BMC Medical Informatics and Decision Making最新文献

Real-world data to support post-market safety and performance of embolization coils: evidence generation from a medical device manufacturer and data institute partnership. 支持栓塞线圈上市后安全性和性能的真实世界数据：从医疗器械制造商和数据机构合作中生成证据。

IF 3.3 3区医学 Q2 MEDICAL INFORMATICS

BMC Medical Informatics and Decision Making

Pub Date : 2024-09-19 DOI: 10.1186/s12911-024-02659-0

Amelia Hochreiter-Hufford, Jennifer Gatz, Amy M Griggs, Ryan D Schoch, Kimberly M Birmingham, Christopher Frederick, John Price, Scott Snyder

Background: Recognizing the limitations of pre-market clinical data, regulatory authorities have embraced total product lifecycle management with post-market surveillance (PMS) data to assess medical device safety and performance. One method of proactive PMS involves the analysis of real-world data (RWD) through retrospective review of electronic health records (EHR). Because EHRs are patient-centered and focused on providing tools that clinicians use to determine care rather than collecting information on individual medical products, the process of transforming RWD into real-world evidence (RWE) can be laborious, particularly for medical devices with broad clinical use and extended clinical follow-up. This study describes a method to extract RWD from EHR to generate RWE on the safety and performance of embolization coils.

Methods: Through a partnership between a non-profit data institute and a medical device manufacturer, information on implantable embolization coils' use was extracted, linked, and analyzed from clinical data housed in an electronic data warehouse from the state of Indiana's largest health system. To evaluate the performance and safety of the embolization coils, technical success and safety were defined as per the Society of Interventional Radiology guidelines. A multi-prong strategy including electronic and manual review of unstructured (clinical chart notes) and structured data (International Classification of Disease codes), was developed to identify patients with relevant devices and extract data related to the endpoints.

Results: A total of 323 patients were identified as treated using Cook Medical Tornado, Nester, or MReye embolization coils between 1 January 2014 and 31 December 2018. Available clinical follow-up for these patients was 1127 ± 719 days. Indications for use, adverse events, and procedural success rates were identified via automated extraction of structured data along with review of available unstructured data. The overall technical success rate was 96.7%, and the safety events rate was 5.3% with 18 major adverse events in 17 patients. The calculated technical success and safety rates met pre-established performance goals (≥ 85% for technical success and ≤ 12% for safety), highlighting the relevance of this surveillance method.

Conclusions: Generating RWE from RWD requires careful planning and execution. The process described herein provided valuable longitudinal data for PMS of real-world device safety and performance. This cost-effective approach can be translated to other medical devices and similar RWD database systems.

背景：由于认识到上市前临床数据的局限性，监管机构已开始利用上市后监测（PMS）数据对整个产品生命周期进行管理，以评估医疗器械的安全性和性能。主动 PMS 的一种方法是通过回顾性审查电子健康记录 (EHR) 来分析真实世界数据 (RWD)。由于电子病历以患者为中心，侧重于提供临床医生用来决定护理的工具，而不是收集单个医疗产品的信息，因此将 RWD 转化为真实世界证据 (RWE) 的过程可能会很费力，特别是对于临床使用广泛、临床随访时间较长的医疗器械而言。本研究介绍了一种从电子病历中提取 RWD 以生成有关栓塞线圈安全性和性能的 RWE 的方法：方法：通过一家非营利性数据机构和一家医疗设备制造商之间的合作，从印第安纳州最大的医疗系统电子数据仓库中的临床数据中提取、链接和分析了植入式栓塞线圈的使用信息。为了评估栓塞线圈的性能和安全性，根据介入放射学会指南对技术成功率和安全性进行了定义。我们制定了一项多管齐下的策略，包括对非结构化数据（临床病历记录）和结构化数据（国际疾病分类代码）进行电子和人工审查，以确定使用相关设备的患者，并提取与终点相关的数据：2014年1月1日至2018年12月31日期间，共有323名患者被确认使用Cook Medical Tornado、Nester或MReye栓塞线圈进行治疗。这些患者的可用临床随访时间为（1127 ± 719）天。通过自动提取结构化数据和审查可用的非结构化数据，确定了使用指征、不良事件和手术成功率。总体技术成功率为 96.7%，安全事件发生率为 5.3%，17 名患者发生了 18 起重大不良事件。计算得出的技术成功率和安全率均达到了预先设定的绩效目标（技术成功率≥85%，安全率≤12%），突出了这一监测方法的相关性：结论：从 RWD 生成 RWE 需要精心策划和执行。本文描述的过程为 PMS 提供了真实世界设备安全性和性能的宝贵纵向数据。这种经济有效的方法可应用于其他医疗器械和类似的 RWD 数据库系统。

