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  1. 381
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    Advanced Online Survival Analysis Tool for Predictive Modelling in Clinical Data Science
  2. 382
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    Intelligent biology and medicine in 2015: advancing interdisciplinary education, collaboration, and data science
  3. 383
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    Erratum: Deep data science to prevent and treat growth faltering in Maya children
  4. 384
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    Revolution in Health Care: How Will Data Science Impact Doctor–Patient Relationships?
  5. 385
    “…At the same time, there is a growing need not only for strong data science skills, but foremost for the ability to both translate between tools and methods on the one hand, and application and problems on the other. …”
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    Training for translation between disciplines: a philosophy for life and data sciences curricula
  6. 386
    “…This paper outlines the field of population data science, the International Population Data Linkage Network (IPDLN), and their roles in advancing data-intensive research. …”
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    Population data science: advancing the safe use of population data for public benefit
  7. 387
    por Siebes, Arno
    Publicado 2018
    “…In this paper, I posit that from a research point of view, Data Science is a language. More precisely Data Science is doing Science using computer science as a language for datafied sciences; much as mathematics is the language of, e.g., physics. …”
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    Data science as a language: challenges for computer science—a position paper
  8. 388
    por Dammann, Olaf
    Publicado 2019
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    Data, Information, Evidence, and Knowledge:: A Proposal for Health Informatics and Data Science
  9. 389
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    Collaborating on Data, Science, and Infrastructure: The 20-Year Journey of the Cancer Research Network
  10. 390
    “…Recent advances have led to the rise of outbreak analytics, an emerging data science focused on the technological and methodological aspects of the outbreak data pipeline, from collection to analysis, modelling and reporting to inform outbreak response. …”
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    Outbreak analytics: a developing data science for informing the response to emerging pathogens
  11. 391
    “…Besides the traditional indices such as biochemistry, arterial blood gas, rapid shallow breathing index (RSBI), acute physiology and chronic health evaluation (APACHE) II score, this study suggests a data science framework for extubation prediction in the surgical intensive care unit (SICU) and investigates the value of the information our prediction model provides. …”
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    Data Science for Extubation Prediction and Value of Information in Surgical Intensive Care Unit
  12. 392
    “…OBJECTIVE: To discuss and illustrate the utility of two open collaborative data science platforms, and how they would benefit data science and informatics education. …”
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    An overview of two open interactive computing environments useful for data science education
  13. 393
    “…There are an ever-increasing number of reports and commentaries that describe the challenges and opportunities associated with the use of big data and data science (DS) in the context of biomedical education, research, and practice. …”
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    Biomedical informatics meets data science: current state and future directions for interaction
  14. 394
    “…A concise list includes: (a) successful harmonization of intra/inter-modality radiomic measurements to facilitate the association with other data domains (genetic, clinical, lifestyle aspects, etc.); (b) ability of data science to revise model strategies and analytics tools to tackle multiple data types and structures (electronic medical records, personal histories, hospitalization data, genomic from various specimens, imaging, etc.) and to offer data-agnostic solutions for patient outcomes prediction; (c) and model validation with independent datasets to ensure generalization of results, clinical value of new risk stratifications, and support to clinical decisions for highly individualized patient management.…”
