Health Informatics Journal最新文献_第6页

Automated breast imaging report generation based on the integration of multiple image features in a metadata format for shared decision-making. 在元数据格式中整合多种图像特征的基础上自动生成乳腺成像报告，以便共享决策。

IF 2.2 3区医学

Health Informatics Journal Pub Date : 2024-07-01 DOI: 10.1177/14604582241288460

Chung-Ming Lo, Hui-Ru Chen

{"title":"Automated breast imaging report generation based on the integration of multiple image features in a metadata format for shared decision-making.","authors":"Chung-Ming Lo, Hui-Ru Chen","doi":"10.1177/14604582241288460","DOIUrl":"https://doi.org/10.1177/14604582241288460","url":null,"abstract":"Importance: Medical imaging increases the workload involved in writing reports. Given the lack of a standardized format for reports, reports are not easily used as communication tools. Objective: During medical team-patient communication, the descriptions in reports also need to be understood. Automatically generated imaging reports with rich and understandable information can improve medical quality. Design, setting, and participants: The image analysis theory of Panofsky and Shatford from the perspective of image metadata was used in this study to establish a medical image interpretation template (MIIT) for automated image report generation. Main outcomes and measures: The image information included digital imaging and communications in medicine (DICOM), reporting and data systems (RADSs), and image features used in computer-aided diagnosis (CAD). The utility of the images was evaluated by a questionnaire survey to determine whether the image content could be better understood. Results: In 100 responses, exploratory factor analysis revealed that the factor loadings of the facets were greater than 0.5, indicating construct validity, and the overall Cronbach's alpha was 0.916, indicating reliability. No significant differences were noted according to sex, age or education. Conclusions and relevance: Overall, the results show that MIIT is helpful for understanding the content of medical images.","PeriodicalId":55069,"journal":{"name":"Health Informatics Journal","volume":"30 3","pages":"14604582241288460"},"PeriodicalIF":2.2,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142301398","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Predicting mortality amongst Jordanian men with heart attacks using the chi-square automatic interaction detection model. 利用卡方自动交互检测模型预测约旦男性心脏病患者的死亡率。

IF 2.2 3区医学

Health Informatics Journal Pub Date : 2024-07-01 DOI: 10.1177/14604582241270830

Salam Bani Hani, Muayyad Ahmad

{"title":"Predicting mortality amongst Jordanian men with heart attacks using the chi-square automatic interaction detection model.","authors":"Salam Bani Hani, Muayyad Ahmad","doi":"10.1177/14604582241270830","DOIUrl":"10.1177/14604582241270830","url":null,"abstract":"Background: One of the most complicated cardiovascular diseases in the world is heart attack. Since men are the most likely to develop cardiac diseases, accurate prediction of these conditions can help save lives in this population. This study proposed the Chi-Squared Automated Interactive Detection (CHAID) model as a prediction algorithm to forecast death versus life among men who might experience heart attacks. Methods: Data were extracted from the electronic health solution system in Jordan using a retrospective, predictive study. Between 2015 and 2021, information on men admitted to public hospitals in Jordan was gathered. Results: The CHAID algorithm had a higher accuracy of 93.72% and an area under the curve of 0.792, making it the best top model created to predict mortality among Jordanian men. It was discovered that among Jordanian men, governorates, age, pulse oximetry, medical diagnosis, pulse pressure, heart rate, systolic blood pressure, and pulse pressure were the most significant predicted risk factors of mortality from heart attack. Conclusion: With heart attack complaints as the primary risk factors that were predicted using machine learning algorithms like the CHAID model, demographic characteristics and hemodynamic readings were presented.","PeriodicalId":55069,"journal":{"name":"Health Informatics Journal","volume":"30 3","pages":"14604582241270830"},"PeriodicalIF":2.2,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141903664","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A review of incidents related to health information technology in Swedish healthcare to characterise system issues as a basis for improvement in clinical practice. 对瑞典医疗保健领域与医疗信息技术有关的事件进行审查，以确定系统问题的特征，为改进临床实践奠定基础。

