Online journal of public health informatics最新文献_第5页

Framework for Classifying Explainable Artificial Intelligence (XAI) Algorithms in Clinical Medicine. 临床医学可解释人工智能(XAI)算法分类框架

Online journal of public health informatics Pub Date : 2023-09-01 eCollection Date: 2023-01-01 DOI: 10.2196/50934

Thomas Gniadek, Jason Kang, Talent Theparee, Jacob Krive

{"title":"Framework for Classifying Explainable Artificial Intelligence (XAI) Algorithms in Clinical Medicine.","authors":"Thomas Gniadek, Jason Kang, Talent Theparee, Jacob Krive","doi":"10.2196/50934","DOIUrl":"10.2196/50934","url":null,"abstract":"Artificial intelligence (AI) applied to medicine offers immense promise, in addition to safety and regulatory concerns. Traditional AI produces a core algorithm result, typically without a measure of statistical confidence or an explanation of its biological-theoretical basis. Efforts are underway to develop explainable AI (XAI) algorithms that not only produce a result but also an explanation to support that result. Here we present a framework for classifying XAI algorithms applied to clinical medicine: An algorithm's clinical scope is defined by whether the core algorithm output leads to observations (eg, tests, imaging, clinical evaluation), interventions (eg, procedures, medications), diagnoses, and prognostication. Explanations are classified by whether they provide empiric statistical information, association with a historical population or populations, or association with an established disease mechanism or mechanisms. XAI implementations can be classified based on whether algorithm training and validation took into account the actions of health care providers in response to the insights and explanations provided or whether training was performed using only the core algorithm output as the end point. Finally, communication modalities used to convey an XAI explanation can be used to classify algorithms and may affect clinical outcomes. This framework can be used when designing, evaluating, and comparing XAI algorithms applied to medicine.","PeriodicalId":74345,"journal":{"name":"Online journal of public health informatics","volume":"1 1","pages":"e50934"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10689048/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44432749","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Discussions with End Users to Inform the Vision for a Shared Care Record in Ontario: Qualitative Interview Study (Preprint) 与最终用户讨论，了解安大略省共享护理记录的愿景：定性访谈研究（预印本）

Online journal of public health informatics Pub Date : 2023-07-27 DOI: 10.2196/51231

Marta Chmielewski, Matthew J. Meyer

引用次数: 0

Completion of the Transfer of the Online Journal of Public Health Informatics (OJPHI) to JMIR Publications. 完成公共卫生信息学在线期刊(OJPHI)向JMIR出版物的转移

Online journal of public health informatics Pub Date : 2023-07-18 eCollection Date: 2023-01-01 DOI: 10.2196/50243

Edward Mensah

{"title":"Completion of the Transfer of the Online Journal of Public Health Informatics (OJPHI) to JMIR Publications.","authors":"Edward Mensah","doi":"10.2196/50243","DOIUrl":"10.2196/50243","url":null,"abstract":"Founded in 2009, the Online Journal of Public Health Informatics (OJPHI) strives to provide an unparalleled experience as the platform of choice to advance public and population health informatics. As a premier peer-reviewed journal in this field, OJPHI's mission is to serve as an advocate for the discipline through the dissemination of public health informatics research results and best practices among practitioners, researchers, policymakers, and educators. However, in the current environment, running an independent open access journal has not been without challenges. Judging from the low geographic spread of our current stakeholders, the overreliance on a small volunteer management staff, the limited scope of topics published by the journal, and the long article turnaround time, it is obvious that OJPHI requires a change in direction in order to fully achieve its mission. Fortunately, our new publisher JMIR Publications is the leading brand in this field, with a portfolio of top peer-reviewed journals covering innovation, technology, digital medicine and health services research in the internet age. Under the leadership of JMIR Publications, OJPHI plans to expand its scope to include new topics such as precision public health informatics, the use of artificial intelligence and machine learning in public health research and practice, and infodemiology in public health informatics.","PeriodicalId":74345,"journal":{"name":"Online journal of public health informatics","volume":" ","pages":"e50243"},"PeriodicalIF":0.0,"publicationDate":"2023-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10426380/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45400187","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Patient Characteristics Associated with Phone and Video Visits at a Tele-Urgent Care Center During the Initial COVID-19 Response in North Carolina (Preprint) 北卡罗来纳州 COVID-19 初次响应期间与远程急诊中心电话和视频就诊相关的患者特征（预印本）

