Health Informatics Journal最新文献_第7页

Smart variant filtering - A blueprint solution for massively parallel sequencing-based variant analysis. 智能变异过滤--基于大规模并行测序的变异分析蓝图解决方案。

IF 2.2 3区医学

Health Informatics Journal Pub Date : 2024-10-01 DOI: 10.1177/14604582241290725

Orlinda Brahimllari, Sandra Eloranta, Patrik Georgii-Hemming, Zahra Haider, Sabine Koch, Aleksandra Krstic, Frantzeska Papadopoulou Skarp, Richard Rosenquist, Karin E Smedby, Fulya Taylan, Birna Thorvaldsdottir, Valtteri Wirta, Tove Wästerlid, Magnus Boman

{"title":"Smart variant filtering - A blueprint solution for massively parallel sequencing-based variant analysis.","authors":"Orlinda Brahimllari, Sandra Eloranta, Patrik Georgii-Hemming, Zahra Haider, Sabine Koch, Aleksandra Krstic, Frantzeska Papadopoulou Skarp, Richard Rosenquist, Karin E Smedby, Fulya Taylan, Birna Thorvaldsdottir, Valtteri Wirta, Tove Wästerlid, Magnus Boman","doi":"10.1177/14604582241290725","DOIUrl":"https://doi.org/10.1177/14604582241290725","url":null,"abstract":"Massively parallel sequencing helps create new knowledge on genes, variants and their association with disease phenotype. This important technological advancement simultaneously makes clinical decision making, using genomic information for cancer patients, more complex. Currently, identifying actionable pathogenic variants with diagnostic, prognostic, or predictive impact requires substantial manual effort. Objective: The purpose is to design a solution for clinical diagnostics of lymphoma, specifically for systematic variant filtering and interpretation. Methods: A scoping review and demonstrations from specialists serve as a basis for a blueprint of a solution for massively parallel sequencing-based genetic diagnostics. Results: The solution uses machine learning methods to facilitate decision making in the diagnostic process. A validation round of interviews with specialists consolidated the blueprint and anchored it across all relevant expert disciplines. The scoping review identified four components of variant filtering solutions: algorithms and Artificial Intelligence (AI) applications, software, bioinformatics pipelines and variant filtering strategies. The blueprint describes the input, the AI model and the interface for dynamic browsing. Conclusion: An AI-augmented system is designed for predicting pathogenic variants. While such a system can be used to classify identified variants, diagnosticians should still evaluate the classification's accuracy, make corrections when necessary, and ultimately decide which variants are truly pathogenic.","PeriodicalId":55069,"journal":{"name":"Health Informatics Journal","volume":"30 4","pages":"14604582241290725"},"PeriodicalIF":2.2,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142407224","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

Epidemiological investigation support application and user evaluation based on infectious disease self-management model in the endemic era. 基于传染病流行时期自我管理模式的流行病学调查支持应用和用户评估。

IF 2.2 3区医学

Health Informatics Journal Pub Date : 2024-10-01 DOI: 10.1177/14604582241294208

Jihwan Park, Mi Jung Rho

{"title":"Epidemiological investigation support application and user evaluation based on infectious disease self-management model in the endemic era.","authors":"Jihwan Park, Mi Jung Rho","doi":"10.1177/14604582241294208","DOIUrl":"https://doi.org/10.1177/14604582241294208","url":null,"abstract":"Objectives: Rapid epidemiological investigations are fundamental to prevent the spread of infectious diseases such as coronavirus disease 2019. An epidemiological investigation presents significant challenges for both epidemiologists and infected individuals. It requires creating an environment that enables people to independently manage infectious diseases and voluntarily participate in epidemiological investigations. Methods: We developed the KODARI application, an epidemiological investigation support system that users can voluntarily use. We developed the questionnaires based on literature reviews. We evaluated the application through an online survey from December 2 to 14, 2022. Results: The application automatically or manually collect epidemiological investigation information. The application improved data accuracy through accurate information collection. It voluntarily can transmit self-management information to epidemiologist terminals or users in real time. We collected 248 users from an online survey. Most users had high ratings and willingness to use. They have willingness to manage infectious patients was substantial. The application was evaluated as helpful for epidemiological investigations and could shorten the time required for epidemiological investigations by more than 30 min. Conclusion: The application proposes a model based on people's voluntary participation. We demonstrated that the application could enhance epidemiological investigations and diminish the duration of existing epidemiological investigation processes.","PeriodicalId":55069,"journal":{"name":"Health Informatics Journal","volume":"30 4","pages":"14604582241294208"},"PeriodicalIF":2.2,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142513297","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 scoping review of the drivers and barriers influencing healthcare professionals' behavioral intentions to comply with electronic health record data privacy policy. 对影响医疗保健专业人员遵守电子健康记录数据隐私政策的行为意向的驱动因素和障碍进行范围审查。