IF 2.2 3区医学

Health Informatics Journal Pub Date : 2024-07-01 DOI: 10.1177/14604582241270742

Ding Pan, Evalill Nilsson, Md Shafiqur Rahman Jabin

引用次数: 0

Research on entity relation extraction for Chinese medical text. 中文医学文本实体关系提取研究。

IF 2.2 3区医学

Health Informatics Journal Pub Date : 2024-07-01 DOI: 10.1177/14604582241274762

Yonghe Lu, Hongyu Chen, Yueyun Zhang, Jiahui Peng, Dingcheng Xiang, Jinxia Zhang

引用次数: 0

'Humans think outside the pixels' - Radiologists' perceptions of using artificial intelligence for breast cancer detection in mammography screening in a clinical setting. 人类的思维不局限于像素"--放射科医生对在临床乳房 X 射线摄影筛查中使用人工智能检测乳腺癌的看法。

IF 2.2 3区医学

Health Informatics Journal Pub Date : 2024-07-01 DOI: 10.1177/14604582241275020

Jennifer Viberg Johansson, Emma Engström

{"title":"'Humans think outside the pixels' - Radiologists' perceptions of using artificial intelligence for breast cancer detection in mammography screening in a clinical setting.","authors":"Jennifer Viberg Johansson, Emma Engström","doi":"10.1177/14604582241275020","DOIUrl":"10.1177/14604582241275020","url":null,"abstract":"Objective: This study aimed to explore radiologists' views on using an artificial intelligence (AI) tool named ScreenTrustCAD with Philips equipment) as a diagnostic decision support tool in mammography screening during a clinical trial at Capio Sankt Göran Hospital, Sweden.Methods: We conducted semi-structured interviews with seven breast imaging radiologists, evaluated using inductive thematic content analysis.Results: We identified three main thematic categories: AI in society, reflecting views on AI's contribution to the healthcare system; AI-human interactions, addressing the radiologists' self-perceptions when using the AI and its potential challenges to their profession; and AI as a tool among others. The radiologists were generally positive towards AI, and they felt comfortable handling its sometimes-ambiguous outputs and erroneous evaluations. While they did not feel that it would undermine their profession, they preferred using it as a complementary reader rather than an independent one.Conclusion: The results suggested that breast radiology could become a launch pad for AI in healthcare. We recommend that this exploratory work on subjective perceptions be complemented by quantitative assessments to generalize the findings.","PeriodicalId":55069,"journal":{"name":"Health Informatics Journal","volume":"30 3","pages":"14604582241275020"},"PeriodicalIF":2.2,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142001405","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Improving the academic resilience of hospital nursing interns through a hybrid multi-criteria decision analysis model. 通过混合多标准决策分析模型提高医院护理实习生的学习适应能力。

IF 2.2 3区医学

Health Informatics Journal Pub Date : 2024-07-01 DOI: 10.1177/14604582241272771

Mao Ye, Weifang Xu, Lili Feng, Siqi Liu, Jianhong Yang, Yen-Ching Chuang, Fuqin Tang

{"title":"Improving the academic resilience of hospital nursing interns through a hybrid multi-criteria decision analysis model.","authors":"Mao Ye, Weifang Xu, Lili Feng, Siqi Liu, Jianhong Yang, Yen-Ching Chuang, Fuqin Tang","doi":"10.1177/14604582241272771","DOIUrl":"10.1177/14604582241272771","url":null,"abstract":"Purpose: To identify the main variables affecting the academic adaptability of hospital nursing interns and key areas for improvement in preparing for future unpredictable epidemics. Methods: The importance of academic resilience-related variables for all nursing interns was analyzed using the random forest method, and key variables were further identified. An importance-performance analysis was used to identify the key improvement gaps regarding the academic resilience of nursing interns in the case hospital. Results: The random forest showed that five items related to cooperation, motivation, confidence, communication, and difficulty with coping were the main variables impacting the academic resilience of nursing interns. Moreover, the importance-performance analysis revealed that three items regarding options examination, communication, and confidence were the key improvement areas for participating nursing interns in the case hospital. Conclusions: For the prevention and control of future unpredictable pandemics, hospital nursing departments can strengthen the link between interns, nurses, and physicians and promote their cooperation and communication during clinical practice. At the same time, an application can be created considering the results of this study and combined with machine learning methods for more in-depth research. These will improve the academic resilience of nursing interns during the routine management of pandemics within hospitals.","PeriodicalId":55069,"journal":{"name":"Health Informatics Journal","volume":"30 3","pages":"14604582241272771"},"PeriodicalIF":2.2,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141903662","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

