Impact of Digital Twins and Metaverse on Cities: History, Current Situation, and Application Perspectives

Applied Sciences, Journal Year: 2022, Volume and Issue: 12(24), P. 12820 - 12820

Published: Dec. 14, 2022

To promote the expansion and adoption of Digital Twins (DTs) in Smart Cities (SCs), a detailed review impact DTs digitalization on cities is made to assess progression standardization their management mode. Combined with technical elements DTs, coupling effect technology urban construction internal logic embedded are discussed. Relevant literature covering full range technologies applications collected, evaluated, collated, relevant studies concatenated, accepted conclusions summarized by modules. First, historical process content City (DC) under modern demand analyzed, main ideas DC design discussed combination key DTs. Then, metaverse product various different scenes. It component integration real world digital can provide more advanced support DC. architecture composed an infrastructure terminal information center application server end. Urban intelligent realized through physical data collection, transmission, processing, visualization. The platform improve city’s perception decision-making ability bring broader vision for future planning progression. interactive experience virtual covered effectively real, will also greatly SCs. In summary, this work important reference value overall development practical cities, which improves operation efficiency governance level cities.

Language: Английский

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Unveiling the prevalence and risk factors of early stage postpartum depression: a hybrid deep learning approach

Multimedia Tools and Applications, Journal Year: 2024, Volume and Issue: 83(26), P. 68281 - 68315

Published: Jan. 25, 2024

Language: Английский

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Unravelling the complexities of depression with medical intelligence: exploring the interplay of genetics, hormones, and brain function

Complex & Intelligent Systems, Journal Year: 2024, Volume and Issue: 10(4), P. 5883 - 5915

Published: April 4, 2024

Abstract Depression is a multifactorial disease with unknown etiology affecting globally. It’s the second most significant reason for infirmity in 2020, about 50 million people worldwide, 80% living developing nations. Recently, surge depression research has been witnessed, resulting multitude of emerging techniques developed prediction, evaluation, detection, classification, localization, and treatment. The main purpose this study to determine volume conducted on different aspects such as genetics, proteins, hormones, oxidative stress, inflammation, mitochondrial dysfunction, associations other mental disorders like anxiety stress using traditional medical intelligence (medical AI). In addition, it also designs comprehensive survey treatment planning, genetic predisposition, along future recommendations. This work designed through methods, including systematic mapping process, literature review, network visualization. we used VOSviewer software some authentic databases Google Scholar, Scopus, PubMed, Web Science data collection, analysis, designing picture study. We analyzed 60 articles related intelligence, 47 from machine learning 513,767 subjects (mean ± SD = 10,931.212 35,624.372) 13 deep 37,917 3159.75 6285.57). Additionally, found that stressors impact brain's cognitive autonomic functioning, increased production catecholamine, decreased cholinergic glucocorticoid activity, cortisol. These factors lead chronic inflammation hinder normal leading depression, anxiety, cardiovascular disorders. brain, reactive oxygen species (ROS) by IL-6 stimulation cytochrome c oxidase inhibited nitric oxide, potent inhibitor. Proteins, lipids, phosphorylation enzymes, mtDNA are further disposed impairment mitochondria. Consequently, dysfunction exacerbates impairs DNA (mtDNA) or deletions mtDNA, increases intracellular Ca 2+ levels, changes fission/fusion morphology, lastly leads neuronal death. highlights multidisciplinary approaches intelligence. It will open new way technologies.

Language: Английский

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Development and preliminary testing of Health Equity Across the AI Lifecycle (HEAAL): A framework for healthcare delivery organizations to mitigate the risk of AI solutions worsening health inequities

PLOS Digital Health, Journal Year: 2024, Volume and Issue: 3(5), P. e0000390 - e0000390