Online journal of public health informatics Pub Date : 2023-07-18 DOI: 10.2196/50962

Saif Khairat, Roshan John, Malvika Pillai, Barbara Edson, R. Gianforcaro

引用次数: 0

Trends in ophthalmic workforce and eye care infrastructure in South India (Preprint) 南印度眼科医务人员和眼科护理基础设施的发展趋势（预印本）

Online journal of public health informatics Pub Date : 2023-07-17 DOI: 10.2196/50921

Srinivasa Reddy Pallerla, Madhurima Reddy Pallerla, Krishnaiah Sannappaneni

引用次数: 0

Designing a Browser Extension for Reliable Online Health Information Retrieval Among Older Adults Using Design Thinking. 用设计思维设计一个可靠的老年人在线健康信息检索浏览器扩展。

Online journal of public health informatics Pub Date : 2022-11-07 eCollection Date: 2022-01-01 DOI: 10.5210/ojphi.v14i1.12593

Eden Shaveet, Marrissa Gallegos, Jonathan Castle, Lisa Gualtieri

{"title":"Designing a Browser Extension for Reliable Online Health Information Retrieval Among Older Adults Using Design Thinking.","authors":"Eden Shaveet, Marrissa Gallegos, Jonathan Castle, Lisa Gualtieri","doi":"10.5210/ojphi.v14i1.12593","DOIUrl":"https://doi.org/10.5210/ojphi.v14i1.12593","url":null,"abstract":"The pervasiveness of online mis/disinformation escalated during the COVID-19 pandemic. To address the proliferation of online mis/disinformation, it is critical to build reliability into the tools older adults use to seek health information. On average, older adult populations demonstrate disproportionate susceptibility to false messages spread under the guise of accuracy and were the most engaged with false information about COVID-19 across online platforms when compared to other age-groups. In a design-thinking challenge posed by AARP to graduate students in a Digital Health course at Tufts University School of Medicine, students leveraged existing solutions to design a web browser extension that is responsive to both passive and active health information-seeking methods utilized by older adults in the United States. This paper details the design-thinking process employed, insights gained from primary research, an overview of the prototyped solution, and insights relating to the design of effective health information-seeking platforms for older adults.","PeriodicalId":74345,"journal":{"name":"Online journal of public health informatics","volume":" ","pages":"e6"},"PeriodicalIF":0.0,"publicationDate":"2022-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9699827/pdf/ojphi-14-1-e6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40457546","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Sara Alert: An automated symptom monitoring tool for COVID-19 in 11 jurisdictions in the United States, June - August, 2021. Sara Alert: 2021年6月至8月，美国11个司法管辖区的COVID-19自动症状监测工具。

Online journal of public health informatics Pub Date : 2022-11-07 eCollection Date: 2022-01-01 DOI: 10.5210/ojphi.v14i1.12449

Carla Bezold, Erin Sizemore, Heather Halter, Diana Bartlett, Kelly Hay, Hammad Ali

