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Generalized Confidence Intervals for Ratios of Standard Deviations Based on Log-Normal Distribution when Times Follow Weibull Distributions. 当时间服从威布尔分布时,基于对数正态分布的标准偏差比率的广义置信区间。
IF 5.3 3区 医学 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2024-06-01 DOI: 10.1007/s10916-024-02073-z
Pei-Fu Chen, Franklin Dexter

Modern anesthetic drugs ensure the efficacy of general anesthesia. Goals include reducing variability in surgical, tracheal extubation, post-anesthesia care unit, or intraoperative response recovery times. Generalized confidence intervals based on the log-normal distribution compare variability between groups, specifically ratios of standard deviations. The alternative statistical approaches, performing robust variance comparison tests, give P-values, not point estimates nor confidence intervals for the ratios of the standard deviations. We performed Monte-Carlo simulations to learn what happens to confidence intervals for ratios of standard deviations of anesthesia-associated times when analyses are based on the log-normal, but the true distributions are Weibull. We used simulation conditions comparable to meta-analyses of most randomized trials in anesthesia, n 25 and coefficients of variation 0.30 . The estimates of the ratios of standard deviations were positively biased, but slightly, the ratios being 0.11% to 0.33% greater than nominal. In contrast, the 95% confidence intervals were very wide (i.e., > 95% of P ≥ 0.05). Although substantive inferentially, the differences in the confidence limits were small from a clinical or managerial perspective, with a maximum absolute difference in ratios of 0.016. Thus, P < 0.05 is reliable, but investigators should plan for Type II errors at greater than nominal rates.

现代麻醉药物可确保全身麻醉的疗效。目标包括减少手术、气管拔管、麻醉后护理病房或术中反应恢复时间的变异性。基于对数正态分布的广义置信区间可比较组间变异性,特别是标准偏差比。另一种统计方法是进行稳健方差比较测试,给出的是 P 值,而不是标准差比率的点估计值或置信区间。我们进行了蒙特卡罗模拟,以了解当分析以对数正态分布为基础,而真实分布为Weibull时,麻醉相关时间标准差比率的置信区间会发生什么变化。我们使用的模拟条件与大多数麻醉随机试验的荟萃分析相当,即 n ≈ 25,变异系数≈ 0.30。标准偏差比率的估计值呈正偏差,但偏差较小,比率比标称值大 0.11% 至 0.33%。相反,95% 置信区间非常宽(即 P≥0.05 的 >95%)。从临床或管理的角度来看,置信区间的差异虽然是实质性的,但却很小,比率的最大绝对差异为 0.016。因此,P
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引用次数: 0
Comparison Between a Single-Lead ECG Garment Device and a Holter Monitor: A Signal Quality Assessment. 单导联心电图服装设备与 Holter 监护仪的比较:信号质量评估
IF 5.3 3区 医学 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2024-05-27 DOI: 10.1007/s10916-024-02077-9
Luca Neri, Ivan Corazza, Matt T Oberdier, Jessica Lago, Ilaria Gallelli, Arrigo F G Cicero, Igor Diemberger, Alessandro Orro, Amir Beker, Nazareno Paolocci, Henry R Halperin, Claudio Borghi

Wearable electronics are increasingly common and useful as health monitoring devices, many of which feature the ability to record a single-lead electrocardiogram (ECG). However, recording the ECG commonly requires the user to touch the device to complete the lead circuit, which prevents continuous data acquisition. An alternative approach to enable continuous monitoring without user initiation is to embed the leads in a garment. This study assessed ECG data obtained from the YouCare device (a novel sensorized garment) via comparison with a conventional Holter monitor. A cohort of thirty patients (age range: 20-82 years; 16 females and 14 males) were enrolled and monitored for twenty-four hours with both the YouCare device and a Holter monitor. ECG data from both devices were qualitatively assessed by a panel of three expert cardiologists and quantitatively analyzed using specialized software. Patients also responded to a survey about the comfort of the YouCare device as compared to the Holter monitor. The YouCare device was assessed to have 70% of its ECG signals as "Good", 12% as "Acceptable", and 18% as "Not Readable". The R-wave, independently recorded by the YouCare device and Holter monitor, were synchronized within measurement error during 99.4% of cardiac cycles. In addition, patients found the YouCare device more comfortable than the Holter monitor (comfortable 22 vs. 5 and uncomfortable 1 vs. 18, respectively). Therefore, the quality of ECG data collected from the garment-based device was comparable to a Holter monitor when the signal was sufficiently acquired, and the garment was also comfortable.