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引用次数: 0

Development of message passing-based graph convolutional networks for classifying cancer pathology reports 开发用于癌症病理报告分类的基于消息传递的图卷积网络

IF 3.5 3区医学 Q2 MEDICAL INFORMATICS

BMC Medical Informatics and Decision Making

Pub Date : 2024-09-17 DOI: 10.1186/s12911-024-02662-5

Hong-Jun Yoon, Hilda B. Klasky, Andrew E. Blanchard, J. Blair Christian, Eric B. Durbin, Xiao-Cheng Wu, Antoinette Stroup, Jennifer Doherty, Linda Coyle, Lynne Penberthy, Georgia D. Tourassi

Applying graph convolutional networks (GCN) to the classification of free-form natural language texts leveraged by graph-of-words features (TextGCN) was studied and confirmed to be an effective means of describing complex natural language texts. However, the text classification models based on the TextGCN possess weaknesses in terms of memory consumption and model dissemination and distribution. In this paper, we present a fast message passing network (FastMPN), implementing a GCN with message passing architecture that provides versatility and flexibility by allowing trainable node embedding and edge weights, helping the GCN model find the better solution. We applied the FastMPN model to the task of clinical information extraction from cancer pathology reports, extracting the following six properties: main site, subsite, laterality, histology, behavior, and grade. We evaluated the clinical task performance of the FastMPN models in terms of micro- and macro-averaged F1 scores. A comparison was performed with the multi-task convolutional neural network (MT-CNN) model. Results show that the FastMPN model is equivalent to or better than the MT-CNN. Our implementation revealed that our FastMPN model, which is based on the PyTorch platform, can train a large corpus (667,290 training samples) with 202,373 unique words in less than 3 minutes per epoch using one NVIDIA V100 hardware accelerator. Our experiments demonstrated that using this implementation, the clinical task performance scores of information extraction related to tumors from cancer pathology reports were highly competitive.

研究证实，将图卷积网络（GCN）应用于利用词图特征（TextGCN）对自由形式的自然语言文本进行分类，是描述复杂自然语言文本的有效方法。然而，基于 TextGCN 的文本分类模型在内存消耗、模型传播和分发方面存在弱点。在本文中，我们提出了一种快速消息传递网络（FastMPN），它实现了具有消息传递架构的 GCN，通过允许可训练的节点嵌入和边缘权重，提供了通用性和灵活性，帮助 GCN 模型找到更好的解决方案。我们将 FastMPN 模型应用于从癌症病理报告中提取临床信息的任务，提取了以下六个属性：主要部位、亚部位、侧位、组织学、行为和分级。我们根据微观和宏观平均 F1 分数评估了 FastMPN 模型的临床任务性能。并与多任务卷积神经网络（MT-CNN）模型进行了比较。结果表明，FastMPN 模型等同于或优于 MT-CNN。我们的实施表明，我们的 FastMPN 模型基于 PyTorch 平台，使用一台英伟达 V100 硬件加速器，每个历时不到 3 分钟就能训练出包含 202,373 个独特单词的大型语料库（667,290 个训练样本）。我们的实验表明，使用该实现，从癌症病理报告中提取肿瘤相关信息的临床任务性能得分极具竞争力。

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引用次数: 0

Machine learning-based evaluation of prognostic factors for mortality and relapse in patients with acute lymphoblastic leukemia: a comparative simulation study 基于机器学习的急性淋巴细胞白血病患者死亡率和复发预后因素评估：一项比较模拟研究

IF 3.5 3区医学 Q2 MEDICAL INFORMATICS

BMC Medical Informatics and Decision Making

Pub Date : 2024-09-16 DOI: 10.1186/s12911-024-02645-6

Zahra Mehrbakhsh, Roghayyeh Hassanzadeh, Nasser Behnampour, Leili Tapak, Ziba Zarrin, Salman Khazaei, Irina Dinu

