Social Media-Based Surveillance Systems for Health Informatics Using Machine and Deep Learning Techniques:A Comprehensive Review and Open Challenges

Social media(SM)based surveillance systems,combined with machine learning(ML)and deep learning(DL)techniques,have shown potential for early detection of epidemic outbreaks.This review discusses the current state of SM-based surveillance methods for early epidemic outbreaks and the role of ML and DL in enhancing their performance.Since,every year,a large amount of data related to epidemic outbreaks,particularly Twitter data is generated by SM.This paper outlines the theme of SM analysis for tracking health-related issues and detecting epidemic outbreaks in SM,along with the ML and DL techniques that have been configured for the detection of epidemic outbreaks.DL has emerged as a promising ML technique that adaptsmultiple layers of representations or features of the data and yields state-of-the-art extrapolation results.In recent years,along with the success of ML and DL in many other application domains,both ML and DL are also popularly used in SM analysis.This paper aims to provide an overview of epidemic outbreaks in SM and then outlines a comprehensive analysis of ML and DL approaches and their existing applications in SM analysis.Finally,this review serves the purpose of offering suggestions,ideas,and proposals,along with highlighting the ongoing challenges in the field of early outbreak detection that still need to be addressed.

Managing Health Treatment by Optimizing Complex Lab-Developed Test Configurations: A Health Informatics Perspective

A complex Laboratory Developed Test(LDT)is a clinical test developed within a single laboratory.It is typically configured from many fea-ture constraints from clinical repositories,which are part of the existing Lab-oratory Information Management System(LIMS).Although these clinical repositories are automated,support for managing patient information with test results of an LDT is also integrated within the existing LIMS.Still,the support to configure LDTs design needs to be made available even in standard LIMS packages.The manual configuration of LDTs is a complex process and can generate configuration inconsistencies because many constraints between features can remain unsatisfied.It is a risky process and can lead patients to undergo unnecessary treatments.We proposed an optimized solution(opt-LDT)based on Genetic Algorithms to automate the configuration and resolve the inconsistencies in LDTs.Opt-LDT encodes LDT configuration as an optimization problem and generates a consistent configuration that satisfies the constraints of the features.We tested and validated opt-LDT for a local secondary care hospital in a real healthcare environment.Our results,averaged over ten runs,show that opt-LDT resolves 90%of inconsistencies while taking between 6 and 6.5 s for each configuration.Moreover,positive feedback based on a subjective questionnaire from clinicians regarding the performance,acceptability,and efficiency of opt-LDT motivates us to present our results for regulatory approval.

Characteristics and Enlightenment of Health Informatics Education in American Universities

The demand for health information is increasing in China,and China has gradually paid attention to health informatics education.The successful experience of American health informatics education can effectively promote the development of health informatics education in China.This paper analyzes the main characteristics of health informatics education in American colleges and universities by literature survey and network survey,and concludes that Chinese colleges and universities should strengthen practical education,enhance teachers’strength,increase the form of educational projects,and perfect the curriculum content system.

Mobile health interventions for improving maternal and child health outcomes in South Africa:a systematic review

