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Data-Centric Machine Learning in Nursing: A Concept Clarification. 护理学中以数据为中心的机器学习:概念澄清。
IF 1.3 4区 医学 Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-05-01 DOI: 10.1097/CIN.0000000000001102
Patricia A Ball Dunlap, Eun-Shim Nahm, Elizabeth E Umberfield

The ubiquity of electronic health records and health information exchanges has generated abundant administrative and clinical healthcare data. The vastness of this rich dataset presents an opportunity for emerging technologies (eg, artificial intelligence and machine learning) to assist clinicians and healthcare administrators with decision-making, predictive analytics, and more. Multiple studies have cited various applications for artificial intelligence and machine learning in nursing. However, what is unknown in the nursing discipline is that while greater than 90% of machine-learning implementations use a model-centric strategy, a fundamental change is occurring. Because of the limitations of this approach, the industry is beginning to pivot toward data-centric artificial intelligence. Nurses should be aware of the differences, including how each approach affects their engagement in designing human-intelligent-like technologies and their data usage, especially regarding electronic health records. Using the Norris Concept Clarification method, this article elucidates the data-centric machine learning concept for nursing. This is accomplished by (1) exploring the concept's origins in the data and computer science disciplines; (2) differentiating data- versus model-centric machine learning approaches, including introducing the machine-learning operation life cycle and process; and (3) explaining the advantages of the data-centric phenomenon, especially concerning nurses' engagement in technological design and proper data usage.

电子健康记录和健康信息交换的普及产生了大量的行政和临床医疗数据。庞大的数据集为新兴技术(如人工智能和机器学习)提供了机会,可帮助临床医生和医疗管理人员进行决策、预测分析等。多项研究列举了人工智能和机器学习在护理领域的各种应用。然而,护理学科不为人知的是,虽然超过 90% 的机器学习实施都采用了以模型为中心的策略,但正在发生根本性的变化。由于这种方法的局限性,业界开始转向以数据为中心的人工智能。护士应该了解其中的差异,包括每种方法如何影响他们参与设计类人智能技术及其数据使用,尤其是在电子健康记录方面。本文采用诺里斯概念澄清法,阐明了护理领域以数据为中心的机器学习概念。具体做法是:(1)探索这一概念在数据和计算机科学学科中的起源;(2)区分以数据为中心和以模型为中心的机器学习方法,包括介绍机器学习操作生命周期和流程;以及(3)解释以数据为中心现象的优势,尤其是在护士参与技术设计和正确使用数据方面。
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引用次数: 0
Development of an Artificial Intelligence Teaching Assistant System for Undergraduate Nursing Students: A Field Testing Study. 护理本科生人工智能助教系统的开发:实地测试研究。
IF 1.3 4区 医学 Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-05-01 DOI: 10.1097/CIN.0000000000001103
Yanika Kowitlawakul, Jocelyn Jie Min Tan, Siriwan Suebnukarn, Hoang D Nguyen, Danny Chiang Choon Poo, Joseph Chai, Devi M Kamala, Wenru Wang

Keeping students engaged and motivated during online or class discussion may be challenging. Artificial intelligence has potential to facilitate active learning by enhancing student engagement, motivation, and learning outcomes. The purpose of this study was to develop, test usability of, and explore undergraduate nursing students' perceptions toward the Artificial Intelligence-Teaching Assistant System. The system was developed based on three main components: machine tutor intelligence, a graphical user interface, and a communication connector. They were included in the system to support contextual machine tutoring. A field-testing study design, a mixed-method approach, was utilized with questionnaires and focus group interview. Twenty-one undergraduate nursing students participated in this study, and they interacted with the system for 2 hours following the required activity checklist. The students completed the validated usability questionnaires and then participated in the focus group interview. Descriptive statistics were used to analyze quantitative data, and thematic analysis was used to analyze qualitative data from the focus group interviews. The results showed that the Artificial Intelligence-Teaching Assistant System was user-friendly. Four main themes emerged, namely, functionality, feasibility, artificial unintelligence, and suggested learning modality. However, Artificial Intelligence-Teaching Assistant System functions, user interface, and content can be improved before full implementation.