Predicting mortality and relapse in children with acute lymphoblastic leukemia (ALL) is crucial for effective treatment and follow-up management. ALL is a common and deadly childhood cancer that often relapses after remission. In this study, we aimed to apply and evaluate machine learning-based models for predicting mortality and relapse in pediatric ALL patients. This retrospective cohort study was conducted on 161 children aged less than 16 years with ALL. Survival status (dead/alive) and patient experience of relapse (yes/no) were considered as the outcome variables. Ten machine learning (ML) algorithms were used to predict mortality and relapse. The performance of the algorithms was evaluated by cross-validation and reported as mean sensitivity, specificity, accuracy and area under the curve (AUC). Finally, prognostic factors were identified based on the best algorithms. The mean accuracy of the ML algorithms for prediction of patient mortality ranged from 64 to 74% and for prediction of relapse, it varied from 64 to 84% on test data sets. The mean AUC of the ML algorithms for mortality and relapse was above 64%. The most important prognostic factors for predicting both mortality and relapse were identified as age at diagnosis, hemoglobin and platelets. In addition, significant prognostic factors for predicting mortality included clinical side effects such as splenomegaly, hepatomegaly and lymphadenopathy. Our results showed that artificial neural networks and bagging algorithms outperformed other algorithms in predicting mortality, while boosting and random forest algorithms excelled in predicting relapse in ALL patients across all criteria. These results offer significant clinical insights into the prognostic factors for children with ALL, which can inform treatment decisions and improve patient outcomes.

预测急性淋巴细胞白血病（ALL）患儿的死亡率和复发率对于有效治疗和后续管理至关重要。急性淋巴细胞白血病是一种常见且致命的儿童癌症，常常在缓解后复发。在这项研究中，我们旨在应用和评估基于机器学习的模型来预测儿童 ALL 患者的死亡率和复发率。这项回顾性队列研究的对象是 161 名年龄小于 16 岁的 ALL 儿童。生存状态（死亡/存活）和复发经历（是/否）被视为结果变量。十种机器学习（ML）算法用于预测死亡率和复发率。算法的性能通过交叉验证进行评估，并以平均灵敏度、特异性、准确性和曲线下面积（AUC）进行报告。最后，根据最佳算法确定了预后因素。在测试数据集上，ML 算法预测患者死亡率的平均准确率在 64% 到 74% 之间，预测复发的准确率在 64% 到 84% 之间。ML 算法预测死亡率和复发率的平均 AUC 均在 64% 以上。预测死亡率和复发的最重要预后因素是诊断时的年龄、血红蛋白和血小板。此外，预测死亡率的重要预后因素还包括脾大、肝大和淋巴结病等临床副作用。我们的研究结果表明，人工神经网络和bagging算法在预测死亡率方面优于其他算法，而boosting和随机森林算法在预测所有标准的ALL患者复发方面表现出色。这些结果为了解ALL患儿的预后因素提供了重要的临床见解，可为治疗决策提供依据并改善患者预后。

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引用次数: 0

Development and validation of a nomogram for predicting critical respiratory events during early anesthesia recovery in elderly patients 开发并验证用于预测老年患者早期麻醉恢复期间呼吸系统危急事件的提名图

IF 3.5 3区医学 Q2 MEDICAL INFORMATICS

BMC Medical Informatics and Decision Making

Pub Date : 2024-09-16 DOI: 10.1186/s12911-024-02671-4

Jingying Huang, Jin Yang, Haiou Qi, Xin Xu, Yiting Zhu, Miaomiao Xu, Yuting Wang

Elderly patients undergoing recovery from general anesthesia face a heightened risk of critical respiratory events (CREs). Despite this, there is a notable absence of effective predictive tools tailored to this specific demographic. This study aims to develop and validate a predictive model (nomogram) to address this gap. CREs pose significant risks to elderly patients during the recovery phase from general anesthesia, making it an important issue in perioperative care. With the increasing aging population and the complexity of surgical procedures, it is crucial to develop effective predictive tools to improve patient outcomes and ensure patient safety during post-anesthesia care unit (PACU) recovery. A total of 324 elderly patients who underwent elective general anesthesia in a grade A tertiary hospital from January 2023 to June 2023 were enrolled. Risk factors were identified using least absolute shrinkage and selection operator (LASSO) regression. A multivariate logistic regression model was constructed and represented as a nomogram. Internal validation of the model was performed using Bootstrapping. This study followed the TRIPOD checklist for reporting. The indicators included in the nomogram were frailty, snoring, patient-controlled intravenous analgesia (PCIA), emergency delirium and cough intensity at extubation. The diagnostic performance of the nomogram model was satisfactory, with AUC values of 0.990 and 0.981 for the training set and internal validation set, respectively. The optimal cutoff value was determined to be 0.22, based on a Youden index of 0.911. The F1-score was 0.927, and the MCC was 0.896. The calibration curve, Brier score (0.046), and HL test demonstrated acceptable consistency between the predicted and actual results. DCA revealed high net benefits of the nomogram prediction across all threshold probabilities. This study developed and validated a nomogram to identify elderly patients in the PACU who are at higher risk of CREs. The identified predictive factors included frailty condition, snoring syndrome, PCIA, emergency delirium, and cough intensity at extubation. By identifying patients at higher risk of CREs early on, medical professionals can implement targeted strategies to mitigate the occurrence of complications and provide better postoperative care for elderly patients recovering from general anesthesia.