Background:Maternal and child health(MCH)remains a significant public health concern globally despite previous efforts made to improve MCH services.Initiatives such as antenatal care,postnatal care services exclusive breastfeeding,child vaccination and supplements have been rolled out to improve MCH outcomes.However,inadequate maternal healthcare,socioeconomic factors,obstetric haemorrhaging,complications of hypertension during pregnancy,lack of maternal information,poor universal health coverage and uptake of MCH services exacerbate maternal mortality and child mortality rates,especially in resource-constrained areas in many sub-Saharan African countries including South Africa.Objective:This study aimed to review mobile health(mHealth)interventions deployed to improve maternal and child health outcomes.Methods:The study adopted the Preferred Reporting Items for Systematic Reviews and Meta-Analyses model to search and retrieve relevant literature from reputable,prominent electronic databases(Google Scholar,Scopus,PubMed,Embase,CINAHL,Web of Science,etc.).A total of 26 papers were selected and analyzed.Results:The findings revealed several mHealth interventions such as MomConnect,Mobile Alliance for Maternal Action,NurseConnect,ChildConnect,CommCare,Road to Health Application and Philani Mobile Video Intervention for Exclusive Breastfeeding have been utilized by healthcare workers and women to improve access to MCH services.However,inadequate digital infrastructure,digital divide,resistance to change,inadequate funding,language barriers,short message service and data costs,lack of digital skills and support,compatibility,scalability and interoperability issues,legislative and policy compliance,lack of mHealth awareness,data security and privacy concerns hinder uptake and utilisation of mHealth interventions.There is a need to scale up and sustain mHealth interventions and update existing regulatory framework,policies and strategies.Conclusion:mHealth interventions offer unprecedented opportunities to improve access to maternal information and substantially improve maternal and child health services.Stakeholder engagement and the development of sustainable funding strategies are important for successfully implementing and scaling mHealth projects while addressing existing and emerging key issues.

Health awareness and lifestyle toward healthcare-seeking behavior among students in Universitas Advent Indonesia

Objective:Healthcare-seeking behavior(HSB)would affect the prevalence of morbidity and mortality.There are various factors that affect one's HSB.This study aimed to determine if health awareness and lifestyle might relate to HSB.Methods:A cross-sectional study was applied by using three questionnaires to determine par ticipants'health awareness,lifestyle,and HSB.This study took place in Universitas Advent Indonesia and the students were recruited to be par ticipants.Results:There were 39 par ticipants joined in this study.Most of the par ticipants were females,third-year students,and from Accounting major.Almost all participants were aware of their low risk of health issues,had a fine lifestyle,and had moderate HSB.Conclusions:One's urge to seek health care facilities was not related to their health awareness and lifestyle.There was no fur ther study to contradict with this finding at this moment.

Towards an actionable One Health approach

Background Despite the increasing focus on strengthening One Health capacity building on global level,challenges remain in devising and implementing real-world interventions particularly in the Asia-Pacific region.Recognizing these gaps,the One Health Action Commission(OHAC)was established as an academic community for One Health action with an emphasis on research agenda setting to identify actions for highest impact.Main text This viewpoint describes the agenda of,and motivation for,the recently formed OHAC.Recognizing the urgent need for evidence to support the formulation of necessary action plans,OHAC advocates the adoption of both bottom-up and top-down approaches to identify the current gaps in combating zoonoses,antimicrobial resistance,addressing food safety,and to enhance capacity building for context-sensitive One Health implementation.Conclusions By promoting broader engagement and connection of multidisciplinary stakeholders,OHAC envisions a collaborative global platform for the generation of innovative One Health knowledge,distilled practical experience and actionable policy advice,guided by strong ethical principles of One Health.

Charting the course:India's health expenditure projections for 2035

Objectives Understanding past trends and forecasting future changes in health spending is vital for planning and reducing reliance on out-of-pocket(OOP)expenses.The current study analyzed health expenditure patterns in India and forecasted future trends and patterns until 2035.Methods Data on health expenditure in India from 2000 to 2019 was collected from the Organisation for Economic Co-operation and Development(OECD)iLibrary and National Health Accounts 2019 databases.Gross domestic product(GDP)data from the World Bank was also utilized.Descriptive statistics analyzed the composition and pattern,while the exponential smoothing model forecasted future health expenditures.Results The findings revealed that expenditure made by OOP is the primary health financing source,followed by government and pre-paid private spending.The percentage of GDP allocated to total health expenditure remains stable,while the per capita health expenditure fluctuates.Variations in expenditure among states are observed,with Karnataka relying heavily on pre-paid private coverage.Future projections suggest a decline in per capita and total health expenditure as a share of GDP,with a slight increase in the government’s share.Pre-paid private expenditure per capita and OOP health expenditure as a share of the total is projected to remain relatively constant but still high in absolute terms.Conclusion The study highlights variations in health spending in India,characterized by high OOP spending,limited public coverage,and a need for investments,and reforms to improve healthcare access and equity.