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    From Medical Imaging to Radiomics: Role of Data Science for Advancing Precision Health
  15. 395
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    Data science and the role of Artificial Intelligence in achieving the fast diagnosis of Covid-19
  16. 396
    por Hemingway, H, Lyons, R, Li, Q, Buchan, I, Ainsworth, J, Pell, J, Morris, A, Barnes, Michael R, Bedford, Helen, Bennie, Marion, Blandford, Ann, Briggs, Andy, Brocklehurst, Peter, Brophy, Sinead, Brown, Gavin, Burton, Paul, Burton, Paul, Butler, Christopher, Capewell, Simon, Carpenter, James, Carroll, John, Cassell, Jackie A., Castillo, Fortunato, Catchpole, Mike, Caulfield, Mark, Colhoun, Helen, Coveney, Peter, Cunningham-Burley, Sarah, Custovic, Adnan, Deanfield, John, Denaxas, Spiros, Dennis, Michael, Dezateux, Carol, Dibben, Chris, Diggle, Peter, Dixon, Will, Dunn, Graham, El Emam, Khaled, Fone, David, Ford, David, Ford, Ian, Frank, John, Freemantle, Nick, Gabbe, Belinda, Gallacher, John, Gibson, Martin, Gilbert, Ruth, Gissler, Mika, Goble, Carol, Goldberg, Andy, Gravenor, Mike, Gunnell, David, Hannaford, Phil, Hayward, Andrew, Hickman, Matthew, Hingorani, Aroon, Holm, Soren, Holman, Cashel, John, Gareth, John, Ann, Jones, Kerina, Kalra, Dipak, Laurie, Graeme, Lewis, Shon, Lloyd, Keith, Lowe, Sarah, McCowan, Colin, Macleod, John, Martin, Richard M., Moore, Anthony (Tony), Moore, Laurence, Nazareth, Irwin, Nenadic, Goran, Paranjothy, Shantini, Parmar, Max, Pebody, Richard, Petersen, Steffen, Petersen, Irene, Pillay, Deenan, Preen, David, Pickett, Kate, Pritchard-Jones, Kathy, Przulj, Natasa, Renehan, Andrew, Roberts, Stephen, Robson, John, Rodgers, Sarah, Rossor, Martin, Russell, Ian, Shawe-Taylor, John, Sheikh, Aziz, Siebert, Stefan, Snooks, Helen, Sperrin, Matthew, Stephenson, Judith, Sullivan, Frank, Taylor, Chris, Taylor, Paul, Timmis, Adam, Ward, Hester J T, Williams, John, Williamson, Paula, Wilson, Alan, Wu, Olivia, Aitken, Mhairi, Akbari, Ashley, Al Sallakh, Mohammad, Al-Adely, Sarah, Alvarez-Madrazo, Samantha, Anne, Leslie, Aucott, Lorna, Bailey, Rowena, Balatsoukas, Panos, Bandyopadhyay, Amrita, Bartlett, Mike, Barutcu, Katherine, Batchelder, Catherine, Beales, Denise, Belmonte, Miguel, Blower, Brian, Brierley, Liam, Broadbent, Andrew, Burton, Paul, Castagnet, Mathilde, Cave, Daniel, Ceroni, Giovanna, Clemens, Tom, Collins, Huw, Conley, Ed, Costello, Carol-Ann, Couch, Phil, Cross, Lynsey, Cunningham, James, Daskalopoulou, Marina, Davies, Emma, Demmler, Joanne, Dixon, Emma, Dobbs, Christine, Dredge, Samuel, Drga, Vit, Evans, Hannah, Evans, Rachel, Farran, Bassam, Fitzpatrick, Natalie, Fleming, Michael, Fonferko-Shadrach, Beata, Fox, Sarah, Fry, Richard, Goddard, Catharine, Gonzalez-Izquierdo, Arturo, Gordon, Sharon, Green, Ben, Green, Benjamin, Gresham, Nick, Griffiths, Lucy, Griffiths, Rhiannon, Griffiths, Rowena, Haigh-Hutchinson, Kathy, Hainke, Melanie-Jayne, Hannaford, Phil, Hardy, Douglas, Harris, Steven, Hayes-Thompson, Nina, Heaven, Martin, Hillen, Neil, Holmes, Kate, Irvine, Elizabeth, Jackson, Guy, Jackson, Nadia, Jefferson, Emily, Jenkins, David, Johnson, Camille, Johnston, Jennifer, Katsoulis, Michail, Kirkwood, Daisy, Kunz, Holger, Kurz, Sabine, Lacey, Arron, Lamb, Amanda, Lea, Nathan, Lloyd, Stephen, Lyons, Jane, Machin, Matt, Malod-Dognin, Noel, Marryat, Louise, Martin, Cherry, McAllister, Gordon, McCleary, Nicola, McCloughan, Lucy, McIntosh, Paul, Melia, Stephen, Mizen, Amy, Mölter, Anna, Momo, Remi, Morgan, Alysha, Morrice, Lynn, Mortimer, Kate, Moayyeri, Alireza, Munro, Chris, Nangle, Clifford, Nind, Thomas, Norris, Ruth, North, Laura, O'Malley, Kieran, Owen, Rhydian, Panagiotopoulos, Adam, Papez, Vaclav, Papworth, Richard, Pasea, Laura, Pavis, Steve, Pell, Jill, Petschnigg, Julia, Porteous, Carol, Raza, Haider, Riordan, Emma, Robinson, Heather, Sethi, Nayha, Singh, Aneesha, Smith, Claire, Song, Jiao, Steiner, Markus, Stephenson, Paul, Tandon, Raj, Tempest, Ed, Thompson, Rachel, Toomey, Sarah, Torabi, Fatemeh, Tramma, Leandro, Tsang, Sandro, Tung, Wing-Chau, Turner, Sam, Turner, Victoria, Veer, Sabine