从全身麻醉中恢复的老年患者面临着更高的危重呼吸事件（CRE）风险。尽管如此，针对这一特殊人群的有效预测工具却明显缺乏。本研究旨在开发并验证一种预测模型（提名图），以填补这一空白。在全身麻醉恢复阶段，CRE 对老年患者构成重大风险，因此成为围手术期护理的一个重要问题。随着人口老龄化的加剧和外科手术的复杂化，开发有效的预测工具以改善患者预后并确保麻醉后护理病房（PACU）恢复期间的患者安全至关重要。2023 年 1 月至 2023 年 6 月期间，在一家甲级三等医院接受择期全身麻醉的老年患者共有 324 人。采用最小绝对收缩和选择算子（LASSO）回归法确定了风险因素。构建了一个多变量逻辑回归模型，并以提名图的形式表示。模型的内部验证采用 Bootstrapping 方法进行。本研究遵循 TRIPOD 清单进行报告。提名图中包括的指标有体弱、打鼾、患者自控静脉镇痛（PCIA）、急诊谵妄和拔管时的咳嗽强度。提名图模型的诊断效果令人满意，训练集和内部验证集的 AUC 值分别为 0.990 和 0.981。根据尤登指数 0.911，确定最佳临界值为 0.22。F1 分数为 0.927，MCC 为 0.896。校准曲线、布赖尔得分（0.046）和 HL 检验表明，预测结果与实际结果之间的一致性是可以接受的。DCA 显示，在所有阈值概率中，提名图预测的净效益都很高。本研究开发并验证了一种提名图，用于识别 PACU 中发生 CRE 风险较高的老年患者。确定的预测因素包括虚弱状况、打鼾综合征、PCIA、急诊谵妄和拔管时的咳嗽强度。通过早期识别CREs风险较高的患者，医务人员可以实施有针对性的策略来减少并发症的发生，并为全身麻醉后恢复的老年患者提供更好的术后护理。

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引用次数: 0

A cross domain access control model for medical consortium based on DBSCAN and penalty function 基于 DBSCAN 和惩罚函数的医疗联合体跨域访问控制模型

IF 3.5 3区医学 Q2 MEDICAL INFORMATICS

BMC Medical Informatics and Decision Making

Pub Date : 2024-09-16 DOI: 10.1186/s12911-024-02638-5

Chuanjia Yao, Rong Jiang, Bin Wu, Pinghui Li, Chenguang Wang

Graded diagnosis and treatment, referral, and expert consultations between medical institutions all require cross domain access to patient medical information to support doctors’ treatment decisions, leading to an increase in cross domain access among various medical institutions within the medical consortium. However, patient medical information is sensitive and private, and it is essential to control doctors’ cross domain access to reduce the risk of leakage. Access control is a continuous and long-term process, and it first requires verification of the legitimacy of user identities, while utilizing control policies for selection and management. After verifying user identity and access permissions, it is also necessary to monitor unauthorized operations. Therefore, the content of access control includes authentication, implementation of control policies, and security auditing. Unlike the existing focus on authentication and control strategy implementation in access control, this article focuses on the control based on access log security auditing for doctors who have obtained authorization to access medical resources. This paper designs a blockchain based doctor intelligent cross domain access log recording system, which is used to record, query and analyze the cross domain access behavior of doctors after authorization. Through DBSCAN clustering analysis of doctors’ cross domain access logs, we find the abnormal phenomenon of cross domain access, and build a penalty function to dynamically control doctors’ cross domain access process, so as to reduce the risk of Data breach. Finally, through comparative analysis and experiments, it is shown that the proposed cross domain access control model for medical consortia based on DBSCAN and penalty function has good control effect on the cross domain access behavior of doctors in various medical institutions of the medical consortia, and has certain feasibility for the cross domain access control of doctors.

医疗机构之间的分级诊疗、转诊和专家会诊都需要跨域访问病人的医疗信息，以支持医生的治疗决策，这导致医疗联合体内各医疗机构之间的跨域访问越来越多。然而，病人的医疗信息具有敏感性和私密性，必须对医生的跨域访问进行控制，以降低泄漏风险。访问控制是一个持续和长期的过程，首先需要验证用户身份的合法性，同时利用控制策略进行选择和管理。在验证用户身份和访问权限后，还需要对未经授权的操作进行监控。因此，访问控制的内容包括身份验证、控制策略的实施和安全审计。不同于现有访问控制中对身份验证和控制策略实施的关注，本文重点关注基于访问日志安全审计的控制，对获得授权的医生访问医疗资源进行安全审计。本文设计了基于区块链的医生智能跨域访问日志记录系统，用于记录、查询和分析医生授权后的跨域访问行为。通过对医生跨域访问日志进行DBSCAN聚类分析，发现跨域访问的异常现象，并构建惩罚函数对医生跨域访问过程进行动态控制，从而降低数据泄露风险。最后，通过对比分析和实验表明，提出的基于DBSCAN和惩罚函数的医疗联合体跨域访问控制模型对医疗联合体各医疗机构医生的跨域访问行为具有良好的控制效果，对医生的跨域访问控制具有一定的可行性。