在网上或课堂讨论中保持学生的参与度和积极性可能具有挑战性。人工智能有可能通过提高学生的参与度、积极性和学习效果来促进主动学习。本研究旨在开发和测试人工智能助教系统的可用性,并探讨护理专业本科生对该系统的看法。该系统的开发基于三个主要组成部分:机器智能辅导、图形用户界面和通信连接器。系统中包含这些组件是为了支持情境机器辅导。该系统采用了实地测试研究设计、混合方法、问卷调查和焦点小组访谈。21 名护理专业本科生参与了这项研究,他们按照规定的活动清单与系统进行了 2 个小时的互动。学生们填写了经过验证的可用性问卷,然后参加了焦点小组访谈。描述性统计用于分析定量数据,主题分析用于分析焦点小组访谈的定性数据。结果表明,人工智能助教系统对用户友好。出现了四大主题,即功能性、可行性、人工非智能性和建议的学习模式。然而,人工智能助教系统的功能、用户界面和内容在全面实施前还有待改进。
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引用次数: 0
Letter to the Editor. 致编辑的信
IF 1.3 4区 医学 Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-05-01 DOI: 10.1097/CIN.0000000000001147
Hinpetch Daungsupawong, Viroj Wiwanitkit
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引用次数: 0
Foundation Models, Generative AI, and Large Language Models: Essentials for Nursing. 基础模型、生成式人工智能和大型语言模型:护理要点》。
IF 1.3 4区 医学 Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-05-01 DOI: 10.1097/CIN.0000000000001149
Angela Ross, Kathleen McGrow, Degui Zhi, Laila Rasmy

We are in a booming era of artificial intelligence, particularly with the increased availability of technologies that can help generate content, such as ChatGPT. Healthcare institutions are discussing or have started utilizing these innovative technologies within their workflow. Major electronic health record vendors have begun to leverage large language models to process and analyze vast amounts of clinical natural language text, performing a wide range of tasks in healthcare settings to help alleviate clinicians' burden. Although such technologies can be helpful in applications such as patient education, drafting responses to patient questions and emails, medical record summarization, and medical research facilitation, there are concerns about the tools' readiness for use within the healthcare domain and acceptance by the current workforce. The goal of this article is to provide nurses with an understanding of the currently available foundation models and artificial intelligence tools, enabling them to evaluate the need for such tools and assess how they can impact current clinical practice. This will help nurses efficiently assess, implement, and evaluate these tools to ensure these technologies are ethically and effectively integrated into healthcare systems, while also rigorously monitoring their performance and impact on patient care.

我们正处于一个人工智能蓬勃发展的时代,尤其是随着可以帮助生成内容的技术(如 ChatGPT)的日益普及。医疗机构正在讨论或已经开始在工作流程中使用这些创新技术。主要的电子健康记录供应商已经开始利用大型语言模型来处理和分析大量的临床自然语言文本,在医疗机构中执行各种任务,帮助减轻临床医生的负担。尽管此类技术在患者教育、起草对患者问题和电子邮件的回复、病历摘要和促进医学研究等应用中很有帮助,但人们对这些工具在医疗保健领域的使用准备情况和现有员工的接受程度仍存在担忧。本文旨在让护士们了解目前可用的基础模型和人工智能工具,使他们能够评估对这些工具的需求,并评估它们如何影响当前的临床实践。这将有助于护士有效地评估、实施和评价这些工具,确保这些技术符合道德规范并有效地融入医疗保健系统,同时严格监控其性能和对患者护理的影响。
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引用次数: 0
Development of a Predictive Model for Survival Over Time in Patients With Out-of-Hospital Cardiac Arrest Using Ensemble-Based Machine Learning. 利用基于集合的机器学习技术开发院外心脏骤停患者随时间变化的存活率预测模型。
IF 1.3 4区 医学 Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-05-01 DOI: 10.1097/CIN.0000000000001145
Hong-Jae Choi, Changhee Lee, JinHo Chun, Roma Seol, Yun Mi Lee, Youn-Jung Son

