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Networking Aspects of the Electronic Health Records: Hypertext Transfer Protocol Version 2 (HTTP/2) vs HTTP/3. 电子健康记录的网络方面:超文本传输协议版本 2 (HTTP/2) 与 HTTP/3。
IF 5.3 3区 医学 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2024-06-15 DOI: 10.1007/s10916-024-02080-0
Nikola Kirilov, E Bischoff

The rapid development of the digital healthcare and the electronic health records (EHR) requires smooth networking infrastructure to access data using Hypertext Transfer Protocol (HTTP)-based applications. The new HTTP/3 standard should provide performance and security improvements over HTTP/2. The goal of our work was to test the performance of HTTP/2 and HTTP/3 in the context of the EHRs. We used 45,000 test FHIR Patient resources downloaded and uploaded using 20, 50, 100 and 200 resources per Bundle, which resulted in 2251, 901, 451 and 226 HTTP GET and POST requests respectively. The first test downloading 20 resources per Bundle showed that HTTP/3 outperformed HTTP/2 in the local (mean request time 16.57 ms ± 7.2 standard deviation [SD]) and in the remote network (71.45 ms ± 43.5 SD) which is almost 3 times faster. In the 50 and 100 resources per Bundle test the HTTP/3 protocol demonstrated again more than two times gain in downloading performance for remote requests with mean request time 91.13 ms ± 34.54 SD and 88.09 ms ± 21.66 SD respectively. Furthermore, HTTP/3 outperformed HTTP/2 in the constructed clinical dataset remote transfer. In the upload tests HTTP/3 showed only a slight gain in performance merely in the remote network. The HTTP/3 protocol is a relatively new development and a major improvement for the worldwide web. This new technology is still missing in the digital health and EHRs. Its use could offer a major performance gain in situations where data is gathered from multiple remote locations.

数字医疗和电子病历(EHR)的快速发展需要流畅的网络基础设施,以便使用基于超文本传输协议(HTTP)的应用程序访问数据。与 HTTP/2 相比,新的 HTTP/3 标准应在性能和安全性方面有所改进。我们的工作目标是在 EHR 的背景下测试 HTTP/2 和 HTTP/3 的性能。我们使用 45,000 个测试 FHIR 患者资源,每个捆绑包下载和上传 20、50、100 和 200 个资源,分别产生 2251、901、451 和 226 个 HTTP GET 和 POST 请求。第一次测试每束下载 20 个资源显示,HTTP/3 在本地(平均请求时间为 16.57 毫秒 ± 7.2 标准差 [SD])和远程网络(71.45 毫秒 ± 43.5 标准差)的表现优于 HTTP/2,几乎快了 3 倍。在每束 50 个和 100 个资源测试中,HTTP/3 协议的远程请求下载性能再次提高了两倍多,平均请求时间分别为 91.13 ms ± 34.54 SD 和 88.09 ms ± 21.66 SD。此外,在构建的临床数据集远程传输中,HTTP/3 的性能优于 HTTP/2。在上传测试中,HTTP/3 的性能仅在远程网络中略有提升。HTTP/3 协议是一项相对较新的开发,是对全球网络的重大改进。这项新技术在数字医疗和电子病历中仍然缺失。在从多个远程地点收集数据的情况下,使用这种技术可以大大提高性能。
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引用次数: 0
A Mobile Post Anesthesia Care Unit Order Reminder System Improves Timely Order Entry. 移动式麻醉后护理单元订单提醒系统提高了订单输入的及时性。
IF 5.3 3区 医学 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2024-06-10 DOI: 10.1007/s10916-024-02079-7
Jacob C Clifton, Holly B Ende, Chandramouli Rathnam, Robert E Freundlich, Warren S Sandberg, Jonathan P Wanderer

Transition to the postanesthesia care unit (PACU) requires timely order placement by anesthesia providers. Computerized ordering enables automated order reminder systems, but their value is not fully understood. We performed a single-center, retrospective cohort study to estimate the association between automated PACU order reminders and primary outcomes (1) on-time order placement and (2) the degree of delay in placement. As a secondary post-hoc analysis, we studied the association between late order placement and PACU outcomes. We included patients with a qualifying postprocedure order from January 1, 2019, to May 31, 2023. We excluded cases transferred directly to the ICU, whose anesthesia provider was involved in the pilot testing of the reminder system, or those with missing covariate data. Order reminder system usage was defined by the primary attending anesthesiologist's receipt of a push notification reminder on the day of surgery. We estimated the association between reminder system usage and timely order placement using a logistic regression. For patients with late orders, we performed a survival analysis of order placement. The significance level was 0.05. Patient (e.g., age, race), procedural (e.g., anesthesia duration), and provider-based (e.g., ordering privileges) variables were used as covariates within the analyses. Reminders were associated with 51% increased odds of order placement prior to PACU admission (Odds Ratio: 1.51; 95% Confidence Interval: 1.43, 1.58; p ≤ 0.001), reducing the incidence of late PACU orders from 17.5% to 12.6% (p ≤ 0.001). In patients with late orders, the reminders were associated with 10% quicker placement (Hazard Ratio: 1.10; 95% CI 1.05, 1.15; p < 0.001). On-time order placement was associated with decreased PACU duration (p < 0.001), decreased odds of peak PACU pain score (p < 0.001), and decreased odds of multiple administration of antiemetics (p = 0.02). An order reminder system was associated with an increase in order placement prior to PACU arrival and a reduction in delay in order placement after arrival.

