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  1. 2041
    “…We concluded that multidisciplinary collaboration, involving experts in health care (including senior staff in surgery, nursing, and anesthesia), data science, health care management, and health information technology, can help build an evidence-based stratified surgical patient safety improvement system. …”
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    Key Issues in the Development of an Evidence-Based Stratified Surgical Patient Safety Improvement Information System: Experience From a Multicenter Surgical Safety Program
  2. 2042
    “…METHODS: CDC analyzed 2012–2018 retail pharmacy data from IQVIA, a health care, data science, and technology company, to assess U.S. naloxone dispensing by U.S. …”
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    Vital Signs: Pharmacy-Based Naloxone Dispensing — United States, 2012–2018
  3. 2043
    “…METHODS: Standardized tidal volumes (mL/kg predicted body weight) were chosen as a proxy for physician decision-making behavior. Using data-science approaches, we quantified the effect of eight factors (six severity of illness, two physician behaviors) on selected standardized tidal volumes in ARDS and control patients. …”
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    A quantitative approach for the analysis of clinician recognition of acute respiratory distress syndrome using electronic health record data
  4. 2044
    por Helliwell, John R.
    Publicado 2019
    “…Some social scientists also emphasize more than FAIR being needed, the data should be “FACT,” which is an acronym meaning Fair, Accurate, Confidential, and Transparent [van der Aalst et al., “Responsible data science,” Bus Inf. Syst. Eng. 59(5), 311–313 (2017)], this being the issue of ensuring reproducibility not just reusability. …”
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    FACT and FAIR with Big Data allows objectivity in science: The view of crystallography
  5. 2045
    “…RESULTS: The applications of AI have mainly impacted clinical settings (enhanced prognosis and diagnosis, robot-assisted surgery, and rehabilitation), data science and precision medicine (collecting individual data for precision medicine), and policy making (raising ethical and legal issues, especially regarding privacy and confidentiality of data). …”
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    Modeling Research Topics for Artificial Intelligence Applications in Medicine: Latent Dirichlet Allocation Application Study
  6. 2046
    “…We outline a programme of research which aims to co-design AMS interventions across healthcare settings, by integrating data-science, evidence- synthesis, behavioural-science and user-centred design. …”
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    An interdisciplinary mixed-methods approach to developing antimicrobial stewardship interventions: Protocol for the Preserving Antibiotics through Safe Stewardship (PASS) Research Programme
  7. 2047
    “…From June 19–23 in 2017, the NIH and NSF jointly held an Innovation Lab on Quantitative Approaches to Biomedical Data Science Challenges in our Understanding of the Microbiome. …”
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    Emerging Priorities for Microbiome Research
  8. 2048
    “…We also collected information from the Data Science Center of the Taiwan Cancer Registry (TCR) between 2007 and 2015. …”
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    BRCA1/2 mutation status in patients with metachronous breast and ovarian malignancies: clues towards the implementation of genetic counseling
  9. 2049
    “…Here, we address this challenge using advanced data science to capture, process and analyze 52 clinical, biological and high-resolution CBCT (radiomics) markers from TMJ OA patients and controls. …”
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    Osteoarthritis of the Temporomandibular Joint can be diagnosed earlier using biomarkers and machine learning
  10. 2050
    por Shokraneh, Farhad
    Publicado 2020
    “…Within the pharmacological evaluations we identified 28 ‘qualifiers’ including dose, route, and timing of drug delivery. Using Data Science approaches, this research revealed unique antipsychotic drugs that are being prescribed only in certain countries such as Japan but the West is not aware of them. …”
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    T129. PHARMACOLOGICAL INTERVENTIONS IN TRIALS OF PEOPLE WITH SCHIZOPHRENIA: A REGISTER-BASED CLASSIFICATION OF SEVENTY YEARS OF RESEARCH
  11. 2051
    “…On the other hand, modern-era largely depends on Artificial Intelligence(AI) including Data Science; and Deep Learning(DL) is one of the current flag-bearer of these techniques. …”
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    A Survey on Deep Transfer Learning to Edge Computing for Mitigating the COVID-19 Pandemic
  12. 2052
    “…Here we report on an event where we collected attitudes towards recent opportunities and controversies within health data science. Methods: The event was attended by ~250 individuals (cohort members and their guests), who had been invited through the offices of their participating cohort studies. …”
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    Using a knowledge exchange event to assess study participants’ attitudes to research in a rapidly evolving research context
  13. 2053
    “…This position paper builds on this workshop to describe the current state of the art for digital clinical trials including (1) defining and outlining the composition and elements of digital trials; (2) describing recruitment and retention using digital technology; (3) outlining data collection elements including mobile health, wearable technologies, application programming interfaces (APIs), digital transmission of data, and consideration of regulatory oversight and guidance for data security, privacy, and remotely provided informed consent; (4) elucidating digital analytics and data science approaches leveraging artificial intelligence and machine learning algorithms; and (5) setting future priorities and strategies that should be addressed to successfully harness digital methods and the myriad benefits of such technologies for clinical research.…”
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    Digitizing clinical trials
  14. 2054
    “…Embedded into these big data are implicit, previously unknown and potentially useful information and valuable knowledge that can be discovered by data science solutions, which apply techniques like data mining. …”
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    Predictive analytics on open big data for supporting smart transportation services
  15. 2055
    “…We recognise that well conducted studies need a wide range of diverse challenges to be skilfully addressed in unison: for example, epidemiology, data science and biostatistics, psychometrics, behavioural and social science, software engineering, user interface design, information governance, data management and patient and public involvement and engagement. …”
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    Charting a course for smartphones and wearables to transform population health research
  16. 2056
    “…In such critical situation, not only the medical research community, but also the scientists in different fields like microbiology, pharmacy, bioinformatics and data science are also sharing effort to accelerate the process of vaccine development, virus prediction, forecasting the transmissible probability and reproduction cases of virus for social awareness. …”
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    Topological Analysis for Sequence Variability: Case Study on more than 2K SARS-CoV-2 sequences of COVID-19 infected 54 countries in comparison with SARS-CoV-1 and MERS-CoV
  17. 2057
    “…The downstream analysis section contains modules for data science such as correlation, clustering (i.e., heatmap) and feature selection methods. …”
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    MuSA: a graphical user interface for multi-OMICs data integration in radiogenomic studies
  18. 2058
    por Kotorov, Rado, Chi, Lianhua, Shen, Min
    Publicado 2020
    “…In addition, it addresses the fact that doctors and nurses typically do not have data science training and the skills needed to configure, understand, and even trust existing black box machine learning models.…”
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    Personalized Monitoring Model for Electrocardiogram Signals: Diagnostic Accuracy Study
  19. 2059
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    Confound Removal and Normalization in Practice: A Neuroimaging Based Sex Prediction Case Study
  20. 2060
    “…This has led to a broad spectrum of statistical and machine learning challenges that range from study design, data processing, and standardization to analysis, modeling, cross-study comparison, prediction, data science ecosystems, and reproducible reporting. …”
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    Statistical and Machine Learning Techniques in Human Microbiome Studies: Contemporary Challenges and Solutions
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