可穿戴电子设备作为健康监测设备越来越普遍和有用,其中许多设备都具有记录单导联心电图(ECG)的功能。然而,记录心电图通常需要用户触摸设备来完成导联电路,这就妨碍了连续数据采集。另一种无需用户启动即可实现连续监测的方法是将导联嵌入服装中。本研究通过与传统 Holter 监护仪的比较,评估了从 YouCare 设备(一种新型感应服装)获得的心电图数据。研究人员招募了 30 名患者(年龄范围:20-82 岁;16 名女性和 14 名男性),使用 YouCare 设备和 Holter 监护仪对他们进行了 24 小时的监测。由三位心脏病专家组成的小组对两种设备的心电图数据进行了定性评估,并使用专业软件进行了定量分析。患者还对 YouCare 设备与 Holter 监护仪的舒适度进行了调查。经评估,YouCare 设备 70% 的心电图信号为 "良好",12% 为 "可接受",18% 为 "不可读"。在 99.4% 的心动周期中,由 YouCare 设备和 Holter 监护仪独立记录的 R 波在测量误差范围内同步。此外,患者认为 YouCare 设备比 Holter 监护仪更舒适(分别为舒适 22 对 5 和不舒适 1 对 18)。因此,在充分采集信号的情况下,服装式设备采集的心电图数据质量与 Holter 监护仪相当,而且服装也很舒适。
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引用次数: 0
Navigating Challenges and Seizing Opportunities in China's Era of Online Health Education. 把握中国在线健康教育时代的挑战和机遇。
IF 5.3 3区 医学 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2024-05-27 DOI: 10.1007/s10916-024-02078-8
Yang Cao, Guochao Zhang, You Wu, Hang Yi

The rapid growth of internet users in China presents opportunities for advancing the "Healthy China 2030" initiative through online health education. Platforms like "Shanghai Health Cloud" and "National Health Information Platform" improve health literacy and management, enhancing overall public health. However, challenges such as the digital divide and the spread of unverified health information hinder progress. Addressing these issues requires enhancing digital infrastructure, employing advanced technologies for information validation, and setting high standards for online health services. Integrated efforts from various sectors are essential to maximize the benefits of online health education in China.

中国网民的快速增长为通过在线健康教育推进 "健康中国 2030 "倡议提供了机遇。上海健康云 "和 "全民健康信息平台 "等平台提高了健康素养和管理水平,提升了公众的整体健康水平。然而,数字鸿沟和未经核实的健康信息传播等挑战阻碍了进展。要解决这些问题,就必须加强数字基础设施建设,采用先进的信息验证技术,并为在线卫生服务制定高标准。要使中国在线健康教育的效益最大化,各部门的综合努力至关重要。
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引用次数: 0
Art or Artifact: Evaluating the Accuracy, Appeal, and Educational Value of AI-Generated Imagery in DALL·E 3 for Illustrating Congenital Heart Diseases. 艺术还是人工制品:评估《DALL-E 3》中人工智能生成的图像在说明先天性心脏病方面的准确性、吸引力和教育价值。
IF 5.3 3区 医学 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2024-05-23 DOI: 10.1007/s10916-024-02072-0
Mohamad-Hani Temsah, Abdullah N Alhuzaimi, Mohammed Almansour, Fadi Aljamaan, Khalid Alhasan, Munirah A Batarfi, Ibraheem Altamimi, Amani Alharbi, Adel Abdulaziz Alsuhaibani, Leena Alwakeel, Abdulrahman Abdulkhaliq Alzahrani, Khaled B Alsulaim, Amr Jamal, Afnan Khayat, Mohammed Hussien Alghamdi, Rabih Halwani, Muhammad Khurram Khan, Ayman Al-Eyadhy, Rakan Nazer