向麻醉后护理病房(PACU)过渡需要麻醉服务提供者及时下达医嘱。计算机化下单使自动下单提醒系统成为可能,但其价值尚不完全清楚。我们进行了一项单中心回顾性队列研究,以估计 PACU 自动下单提醒与主要结果(1)按时下单和(2)延迟下单程度之间的关系。作为次要的事后分析,我们研究了延迟下单与 PACU 结果之间的关联。我们纳入了 2019 年 1 月 1 日至 2023 年 5 月 31 日期间开具合格术后医嘱的患者。我们排除了直接转入重症监护室的病例、麻醉提供者参与了提醒系统试点测试的病例或协变量数据缺失的病例。订单提醒系统的使用是指主要主治麻醉医师在手术当天收到推送通知提醒。我们使用逻辑回归估算了提醒系统的使用与及时下单之间的关系。对于延迟下单的患者,我们对下单情况进行了生存分析。显著性水平为 0.05。患者(如年龄、种族)、手术过程(如麻醉持续时间)和医疗服务提供者(如下单权限)变量被用作分析中的协变量。提醒与在 PACU 入院前下达医嘱的几率增加 51% 相关(比值比:1.51;95% 置信区间:1.43, 1.58;p ≤ 0.001),将 PACU 迟下医嘱的发生率从 17.5% 降至 12.6%(p ≤ 0.001)。在延迟下达医嘱的患者中,提醒与加快 10%的置管速度相关(危险比:1.10;95% CI 1.05,1.15;P<0.001)。
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引用次数: 0
ChatGPT: A Conceptual Review of Applications and Utility in the Field of Medicine. ChatGPT:医学领域应用和实用性的概念回顾。
IF 5.3 3区 医学 Q1 Computer Science Pub Date : 2024-06-05 DOI: 10.1007/s10916-024-02075-x
Shiavax J Rao, Ameesh Isath, Parvathy Krishnan, Jonathan A Tangsrivimol, Hafeez Ul Hassan Virk, Zhen Wang, Benjamin S Glicksberg, Chayakrit Krittanawong

Artificial Intelligence, specifically advanced language models such as ChatGPT, have the potential to revolutionize various aspects of healthcare, medical education, and research. In this narrative review, we evaluate the myriad applications of ChatGPT in diverse healthcare domains. We discuss its potential role in clinical decision-making, exploring how it can assist physicians by providing rapid, data-driven insights for diagnosis and treatment. We review the benefits of ChatGPT in personalized patient care, particularly in geriatric care, medication management, weight loss and nutrition, and physical activity guidance. We further delve into its potential to enhance medical research, through the analysis of large datasets, and the development of novel methodologies. In the realm of medical education, we investigate the utility of ChatGPT as an information retrieval tool and personalized learning resource for medical students and professionals. There are numerous promising applications of ChatGPT that will likely induce paradigm shifts in healthcare practice, education, and research. The use of ChatGPT may come with several benefits in areas such as clinical decision making, geriatric care, medication management, weight loss and nutrition, physical fitness, scientific research, and medical education. Nevertheless, it is important to note that issues surrounding ethics, data privacy, transparency, inaccuracy, and inadequacy persist. Prior to widespread use in medicine, it is imperative to objectively evaluate the impact of ChatGPT in a real-world setting using a risk-based approach.

人工智能,特别是诸如 ChatGPT 这样的高级语言模型,有可能彻底改变医疗保健、医学教育和研究的各个方面。在这篇叙述性综述中,我们评估了 ChatGPT 在不同医疗保健领域的大量应用。我们讨论了它在临床决策中的潜在作用,探讨了它如何通过为诊断和治疗提供快速、数据驱动的见解来帮助医生。我们回顾了 ChatGPT 在个性化患者护理方面的优势,尤其是在老年病护理、药物管理、减肥和营养以及体育锻炼指导方面。通过分析大型数据集和开发新型方法,我们进一步深入探讨了 ChatGPT 在促进医学研究方面的潜力。在医学教育领域,我们研究了 ChatGPT 作为信息检索工具和个性化学习资源对医学生和专业人员的实用性。ChatGPT 的应用前景广阔,很可能引发医疗实践、教育和研究领域的范式转变。在临床决策、老年护理、药物管理、减肥和营养、体育健身、科学研究和医学教育等领域,使用 ChatGPT 可能会带来诸多益处。不过,需要注意的是,围绕伦理、数据隐私、透明度、不准确性和不足等问题依然存在。在医学领域广泛使用之前,必须使用基于风险的方法客观评估 ChatGPT 在真实世界环境中的影响。
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引用次数: 0
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 Computer Science 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 Computer Science 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 Computer Science 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 Computer Science 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 Computer Science 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 Computer Science 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 Computer Science 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
期刊
Journal of Medical Systems
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