{"title":"Sara Alert: An automated symptom monitoring tool for COVID-19 in 11 jurisdictions in the United States, June - August, 2021.","authors":"Carla Bezold, Erin Sizemore, Heather Halter, Diana Bartlett, Kelly Hay, Hammad Ali","doi":"10.5210/ojphi.v14i1.12449","DOIUrl":"https://doi.org/10.5210/ojphi.v14i1.12449","url":null,"abstract":"Objectives: Health department personnel conduct daily active symptom monitoring for persons potentially exposed to SARS-CoV-2. This can be resource-intensive. Automation and digital tools can improve efficiency. We describe use of a digital tool, Sara Alert, for automated daily symptom monitoring across multiple public health jurisdictions.Methods: Eleven of the 20 U.S. public health jurisdictions using Sara Alert provided average daily activity data during June 29 to August 30, 2021. Data elements included demographics, communication preferences, timeliness of symptom monitoring initiation, responsiveness to daily messages, and reports of symptoms.Results: Participating jurisdictions served a U.S. population of over 22 million persons. Health department personnel used this digital tool to monitor more than 12,000 persons per day on average for COVID-19 symptoms. On average, monitoring began 3.9 days following last exposure and was conducted for an average of 5.7 days. Monitored persons were frequently < 18 years old (45%, 5,474/12,450) and preferred communication via text message (47%). Seventy-four percent of monitored persons responded to at least one daily automated symptom message.Conclusions: In our geographically diverse sample, we found that use of an automated digital tool might improve public health capacity for daily symptom monitoring, allowing staff to focus their time on interventions for persons most at risk or in need of support. Future work should include identifying jurisdictional successes and challenges implementing digital tools; the effectiveness of digital tools in identifying symptomatic individuals, ensuring appropriate isolation, and testing to disrupt transmission; and impact on public health staff efficiency and program costs.","PeriodicalId":74345,"journal":{"name":"Online journal of public health informatics","volume":" ","pages":"e7"},"PeriodicalIF":0.0,"publicationDate":"2022-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9699828/pdf/ojphi-14-1-e7.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40457550","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The Representation of Causality and Causation with Ontologies: A Systematic Literature Review. 因果关系和因果关系的本体论表示:系统的文献综述。

Online journal of public health informatics Pub Date : 2022-09-07 eCollection Date: 2022-01-01 DOI: 10.5210/ojphi.v14i1.12577

Suhila Sawesi, Mohamed Rashrash, Olaf Dammann

{"title":"The Representation of Causality and Causation with Ontologies: A Systematic Literature Review.","authors":"Suhila Sawesi, Mohamed Rashrash, Olaf Dammann","doi":"10.5210/ojphi.v14i1.12577","DOIUrl":"https://doi.org/10.5210/ojphi.v14i1.12577","url":null,"abstract":"Objective: To explore how disease-related causality is formally represented in current ontologies and identify their potential limitations.Methods: We conducted a systematic literature search on eight databases (PubMed, Institute of Electrical and Electronic Engendering (IEEE Xplore), Association for Computing Machinery (ACM), Scopus, Web of Science databases, Ontobee, OBO Foundry, and Bioportal. We included studies published between January 1, 1970, and December 9, 2020, that formally represent the notions of causality and causation in the medical domain using ontology as a representational tool. Further inclusion criteria were publication in English and peer-reviewed journals or conference proceedings. Two authors (SS, RM) independently assessed study quality and performed content analysis using a modified validated extraction grid with pre-established categorization.Results: The search strategy led to a total of 8,501 potentially relevant papers, of which 50 met the inclusion criteria. Only 14 out of 50 (28%) specified the nature of causation, and only 7 (14%) included clear and non-circular natural language definitions. Although several theories of causality were mentioned, none of the articles offers a widely accepted conceptualization of how causation and causality can be formally represented.Conclusion: No current ontology captures the wealth of available concepts of causality. This provides an opportunity for the development of a formal ontology of causation/causality.","PeriodicalId":74345,"journal":{"name":"Online journal of public health informatics","volume":" ","pages":"e4"},"PeriodicalIF":0.0,"publicationDate":"2022-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9473331/pdf/ojphi-14-1-e4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40369363","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Population Segmentation Using a Novel Socio-Demographic Dataset. 利用新颖的社会人口数据集进行人口划分。

Online journal of public health informatics Pub Date : 2022-08-11 eCollection Date: 2022-01-01 DOI: 10.5210/ojphi.v14i1.11651