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引用次数: 0

Differences in changes of data completeness after the implementation of an electronic medical record in three surgical departments of a German hospital–a longitudinal comparative document analysis 德国一家医院的三个外科部门实施电子病历后数据完整性变化的差异--纵向对比文件分析

IF 3.5 3区医学 Q2 MEDICAL INFORMATICS

BMC Medical Informatics and Decision Making

Pub Date : 2024-09-16 DOI: 10.1186/s12911-024-02667-0

Florian Wurster, Christin Herrmann, Marina Beckmann, Natalia Cecon-Stabel, Kerstin Dittmer, Till Hansen, Julia Jaschke, Juliane Köberlein-Neu, Mi-Ran Okumu, Holger Pfaff, Carsten Rusniok, Ute Karbach

The European health data space promises an efficient environment for research and policy-making. However, this data space is dependent on high data quality. The implementation of electronic medical record systems has a positive impact on data quality, but improvements are not consistent across empirical studies. This study aims to analyze differences in the changes of data quality and to discuss these against distinct stages of the electronic medical record’s adoption process. Paper-based and electronic medical records from three surgical departments were compared, assessing changes in data quality after the implementation of an electronic medical record system. Data quality was operationalized as completeness of documentation. Ten information that must be documented in both record types (e.g. vital signs) were coded as 1 if they were documented, otherwise as 0. Chi-Square-Tests were used to compare percentage completeness of these ten information and t-tests to compare mean completeness per record type. A total of N = 659 records were analyzed. Overall, the average completeness improved in the electronic medical record, with a change from 6.02 (SD = 1.88) to 7.2 (SD = 1.77). At the information level, eight information improved, one deteriorated and one remained unchanged. At the level of departments, changes in data quality show expected differences. The study provides evidence that improvements in data quality could depend on the process how the electronic medical record is adopted in the affected department. Research is needed to further improve data quality through implementing new electronical medical record systems or updating existing ones.

欧洲卫生数据空间为研究和决策提供了一个高效的环境。然而，这一数据空间依赖于较高的数据质量。电子病历系统的实施对数据质量产生了积极影响，但各实证研究的改进并不一致。本研究旨在分析数据质量变化的差异，并根据电子病历采用过程的不同阶段进行讨论。研究比较了三个外科部门的纸质病历和电子病历，评估了实施电子病历系统后数据质量的变化。数据质量的可操作性是文档的完整性。两种记录类型都必须记录的十项信息（如生命体征），如果记录了，则编码为 1，否则编码为 0。我们使用 Chi-Square 检验比较这十项信息的完整性百分比，使用 t 检验比较每种记录类型的平均完整性。共分析了 N = 659 条记录。总体而言，电子病历的平均完整性有所提高，从 6.02（标度 = 1.88）提高到 7.2（标度 = 1.77）。在信息层面，8 项信息有所改善，1 项恶化，1 项保持不变。在部门层面，数据质量的变化显示出预期的差异。这项研究提供的证据表明，数据质量的改善可能取决于受影响科室采用电子病历的过程。需要开展研究，通过实施新的电子病历系统或更新现有系统来进一步提高数据质量。

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引用次数: 0

Classification of coronary artery disease using radial artery pulse wave analysis via machine learning 通过机器学习利用桡动脉脉搏波分析进行冠状动脉疾病分类

IF 3.5 3区医学 Q2 MEDICAL INFORMATICS

BMC Medical Informatics and Decision Making

Pub Date : 2024-09-16 DOI: 10.1186/s12911-024-02666-1

Yi Lyu, Hai-Mei Wu, Hai-Xia Yan, Rui Guo, Yu-Jie Xiong, Rui Chen, Wen-Yue Huang, Jing Hong, Rong Lyu, Yi-Qin Wang, Jin Xu

