populate variables remained limited. Governance and management structures are diverse, making it challenging to compare their resourcing. Data collection for clinical registries requires time and necessary skills and imposes a significant administrative burden on the professionals entering data. We highlight the role of clinical registries as powerful instruments for quality improvement. Future work should involve creating a central repository of stroke registries to enable the development of new registries and facilitate international collaboration.

卒中登记是改善护理和促进研究的工具。我们旨在描述现有国家卒中登记的方法和资源配置。我们对已发表的同行评议文献和灰色文献进行了系统检索,研究了 2012 年至 2023 年发表的国家卒中登记的数据收集方法和资源配置。该系统性综述已在 PROSPERO 中注册(CRD42023393841)。确定了 19 个国家 21 个登记处的 101 条记录。这些登记处普遍采用网络平台进行数据收集。数据收集者的主要职业是护士。所有登记都包括急性期护理,28%(6 份)登记了院前(救护车)护理,14%(3 份)包括康复护理。80%(17 个)收集了结果数据。登记处收集结果数据的方法各不相同:9 个登记处由医院收集结果数据,2 个登记处由登记处收集结果数据,7 个登记处与国家行政数据库建立了链接,可对有限的终点进行随访。脑卒中总数的覆盖率从 6% 到 95% 不等。尽管电子健康记录(EHR)得到了广泛应用,但自动填充变量的能力仍然有限。治理和管理结构各不相同,因此很难对其资源进行比较。临床登记处的数据收集工作需要时间和必要的技能,并给输入数据的专业人员带来沉重的行政负担。我们强调临床登记作为质量改进有力工具的作用。未来的工作应包括建立一个卒中登记中心资料库,以便开发新的登记中心并促进国际合作。
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引用次数: 0
An Artificial Intelligent System for Prostate Cancer Diagnosis in Whole Slide Images. 在全切片图像中诊断前列腺癌的人工智能系统
IF 3.5 3区 医学 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2024-10-28 DOI: 10.1007/s10916-024-02118-3
Sajib Saha, Janardhan Vignarajan, Adam Flesch, Patrik Jelinko, Petra Gorog, Eniko Szep, Csaba Toth, Peter Gombas, Tibor Schvarcz, Orsolya Mihaly, Marianna Kapin, Alexandra Zub, Levente Kuthi, Laszlo Tiszlavicz, Tibor Glasz, Shaun Frost

In recent years a significant demand to develop computer-assisted diagnostic tools to assess prostate cancer using whole slide images has been observed. In this study we develop and validate a machine learning system for cancer assessment, inclusive of detection of perineural invasion and measurement of cancer portion to meet clinical reporting needs. The system analyses the whole slide image in three consecutive stages: tissue detection, classification, and slide level analysis. The whole slide image is divided into smaller regions (patches). The tissue detection stage relies upon traditional machine learning to identify WSI patches containing tissue, which are then further assessed at the classification stage where deep learning algorithms are employed to detect and classify cancer tissue. At the slide level analysis stage, entire slide level information is generated by aggregating all the patch level information of the slide. A total of 2340 haematoxylin and eosin stained slides were used to train and validate the system. A medical team consisting of 11 board certified pathologists with prostatic pathology subspeciality competences working independently in 4 different medical centres performed the annotations. Pixel-level annotation based on an agreed set of 10 annotation terms, determined based on medical relevance and prevalence, was created by the team. The system achieved an accuracy of 99.53% in tissue detection, with sensitivity and specificity respectively of 99.78% and 99.12%. The system achieved an accuracy of 92.80% in classifying tissue terms, with sensitivity and specificity respectively 92.61% and 99.25%, when 5x magnification level was used. For 10x magnification, these values were respectively 91.04%, 90.49%, and 99.07%. For 20x magnification they were 84.71%, 83.95%, 90.13%.

近年来,利用整张切片图像评估前列腺癌的计算机辅助诊断工具的开发需求十分旺盛。在本研究中,我们开发并验证了一种用于癌症评估的机器学习系统,该系统包括会厌浸润检测和癌症部位测量,以满足临床报告需求。该系统分三个连续阶段对整张切片图像进行分析:组织检测、分类和切片级分析。整个玻片图像被划分为较小的区域(斑块)。组织检测阶段依靠传统的机器学习来识别含有组织的 WSI 补丁,然后在分类阶段对其进行进一步评估,在此阶段采用深度学习算法来检测和分类癌症组织。在玻片级分析阶段,通过汇总玻片的所有斑块级信息,生成整个玻片级信息。该系统共使用了 2340 张经血红素和伊红染色的幻灯片进行训练和验证。一个由 11 位具有前列腺病理学亚专业能力的认证病理学家组成的医疗团队在 4 个不同的医疗中心独立工作,进行注释。该团队根据医学相关性和普遍性确定了一套商定的 10 个注释术语,并根据这套术语创建了像素级注释。该系统的组织检测准确率达到 99.53%,灵敏度和特异度分别为 99.78% 和 99.12%。使用 5 倍放大率时,系统对组织术语分类的准确率为 92.80%,灵敏度和特异性分别为 92.61% 和 99.25%。放大 10 倍时,这些数值分别为 91.04%、90.49% 和 99.07%。放大 20 倍时,敏感度和特异度分别为 84.71%、83.95% 和 90.13%。
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引用次数: 0
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Journal of Medical Systems
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