Artificial Intelligence (AI), particularly AI-Generated Imagery, has the potential to impact medical and patient education. This research explores the use of AI-generated imagery, from text-to-images, in medical education, focusing on congenital heart diseases (CHD). Utilizing ChatGPT's DALL·E 3, the research aims to assess the accuracy and educational value of AI-created images for 20 common CHDs. In this study, we utilized DALL·E 3 to generate a comprehensive set of 110 images, comprising ten images depicting the normal human heart and five images for each of the 20 common CHDs. The generated images were evaluated by a diverse group of 33 healthcare professionals. This cohort included cardiology experts, pediatricians, non-pediatric faculty members, trainees (medical students, interns, pediatric residents), and pediatric nurses. Utilizing a structured framework, these professionals assessed each image for anatomical accuracy, the usefulness of in-picture text, its appeal to medical professionals, and the image's potential applicability in medical presentations. Each item was assessed on a Likert scale of three. The assessments produced a total of 3630 images' assessments. Most AI-generated cardiac images were rated poorly as follows: 80.8% of images were rated as anatomically incorrect or fabricated, 85.2% rated to have incorrect text labels, 78.1% rated as not usable for medical education. The nurses and medical interns were found to have a more positive perception about the AI-generated cardiac images compared to the faculty members, pediatricians, and cardiology experts. Complex congenital anomalies were found to be significantly more predicted to anatomical fabrication compared to simple cardiac anomalies. There were significant challenges identified in image generation. Based on our findings, we recommend a vigilant approach towards the use of AI-generated imagery in medical education at present, underscoring the imperative for thorough validation and the importance of collaboration across disciplines. While we advise against its immediate integration until further validations are conducted, the study advocates for future AI-models to be fine-tuned with accurate medical data, enhancing their reliability and educational utility.

人工智能(AI),尤其是人工智能生成的图像,有可能对医疗和患者教育产生影响。本研究探讨了人工智能生成的图像(从文本到图像)在医学教育中的应用,重点是先天性心脏病(CHD)。这项研究旨在利用 ChatGPT 的 DALL-E 3 评估人工智能生成的 20 种常见先天性心脏病图像的准确性和教育价值。在这项研究中,我们利用 DALL-E 3 生成了一套完整的 110 张图像,其中包括 10 张描绘正常人心脏的图像和 20 种常见先天性心脏病的各 5 张图像。生成的图像由 33 位不同的医疗保健专业人员进行评估。其中包括心脏病学专家、儿科医生、非儿科专业教师、受训人员(医学生、实习生、儿科住院医师)和儿科护士。利用结构化框架,这些专业人员对每张图片的解剖准确性、图片内文字的实用性、对医学专业人员的吸引力以及图片在医学演示中的潜在适用性进行了评估。每项评估均采用李克特三段式量表。评估共产生了 3630 张图像的评估结果。大多数人工智能生成的心脏图像都被评为较差,具体如下:80.8%的图像被评为解剖不正确或捏造,85.2%的图像被评为文本标签不正确,78.1%的图像被评为不能用于医学教育。与教师、儿科医生和心脏病学专家相比,护士和实习医生对人工智能生成的心脏图像有更积极的看法。与简单的心脏畸形相比,复杂的先天性畸形更容易预测解剖结构。在图像生成方面发现了一些重大挑战。基于我们的研究结果,我们建议目前在医学教育中使用人工智能生成的图像时要保持警惕,强调彻底验证的必要性和跨学科合作的重要性。虽然我们建议在进行进一步验证之前不要立即将其应用到医学教育中,但本研究主张利用准确的医学数据对未来的人工智能模型进行微调,以提高其可靠性和教育效用。
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引用次数: 0
Back to the Roots: Reconstructing Large and Complex Cranial Defects using an Image-based Statistical Shape Model. 回归本源:使用基于图像的统计形状模型重建大型复杂颅骨缺损。
IF 5.3 3区 医学 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2024-05-23 DOI: 10.1007/s10916-024-02066-y
Jianning Li, David G Ellis, Antonio Pepe, Christina Gsaxner, Michele R Aizenberg, Jens Kleesiek, Jan Egger

Designing implants for large and complex cranial defects is a challenging task, even for professional designers. Current efforts on automating the design process focused mainly on convolutional neural networks (CNN), which have produced state-of-the-art results on reconstructing synthetic defects. However, existing CNN-based methods have been difficult to translate to clinical practice in cranioplasty, as their performance on large and complex cranial defects remains unsatisfactory. In this paper, we present a statistical shape model (SSM) built directly on the segmentation masks of the skulls represented as binary voxel occupancy grids and evaluate it on several cranial implant design datasets. Results show that, while CNN-based approaches outperform the SSM on synthetic defects, they are inferior to SSM when it comes to large, complex and real-world defects. Experienced neurosurgeons evaluate the implants generated by the SSM to be feasible for clinical use after minor manual corrections. Datasets and the SSM model are publicly available at https://github.com/Jianningli/ssm .