Published: May 9, 2024

The use of data-driven technologies such as Artificial Intelligence (AI) and Machine Learning (ML) is growing in healthcare. However, the proliferation healthcare AI tools has outpaced regulatory frameworks, accountability measures, governance standards to ensure safe, effective, equitable use. To address these gaps tackle a common challenge faced by delivery organizations, case-based workshop was organized, framework developed evaluate potential impact implementing an solution on health equity. Health Equity Across Lifecycle (HEAAL) co-designed with extensive engagement clinical, operational, technical, leaders across organizations ecosystem partners US. It assesses 5 equity assessment domains–accountability, fairness, fitness for purpose, reliability validity, transparency–across span eight key decision points adoption lifecycle. process-oriented containing 37 step-by-step procedures evaluating existing 34 new total. Within each procedure, it identifies relevant stakeholders data sources used conduct procedure. HEAAL guides how may mitigate risk solutions worsening inequities. also informs much resources support are required assess

Language: Английский

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Prediction of Postpartum Depression With Dataset Using Hybrid Data Mining Classification Technique

International Journal of Computational and Experimental Science and Engineering, Journal Year: 2025, Volume and Issue: 11(1)

Published: Jan. 12, 2025

Postpartum Depression is a condition or state which usually affects the woman immediately after child birth. The birth of baby not only brings delighted emotions such as excitement, but also fear and anxiety may sometimes lead to depression. It period physical, emotional behavioral changes that happen in some delivery. Apart from chemical changes, there are many factors affect during pregnancy period. If PPD identified treated at earlier stages, it serious issues for mother child. therefore vital importance sift through any early stage prevent consequences. objective this study find out presence without getting worse. Data mining plays an important role health care industry with successful outcome. helps hidden patterns, trends anomalies large dataset make predictions. proposed system combined classification technique prediction postpartum depression uses Support vector machine, Artificial Neural Network Hybrid classifier algorithm produce best result.

Language: Английский

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Predicting women with depressive symptoms postpartum with machine learning methods

Scientific Reports, Journal Year: 2021, Volume and Issue: 11(1)

Published: April 12, 2021

Postpartum depression (PPD) is a detrimental health condition that affects 12% of new mothers. Despite negative effects on mothers' and children's health, many women do not receive adequate care. Preventive interventions are cost-efficient among high-risk women, but our ability to identify these poor. We leveraged the power clinical, demographic, psychometric data assess if machine learning methods can make accurate predictions postpartum depression. Data were obtained from population-based prospective cohort study in Uppsala, Sweden, collected between 2009 2018 (BASIC study, n = 4313). Sub-analyses without previous performed. The extremely randomized trees method provided robust performance with highest accuracy well-balanced sensitivity specificity (accuracy 73%, 72%, 75%, positive predictive value 33%, 94%, area under curve 81%). Among earlier mental issues, was 64%. variables setting at most risk for PPD anxiety during pregnancy, as well related resilience personality. Future clinical models could be implemented directly after delivery might consider including order high facilitate individualized follow-up cost-effectiveness.

Language: Английский

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Development and validation of a machine learning‐based postpartum depression prediction model: A nationwide cohort study

Depression and Anxiety, Journal Year: 2020, Volume and Issue: 38(4), P. 400 - 411

Published: Dec. 7, 2020

Currently, postpartum depression (PPD) screening is mainly based on self-report symptom-based assessment, with lack of an objective, integrative tool which identifies women at increased risk, before the emergent PPD. We developed and validated a machine learning-based PPD prediction model utilizing electronic health record (EHR) data, identified novel predictors. A nationwide longitudinal cohort that included 214,359 births between January 2008 December 2015, divided into training validation sets, was constructed Israel largest maintenance organization's EHR-database. defined as new diagnosis depressive episode or antidepressant prescription within first year postpartum. gradient-boosted decision tree algorithm applied to EHR-derived sociodemographic, clinical, obstetric features. Among birth cohort, 1.9% (n = 4104) met case definition new-onset In set, achieved area under curve (AUC) 0.712 (95% confidence interval, 0.690-0.733), sensitivity 0.349 specificity 0.905 90th percentile risk threshold, identifying PPDs rate more than three times higher overall set (positive negative predictive values were 0.074 0.985, respectively). The model's strongest predictors both well-recognized (e.g., past depression) less-recognized (differing patterns blood tests) factors. Machine models incorporating predictors, could augment practice by high-risk population greatest need for preventive intervention, development