AI-based epidemic and pandemic early warning systems: A systematic scoping review. 基于人工智能的流行病和大流行病预警系统：系统性范围审查。

IF 4.6 3区医学

Health Informatics Journal Pub Date : 2024-07-01 DOI: 10.1177/14604582241275844

Christo El Morr, Deniz Ozdemir, Yasmeen Asdaah, Antoine Saab, Yahya El-Lahib, Elie Salem Sokhn

{"title":"AI-based epidemic and pandemic early warning systems: A systematic scoping review.","authors":"Christo El Morr, Deniz Ozdemir, Yasmeen Asdaah, Antoine Saab, Yahya El-Lahib, Elie Salem Sokhn","doi":"10.1177/14604582241275844","DOIUrl":"10.1177/14604582241275844","url":null,"abstract":"Background: Timely detection of disease outbreaks is critical in public health. Artificial Intelligence (AI) can identify patterns in data that signal the onset of epidemics and pandemics. This scoping review examines the effectiveness of AI in epidemic and pandemic early warning systems (EWS). Objective: To assess the capability of AI-based systems in predicting epidemics and pandemics and to identify challenges and strategies for improvement. Methods: A systematic scoping review was conducted. The review included studies from the last 5 years, focusing on AI and machine learning applications in EWS. After screening 1087 articles, 33 were selected for thematic analysis. Results: The review found that AI-based EWS have been effectively implemented in various contexts, using a range of algorithms. Key challenges identified include data quality, model explainability, bias, data volume, velocity, variety, availability, and granularity. Strategies for mitigating AI bias and improving system adaptability were also discussed. Conclusion: AI has shown promise in enhancing the speed and accuracy of epidemic detection. However, challenges related to data quality, bias, and model transparency need to be addressed to improve the reliability and generalizability of AI-based EWS. Continuous monitoring and improvement, as well as incorporating social and environmental data, are essential for future development.","PeriodicalId":55069,"journal":{"name":"Health Informatics Journal","volume":"30 3","pages":"14604582241275844"},"PeriodicalIF":4.6,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142037828","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Epid data explorer: A visualization tool for exploring and comparing spatio-temporal epidemiological data. Epid 数据资源管理器：用于探索和比较时空流行病学数据的可视化工具。

IF 2.2 3区医学

Health Informatics Journal Pub Date : 2024-07-01 DOI: 10.1177/14604582241279720

Laetitia Viau, Jérôme Azé, Fati Chen, Pierre Pompidor, Pascal Poncelet, Vincent Raveneau, Nancy Rodriguez, Arnaud Sallaberry

{"title":"Epid data explorer: A visualization tool for exploring and comparing spatio-temporal epidemiological data.","authors":"Laetitia Viau, Jérôme Azé, Fati Chen, Pierre Pompidor, Pascal Poncelet, Vincent Raveneau, Nancy Rodriguez, Arnaud Sallaberry","doi":"10.1177/14604582241279720","DOIUrl":"10.1177/14604582241279720","url":null,"abstract":"The analysis of large sets of spatio-temporal data is a fundamental challenge in epidemiological research. As the quantity and the complexity of such kind of data increases, automatic analysis approaches, such as statistics, data mining, machine learning, etc., can be used to extract useful information. While these approaches have proven effective, they require a priori knowledge of the information being sought, and some interesting insights into the data may be missed. To bridge this gap, information visualization offers a set of techniques for not only presenting known information, but also exploring data without having a hypothesis formulated beforehand. In this paper, we introduce Epid Data Explorer (EDE), a visualization tool that enables exploration of spatio-temporal epidemiological data. EDE allows easy comparisons of indicators and trends across different geographical areas and times. It facilitates this exploration through ready-to-use pre-loaded datasets as well as user-chosen datasets. The tool also provides a secure architecture for easily importing new datasets while ensuring confidentiality. In two use cases using data associated with the COVID-19 epidemic, we demonstrate the substantial impact of implemented lockdown measures on mobility and how EDE allows assessing correlations between the spread of COVID-19 and weather conditions.","PeriodicalId":55069,"journal":{"name":"Health Informatics Journal","volume":"30 3","pages":"14604582241279720"},"PeriodicalIF":2.2,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142121215","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Machine learning model for osteoporosis diagnosis based on bone turnover markers. 基于骨转换标志物的骨质疏松症诊断机器学习模型。

