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Your search keyword 'LN cat08778a' showing total 3 results
3 results on 'LN cat08778a'

1. The Convergence of Human and Artificial Intelligence on Clinical Care - Part I.

Author

Abedi, Vida and Abedi, Vida

Subjects
Medicine, machine learning-enabled decision support system, improving diagnosis accuracy, Bayesian network, bariatric surgery, health-related quality of life, comorbidity, voice change, larynx cancer, machine learning, deep learning, voice pathology classification, imputation, electronic health records, EHR, laboratory measures, medical informatics, inflammatory bowel disease, C. difficile infection, osteoarthritis, complex diseases, healthcare, artificial intelligence, interpretable machine learning, explainable machine learning, septic shock, clinical decision support system, electronic health record, cerebrovascular disorders, stroke, SARS-CoV-2, COVID-19, cluster analysis, risk factors, ischemic stroke, outcome prediction, recurrent stroke, cardiac ultrasound, echocardiography, portable ultrasound, aneurysm surgery, temporary artery occlusion, clipping time, artificial neural network, digital imaging, monocytes, promonocytes and monoblasts, chronic myelomonocytic leukemia (CMML) and acute myeloid leukemia (AML) for acute monoblastic leukemia and acute monocytic leukemia, concordance between hematopathologists, mechanical ventilation, respiratory failure, ADHD, social media, Twitter, pharmacotherapy, stimulants, alpha-2-adrenergic agonists, non-stimulants, trust, passive adherence, human factors
Abstract

Summary: This edited book contains twelve studies, large and pilots, in five main categories: (i) adaptive imputation to increase the density of clinical data for improving downstream modeling; (ii) machine-learning-empowered diagnosis models; (iii) machine learning models for outcome prediction; (iv) innovative use of AI to improve our understanding of the public view; and (v) understanding of the attitude of providers in trusting insights from AI for complex cases. This collection is an excellent example of how technology can add value in healthcare settings and hints at some of the pressing challenges in the field. Artificial intelligence is gradually becoming a go-to technology in clinical care; therefore, it is important to work collaboratively and to shift from performance-driven outcomes to risk-sensitive model optimization, improved transparency, and better patient representation, to ensure more equitable healthcare for all.

2. COVID-19. Impact on Public Health and Healthcare.

Author

Batra, Kavita, Sharma, Manoj, and Batra, Kavita

Subjects
Humanities, Social interaction, COVID-19, novel coronavirus, social lockdown, protection motivation theory, health behavior, health communication, pregnant woman, coronavirus, infectious disease transmission, vertical transmission, obstetric management, SARS-CoV-2, systematic review, computerized tomography, pneumonia, risk factors, echocardiography, healthcare, mental health, Impact of event scale, negative attitude, Saudi Arabian females, health services, cost, manual therapy, chiropractic, osteopathy, physiotherapy, direct RT-PCR, molecular detection, dental care, dental health services, dental visits, dental service use, postponed dental visits, check-up, dental examination, pain, dental complaints, oral health, Saudi Arabia, blood donors, seroprevalence, ELISA, antibodies, lockdown, multi-theory model, behavior change, pandemic, handwashing, young adults, college students, protective behavior changes, individual, family, environmental factor, COVID-19 spreading, online survey, awareness and knowledge, ships, seafarers, SARS-COV-2, anxiety, depression, stress, suicidal ideation, students, time-series, ARIMA, forecasting, confirmed cases, infectious disease, international cruises, health policy and regulation, control strategies, international cooperation, global health governance, ICF, healthcare services, interprofessional education, medical students, pharmacy students, telehealth, older adults, artificial intelligence, machine learning, bibliometric analysis, health, novel design, fabrication, automated dispenser, LDR based controller, reduction of COVID-19 spread, psychological symptom, college student, avoidance of infection, social distancing, free tickets for the aged, subway use demand, e-learning, youth and children health, visual health, myopia, routine care, global pandemic, role conflict, role ambiguity, social support, dental precautions, dental students, India, infection control, knowledge, perception, survey, face masks, young people, behaviors, dentist, infection control practices, concerns, dental practice, social isolation, social connectedness, loneliness, technology, internet, smartphones, m-health, severe acute respiratory syndrome, post-graduate year training, self-efficacy, emotional traits, Coronavirus, prevention, community, public health nurse, telephone consultation, vaccine literacy, Japan, family carers for older adults, sustainable ageing society, health communications, mass media, HCWs, personality traits, intolerance of uncertainty, coping strategies, perceived stress, resilience, migration, refugees, fear, modeling, data analysis, assessment, effectiveness, incidence rate, restriction, epidemic pattern, exponential growth, basic reproduction number (R0), spatio-temporal analysis, demographic risk factor, observational study, public health, Southeast Asia (SEA), vaccination rate, basic reproduction number, SARS-CoV, African American, COVID-19 vaccine, vaccine hesitancy, vaccine hesitant, text classification, SARS-CoV-2 infection, survival rate, hospitalized patients, Hidalgo Mexico, multimorbidity, vaccination, level 1 trauma, health care workers, information source trust, COVID-19 stressor, global south
Abstract

Summary: This compendium describes the impact of COVID-19 pandemic on all aspects of people lives. Data presented in this collection will be useful to understand the disruption in healthcare, learning, and socio-economic aspects amidst the pandemic. The sooner we begin to understand the impact, the better placed we will be to address the unmet needs of vulnerable population groups.

3. Data Science in Healthcare.

Author

Hulsen, Tim and Hulsen, Tim

Subjects
Medicine, Pharmacology, data sharing, data management, data science, big data, healthcare, depression, psychological treatment, task sharing, primary care, pilot study, non-specialist health worker, training, digital technology, mental health, COVID-19, SARS-CoV-2, pneumonia, computed tomography, case fatality rate, social distancing, smoking, metabolically healthy obese phenotype, metabolic syndrome, obesity, coronavirus, machine learning, social media, apache spark, Twitter, Arabic language, distributed computing, smart cities, smart healthcare, smart governance, Triple Bottom Line (TBL), thoracic pain, tree classification, cross-validation, hand-foot-and-mouth disease, early-warning model, neural network, genetic algorithm, sentinel surveillance system, outbreak prediction, artificial intelligence, vascular access surveillance, arteriovenous fistula, end stage kidney disease, dialysis, kidney failure, chronic kidney disease (CKD), end-stage kidney disease (ESKD), kidney replacement therapy (KRT), risk prediction, naïve Bayes classifiers, precision medicine, machine learning models, data exploratory techniques, breast cancer diagnosis, tumors classification, n/a
Abstract

Summary: Data science is an interdisciplinary field that applies numerous techniques, such as machine learning, neural networks, and deep learning, to create value based on extracting knowledge and insights from available data. Advances in data science have a significant impact on healthcare. While advances in the sharing of medical information result in better and earlier diagnoses as well as more patient-tailored treatments, information management is also affected by trends such as increased patient centricity (with shared decision making), self-care (e.g., using wearables), and integrated care delivery. The delivery of health services is being revolutionized through the sharing and integration of health data across organizational boundaries. Via data science, researchers can deliver new approaches to merge, analyze, and process complex data and gain more actionable insights, understanding, and knowledge at the individual and population levels. This Special Issue focuses on how data science is used in healthcare (e.g., through predictive modeling) and on related topics, such as data sharing and data management.

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