Language: Английский

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Impact of the Covid-19 pandemic on perinatal mental health (Riseup-PPD-COVID-19): protocol for an international prospective cohort study

BMC Public Health, Journal Year: 2021, Volume and Issue: 21(1)

Published: Feb. 17, 2021

Abstract Background Corona Virus Disease 19 (COVID-19) is a new pandemic, declared public health emergency by the World Health Organization, which could have negative consequences for pregnant and postpartum women. The scarce evidence published to date suggests that perinatal mental has deteriorated since COVID-19 outbreak. However, few studies so far some limitations, such as cross-sectional design omission of important factors understanding health, including governmental restriction measures healthcare practices implemented at maternity hospitals. Within Riseup-PPD COST Action, study underway assess impact in health. primary objectives are (1) evaluate changes outcomes; (2) determine risk protective during pandemic. Additionally, we will compare results between countries participating study. Methods This an international prospective cohort study, with baseline three follow-up assessments over six-month period. It being carried out 11 European (Albania, Bulgaria, Cyprus, France, Greece, Israel, Malta, Portugal, Spain, Turkey, United Kingdom), Argentina, Brazil Chile. sample consists adult women (with infants up 6 months age). assessment includes on epidemiology (Oxford Government Response Tracker dataset), Coronavirus Perinatal Experiences (COPE questionnaires), psychological distress (BSI-18), depression (EPDS), anxiety (GAD-7) post-traumatic stress symptoms (PTSD checklist DSM-V). Discussion provide information pandemic well-being, identification potential implementing predictive models using machine learning techniques. findings help policymakers develop suitable guidelines prevention strategies contribute designing tailored interventions. Trial registration ClinicalTrials.gov Identifier: NCT04595123 .

Language: Английский

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Predicting depression among rural and urban disabled elderly in China using a random forest classifier

BMC Psychiatry, Journal Year: 2022, Volume and Issue: 22(1)

Published: Feb. 15, 2022

With global aging, the number of elderly with physical disabilities is also increasing. Compared ordinary elderly, who lose their independence are more likely to have symptoms depression. Reducing depression may help alleviate disability process those find themselves in disabled stages. Therefore, purpose this study explore predictive effects demographic characteristics, health behavior, status, family relations, social and subjective attitude on rural urban improve early symptom recognition.A total 1460 older adults aged 60 were selected from China Family Panel Studies (CFPS). Depression was assessed according The Center for Epidemiologic Scale (CES-D). This paper used random forest classifier predict six aspects: relationship, relationship. prediction model established based 70% training set 30% test set. rate 57.67%, that 44.59%. mean values 10-k cross-validated results 0.71 areas 0.70 areas. AUC:0.71, specificity: 65.3%, sensitivity: 80.6% depression; AUC:0.78, 78.1%, 64.2% depression, respectively. There apparent differences top ten predictors between elderly. common self-rated health, changing perceived disease or accidence experience within past 2 weeks, life satisfaction, trusting people, BMI, having trust future. Non-common chronic diseases, neighborly medical expenses 1 year, community emotion, sleep duration, per capita income. Using data lead detection

Language: Английский

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Analysis of Publication Activity and Research Trends in the Field of AI Medical Applications: Network Approach

International Journal of Environmental Research and Public Health, Journal Year: 2023, Volume and Issue: 20(7), P. 5335 - 5335

Published: March 30, 2023

Artificial intelligence (AI) has revolutionized numerous industries, including medicine. In recent years, the integration of AI into medical practices shown great promise in enhancing accuracy and efficiency diagnosing diseases, predicting patient outcomes, personalizing treatment plans. This paper aims at exploration AI-based medicine research using network approach analysis existing trends based on PubMed. Our findings are results PubMed search queries number papers obtained by different queries. goal is to explore how methods used healthcare research, which approaches techniques most popular, discuss potential reasoning behind results. Using co-occurrence constructed VOSviewer software, we detected main clusters interest research. Then, proceeded with thorough publication activity various categories applied types data. We analyzed query processing database over past 5 years via a specifically designed strategy for generating selection keywords from interest. provide comprehensive applications data modalities, context fields specific diseases that carry greatest danger human population.

Language: Английский

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