即使对于专业设计人员来说,为巨大而复杂的颅骨缺损设计植入体也是一项极具挑战性的任务。目前,设计过程自动化的工作主要集中在卷积神经网络(CNN)上,它在重建合成缺陷方面取得了最先进的成果。然而,现有的基于卷积神经网络的方法很难应用到颅骨整形的临床实践中,因为它们在大型复杂颅骨缺损上的表现仍不能令人满意。在本文中,我们提出了一种统计形状模型(SSM),该模型直接建立在以二元体素占位网格表示的头骨分割掩模上,并在多个颅骨植入物设计数据集上对其进行了评估。结果表明,虽然基于 CNN 的方法在合成缺陷上优于 SSM,但在大型、复杂和真实世界的缺陷上却不如 SSM。根据经验丰富的神经外科医生的评估,SSM 生成的植入物在经过少量手动修正后可用于临床。数据集和 SSM 模型可通过 https://github.com/Jianningli/ssm 公开获取。
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引用次数: 0
A Novel Deep Learning Approach for Forecasting Myocardial Infarction Occurrences with Time Series Patient Data. 利用时间序列患者数据预测心肌梗死发生率的新型深度学习方法。
IF 5.3 3区 医学 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2024-05-22 DOI: 10.1007/s10916-024-02076-w
Mohammad Saiduzzaman Sayed, Mohammad Abu Tareq Rony, Mohammad Shariful Islam, Ali Raza, Sawsan Tabassum, Mohammad Sh Daoud, Hazem Migdady, Laith Abualigah

Myocardial Infarction (MI) commonly referred to as a heart attack, results from the abrupt obstruction of blood supply to a section of the heart muscle, leading to the deterioration or death of the affected tissue due to a lack of oxygen. MI, poses a significant public health concern worldwide, particularly affecting the citizens of the Chittagong Metropolitan Area. The challenges lie in both prevention and treatment, as the emergence of MI has inflicted considerable suffering among residents. Early warning systems are crucial for managing epidemics promptly, especially given the escalating disease burden in older populations and the complexities of assessing present and future demands. The primary objective of this study is to forecast MI incidence early using a deep learning model, predicting the prevalence of heart attacks in patients. Our approach involves a novel dataset collected from daily heart attack incidence Time Series Patient Data spanning January 1, 2020, to December 31, 2021, in the Chittagong Metropolitan Area. Initially, we applied various advanced models, including Autoregressive Integrated Moving Average (ARIMA), Error-Trend-Seasonal (ETS), Trigonometric seasonality, Box-Cox transformation, ARMA errors, Trend and Seasonal (TBATS), and Long Short Time Memory (LSTM). To enhance prediction accuracy, we propose a novel Myocardial Sequence Classification (MSC)-LSTM method tailored to forecast heart attack occurrences in patients using the newly collected data from the Chittagong Metropolitan Area. Comprehensive results comparisons reveal that the novel MSC-LSTM model outperforms other applied models in terms of performance, achieving a minimum Mean Percentage Error (MPE) score of 1.6477. This research aids in predicting the likely future course of heart attack occurrences, facilitating the development of thorough plans for future preventive measures. The forecasting of MI occurrences contributes to effective resource allocation, capacity planning, policy creation, budgeting, public awareness, research identification, quality improvement, and disaster preparedness.