Coronary artery disease (CAD) is a major global cardiovascular health threat and the leading cause of death in many countries. The disease has a significant impact in China, where it has become the leading cause of death. There is an urgent need to develop non-invasive, rapid, cost-effective, and reliable techniques for the early detection of CAD using machine learning (ML). Six hundred eight participants were divided into three groups: healthy, hypertensive, and CAD. The raw data of pulse wave from those participants was collected. The data were de-noised, normalized, and analyzed using several applications. Seven ML classifiers were used to model the processed data, including Decision Tree (DT), Random Forest (RF), Gradient Boosting Decision Tree (GBDT), Extra Trees (ET), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting (LightGBM), and Unbiased Boosting with Categorical Features (CatBoost). The Extra Trees classifier demonstrated the best classification performance. After tunning, the results performance evaluation on test set are: 0.8579 accuracy, 0.9361 AUC, 0.8561 recall, 0.8581 precision, 0.8571 F1 score, 0.7859 kappa coefficient, and 0.7867 MCC. The top 10 feature importances of ET model are w/t1, t3/tmax, tmax, t3/t1, As, hf/3, tf/3/tmax, tf/5, w and tf/3/t1. Radial artery pulse wave can be used to identify healthy, hypertensive and CAD participants by using Extra Trees Classifier. This method provides a potential pathway to recognize CAD patients by using a simple, non-invasive, and cost-effective technique.

冠状动脉疾病（CAD）是全球心血管健康的主要威胁，也是许多国家的主要死因。在中国，冠心病的影响非常大，已成为导致死亡的主要原因。利用机器学习（ML）开发无创、快速、经济、可靠的早期检测 CAD 的技术迫在眉睫。68 名参与者被分为三组：健康组、高血压组和 CAD 组。收集了这些参与者的脉搏波原始数据。数据经过去噪、归一化处理，并使用多个应用程序进行分析。在对处理后的数据建模时使用了七种 ML 分类器，包括决策树 (DT)、随机森林 (RF)、梯度提升决策树 (GBDT)、额外树 (ET)、极端梯度提升 (XGBoost)、轻梯度提升 (LightGBM) 和带分类特征的无偏提升 (CatBoost)。Extra Trees 分类器的分类效果最好。经过调整后，测试集的性能评估结果如下准确率为 0.8579，AUC 为 0.9361，召回率为 0.8561，精度为 0.8581，F1 分数为 0.8571，卡帕系数为 0.7859，MCC 为 0.7867。ET 模型的前 10 个重要特征是 w/t1、t3/tmax、tmax、t3/t1、As、hf/3、tf/3/tmax、tf/5、w 和 tf/3/t1。利用 Extra Trees 分类器，桡动脉脉搏波可用于识别健康、高血压和 CAD 患者。这种方法提供了一种利用简单、无创和经济有效的技术识别 CAD 患者的潜在途径。

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引用次数: 0

RCC-Supporter: supporting renal cell carcinoma treatment decision-making using machine learning RCC-Supporter：利用机器学习支持肾细胞癌治疗决策

IF 3.5 3区医学 Q2 MEDICAL INFORMATICS

BMC Medical Informatics and Decision Making

Pub Date : 2024-09-16 DOI: 10.1186/s12911-024-02660-7

Won Hoon Song, Meeyoung Park

The population diagnosed with renal cell carcinoma, especially in Asia, represents 36.6% of global cases, with the incidence rate of renal cell carcinoma in Korea steadily increasing annually. However, treatment options for renal cell carcinoma are diverse, depending on clinical stage and histologic characteristics. Hence, this study aims to develop a machine learning based clinical decision-support system that recommends personalized treatment tailored to the individual health condition of each patient. We reviewed the real-world medical data of 1,867 participants diagnosed with renal cell carcinoma between November 2008 and June 2021 at the Pusan National University Yangsan Hospital in South Korea. Data were manually divided into a follow-up group where the patients did not undergo surgery or chemotherapy (Surveillance), a group where the patients underwent surgery (Surgery), and a group where the patients received chemotherapy before or after surgery (Chemotherapy). Feature selection was conducted to identify the significant clinical factors influencing renal cell carcinoma treatment decisions from 2,058 features. These features included subsets of 20, 50, 75, 100, and 150, as well as the complete set and an additional 50 expert-selected features. We applied representative machine learning algorithms, namely Decision Tree, Random Forest, and Gradient Boosting Machine (GBM). We analyzed the performance of three applied machine learning algorithms, among which the GBM algorithm achieved an accuracy score of 95% (95% CI, 92–98%) for the 100 and 150 feature sets. The GBM algorithm using 100 and 150 features achieved better performance than the algorithm using features selected by clinical experts (93%, 95% CI 89–97%). We developed a preliminary personalized treatment decision-support system (TDSS) called “RCC-Supporter” by applying machine learning (ML) algorithms to determine personalized treatment for the various clinical situations of RCC patients. Our results demonstrate the feasibility of using machine learning-based clinical decision support systems for treatment decisions in real clinical settings.

