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  1. 2181
    “…METHODS: We used advanced data science techniques to first preprocess the data and then train machine learning classifiers to predict the probability of developing PIs. …”
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    Modeling and prediction of pressure injury in hospitalized patients using artificial intelligence
  2. 2182
    “…By using basic as well as advanced analytic tools ranging from simple aggregation and display of trends to data science application, we provided commanders and clinicians with access to trusted, accurate, and personalized information and tools that were designed to foster operational changes and mitigate the propagation of the pandemic. …”
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    Online Artículo Texto
    Data Empowerment of Decision-Makers in an Era of a Pandemic: Intersection of “Classic” and Artificial Intelligence in the Service of Medicine
  3. 2183
    “…Lack of COVID-19 patient data has hindered the data science community in developing models to aid in the response to the pandemic. …”
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    Online Artículo Texto
    A Continuously Benchmarked and Crowdsourced Challenge for Rapid Development and Evaluation of Models to Predict COVID-19 Diagnosis and Hospitalization
  4. 2184
    “…R packages of genomic data science were executed for statistical evaluations and data visualization. …”
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    Identification of Prognostic Biomarkers Originating From the Tumor Stroma of Betel Quid-Associated Oral Cancer Tissues
  5. 2185
    “…RESULTS/ANTICIPATED RESULTS: o The program served 130 students in summer 2020. o We were able to recruit new faculty and industry mentors involved in data science research. As a result, we have now increased our mentor pool to serve more students in the future. o Because student participation was virtual, we were able to accept students from further distances (up to 120 miles away) across the state. …”
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    11039 Indiana CTSI High-School STEM Summer Research Program: Future opportunities from a 2020 virtual program
  6. 2186
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    Modeling and predicting the spread of COVID-19: a continental analysis
  7. 2187
    “…Statistical software for data science (STATA V16) software using a random-effects model was used to determine the pooled prevalence and type-specific distribution of HPV with 95% confidence intervals (CI). …”
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    Prevalence and Genotype Distribution of High-Risk Human Papillomavirus Infection Among Sub-Saharan African Women: A Systematic Review and Meta-Analysis
  8. 2188
    “…AIM: To develop a binary classifier for the outcome of death in ICU patients based on clinical and laboratory parameters, a set formed by 1087 instances and 50 variables from ICU patients admitted to the emergency department was obtained in the “WiDS (Women in Data Science) Datathon 2020: ICU Mortality Prediction” dataset. …”
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    Prediction of hospital mortality in intensive care unit patients from clinical and laboratory data: A machine learning approach
  9. 2189
    “…METHODS: We examined education-related inequalities in four COVID-19 prevention and testing indicators within 90 countries, using data from the University of Maryland Social Data Science Center Global COVID-19 Trends and Impact Survey, in partnership with Facebook, over the period 1 June 2021 to 31 December 2021. …”
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    Education-related inequalities in beliefs and behaviors pertaining to COVID-19 non-pharmaceutical interventions
  10. 2190
    “…CONCLUSIONS: This UK-based survey study shows that willingness to share commercial data for health research varies; however, researchers should focus on effectively communicating their data practices to minimize concerns about data misuse and improve public trust in data science. The results of this study can be further used as a guide to consider methods to improve recruitment strategies in health-related research and to improve response rates and participant retention.…”
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    Understanding Public Attitudes and Willingness to Share Commercial Data for Health Research: Survey Study in the United Kingdom
  11. 2191
    “…CONCLUSION: There are numerous opportunities for innovation within the intersection of healthcare and data science. Pharmaceutical manufacturers, with one of their medical affairs responsibilities being the collection of unsolicited inquiries, particularly from HCPs, stand poised to leverage machine learning capabilities to optimize its processes. …”
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    Advancing medical affair capabilities and insight generation through machine learning techniques
  12. 2192
    “…Machine learning has won most data science competitions and could support many clinical activities, yet only 15% of hospitals use it for even limited purposes. …”
