Health Informatics Journal最新文献

Demonstrating the data integrity of routinely collected healthcare systems data for clinical trials (DEDICaTe): A proof-of-concept study 展示用于临床试验的常规医疗保健系统数据的完整性（DEDICaTe）：概念验证研究

IF 3 3区医学

Health Informatics Journal Pub Date : 2024-09-19 DOI: 10.1177/14604582241276969

Macey L Murray, Laura Sato, Jaspal Panesar, Sharon B Love, Rebecca Lee, James R Carpenter, Marion Mafham, Mahesh KB Parmar, Heather Pinches, Matthew R Sydes

{"title":"Demonstrating the data integrity of routinely collected healthcare systems data for clinical trials (DEDICaTe): A proof-of-concept study","authors":"Macey L Murray, Laura Sato, Jaspal Panesar, Sharon B Love, Rebecca Lee, James R Carpenter, Marion Mafham, Mahesh KB Parmar, Heather Pinches, Matthew R Sydes","doi":"10.1177/14604582241276969","DOIUrl":"https://doi.org/10.1177/14604582241276969","url":null,"abstract":"Introduction/aims: Healthcare systems data (also known as real-world or routinely collected health data) could transform the conduct of clinical trials. Demonstrating integrity and provenance of these data is critical for clinical trials, to enable their use where appropriate and avoid duplication using scarce trial resources. Building on previous work, this proof-of-concept study used a data intelligence tool, the “Central Metastore,” to provide metadata and lineage information of nationally held data. Methods: The feasibility of NHS England’s Central Metastore to capture detailed records of the origins, processes, and methods that produce four datasets was assessed. These were England’s Hospital Episode Statistics (Admitted Patient Care, Outpatients, Critical Care) and the Civil Registration of Deaths (England and Wales). The process comprised: information gathering; information ingestion using the tool; and auto-generation of lineage diagrams/content to show data integrity. A guidance document to standardise this process was developed. Results/Discussion: The tool can ingest, store and display data provenance in sufficient detail to support trust and transparency in using these datasets for trials. The slowest step was information gathering from multiple sources, so consistency in record-keeping is essential.","PeriodicalId":55069,"journal":{"name":"Health Informatics Journal","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142252743","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 and disability: A systematic scoping review 人工智能与残疾：系统性范围界定审查

IF 3 3区医学

Health Informatics Journal Pub Date : 2024-09-18 DOI: 10.1177/14604582241285743

Christo El Morr, Bushra Kundi, Fariah Mobeen, Sarah Taleghani, Yahya El-Lahib, Rachel Gorman

{"title":"AI and disability: A systematic scoping review","authors":"Christo El Morr, Bushra Kundi, Fariah Mobeen, Sarah Taleghani, Yahya El-Lahib, Rachel Gorman","doi":"10.1177/14604582241285743","DOIUrl":"https://doi.org/10.1177/14604582241285743","url":null,"abstract":"Background: Artificial intelligence (AI) can enhance life experiences and present challenges for people with disabilities. Objectives: This study aims to investigate the relationship between AI and disability, exploring the potential benefits and challenges of using AI for people with disabilities. Methods: A systematic scoping review was conducted using eight online databases; 45 scholarly articles from the last 5 years were identified and selected for thematic analysis. Results: The review’s findings revealed AI’s potential to enhance healthcare; however, it showed a high prevalence of a narrow medical model of disability and an ableist perspective in AI research. This raises concerns about the perpetuation of biases and discrimination against individuals with disabilities in the development and deployment of AI technologies. Conclusion: We recommend shifting towards a social model of disability, promoting interdisciplinary collaboration, addressing AI bias and discrimination, prioritizing privacy and security in AI development, focusing on accessibility and usability, investing in education and training, and advocating for robust policy and regulatory frameworks. The review emphasizes the urgent need for further research to ensure that AI benefits all members of society equitably and that future AI systems are designed with inclusivity and accessibility as core principles.","PeriodicalId":55069,"journal":{"name":"Health Informatics Journal","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142252745","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

Comparative analysis of machine learning algorithms for predicting diarrhea among under-five children in Ethiopia: Evidence from 2016 EDHS 预测埃塞俄比亚五岁以下儿童腹泻的机器学习算法比较分析：来自 2016 年埃塞俄比亚人口与健康调查的证据