被确诊为肾细胞癌的人群，尤其是在亚洲，占全球病例的 36.6%，韩国的肾细胞癌发病率每年都在稳步上升。然而，根据临床分期和组织学特征，肾细胞癌的治疗方案多种多样。因此，本研究旨在开发一种基于机器学习的临床决策支持系统，根据每位患者的个人健康状况推荐个性化治疗方案。我们回顾了韩国釜山大学梁山医院在 2008 年 11 月至 2021 年 6 月期间诊断出患有肾细胞癌的 1867 名参与者的真实医疗数据。数据被人工分为未接受手术或化疗的随访组（监测组）、接受手术的随访组（手术组）和术前或术后接受化疗的随访组（化疗组）。通过特征选择，从 2058 个特征中找出影响肾细胞癌治疗决策的重要临床因素。这些特征包括 20、50、75、100 和 150 个子集，以及整套特征和另外 50 个专家选择的特征。我们应用了代表性的机器学习算法，即决策树、随机森林和梯度提升机（GBM）。我们分析了三种应用机器学习算法的性能，其中 GBM 算法在 100 和 150 个特征集上的准确率达到了 95%（95% CI，92-98%）。使用 100 和 150 个特征的 GBM 算法比使用临床专家选择的特征的算法取得了更好的性能（93%，95% CI 89-97%）。我们应用机器学习（ML）算法开发了一个名为 "RCC-Supporter "的初步个性化治疗决策支持系统（TDSS），以针对 RCC 患者的各种临床情况确定个性化治疗方案。我们的研究结果证明了在实际临床环境中使用基于机器学习的临床决策支持系统进行治疗决策的可行性。

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引用次数: 0

Enhancing clinical data retrieval with Smart Watchers: a NiFi-based ETL pipeline for Elasticsearch queries 利用智能监视器加强临床数据检索：基于 NiFi 的 ETL 管道用于 Elasticsearch 查询

IF 3.5 3区医学 Q2 MEDICAL INFORMATICS

BMC Medical Informatics and Decision Making

Pub Date : 2024-09-16 DOI: 10.1186/s12911-024-02633-w

Mohammad Al-Agil, Stephen J. Obee, Vlad Dinu, James Teo, David Brawand, Piers E. M. Patten, Anwar Alhaq

The aim is to develop and deploy an automated clinical alert system to enhance patient care and streamline healthcare operations. Structured and unstructured data from multiple sources are used to generate near real-time alerts for specific clinical scenarios, with an additional goal to improve clinical decision-making through accuracy and reliability. The automated clinical alert system, named Smart Watchers, was developed using Apache NiFi and Python scripts to create flexible data processing pipelines and customisable clinical alerts. A comparative analysis between Smart Watchers and the legacy Elastic Watchers was conducted to evaluate performance metrics such as accuracy, reliability, and scalability. The evaluation involved measuring the time taken for manual data extraction through the electronic patient record (EPR) front-end and comparing it with the automated data extraction process using Smart Watchers. Deployment of Smart Watchers showcased a consistent time savings between 90% to 98.67% compared to manual data extraction through the EPR front-end. The results demonstrate the efficiency of Smart Watchers in automating data extraction and alert generation, significantly reducing the time required for these tasks when compared to manual methods in a scalable manner. The research underscores the utility of employing an automated clinical alert system, and its portability facilitated its use across multiple clinical settings. The successful implementation and positive impact of the system lay a foundation for future technological innovations in this rapidly evolving field.

其目的是开发和部署一个自动临床警报系统，以加强病人护理和简化医疗保健操作。来自多个来源的结构化和非结构化数据被用于针对特定临床场景生成近乎实时的警报，其额外目标是通过准确性和可靠性改进临床决策。自动临床警报系统名为 Smart Watchers，是使用 Apache NiFi 和 Python 脚本开发的，用于创建灵活的数据处理管道和可定制的临床警报。对智能观察者和传统的弹性观察者进行了比较分析，以评估准确性、可靠性和可扩展性等性能指标。评估包括测量通过电子病历（EPR）前端进行手动数据提取所需的时间，并将其与使用 Smart Watchers 的自动数据提取流程进行比较。与通过电子病历前端进行人工数据提取相比，部署 Smart Watchers 可持续节省 90% 至 98.67% 的时间。结果表明，Smart Watchers 在自动数据提取和警报生成方面非常高效，与人工方法相比，以可扩展的方式大大减少了这些任务所需的时间。这项研究强调了采用自动临床警报系统的实用性，其便携性有助于在多种临床环境中使用。该系统的成功实施和积极影响为这一快速发展领域未来的技术创新奠定了基础。