Health Systems Strengthening to Tackle the Global Burden of Pediatric and Congenital Heart Disease: A Diagonal Approach

1 Background Congenital heart disease(CHD)is the most common major congenital anomaly,affecting approximately one in every 100 live births[1].Among congenital anomalies,66%of preventable deaths are due to CHD,and 58%of the avertable morbidity and mortality due to congenital anomalies would result from scaling congenital heart surgery services[2].Every year,nearly 300,000 children and adults die from CHD,the majority of whom live in low-and middle-income countries(LMICs)[3].Approximately 49%of all individuals with CHD will require surgical or interventional care at some point in their lifetime[4];as a result of advances in access to and the delivery of such services,over 95%of children born with CHD in high-income countries now live into adulthood[3].Here,adults have surpassed children in the number of CHD cases at a ratio of 2:1[5].

Integrating the health belief model into health education programs in a clinical setting

The article demonstrates that health belief model(HBM)-based health education in hypertensive patients effectively improves blood pressure control and medication adherence at 3 months and 6 months.The HBM addresses perceived barriers,benefits,susceptibility,severity,and self-efficacy,leading to better health behaviors.HBM-based education has been effective in various contexts,including managing chronic diseases,promoting cancer screenings,and preventing infectious diseases.However,the model has limitations,such as cultural applicability and addressing complex health behaviors influenced by environmental factors.Future research should integrate HBM with other theories and conduct longitudinal studies to assess long-term impacts.Despite these limitations,HBM-based education significantly improves patient outcomes,highlighting its potential in health education and promotion when appropriately adapted and implemented.This reinforces the model's value in designing effective health interventions and advancing public health.

Informatics and Health征稿启事

Informatics and Health(《信息学与健康》)是由中国医学科学院北京协和医学院主办,中国医学科学院医学信息研究所与科爱公司合作编辑出版,旨在反映医学卫生健康领域与信息科学技术相关的前沿学术研究进展的英文期刊(ISSN:2949-9534),本刊由中国工程院院士、中国医学科学院北京协和医学院院校长王辰教授担任主编,中国医学科学院医学信息研究所所长刘辉研究员担任执行主编。

Informatics and Health征稿启事

Informatics and Health(《信息学与健康》)是由中国医学科学院北京协和医学院主办,中国医学科学院医学信息研究所与科爱公司合作编辑出版,旨在反映医学卫生健康领域与信息科学技术相关的前沿学术研究进展的英文期刊(ISSN:2949-9534),本刊由中国工程院院士、中国医学科学院北京协和医学院院校长王辰教授担任主编,中国医学科学院医学信息研究所所长刘辉研究员担任执行主编。

Informatics and Health征稿启事

Informatics and Health(《信息学与健康》)是由中国医学科学院北京协和医学院主办,中国医学科学院医学信息研究所与科爱公司合作编辑出版,旨在反映医学卫生健康领域与信息科学技术相关的前沿学术研究进展的英文期刊(ISSN:2949-9534),本刊由中国工程院院士、中国医学科学院北京协和医学院院校长王辰教授担任主编,中国医学科学院医学信息研究所所长刘辉研究员担任执行主编。

Informatics and Health征稿启事

Informatics and Health(《信息学与健康》)是由中国医学科学院北京协和医学院主办,中国医学科学院医学信息研究所与科爱公司合作编辑出版,旨在反映医学卫生健康领域与信息科学技术相关的前沿学术研究进展的英文期刊(ISSN:2949-9534),本刊由中国工程院院士、中国医学科学院北京协和医学院院校长王辰教授担任主编,中国医学科学院医学信息研究所所长刘辉研究员担任执行主编。

Informatics and Health征稿启事

Informatics and Health(《信息学与健康》)是由中国医学科学院北京协和医学院主办,中国医学科学院医学信息研究所与科爱公司合作编辑出版,旨在反映医学卫生健康领域与信息科学技术相关的前沿学术研究进展的英文期刊(ISSN:2949-9534),本刊由中国工程院院士、中国医学科学院北京协和医学院院校长王辰教授担任主编,中国医学科学院医学信息研究所所长刘辉研究员担任执行主编。