心肌梗塞(MI)通常被称为心脏病发作,是由于部分心肌供血突然受阻,导致受影响的组织因缺氧而恶化或死亡。心肌梗死是全球关注的重大公共卫生问题,对吉大港大都市区的居民影响尤为严重。由于心肌缺血的出现给居民带来了巨大的痛苦,因此预防和治疗都面临着挑战。早期预警系统对于及时处理流行病至关重要,特别是考虑到老年人群的疾病负担不断加重,以及评估当前和未来需求的复杂性。本研究的主要目的是利用深度学习模型及早预测心肌梗死的发病率,预测患者心脏病发作的流行率。我们的方法涉及从吉大港大都市区 2020 年 1 月 1 日至 2021 年 12 月 31 日期间每日心脏病发作发病率时间序列患者数据中收集的新型数据集。最初,我们应用了各种先进的模型,包括自回归综合移动平均(ARIMA)、误差-趋势-季节(ETS)、三角季节性、箱-考克斯变换、ARMA 误差、趋势和季节(TBATS)以及长短时间记忆(LSTM)。为了提高预测的准确性,我们提出了一种新的心肌序列分类(MSC)-LSTM 方法,利用从吉大港大都市区收集的新数据预测心脏病患者的发病率。综合结果比较显示,新型 MSC-LSTM 模型的性能优于其他应用模型,平均百分比误差 (MPE) 最小值为 1.6477。这项研究有助于预测未来心脏病发作的可能过程,从而为制定未来预防措施的周密计划提供便利。对心肌梗死发生率的预测有助于有效的资源分配、能力规划、政策制定、预算编制、公众意识、研究鉴定、质量改进和备灾。
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引用次数: 0
Publication Trends and Hot Spots of ChatGPT's Application in the Medicine. ChatGPT 在医学中应用的出版趋势和热点。
IF 5.3 3区 医学 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2024-05-18 DOI: 10.1007/s10916-024-02074-y
Zhi-Qiang Li, Xue-Feng Wang, Jian-Ping Liu

This study aimed to analyze the current landscape of ChatGPT application in the medical field, assessing the current collaboration patterns and research topic hotspots to understand the impact and trends. By conducting a search in the Web of Science, we collected literature related to the applications of ChatGPT in medicine, covering the period from January 1, 2000 up to January 16, 2024. Bibliometric analyses were performed using CiteSpace (V6.2., Drexel University, PA, USA) and Microsoft Excel (Microsoft Corp.,WA, USA) to map the collaboration among countries/regions, the distribution of institutions and authors, and clustering of keywords. A total of 574 eligible articles were included, with 97.74% published in 2023. These articles span various disciplines, particularly in Health Care Sciences Services, with extensive international collaboration involving 73 countries. In terms of countries/regions studied, USA, India, and China led in the number of publications. USA ot only published nearly half of the total number of papers but also exhibits a highest collaborative capability. Regarding the co-occurrence of institutions and scholars, the National University of Singapore and Harvard University held significant influence in the cooperation network, with the top three authors in terms of publications being Wiwanitkit V (10 articles), Seth I (9 articles), Klang E (7 articles), and Kleebayoon A (7 articles). Through keyword clustering, the study identified 9 research theme clusters, among which "digital health"was not only the largest in scale but also had the most citations. The study highlights ChatGPT's cross-disciplinary nature and collaborative research in medicine, showcasing its growth potential, particularly in digital health and clinical decision support. Future exploration should examine the socio-economic and cultural impacts of this trend, along with ChatGPT's specific technical uses in medical practice.

本研究旨在分析 ChatGPT 在医学领域的应用现状,评估当前的合作模式和研究课题热点,以了解其影响和趋势。通过在 Web of Science 中进行检索,我们收集了与 ChatGPT 在医学中的应用相关的文献,时间跨度为 2000 年 1 月 1 日至 2024 年 1 月 16 日。我们使用 CiteSpace(V6.2.,德雷塞尔大学,美国宾夕法尼亚州)和 Microsoft Excel(微软公司,美国华盛顿州)进行了文献计量分析,以绘制国家/地区间的合作、机构和作者分布以及关键词聚类图。符合条件的文章共有 574 篇,其中 97.74% 发表于 2023 年。这些文章涉及多个学科,尤其是医疗保健科学服务领域,并有 73 个国家参与了广泛的国际合作。从研究的国家/地区来看,美国、印度和中国的论文数量居前。美国不仅发表了论文总数的近一半,而且合作能力也最强。在机构和学者的共同出现方面,新加坡国立大学和哈佛大学在合作网络中具有重要影响力,发表论文数量排名前三的作者分别是 Wiwanitkit V(10 篇)、Seth I(9 篇)、Klang E(7 篇)和 Kleebayoon A(7 篇)。通过关键词聚类,研究确定了 9 个研究主题集群,其中 "数字健康 "不仅规模最大,而且被引用次数也最多。该研究强调了 ChatGPT 在医学领域的跨学科性质和合作研究,展示了其发展潜力,尤其是在数字健康和临床决策支持方面。未来的探索应研究这一趋势对社会经济和文化的影响,以及 ChatGPT 在医疗实践中的具体技术应用。
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引用次数: 0
An innovative method to strengthen evidence for potential drug safety signals using Electronic Health Records. 利用电子健康记录加强潜在药物安全信号证据的创新方法。
IF 5.3 3区 医学 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2024-05-16 DOI: 10.1007/s10916-024-02070-2
H Abedian Kalkhoran, J Zwaveling, F van Hunsel, A Kant