As of now, a model for predicting the survival of patients with out-of-hospital cardiac arrest has not been established. This study aimed to develop a model for identifying predictors of survival over time in patients with out-of-hospital cardiac arrest during their stay in the emergency department, using ensemble-based machine learning. A total of 26 013 patients from the Korean nationwide out-of-hospital cardiac arrest registry were enrolled between January 1 and December 31, 2019. Our model, comprising 38 variables, was developed using the Survival Quilts model to improve predictive performance. We found that changes in important variables of patients with out-of-hospital cardiac arrest were observed 10 minutes after arrival at the emergency department. The important score of the predictors showed that the influence of patient age decreased, moving from the highest rank to the fifth. In contrast, the significance of reperfusion attempts increased, moving from the fourth to the highest rank. Our research suggests that the ensemble-based machine learning model, particularly the Survival Quilts, offers a promising approach for predicting survival in patients with out-of-hospital cardiac arrest. The Survival Quilts model may potentially assist emergency department staff in making informed decisions quickly, reducing preventable deaths.

迄今为止,预测院外心脏骤停患者存活率的模型尚未建立。本研究旨在利用基于集合的机器学习方法建立一个模型,以确定院外心脏骤停患者在急诊科住院期间的存活率预测因素。在2019年1月1日至12月31日期间,韩国全国院外心脏骤停登记处共登记了26 013名患者。我们的模型由 38 个变量组成,采用生存棉被模型开发,以提高预测性能。我们发现,院外心脏骤停患者的重要变量在到达急诊科 10 分钟后发生了变化。预测因子的重要得分显示,患者年龄的影响力有所下降,从最高级别降至第五位。相比之下,再灌注尝试的重要性则有所上升,从第四位上升到最高位。我们的研究表明,基于集合的机器学习模型,尤其是 "生存之被(Survival Quilts)",为预测院外心脏骤停患者的存活率提供了一种很有前景的方法。生存之被(Survival Quilts)模型有可能帮助急诊科工作人员迅速做出明智的决定,从而减少可预防的死亡。
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引用次数: 0
Artificial Intelligence and the National Violent Death Reporting System: A Rapid Review. 人工智能与国家暴力死亡报告系统:快速回顾。
IF 1.3 4区 医学 Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-05-01 DOI: 10.1097/CIN.0000000000001124
Lisa C Lindley, Christina N Policastro, Brianne Dosch, Joshua G Ortiz Baco, Charles Q Cao

As the awareness on violent deaths from guns, drugs, and suicides emerges as a public health crisis in the United States, attempts to prevent injury and mortality through nursing research are critical. The National Violent Death Reporting System provides public health surveillance of US violent deaths; however, understanding the National Violent Death Reporting System's research utility is limited. The purpose of our rapid review of the 2019-2023 literature was to understand to what extent artificial intelligence methods are being used with the National Violent Death Reporting System. We identified 16 National Violent Death Reporting System artificial intelligence studies, with more than half published after 2020. The text-rich content of National Violent Death Reporting System enabled researchers to center their artificial intelligence approaches mostly on natural language processing (50%) or natural language processing and machine learning (37%). Significant heterogeneity in approaches, techniques, and processes was noted across the studies, with critical methods information often lacking. The aims and focus of National Violent Death Reporting System studies were homogeneous and mostly examined suicide among nurses and older adults. Our findings suggested that artificial intelligence is a promising approach to the National Violent Death Reporting System data with significant untapped potential in its use. Artificial intelligence may prove to be a powerful tool enabling nursing scholars and practitioners to reduce the number of preventable, violent deaths.