IF 3 3区医学

Health Informatics Journal Pub Date : 2024-09-14 DOI: 10.1177/14604582241285769

Alemu Birara Zemariam, Wondosen Abey, Abdulaziz Kebede Kassaw, Ali Yimer

{"title":"Comparative analysis of machine learning algorithms for predicting diarrhea among under-five children in Ethiopia: Evidence from 2016 EDHS","authors":"Alemu Birara Zemariam, Wondosen Abey, Abdulaziz Kebede Kassaw, Ali Yimer","doi":"10.1177/14604582241285769","DOIUrl":"https://doi.org/10.1177/14604582241285769","url":null,"abstract":"Background: Diarrhea is a major cause of mortality and morbidity in under-5 children globally, especially in developing countries like Ethiopia. Limited research has used machine learning to predict childhood diarrhea. This study aimed to compare the predictive performance of ML algorithms for diarrhea in under-5 children in Ethiopia. Methods: The study utilized a dataset of 9501 under-5 children from the Ethiopia Demographic and Health Survey 2016. Five ML algorithms were used to build and compare predictive models. The model performance was evaluated using various metrics in Python. Boruta feature selection was employed, and data balancing techniques such as under-sampling, over-sampling, adaptive synthetic sampling, and synthetic minority oversampling as well as hyper parameter tuning methods were explored. Association rule mining was conducted using the Apriori algorithm in R to determine relationships between independent and target variables. Results: 10.2% of children had diarrhea. The Random Forest model had the best performance with 93.2% accuracy, 98.4% sensitivity, 85.5% specificity, and 0.916 AUC. The top predictors were residence, wealth index, and child age, number of living children, deworming, wasting, mother’s occupation, and education. Association rule mining identified the top 7 rules most associated with under-5 diarrhea in Ethiopia. Conclusion: The RF achieved the highest performance for predicting childhood diarrhea. Policymakers and healthcare providers can use these findings to develop targeted interventions to reduce diarrhea. Customizing strategies based on the identified association rules has the potential to improve child health and decrease the impact of diarrhea in Ethiopia.","PeriodicalId":55069,"journal":{"name":"Health Informatics Journal","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142252744","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 approaches for asthma disease prediction among adults in Sri Lanka 斯里兰卡成人哮喘疾病预测的机器学习方法

IF 3 3区医学

Health Informatics Journal Pub Date : 2024-09-14 DOI: 10.1177/14604582241283968

JRNA Gunawardana, SD Viswakula, Ravindra P Rannan-Eliya, Nilmini Wijemunige

{"title":"Machine learning approaches for asthma disease prediction among adults in Sri Lanka","authors":"JRNA Gunawardana, SD Viswakula, Ravindra P Rannan-Eliya, Nilmini Wijemunige","doi":"10.1177/14604582241283968","DOIUrl":"https://doi.org/10.1177/14604582241283968","url":null,"abstract":"Objectives: Addressing the challenge of cost-effective asthma diagnosis amidst diverse symptom patterns among patients, this study aims to develop a machine learning-based asthma prediction tool for self-detection of asthma. Methods: Data from 6,665 participants in the Sri Lanka Health and Ageing Study (2018-2019) are used for this research. Thirteen machine learning algorithms, including Logistic Regression, Support Vector Machine, Decision Tree, Random Forest, Naïve Bayes, K-Nearest Neighbors, Gradient Boost, XGBoost, AdaBoost, CatBoost, LightGBM, Multi-Layer Perceptron, and Probabilistic Neural Network, are employed. Results: A hybrid version of Logistic Regression and LightGBM outperformed other models, achieving an AUC of 0.9062 and 79.85% sensitivity. Key predictive features for asthma include wheezing, breathlessness with wheezing, shortness of breath attacks, coughing attacks, chest tightness, nasal allergies, physical activity, passive smoking, ethnicity, and residential sector. Conclusion: Combining Logistic Regression and LightGBM models can effectively predict adult asthma based on self-reported symptoms and demographic and behavioural characteristics. The proposed expert system assists clinicians and patients in diagnosing potential asthma cases.","PeriodicalId":55069,"journal":{"name":"Health Informatics Journal","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142252746","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

Artificial intelligence and health information: A bibliometric analysis of three decades of research 人工智能与健康信息：三十年研究的文献计量分析

IF 3 3区医学

Health Informatics Journal Pub Date : 2024-09-12 DOI: 10.1177/14604582241283969

Elham Aldousari, Dennis Kithinji

引用次数: 0

Systematic review of subjective validation methods for computerized colonoscopy simulators 计算机化结肠镜检查模拟器主观验证方法的系统回顾

IF 3 3区医学

Health Informatics Journal Pub Date : 2024-09-10 DOI: 10.1177/14604582241279692

Adrián Lugilde-López, Manuel Caeiro-Rodríguez, Fernando A. Mikic-Fonte, Martín Llamas-Nistal

{"title":"Systematic review of subjective validation methods for computerized colonoscopy simulators","authors":"Adrián Lugilde-López, Manuel Caeiro-Rodríguez, Fernando A. Mikic-Fonte, Martín Llamas-Nistal","doi":"10.1177/14604582241279692","DOIUrl":"https://doi.org/10.1177/14604582241279692","url":null,"abstract":"Introduction: In recent years, different approaches have been used to conduct a subjective assessment of colonoscopy simulators. The purpose of this paper is to review these different approaches, specifically the ones used for computerized simulators, as the first step for the design of a standard validation procedure for this type of simulators. Methods: A systematic review was conducted by searching papers after 2010 in PubMed, Google Scholar, ScienceDirect, and IEEE Xplore databases. Papers were screened and reviewed for procedures regarding the subjective validation of computerized simulators for traditional colonoscopy with an endoscope. Results: An initial search in the databases identified 2094 papers, of which 7 remained after exhaustive review and application of exclusion criteria. All studies used questionnaires for subjective validation, with “face” being the most common validity type tested, while “content” validity and “usability” were less prominent. Conclusions: A classification of subscales for testing face validity was derived from the studies. The Colonoscopy Simulator Realism Questionnaire (CSRQ) was selected as the guide to follow for the development of future questionnaires related to subjective validation. Mislabeling of the validity tested in the studies due to ambiguous interpretations of the validity types was a common occurrence observed in the reviewed studies.","PeriodicalId":55069,"journal":{"name":"Health Informatics Journal","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142213343","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":null,"pages":null},"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":null,"pages":null},"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