Reports from spontaneous reporting systems (SRS) are hypothesis generating. Additional evidence such as more reports is required to determine whether the generated drug-event associations are in fact safety signals. However, underreporting of adverse drug reactions (ADRs) delays signal detection. Through the use of natural language processing, different sources of real-world data can be used to proactively collect additional evidence for potential safety signals. This study aims to explore the feasibility of using Electronic Health Records (EHRs) to identify additional cases based on initial indications from spontaneous ADR reports, with the goal of strengthening the evidence base for potential safety signals. For two confirmed and two potential signals generated by the SRS of the Netherlands Pharmacovigilance Centre Lareb, targeted searches in the EHR of the Leiden University Medical Centre were performed using a text-mining based tool, CTcue. The search for additional cases was done by constructing and running queries in the structured and free-text fields of the EHRs. We identified at least five additional cases for the confirmed signals and one additional case for each potential safety signal. The majority of the identified cases for the confirmed signals were documented in the EHRs before signal detection by the Dutch Medicines Evaluation Board. The identified cases for the potential signals were reported to Lareb as further evidence for signal detection. Our findings highlight the feasibility of performing targeted searches in the EHR based on an underlying hypothesis to provide further evidence for signal generation.

来自自发报告系统(SRS)的报告可产生假设。需要更多的证据,如更多的报告,才能确定所产生的药物-事件关联实际上是否是安全信号。然而,药物不良反应(ADRs)报告不足会延误信号检测。通过使用自然语言处理技术,不同来源的真实世界数据可用于主动收集潜在安全信号的额外证据。本研究旨在探索使用电子健康记录(EHR)根据自发 ADR 报告中的初步迹象识别更多病例的可行性,目的是加强潜在安全信号的证据基础。针对荷兰药物警戒中心 Lareb 的 SRS 生成的两个确诊信号和两个潜在信号,使用基于文本挖掘的工具 CTcue 在莱顿大学医疗中心的电子病历中进行了有针对性的搜索。通过在电子病历的结构化字段和自由文本字段中构建和运行查询来搜索其他病例。我们为已确认的信号确定了至少五个额外病例,并为每个潜在安全信号确定了一个额外病例。大部分已确认信号的病例在荷兰药品评估委员会检测到信号之前就已记录在电子病历中。已确定的潜在信号病例已报告给 Lareb,作为信号检测的进一步证据。我们的研究结果突显了根据基本假设在电子病历中进行有针对性的搜索,为信号生成提供进一步证据的可行性。
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引用次数: 0
Virtual Reality for Cardiopulmonary Resuscitation Healthcare Professionals Training: A Systematic Review. 虚拟现实技术用于心肺复苏医护人员培训:系统回顾。
IF 5.3 3区 医学 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2024-05-15 DOI: 10.1007/s10916-024-02063-1
Roberto Trevi, Stefania Chiappinotto, Alvisa Palese, Alessandro Galazzi

Introduction: Virtual reality (VR) is becoming increasingly popular to train health-care professionals (HCPs) to acquire and/or maintain cardiopulmonary resuscitation (CPR) basic or advanced skills.

Aim: To understand whether VR in CPR training or retraining courses can have benefits for patients (neonatal, pediatric, and adult), HCPs and health-care organizations as compared to traditional CPR training.

Methods: A systematic review (PROSPERO: CRD42023431768) following the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines. In June 2023, the PubMed, Cochrane Library, Scopus and Cumulative Index to Nursing and Allied Health Literature (CINAHL) databases were searched and included studies evaluated in their methodological quality with Joanna Briggs Institute checklists. Data were narratively summarized.

Results: Fifteen studies published between 2013 and 2023 with overall fair quality were included. No studies investigated patients' outcomes. At the HCP level, the virtual learning environment was perceived to be engaging, realistic and facilitated the memorization of the procedures; however, limited decision-making, team building, psychological pressure and frenetic environment were underlined as disadvantages. Moreover, a general improvement in performance was reported in the use of the defibrillator and carrying out the chest compressions. At the organizational level, one study performed a cost/benefit evaluation in favor of VR as compared to traditional CPR training.