Van Der, Vance, William, Walters, Angharad, Whiffen, Tony, Williams, Andrew James, Aldridge, Robert, Alvarez-Madrazo, Samantha, Anastasiou, Athanasios, Banerjee, Amitava, Berridge, Damon, Black, Corri, Burton, Paul, Colhoun, Helen, Cunningham-Burley, Sarah, Romero, Juan-Pablo Casas, Denaxas, Spiros, Dibben, Chris, Dobson, Richard, Fleming, Michael, Hardelid, Pia, Harron, Katie, Kunz, Holger, Martin, Glen, Moulton, Georgina, Mueller, Tanja, Peek, Niels, Nangle, Clifford, Sethi, Nayha, Shallcross, Laura, Sperrin, Matt, Taylor, Paul, van der Veer, Sabine, van Staa, Tjeerd, Watkins, Alan, Williamson, Elizabeth, Waheed, Adeel, Afzal, Nida, Ahmed, Harry, Al Sallakh, Mohammad Annas, Alhlayl, Adel, Allan, Victoria, Alzghaibi, Haitham, Appleby, Phil, Badrick, Ellie, Bailey, Kerry, Berry, Natalie, Bodegraven, Birgitta, Brierley, Helen, Brown, Benjamin, Ciminata, Giorgio, Cornish, Rosie, Cowan, Eilidh, Deliu, Mattea, Fitton, Catherine, Fleming, Michael, Haining, Kenny, Halliday, Kathryn, Horne, Elsie, Howard, Rebecca, Hulme, Will, Kremyda-Vlachou, Myrto, Marchant, Emily, Martin, Glen, Mueller, Tanja, Newman, Jenny, Peconi, Julie, Pham, Tra My, Reeves, Rachel, Rhodes, Daniel, Rudgard, William, Smith, Catherine, Spithourakis, Georgious, Valentine, Dennis Stallone, Wyper, Grant, Yassine, Bilal, Zylbersztejn, Anna, Aldridge, Robert, Azcoaga-Lorenzo, Amaya, Blackburn, Ruth, Brown, Ben, Caminati, Marco, Carragher, Raymond, Chung, Sheng-Chia, Dale, Caroline, Denaxas, Spiros, Edge, Chantal, Fatemifar, Ghazaleh, Fleming, Mike, George, Julie, Hale, Alison, Katsoulis, Michail, Harvey, William, Hassan, Lamiece, Lilaonitkul, Watjana, Lindner, Claudia, Luchenski, Serena, Lumbers, Tom, Niedzwiedz, Claire, Rafiq, Meena, Rannikmae, Kristiina, Shallcross, Laura, Keith, Smith, Strawbridge, Rona, Warren-Gash, Charlotte, Wu, Honghan, Ainsworth, John, Couch, Dr Philip, Ford, Professor David, Thompson, Simon, Pallas, Dr Jacky, Parsons, Professor Mark, Jefferson, Dr Emily, Cunnigham, Professor James, Moulton, Georgina, Anastasiou, Athanasios, Taylor, Professor Paul, Tung, Dr Wing-Chau, Goddard, Dr Catharine, McCowan, Professor Colin, Zhou, Dr Shang-Ming, Governance, Innovative, (Chair), Dr Kerina Jones, Laurie, Professor Graeme, Cunningham, Professor James, Lea, Dr Nathan, Hemingway, Harry, Evans, Ms Rachel, Ford, Professor David, Norris, Ms Ruth, Fitzpatrick, Ms Natalie, (Chair), Professor Sarah Cunningham-Burley, Aitken, Dr Mhairi, Carrigan, Dr Chris, Cross, Dr Lynsey, Denegri, Dr Simon, Fitzpatrick, Ms Natalie, Fox, Dr Sarah, Hassan, Dr Lamiece, Porteous, Ms Carol, Tully, Dr Mary, Buchan, Iain, Wyatt, Jeremy, Lyons, Ronan, Benzeval, Michaela, Delaney, Brendan, Sullivan, Frank, Morgan, Dr Alysha, Owen, Dr Rhydian, Beales, Dr Denise, Jackson, Ms Nadia, Kirkwood, Ms Daisy, Tung, Dr Wing-Chau, Goddard, Dr Catharine, McCloughan, Dr Lucy, Johnston, Ms Jennifer, Lamb, Dr Amanda, Norris, Ms Ruth, Turner, Ms Vicky
    Publicado 2020
    Materias: “…Population Data Science…”
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    A national initiative in data science for health: an evaluation of the UK Farr Institute
  17. 397
    “…We propose the use of an agile data science framework based on the Scrumban framework used in software development. …”
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    Agile clinical research: A data science approach to scrumban in clinical medicine
  18. 398
    “…Image-based tracking of medical instruments is an integral part of surgical data science applications. Previous research has addressed the tasks of detecting, segmenting and tracking medical instruments based on laparoscopic video data. …”
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    Heidelberg colorectal data set for surgical data science in the sensor operating room
  19. 399
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    Data science in clinical and translational research: Improving the health of the data to knowledge pipeline
  20. 400
    Materias: “…Population Data Science…”
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    A Position Statement on Population Data Science: The Science of Data about People
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