随着枪支、毒品和自杀造成的暴力死亡成为美国的公共卫生危机,通过护理研究预防伤害和死亡的尝试至关重要。全国暴力死亡报告系统对美国的暴力死亡事件进行公共卫生监测;然而,人们对全国暴力死亡报告系统的研究效用了解有限。我们对 2019-2023 年文献进行快速审查的目的是了解国家暴力死亡报告系统在多大程度上使用了人工智能方法。我们确定了 16 项国家暴力死亡报告系统人工智能研究,其中一半以上是在 2020 年之后发表的。全国暴力死亡报告系统的文本内容丰富,因此研究人员的人工智能方法大多以自然语言处理(50%)或自然语言处理和机器学习(37%)为中心。这些研究在方法、技术和流程上存在很大的差异,而且往往缺乏关键的方法信息。国家暴力死亡报告系统研究的目的和重点是相同的,大多研究护士和老年人的自杀问题。我们的研究结果表明,人工智能是处理国家暴力死亡报告系统数据的一种很有前途的方法,其使用潜力还有待挖掘。人工智能可能会被证明是一种强大的工具,使护理学者和从业人员能够减少可预防的暴力死亡人数。
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引用次数: 0
Machine Learning-Based Approach to Predict Last-Minute Cancellation of Pediatric Day Surgeries. 基于机器学习的小儿日间手术最后一分钟取消预测方法。
IF 1.3 4区 医学 Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-05-01 DOI: 10.1097/CIN.0000000000001110
Canping Li, Zheming Li, Shoujiang Huang, Xiyan Chen, Tingting Zhang, Jihua Zhu

The last-minute cancellation of surgeries profoundly affects patients and their families. This research aimed to forecast these cancellations using EMR data and meteorological conditions at the time of the appointment, using a machine learning approach. We retrospectively gathered medical data from 13 440 pediatric patients slated for surgery from 2018 to 2021. Following data preprocessing, we utilized random forests, logistic regression, linear support vector machines, gradient boosting trees, and extreme gradient boosting trees to predict these abrupt cancellations. The efficacy of these models was assessed through performance metrics. The analysis revealed that key factors influencing last-minute cancellations included the impact of the coronavirus disease 2019 pandemic, average wind speed, average rainfall, preanesthetic assessments, and patient age. The extreme gradient boosting algorithm outperformed other models in predicting cancellations, boasting an area under the curve value of 0.923 and an accuracy of 0.841. This algorithm yielded superior sensitivity (0.840), precision (0.837), and F1 score (0.838) relative to the other models. These insights underscore the potential of machine learning, informed by EMRs and meteorological data, in forecasting last-minute surgical cancellations. The extreme gradient boosting algorithm holds promise for clinical deployment to curtail healthcare expenses and avert adverse patient-family experiences.

手术在最后一刻取消对患者及其家属造成了深远的影响。这项研究旨在利用EMR数据和预约时的气象条件,采用机器学习方法预测这些取消手术的情况。我们回顾性地收集了 2018 年至 2021 年期间预定手术的 13 440 名儿科患者的医疗数据。经过数据预处理后,我们利用随机森林、逻辑回归、线性支持向量机、梯度提升树和极端梯度提升树来预测这些突然取消的手术。我们通过性能指标评估了这些模型的功效。分析表明,影响最后一刻取消手术的关键因素包括 2019 年冠状病毒疾病大流行的影响、平均风速、平均降雨量、麻醉前评估和患者年龄。极端梯度提升算法在预测取消手术方面的表现优于其他模型,其曲线下面积值为 0.923,准确率为 0.841。该算法的灵敏度(0.840)、精确度(0.837)和 F1 分数(0.838)均优于其他模型。这些见解强调了机器学习在 EMR 和气象数据基础上预测最后一分钟手术取消的潜力。极端梯度提升算法有望在临床应用中减少医疗费用,避免患者和家属的不良体验。
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引用次数: 0
A Microlearning-Based Self-directed Learning Chatbot on Medication Administration for New Nurses: A Feasibility Study. 基于微学习的新护士用药管理自主学习聊天机器人:可行性研究。
IF 1.3 4区 医学 Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-05-01 DOI: 10.1097/CIN.0000000000001119
Ae Ran Kim, Ae Young Park, Soojin Song, Jeong Hee Hong, Kyeongsug Kim

New nurses must acquire accurate knowledge of medication administration, as it directly affects patient safety. This study aimed to develop a microlearning-based self-directed learning chatbot on medication administration for novice nurses. Furthermore, the study had the objective of evaluating the chatbot feasibility. The chatbot covered two main topics: medication administration processes and drug-specific management, along with 21 subtopics. Fifty-eight newly hired nurses on standby were asked to use the chatbot over a 2-week period. Moreover, we evaluated the chatbot's feasibility through a survey that gauged changes in their confidence in medication administration knowledge, intrinsic learning motivation, satisfaction with the chatbot's learning content, and usability. After using the chatbot, participants' confidence in medication administration knowledge significantly improved in all topics ( P < .001) except "Understanding a concept of 5Right" ( P = .077). Their intrinsic learning motivation, satisfaction with the learning content, and usability scored above 5 out of 7 in all subdomains, except for pressure/tension (mean, 2.12; median, 1.90). They scored highest on ease of learning (mean, 6.69; median, 7.00). A microlearning-based chatbot can help new nurses improve their knowledge of medication administration through self-directed learning.