Conclusions: The use of VR for CPR training and retraining is in an early stage of development. Some benefits at the HCP level are promising. However, more research is needed with standardized approaches to ensure a progressive accumulation of the evidence and inform decisions regarding the best training methodology in this field.

导言:目的:了解与传统心肺复苏术培训相比,虚拟现实技术在心肺复苏术培训或再培训课程中的应用是否能为患者(新生儿、儿童和成人)、医护人员和医疗机构带来益处:按照系统综述和元分析首选报告项目(PRISMA)指南进行系统综述(PROSPERO:CRD42023431768)。2023 年 6 月,对 PubMed、Cochrane Library、Scopus 和 Cumulative Index to Nursing and Allied Health Literature (CINAHL) 数据库进行了检索,并根据 Joanna Briggs Institute 的检查表对纳入研究的方法学质量进行了评估。对数据进行了叙述性总结:结果:共纳入 15 项研究,这些研究发表于 2013 年至 2023 年之间,总体质量尚可。没有研究调查了患者的治疗效果。在高级保健人员层面,虚拟学习环境被认为是吸引人、逼真的,并有助于记忆程序;然而,有限的决策、团队建设、心理压力和狂热的环境被强调为缺点。此外,据报告,使用除颤器和进行胸外按压的成绩普遍有所提高。在组织层面,一项研究进行了成本/效益评估,结果显示,与传统心肺复苏术培训相比,VR 更受青睐:结论:将 VR 用于心肺复苏术培训和再培训尚处于早期发展阶段。在心肺复苏术培训和再培训中使用 VR 尚处于早期发展阶段。然而,还需要进行更多标准化方法的研究,以确保逐步积累证据,并为该领域最佳培训方法的决策提供依据。
{"title":"Virtual Reality for Cardiopulmonary Resuscitation Healthcare Professionals Training: A Systematic Review.","authors":"Roberto Trevi, Stefania Chiappinotto, Alvisa Palese, Alessandro Galazzi","doi":"10.1007/s10916-024-02063-1","DOIUrl":"10.1007/s10916-024-02063-1","url":null,"abstract":"<p><strong>Introduction: </strong>Virtual reality (VR) is becoming increasingly popular to train health-care professionals (HCPs) to acquire and/or maintain cardiopulmonary resuscitation (CPR) basic or advanced skills.</p><p><strong>Aim: </strong>To understand whether VR in CPR training or retraining courses can have benefits for patients (neonatal, pediatric, and adult), HCPs and health-care organizations as compared to traditional CPR training.</p><p><strong>Methods: </strong>A systematic review (PROSPERO: CRD42023431768) following the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines. In June 2023, the PubMed, Cochrane Library, Scopus and Cumulative Index to Nursing and Allied Health Literature (CINAHL) databases were searched and included studies evaluated in their methodological quality with Joanna Briggs Institute checklists. Data were narratively summarized.</p><p><strong>Results: </strong>Fifteen studies published between 2013 and 2023 with overall fair quality were included. No studies investigated patients' outcomes. At the HCP level, the virtual learning environment was perceived to be engaging, realistic and facilitated the memorization of the procedures; however, limited decision-making, team building, psychological pressure and frenetic environment were underlined as disadvantages. Moreover, a general improvement in performance was reported in the use of the defibrillator and carrying out the chest compressions. At the organizational level, one study performed a cost/benefit evaluation in favor of VR as compared to traditional CPR training.</p><p><strong>Conclusions: </strong>The use of VR for CPR training and retraining is in an early stage of development. Some benefits at the HCP level are promising. However, more research is needed with standardized approaches to ensure a progressive accumulation of the evidence and inform decisions regarding the best training methodology in this field.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"48 1","pages":"50"},"PeriodicalIF":5.3,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11096216/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140920678","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Investigating Machine Learning Techniques for Predicting Risk of Asthma Exacerbations: A Systematic Review. 研究预测哮喘恶化风险的机器学习技术:系统回顾
IF 5.3 3区 医学 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2024-05-13 DOI: 10.1007/s10916-024-02061-3
Widana Kankanamge Darsha Jayamini, Farhaan Mirza, M Asif Naeem, Amy Hai Yan Chan