Informatics and Health征稿启事

Informatics and Health(《信息学与健康》)是由中国医学科学院北京协和医学院主办,中国医学科学院医学信息研究所与科爱公司合作编辑出版,旨在反映医学卫生健康领域与信息科学技术相关的前沿学术研究进展的英文期刊(ISSN:2949-9534),本刊由中国工程院院士、中国医学科学院北京协和医学院院校长王辰教授担任主编,中国医学科学院医学信息研究所所长刘辉研究员担任执行主编。

Informatics and Health征稿启事

Informatics and Health(《信息学与健康》)是由中国医学科学院北京协和医学院主办,中国医学科学院医学信息研究所与科爱公司合作编辑出版,旨在反映医学卫生健康领域与信息科学技术相关的前沿学术研究进展的英文期刊(ISSN:2949-9534),本刊由中国工程院院士。

Using AI and Precision Nutrition to Support Brain Health during Aging

Artificial intelligence, often referred to as AI, is a branch of computer science focused on developing systems that exhibit intelligent behavior. Broadly speaking, AI researchers aim to develop technologies that can think and act in a way that mimics human cognition and decision-making [1]. The foundations of AI can be traced back to early philosophical inquiries into the nature of intelligence and thinking. However, AI is generally considered to have emerged as a formal field of study in the 1940s and 1950s. Pioneering computer scientists at the time theorized that it might be possible to extend basic computer programming concepts using logic and reasoning to develop machines capable of “thinking” like humans. Over time, the definition and goals of AI have evolved. Some theorists argued for a narrower focus on developing computing systems able to efficiently solve problems, while others aimed for a closer replication of human intelligence. Today, AI encompasses a diverse set of techniques used to enable intelligent behavior in machines. Core disciplines that contribute to modern AI research include computer science, mathematics, statistics, linguistics, psychology and cognitive science, and neuroscience. Significant AI approaches used today involve statistical classification models, machine learning, and natural language processing. Classification methods are widely applicable to problems in various domains like healthcare, such as informing diagnostic or treatment decisions based on patterns in data. Dean and Goldreich, 1998, define ML as an approach through which a computer has to learn a model by itself from the data provided but no specification on the sort of model is provided to the computer. They can then predict values for things that are different from the values used in training the models. NLP looks at two interrelated concerns, the task of training computers to understand human languages and the fact that since natural languages are so complex, they lend themselves very well to serving a number of very useful goals when used by computers.

Informatics and Health征稿启事

Informatics and Health(《信息学与健康》)是由中国医学科学院北京协和医学院主办,中国医学科学院医学信息研究所与科爱公司合作编辑出版,旨在反映医学卫生健康领域与信息科学技术相关的前沿学术研究进展的英文期刊(ISSN:2949-9534),本刊由中国工程院院士、中国医学科学院北京协和医学院院校长王辰教授担任主编,中国医学科学院医学信息研究所所长刘辉研究员担任执行主编。

Informatics and Health征稿启事

Informatics and Health(《信息学与健康》)是由中国医学科学院北京协和医学院主办,中国医学科学院医学信息研究所与科爱公司合作编辑出版,旨在反映医学卫生健康领域与信息科学技术相关的前沿学术研究进展的英文期刊(ISSN:2949-9534),本刊由中国工程院院士、中国医学科学院北京协和医学院院校长王辰教授担任主编,中国医学科学院医学信息研究所所长刘辉研究员担任执行主编。

Informatics and Health征稿启事

Informatics and Health(《信息学与健康》)是由中国医学科学院北京协和医学院主办,中国医学科学院医学信息研究所与科爱公司合作编辑出版,旨在反映医学卫生健康领域与信息科学技术相关的前沿学术研究进展的英文期刊(ISSN:2949-9534),本刊由中国工程院院士、中国医学科学院北京协和医学院院校长王辰教授担任主编,中国医学科学院医学信息研究所所长刘辉研究员担任执行主编。