新护士必须掌握准确的药物管理知识,因为这直接影响到患者的安全。本研究旨在为新手护士开发一个基于微课的用药管理自学聊天机器人。此外,该研究还旨在评估聊天机器人的可行性。聊天机器人涵盖两大主题:用药管理流程和特定药物管理,以及 21 个子主题。我们要求 58 名新招聘的待岗护士在两周内使用聊天机器人。此外,我们还通过调查评估了聊天机器人的可行性,调查内容包括参与者对药物管理知识的信心变化、内在学习动力、对聊天机器人学习内容的满意度以及可用性。使用聊天机器人后,除了 "理解 5Right 的概念"(P = .077)外,参与者对所有主题的用药知识的信心都有了显著提高(P < .001)。除了压力/紧张(平均值为 2.12;中位数为 1.90)外,他们在所有子域的内在学习动力、对学习内容的满意度和可用性都超过了 5 分(满分 7 分)。他们在易学性方面得分最高(平均分 6.69;中位数 7.00)。基于微学习的聊天机器人可以帮助新护士通过自主学习提高用药知识。
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引用次数: 0
Identifying Main Themes in Diabetes Management Interviews Using Natural Language Processing-Based Text Mining. 利用基于自然语言处理的文本挖掘技术识别糖尿病管理访谈中的主要主题。
IF 1.3 4区 医学 Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-05-01 DOI: 10.1097/CIN.0000000000001114
EunSeok Cha, Seonah Lee

This study aimed to identify the main themes from exit interviews of adult patients with type 2 diabetes after completion of a diabetes education program. Eighteen participants with type 2 diabetes completed an exit interview regarding their program experience and satisfaction. Semistructured interview questions were used, and the interviews were auto-recorded. The interview transcripts were preprocessed and analyzed using four natural language processing-based text-mining techniques. The top 30 words from the term frequency and term frequency-inverse document frequency each were derived. In the N-gram analysis, the connection strength of "diabetes" and "education" was the highest, and the simultaneous connectivity of word chains ranged from a maximum of seven words to a minimum of two words. Based on the CONvergence of iteration CORrelation (CONCOR) analysis, three clusters were generated, and each cluster was named as follows: participation in a diabetes education program to control blood glucose, exercise, and use of digital devices. This study using text mining proposes a new and useful approach to visualize data to develop patient-centered diabetes education.

本研究旨在从 2 型糖尿病成年患者完成糖尿病教育项目后的退出访谈中找出主要主题。18 名 2 型糖尿病患者完成了有关其项目体验和满意度的退出访谈。访谈采用了半结构化访谈问题,并进行了自动录音。访谈记录经过预处理,并使用四种基于自然语言处理的文本挖掘技术进行分析。从词频和词频-反文档频率中各提取出前 30 个词。在 N-gram 分析中,"糖尿病 "和 "教育 "的连接强度最高,词链的同时连接性从最多 7 个词到最少 2 个词不等。根据迭代相关性分析(CONCOR),产生了三个聚类,每个聚类的命名如下:参与糖尿病教育项目以控制血糖、运动和使用数字设备。这项使用文本挖掘技术的研究提出了一种新的有用方法,将数据可视化,以开展以患者为中心的糖尿病教育。
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引用次数: 0
Use of Artificial Intelligence in Nursing Care Areas. 人工智能在护理领域的应用。
IF 1.3 4区 医学 Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-05-01 DOI: 10.1097/CIN.0000000000001150
Heather D Carter-Templeton
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引用次数: 0
期刊
Cin-Computers Informatics Nursing
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