Asthma, a common chronic respiratory disease among children and adults, affects more than 200 million people worldwide and causes about 450,000 deaths each year. Machine learning is increasingly applied in healthcare to assist health practitioners in decision-making. In asthma management, machine learning excels in performing well-defined tasks, such as diagnosis, prediction, medication, and management. However, there remain uncertainties about how machine learning can be applied to predict asthma exacerbation. This study aimed to systematically review recent applications of machine learning techniques in predicting the risk of asthma attacks to assist asthma control and management. A total of 860 studies were initially identified from five databases. After the screening and full-text review, 20 studies were selected for inclusion in this review. The review considered recent studies published from January 2010 to February 2023. The 20 studies used machine learning techniques to support future asthma risk prediction by using various data sources such as clinical, medical, biological, and socio-demographic data sources, as well as environmental and meteorological data. While some studies considered prediction as a category, other studies predicted the probability of exacerbation. Only a group of studies applied prediction windows. The paper proposes a conceptual model to summarise how machine learning and available data sources can be leveraged to produce effective models for the early detection of asthma attacks. The review also generated a list of data sources that other researchers may use in similar work. Furthermore, we present opportunities for further research and the limitations of the preceding studies.

哮喘是儿童和成人中常见的慢性呼吸道疾病,影响着全球 2 亿多人,每年导致约 45 万人死亡。机器学习越来越多地应用于医疗保健领域,以协助医疗从业人员做出决策。在哮喘管理中,机器学习在执行诊断、预测、用药和管理等明确任务方面表现出色。然而,如何将机器学习应用于预测哮喘恶化仍存在不确定性。本研究旨在系统回顾机器学习技术在预测哮喘发作风险方面的最新应用,以协助哮喘控制和管理。初步从五个数据库中确定了 860 项研究。经过筛选和全文审阅后,20 项研究被选入本综述。综述考虑了 2010 年 1 月至 2023 年 2 月期间发表的最新研究。这 20 项研究利用机器学习技术,通过使用各种数据源,如临床、医疗、生物和社会人口数据源,以及环境和气象数据,支持未来哮喘风险预测。一些研究将预测作为一个类别,而其他研究则预测病情恶化的概率。只有一组研究使用了预测窗口。本文提出了一个概念模型,总结了如何利用机器学习和可用数据源来生成早期检测哮喘发作的有效模型。该综述还生成了一份数据源清单,其他研究人员可在类似工作中使用这些数据源。此外,我们还提出了进一步研究的机会以及前述研究的局限性。
{"title":"Investigating Machine Learning Techniques for Predicting Risk of Asthma Exacerbations: A Systematic Review.","authors":"Widana Kankanamge Darsha Jayamini, Farhaan Mirza, M Asif Naeem, Amy Hai Yan Chan","doi":"10.1007/s10916-024-02061-3","DOIUrl":"10.1007/s10916-024-02061-3","url":null,"abstract":"<p><p>Asthma, a common chronic respiratory disease among children and adults, affects more than 200 million people worldwide and causes about 450,000 deaths each year. Machine learning is increasingly applied in healthcare to assist health practitioners in decision-making. In asthma management, machine learning excels in performing well-defined tasks, such as diagnosis, prediction, medication, and management. However, there remain uncertainties about how machine learning can be applied to predict asthma exacerbation. This study aimed to systematically review recent applications of machine learning techniques in predicting the risk of asthma attacks to assist asthma control and management. A total of 860 studies were initially identified from five databases. After the screening and full-text review, 20 studies were selected for inclusion in this review. The review considered recent studies published from January 2010 to February 2023. The 20 studies used machine learning techniques to support future asthma risk prediction by using various data sources such as clinical, medical, biological, and socio-demographic data sources, as well as environmental and meteorological data. While some studies considered prediction as a category, other studies predicted the probability of exacerbation. Only a group of studies applied prediction windows. The paper proposes a conceptual model to summarise how machine learning and available data sources can be leveraged to produce effective models for the early detection of asthma attacks. The review also generated a list of data sources that other researchers may use in similar work. Furthermore, we present opportunities for further research and the limitations of the preceding studies.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"48 1","pages":"49"},"PeriodicalIF":5.3,"publicationDate":"2024-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11090925/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140912014","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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Journal of Medical Systems
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