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.
{"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":"<p><p><b>Objective:</b> 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). <b>Methods:</b> 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. <b>Results:</b> 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. <b>Conclusion:</b> 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.</p>","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}
Pub Date : 2024-10-01DOI: 10.1177/14604582241304705
Kennedy Addo, Pabbi Kwaku Agyepong
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.
{"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":"<p><strong>Introduction: </strong>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.</p><p><strong>Methodology: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusion: </strong>Implementing EHR and DHIMS-2 within the confines of developing nations is recommended.</p>","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}
Pub Date : 2024-10-01DOI: 10.1177/14604582241274282
Tamires de Sá Menezes, Mateus Martins Martini, Matheus Lotto, Olivia Santana Jorge, Ana Maria Jucá, Patricia Estefania Ayala Aguirre, Thiago Cruvinel
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.
{"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":"<p><p><b>Objectives:</b> This study characterized toothache-related Portuguese Facebook posts, identifying factors driving misinformation production and user engagement. <b>Methods:</b> 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. <b>Results:</b> 39.6% of posts (<i>n</i> = 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. <b>Conclusion:</b> Toothache-related posts often contain misinformation, shared by regular users in links and video formats, tied to positive sentiments, and generally with financial motivation.</p>","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}
Pub Date : 2024-10-01DOI: 10.1177/14604582241304730
Aida Brankovic, David Cook, Jessica Rahman, Sankalp Khanna, Wenjie Huang
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.
{"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":"<p><strong>Objective: </strong>This study aimed to assess the practicality and trustworthiness of explainable artificial intelligence (XAI) methods used for explaining clinical predictive models.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusion: </strong>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.</p>","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}
Pub Date : 2024-09-19DOI: 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
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.
导言/目的：医疗保健系统数据（也称为真实世界或常规收集的健康数据）可以改变临床试验的开展。证明这些数据的完整性和出处对临床试验至关重要，这样才能在适当的时候使用这些数据，避免重复使用稀缺的试验资源。在以往工作的基础上，这项概念验证研究使用了一种数据智能工具--"中央元数据库"，以提供全国性数据的元数据和来源信息。研究方法我们评估了英格兰国家医疗服务系统中央元数据存储库（NHS England's Central Metastore）详细记录产生四个数据集的来源、过程和方法的可行性。这四个数据集分别是英格兰医院事件统计（入院病人护理、门诊病人、危重病人护理）和死亡民事登记（英格兰和威尔士）。该流程包括：信息收集；使用工具摄取信息；自动生成脉络图/内容以显示数据完整性。为使这一过程标准化，制定了一份指导文件。结果/讨论：该工具可以摄取、存储和显示足够详细的数据来源，以支持在试验中使用这些数据集时的信任度和透明度。最慢的步骤是从多个来源收集信息，因此记录保存的一致性至关重要。
{"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}
Pub Date : 2024-09-18DOI: 10.1177/14604582241285743
Christo El Morr, Bushra Kundi, Fariah Mobeen, Sarah Taleghani, Yahya El-Lahib, Rachel Gorman
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.
{"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}
Pub Date : 2024-09-14DOI: 10.1177/14604582241285769
Alemu Birara Zemariam, Wondosen Abey, Abdulaziz Kebede Kassaw, Ali Yimer
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.
{"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}
Pub Date : 2024-09-14DOI: 10.1177/14604582241283968
JRNA Gunawardana, SD Viswakula, Ravindra P Rannan-Eliya, Nilmini Wijemunige
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.
{"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":"26 1","pages":"14604582241283968"},"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}
Pub Date : 2024-09-12DOI: 10.1177/14604582241283969
Elham Aldousari, Dennis Kithinji
Information on the application of artificial intelligence (AI) in healthcare is needed to align healthcare transformation efforts. This bibliometric analysis aims to establish the patterns of publication activities on the application of AI in health. A total of 1083 scholarly papers published between 1993 and 2023 were retrieved from the Web of Science and Scopus databases. R Studio and VOSviewer were applied to quantify and illustrate publication patterns and citation rates. Publication rates grew by an average rate of 13% yearly, with each document being cited averagely 12 times. The articles had a mean of five co-authors, with a global co-authorship rate of 10%. COVID-19, artificial intelligence, and machine learning dominated the publications. The US, China, UK, Canada, and India coordinated most of the collaborative research. AI-based health information research is growing steadily. International collaborations can be leveraged to ensure the spread and interoperability of AI-based healthcare innovations globally.
需要有关人工智能（AI）在医疗保健领域应用的信息，以调整医疗保健转型工作。本文献计量分析旨在建立有关人工智能在医疗领域应用的出版活动模式。我们从 Web of Science 和 Scopus 数据库中检索了 1993 年至 2023 年间发表的 1083 篇学术论文。应用 R Studio 和 VOSviewer 对发表模式和引用率进行了量化和说明。论文发表率平均每年增长 13%，每篇论文平均被引用 12 次。文章的共同作者平均为五人，全球共同作者率为 10%。COVID-19、人工智能和机器学习在论文发表中占主导地位。美国、中国、英国、加拿大和印度协调了大部分合作研究。基于人工智能的健康信息研究正在稳步发展。可以利用国际合作来确保基于人工智能的医疗创新在全球范围内的传播和互操作性。
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Pub Date : 2024-09-10DOI: 10.1177/14604582241279692
Adrián Lugilde-López, Manuel Caeiro-Rodríguez, Fernando A. Mikic-Fonte, Martín Llamas-Nistal
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.
{"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":"5 1","pages":"14604582241279692"},"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}
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