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    Automating Construction of Machine Learning Models With Clinical Big Data: Proposal Rationale and Methods
  13. 2193
    “…BACKGROUND: To demonstrate how the Observational Healthcare Data Science and Informatics (OHDSI) collaborative network and standardization can be utilized to scale-up external validation of patient-level prediction models by enabling validation across a large number of heterogeneous observational healthcare datasets. …”
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    Online Artículo Texto
    Feasibility and evaluation of a large-scale external validation approach for patient-level prediction in an international data network: validation of models predicting stroke in female patients newly diagnosed with atrial fibrillation
  14. 2194
    “…Moreover, we integrated an easy-to-use cloud platform, called DSaaS (Data Science as a Service), well suited for hospital structures, where healthcare operators might not have specific competences in using programming languages but still, they do need to analyze data as a continuous process. …”
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    Machine learning models predicting multidrug resistant urinary tract infections using “DsaaS”
  15. 2195
    “…Natural history collections are leading successful large-scale projects of specimen digitization (images, metadata, DNA barcodes), thereby transforming taxonomy into a big data science. Yet, little effort has been directed towards safeguarding and subsequently mobilizing the considerable amount of original data generated during the process of naming 15,000–20,000 species every year. …”
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    Repositories for Taxonomic Data: Where We Are and What is Missing
  16. 2196
    por Flores, Mona, Dayan, Ittai, Roth, Holger, Zhong, Aoxiao, Harouni, Ahmed, Gentili, Amilcare, Abidin, Anas, Liu, Andrew, Costa, Anthony, Wood, Bradford, Tsai, Chien-Sung, Wang, Chih-Hung, Hsu, Chun-Nan, Lee, CK, Ruan, Colleen, Xu, Daguang, Wu, Dufan, Huang, Eddie, Kitamura, Felipe, Lacey, Griffin, César de Antônio Corradi, Gustavo, Shin, Hao-Hsin, Obinata, Hirofumi, Ren, Hui, Crane, Jason, Tetreault, Jesse, Guan, Jiahui, Garrett, John, Park, Jung Gil, Dreyer, Keith, Juluru, Krishna, Kersten, Kristopher, Bezerra Cavalcanti Rockenbach, Marcio Aloisio, Linguraru, Marius, Haider, Masoom, AbdelMaseeh, Meena, Rieke, Nicola, Damasceno, Pablo, Cruz e Silva, Pedro Mario, Wang, Pochuan, Xu, Sheng, Kawano, Shuichi, Sriswasdi, Sira, Park, Soo Young, Grist, Thomas, Buch, Varun, Jantarabenjakul, Watsamon, Wang, Weichung, Tak, Won Young, Li, Xiang, Lin, Xihong, Kwon, Fred, Gilbert, Fiona, Kaggie, Josh, Li, Quanzheng, Quraini, Abood, Feng, Andrew, Priest, Andrew, Turkbey, Baris, Glicksberg, Benjamin, Bizzo, Bernardo, Kim, Byung Seok, Tor-Diez, Carlos, Lee, Chia-Cheng, Hsu, Chia-Jung, Lin, Chin, Lai, Chiu-Ling, Hess, Christopher, Compas, Colin, Bhatia, Deepi, Oermann, Eric, Leibovitz, Evan, Sasaki, Hisashi, Mori, Hitoshi, Yang, Isaac, Sohn, Jae Ho, Keshava Murthy, Krishna Nand, Fu, Li-Chen, Furtado de Mendonça, Matheus Ribeiro, Fralick, Mike, Kang, Min Kyu, Adil, Mohammad, Gangai, Natalie, Vateekul, Peerapon, Elnajjar, Pierre, Hickman, Sarah, Majumdar, Sharmila, McLeod, Shelley, Reed, Sheridan, Graf, Stefan, Harmon, Stephanie, Kodama, Tatsuya, Puthanakit, Thanyawee, Mazzulli, Tony, de Lima Lavor, Vitor, Rakvongthai, Yothin, Lee, Yu Rim, Wen, Yuhong
    Publicado 2021
    “…The FL paradigm was successfully applied to facilitate a rapid data science collaboration without data exchange, resulting in a model that generalised across heterogeneous, unharmonized datasets. …”
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    Federated Learning used for predicting outcomes in SARS-COV-2 patients
  17. 2197
    “…To be ready for new roles and tasks, medical students and physicians will need to understand the fundamentals of AI and data science, mathematical concepts, and related ethical and medico-legal issues in addition with the standard medical principles. …”
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    Medical artificial intelligence readiness scale for medical students (MAIRS-MS) – development, validity and reliability study
  18. 2198
    “…RESULTS: We examined the evolution of miRNA–target interaction rules and used data science and ML approaches to investigate whether these rules are transferable between species. …”
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    Comprehensive machine-learning-based analysis of microRNA–target interactions reveals variable transferability of interaction rules across species
  19. 2199
    “…Experts in digital phenotyping, data science, mental health, law, and ethics participated as panelists in the study. …”
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    Ethical Development of Digital Phenotyping Tools for Mental Health Applications: Delphi Study
  20. 2200
    “…Participants consisted of experts in surgery, anaesthesia, and obstetric care, data science, and health indicators from high-, middle-, and low-income countries. …”
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    Global surgery, obstetric, and anaesthesia indicator definitions and reporting: An Utstein consensus report
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