IF 2.2 3区医学

Health Informatics Journal Pub Date : 2024-10-01 DOI: 10.1177/14604582241296398

Nabil D Alhassani, Richard Windle, Stathis Th Konstantinidis

{"title":"A scoping review of the drivers and barriers influencing healthcare professionals' behavioral intentions to comply with electronic health record data privacy policy.","authors":"Nabil D Alhassani, Richard Windle, Stathis Th Konstantinidis","doi":"10.1177/14604582241296398","DOIUrl":"10.1177/14604582241296398","url":null,"abstract":"Objective: Electronic Health Records (EHRs) are now an integral part of health systems in middle and high-income countries despite recognized deficits in the digital competencies of Healthcare Professionals (HCPs). Therefore, we undertook a scoping review of factors influencing compliance with EHR data privacy policies. Methods: Seven databases revealed 27 relevant studies, covering a range of countries, professional groups, and research methods. The diverse nature of these factors meant that 18 separate theoretical frameworks representing technology-acceptance to behavioral psychology were used to interpret these. Results: The predominant factors influencing compliance with EHR data privacy policies included confidence and competence to comply, perceived ease of use, facilitatory environmental factors, perceived usefulness, fear that non-compliance would be detected and/or punished and the expectations of others. Conclusion: Human factors such as attitudes, social pressure, confidence, and perceived usefulness are as important as technical factors and must be addressed to improve compliance.","PeriodicalId":55069,"journal":{"name":"Health Informatics Journal","volume":"30 4","pages":"14604582241296398"},"PeriodicalIF":2.2,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142481343","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

Empowering healthcare education: A multilingual ontology for medical informatics and digital health (MIMO) integrated to artificial intelligence powered training in smart hospitals. 增强医疗保健教育：医疗信息学和数字健康多语言本体论（MIMO）与智能医院的人工智能培训相结合。

IF 2.2 3区医学

Health Informatics Journal Pub Date : 2024-10-01 DOI: 10.1177/14604582241287010

Arriel Benis, Julien Grosjean, Flavien Disson, Mihaela Crisan-Vida, Patrick Weber, Lacramioara Stoicu-Tivadar, Pascal Staccini, Stéfan J Darmoni

{"title":"Empowering healthcare education: A multilingual ontology for medical informatics and digital health (MIMO) integrated to artificial intelligence powered training in smart hospitals.","authors":"Arriel Benis, Julien Grosjean, Flavien Disson, Mihaela Crisan-Vida, Patrick Weber, Lacramioara Stoicu-Tivadar, Pascal Staccini, Stéfan J Darmoni","doi":"10.1177/14604582241287010","DOIUrl":"https://doi.org/10.1177/14604582241287010","url":null,"abstract":"Objective: A comprehensive understanding of professional and technical terms is essential to achieving practical results in multidisciplinary projects dealing with health informatics and digital health. The medical informatics multilingual ontology (MIMO) initiative has been created through international cooperation. MIMO is continuously updated and comprises over 3700 concepts in 37 languages on the Health Terminology/Ontology Portal (HeTOP). Methods: We conducted case studies to assess the feasibility and impact of integrating MIMO into real-world healthcare projects. In HosmartAI, MIMO is used to index technological tools in a dedicated marketplace and improve partners' communication. Then, in SaNuRN, MIMO supports the development of a \"Catalog and Index of Digital Health Teaching Resources\" (CIDHR) backing digital health resources retrieval for health and allied health students. Results: In HosmartAI, MIMO facilitates the indexation of technological tools and smooths partners' interactions. In SaNuRN within CIDHR, MIMO ensures that students and practitioners access up-to-date, multilingual, and high-quality resources to enhance their learning endeavors. Conclusion: Integrating MIMO into training in smart hospital projects allows healthcare students and experts worldwide with different mother tongues and knowledge to tackle challenges facing the health informatics and digital health landscape to find innovative solutions improving initial and continuous education.","PeriodicalId":55069,"journal":{"name":"Health Informatics Journal","volume":"30 4","pages":"14604582241287010"},"PeriodicalIF":2.2,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142378639","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

Evaluating the Health Information system implementation and utilization in healthcare delivery. 评估卫生信息系统在医疗服务中的实施和利用。