IF 2.2 3区医学

Health Informatics Journal Pub Date : 2024-07-01 DOI: 10.1177/14604582241270778

Seung Min Baik, Hi Jeong Kwon, Yeongsic Kim, Jehoon Lee, Young Hoon Park, Dong Jin Park

{"title":"Machine learning model for osteoporosis diagnosis based on bone turnover markers.","authors":"Seung Min Baik, Hi Jeong Kwon, Yeongsic Kim, Jehoon Lee, Young Hoon Park, Dong Jin Park","doi":"10.1177/14604582241270778","DOIUrl":"10.1177/14604582241270778","url":null,"abstract":"To assess the diagnostic utility of bone turnover markers (BTMs) and demographic variables for identifying individuals with osteoporosis. A cross-sectional study involving 280 participants was conducted. Serum BTM values were obtained from 88 patients with osteoporosis and 192 controls without osteoporosis. Six machine learning models, including extreme gradient boosting (XGBoost), light gradient boosting machine (LGBM), CatBoost, random forest, support vector machine, and k-nearest neighbors, were employed to evaluate osteoporosis diagnosis. The performance measures included the area under the receiver operating characteristic curve (AUROC), F1-score, and accuracy. After AUROC optimization, LGBM exhibited the highest AUROC of 0.706. Post F1-score optimization, LGBM's F1-score was improved from 0.50 to 0.65. Combining the top three optimized models (LGBM, XGBoost, and CatBoost) resulted in an AUROC of 0.706, an F1-score of 0.65, and an accuracy of 0.73. BTMs, along with age and sex, were found to contribute significantly to osteoporosis diagnosis. This study demonstrates the potential of machine learning models utilizing BTMs and demographic variables for diagnosing preexisting osteoporosis. The findings highlight the clinical relevance of accessible clinical data in osteoporosis assessment, providing a promising tool for early diagnosis and management.","PeriodicalId":55069,"journal":{"name":"Health Informatics Journal","volume":"30 3","pages":"14604582241270778"},"PeriodicalIF":2.2,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141903663","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Using process model to define the legislative framework of electronic prescription in the Czech Republic. 利用流程模型确定捷克共和国电子处方的立法框架。

IF 2.2 3区医学

Health Informatics Journal Pub Date : 2024-07-01 DOI: 10.1177/14604582241270902

Jiří Berger, Jan Bruthans, Adam Vojtěch, Jiří Kofránek

{"title":"Using process model to define the legislative framework of electronic prescription in the Czech Republic.","authors":"Jiří Berger, Jan Bruthans, Adam Vojtěch, Jiří Kofránek","doi":"10.1177/14604582241270902","DOIUrl":"10.1177/14604582241270902","url":null,"abstract":"Defining legislation for electronic prescription systems (EPS) is inherently challenging due to conflicting interests and requirements. The study aimed to develop a comprehensive EPS within the Czech healthcare framework, integrating legislative, process, and technical aspects to ensure security, user acceptability, and compliance with health regulations. A process modeling tool based on hierarchical state machines was employed to create a detailed process architecture for the EPS. Key participants, scenarios, and state transitions were identified and incorporated into a process model using the Craft.CASE based on the BORM methodology. The final process architecture model facilitated interdisciplinary communication and consensus-building among stakeholders, including healthcare professionals, IT specialists, and legislators. The model served as a foundation for the legislative framework and was included in the explanatory memorandum for the draft amendment to the Pharmaceuticals Act. The use of hierarchical state machines and process modeling tools in developing healthcare legislation effectively reduced misunderstandings and ensured precise implementation. This method can be applied to other complex legislative and system design projects, enhancing stakeholder communication and project success.","PeriodicalId":55069,"journal":{"name":"Health Informatics Journal","volume":"30 3","pages":"14604582241270902"},"PeriodicalIF":2.2,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141903665","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0