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引用次数: 0

Health professionals’ perceptions of electronic health records system: a mixed method study in Ghana 卫生专业人员对电子健康记录系统的看法：加纳的一项混合方法研究

IF 3.5 3区医学 Q2 MEDICAL INFORMATICS

BMC Medical Informatics and Decision Making

Pub Date : 2024-09-16 DOI: 10.1186/s12911-024-02672-3

Nathan Kumasenu Mensah, Godwin Adzakpah, Jonathan Kissi, Kasim Abdulai, Hannah Taylor-Abdulai, Stephen Benyi Johnson, Christabell Opoku, Cephas Hallo, Richard Okyere Boadu

Electronic Health Record systems (EHRs) offer significant benefits and have transformed healthcare in developed countries. However, their implementation and adoption in low- and middle-income countries (LMICs) remains low due to challenges and competing interests. Health professionals’ perception of EHRs can influence their adoption and continued use. The objectives of this study are to explore the perception of health professionals regarding implemented EHR systems in three hospitals in Ghana and identify factors influencing their perception and satisfaction. In this study, we employed a concurrent mixed method design to collect data from study participants from May to June 2023. The quantitative part employed a descriptive-survey and the qualitative (in-depth interview) techniques was applied. After obtaining written informed consent from each respondent, a structured survey questionnaire was filled out by the health professionals from three hospitals. An a priori power calculation was used to determine the sample size for the quantitative component. Two hundred and sixty-three (263) health professionals completed the questionnaire from the three facilities. A purposive sampling technique was used to select fifteen [1] participants for the interviews. A semi-structured interview guide was used for the in-depth interviews. The interviews were audio recorded, transcribed, and coded into themes using QSR Nvivo 12 software before thematic content analysis. Our findings revealed that 213 (80.99%) health professionals perceived the EHRs as beneficial to patients and were generally satisfied. An overwhelming majority, 197 (74.90%) of the health professionals, were satisfied with its use and expressed interest in continuing to use the system. The majority of health professionals viewed the EHRs to have improved their work and workflow processes and provided the desired results. However, few other health professionals were dissatisfied with the system because they viewed the EHRs as frustrating due to unstable internet connectivity and power supply. Other concerns were related to the privacy and confidentiality of patient information. They believe access to patient information should be on a need-to-know basis, and patient information should not be accessible to all other clinicians except those involved directly in their care processes. The study revealed that health professionals have a positive perception of the implemented EHRs, are highly satisfied with them, and are interested in continuing to use them. However, health professionals’ concerns about the unstable power supply, poor internet connectivity, security, and confidentiality of patient’s information need attention, to mitigate their frustrations and boost their confidence in the system.

电子病历系统（EHR）带来了巨大的好处，并改变了发达国家的医疗保健。然而，由于各种挑战和利益冲突，这些系统在中低收入国家（LMICs）的实施和采用率仍然很低。医疗专业人员对电子病历的认知会影响其采用和持续使用。本研究的目的是探讨加纳三家医院的医疗专业人员对已实施的电子病历系统的看法，并确定影响其看法和满意度的因素。在本研究中，我们采用了并行混合法设计，从 2023 年 5 月至 6 月期间向研究参与者收集数据。定量部分采用了描述性调查，定性部分则采用了深入访谈技术。在获得每位受访者的书面知情同意后，三家医院的医务人员填写了一份结构化调查问卷。先验功率计算用于确定定量部分的样本量。三家医院的 263 名医护人员填写了调查问卷。采用目的性抽样技术挑选了 15 [1] 名参与者进行访谈。深度访谈采用了半结构化访谈指南。对访谈进行了录音、转录，并使用 QSR Nvivo 12 软件进行了主题内容分析。研究结果显示，213 名（80.99%）医护人员认为电子健康记录对病人有益，并普遍感到满意。绝大多数医护人员（197 人，占 74.90%）对系统的使用感到满意，并表示有兴趣继续使用该系统。大多数医护人员认为电子健康记录系统改善了他们的工作和工作流程，并提供了预期的效果。不过，也有少数其他医护人员对系统表示不满，因为他们认为电子健康记录系统因互联网连接和电源不稳定而令人沮丧。其他关切问题涉及病人信息的隐私和保密。他们认为，病人信息的获取应建立在 "有必要知道 "的基础上，除了直接参与病人护理过程的医生外，其他所有临床医生都不应获取病人信息。研究显示，医护人员对已实施的电子病历有积极的看法，对其非常满意，并有兴趣继续使用。然而，医护人员对电力供应不稳定、互联网连接不畅、安全性和病人信息保密性的担忧需要得到关注，以减轻他们的挫败感，增强他们对系统的信心。

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引用次数: 0