Elisabeth L Scheufele, Brandi Hodor, George Popa, Suwei Wang, William J Kassler

{"title":"Population Segmentation Using a Novel Socio-Demographic Dataset.","authors":"Elisabeth L Scheufele, Brandi Hodor, George Popa, Suwei Wang, William J Kassler","doi":"10.5210/ojphi.v14i1.11651","DOIUrl":"10.5210/ojphi.v14i1.11651","url":null,"abstract":"Appending market segmentation data to a national healthcare knowledge, attitude and behavior survey and medical claims by geocode can provide valuable insight for providers, payers and public health entities to better understand populations at a hyperlocal level and develop cohort-specific strategies for health improvement. A prolonged use case investigates population factors, including social determinants of health, in depression and develops cohort-level management strategies, utilizing market segmentation and survey data. Survey response scores for each segment were normalized against the average national score and appended to claims data to identify at-risk segment whose scores were compared with three socio-demographically comparable but not at-risk segments via Nonparametric Mann-Whitney U test to identify specific risk factors for intervention. The marketing segment, New Melting Point (NMP), was identified as at-risk. The median scores of three comparable segments differed from NMP in \"Inability to Pay For Basic Needs\" (121% vs 123%), \"Lack of Transportation\" (112% vs 153%), \"Utilities Threatened\" (103% vs 239%), \"Delay Visiting MD\" (67% vs 181%), \"Delay/Not Fill Prescription\" (117% vs 182%), \"Depressed: All/Most Time\" (127% vs 150%), and \"Internet: Virtual Visit\" (55% vs 130%) (all with p<0.001). The appended dataset illustrates NMP as having many stressors (e.g., difficult social situations, delaying seeking medical care). Strategies to improve depression management in NMP could employ virtual visits, or pharmacy incentives. Insights gleaned from appending market segmentation and healthcare utilization survey data can fill in knowledge gaps from claims-based data and provide practical and actionable insights for use by providers, payers and public health entities.","PeriodicalId":74345,"journal":{"name":"Online journal of public health informatics","volume":" ","pages":"e1"},"PeriodicalIF":0.0,"publicationDate":"2022-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9473328/pdf/ojphi-14-1-e1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40369365","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Health Information Technology During the COVID-19 Epidemic: A Review via Text Mining. COVID-19 流行期间的医疗信息技术：通过文本挖掘进行回顾。

Online journal of public health informatics Pub Date : 2022-08-11 eCollection Date: 2022-01-01 DOI: 10.5210/ojphi.v14i1.11090

Meisam Dastani, Alireza Atarodi

{"title":"Health Information Technology During the COVID-19 Epidemic: A Review via Text Mining.","authors":"Meisam Dastani, Alireza Atarodi","doi":"10.5210/ojphi.v14i1.11090","DOIUrl":"10.5210/ojphi.v14i1.11090","url":null,"abstract":"Background: Due to the prevalence of the COVID-19 epidemic in all countries of the world, the need to apply health information technology is of great importance. hence, the study has identified the role of health information technology during the period of the COVID-19 epidemic.Methods: The present research is a review study by employing text mining techniques. Therefore, 941 published documents related to health information technology's role during the COVID-19 epidemic were extracted by keyword searching in the Web of Science database. In order to analyze the data and implement the text mining and topic modeling algorithms, Python programming language was applied.Results: The results indicated that the highest number of publications related to the role of health information technology in the period of the COVID-19 epidemic was respectively on the following topics: \"Models and smart systems,\" \"Telemedicine,\" \"Health care,\" \"Health information technology,\" \"Evidence-based medicine,\" \"Big data and Statistic analysis.\"Conclusion: Health information technology has been extensively used during the COVID-19 epidemic. Therefore, different communities can apply these technologies, considering the conditions and facilities to manage the COVID-19 epidemic better.","PeriodicalId":74345,"journal":{"name":"Online journal of public health informatics","volume":" ","pages":"e3"},"PeriodicalIF":0.0,"publicationDate":"2022-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9473330/pdf/ojphi-14-1-e3.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40369364","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0