IF 2.2 3区医学

Health Informatics Journal Pub Date : 2024-10-01 DOI: 10.1177/14604582241304705

Kennedy Addo, Pabbi Kwaku Agyepong

{"title":"Evaluating the Health Information system implementation and utilization in healthcare delivery.","authors":"Kennedy Addo, Pabbi Kwaku Agyepong","doi":"10.1177/14604582241304705","DOIUrl":"https://doi.org/10.1177/14604582241304705","url":null,"abstract":"Introduction: Information and Communication Technology (ICT) with emphasis on Electronic Health Records (EHR) is growing steadily in most developing countries including Ghana. This is considered the impetus for achieving quality service delivery. The study is intended to evaluate the implementation and utilization of health information systems in health care delivery.Methodology: A descriptive cross-sectional study was conducted to achieve the study objective. The target population included health professionals from diverse settings who interact with Electronic Health Records, the District Health Information and Management System (DHIMS-2). The data collection approach relied on close and open-ended questionnaires, observations, and focus group discussions. The proportionate stratified and simple random sampling techniques were used to obtain a representative group of healthcare professionals. Descriptive statistics was used to analyze user satisfaction, benefits, and challenges of EHR/DHIMS-2. Moreover, Pearson correlation and linear regression analysis were used to analyze the Technology Acceptance Model for the end users.Results: The study revealed that perceived ease of use and usefulness could be significantly predicted to influence end-users' attitude towards technology adoption. The results show significant association between the combined effects of attitude and usefulness on acceptance.Conclusion: Implementing EHR and DHIMS-2 within the confines of developing nations is recommended.","PeriodicalId":55069,"journal":{"name":"Health Informatics Journal","volume":"30 4","pages":"14604582241304705"},"PeriodicalIF":2.2,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142774933","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

Factors driving misinformation production and user engagement with toothache content on Facebook. 推动错误信息生产和用户参与Facebook上令人牙痛的内容的因素。

IF 2.2 3区医学

Health Informatics Journal Pub Date : 2024-10-01 DOI: 10.1177/14604582241274282

Tamires de Sá Menezes, Mateus Martins Martini, Matheus Lotto, Olivia Santana Jorge, Ana Maria Jucá, Patricia Estefania Ayala Aguirre, Thiago Cruvinel

{"title":"Factors driving misinformation production and user engagement with toothache content on Facebook.","authors":"Tamires de Sá Menezes, Mateus Martins Martini, Matheus Lotto, Olivia Santana Jorge, Ana Maria Jucá, Patricia Estefania Ayala Aguirre, Thiago Cruvinel","doi":"10.1177/14604582241274282","DOIUrl":"https://doi.org/10.1177/14604582241274282","url":null,"abstract":"Objectives: This study characterized toothache-related Portuguese Facebook posts, identifying factors driving misinformation production and user engagement. Methods: Investigators qualitatively analyzed 500 posts published between August 2018 and August 2022, screening on language and theme. Posts were selected using CrowdTangle and assessed for motivation, author profile, content, sentiment, facticity, and format. The interaction metrics (total interactions/overperforming scores) were compared between groups of dichotomized characteristics, including time of publication. Data were evaluated by descriptive analysis, the Mann-Whitney U test, and the path analysis by generalized structural equation modeling. Results: 39.6% of posts (n = 198) contained misinformation, significantly linked to noncommercial posts with positive sentiment, links, and videos from regular users motivated by financial motivation. Additionally, user engagement was only positively associated with business/health authors' profiles and the time of publication. Conclusion: Toothache-related posts often contain misinformation, shared by regular users in links and video formats, tied to positive sentiments, and generally with financial motivation.","PeriodicalId":55069,"journal":{"name":"Health Informatics Journal","volume":"30 4","pages":"14604582241274282"},"PeriodicalIF":2.2,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142752122","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

Benchmarking the most popular XAI used for explaining clinical predictive models: Untrustworthy but could be useful. 对用于解释临床预测模型的最流行的XAI进行基准测试：不可信但可能有用。

IF 2.2 3区医学

Health Informatics Journal Pub Date : 2024-10-01 DOI: 10.1177/14604582241304730

Aida Brankovic, David Cook, Jessica Rahman, Sankalp Khanna, Wenjie Huang

{"title":"Benchmarking the most popular XAI used for explaining clinical predictive models: Untrustworthy but could be useful.","authors":"Aida Brankovic, David Cook, Jessica Rahman, Sankalp Khanna, Wenjie Huang","doi":"10.1177/14604582241304730","DOIUrl":"https://doi.org/10.1177/14604582241304730","url":null,"abstract":"Objective: This study aimed to assess the practicality and trustworthiness of explainable artificial intelligence (XAI) methods used for explaining clinical predictive models.Methods: Two popular XAIs used for explaining clinical predictive models were evaluated based on their ability to generate domain-appropriate representations, impact clinical workflow, and consistency. Explanations were benchmarked against true clinical deterioration triggers recorded in the data system and agreement was quantified. The evaluation was conducted using two Electronic Medical Records datasets from major hospitals in Australia. Results were examined and commented on by a senior clinician.Results: Findings demonstrate a violation of consistency criteria and moderate concordance (0.47-0.8) with true triggers, undermining reliability and actionability, criteria for clinicians' trust in XAI.Conclusion: Explanations are not trustworthy to guide clinical interventions, though they may offer useful insights and help model troubleshooting. Clinician-informed XAI development and presentation, clear disclaimers on limitations, and critical clinical judgment can promote informed decisions and prevent over-reliance.","PeriodicalId":55069,"journal":{"name":"Health Informatics Journal","volume":"30 4","pages":"14604582241304730"},"PeriodicalIF":2.2,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142883612","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

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":"38 1","pages":"14604582241276969"},"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":"7 1","pages":""},"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":"23 1","pages":""},"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