Zobrazeno 1 - 10
of 71
pro vyhledávání: '"Daniel B. Neill"'
Publikováno v:
Royal Society Open Science, Vol 8, Iss 2 (2021)
Under-reporting and delayed reporting of rape crime are severe issues that can complicate the prosecution of perpetrators and prevent rape survivors from receiving needed support. Building on a massive database of publicly available criminal reports
Externí odkaz:
https://doaj.org/article/4dd9973f45df41c5bf19823e18dc932a
We propose a new approach, the calibrated nonparametric scan statistic (CNSS), for more accurate detection of anomalous patterns in large-scale, real-world graphs. Scan statistics identify connected subgraphs that are interesting or unexpected throug
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c7e5fb25c815556df84d6ad3fd6a9db2
http://arxiv.org/abs/2206.12786
http://arxiv.org/abs/2206.12786
Autor:
ISAAC BOHART, J. REED CALDWELL, JORDAN SWARTZ, PERRY E. ROSEN, NICHOLAS GENES, CHRISTIAN A. KOZIATEK, DANIEL B. NEILL, DAVID C. LEE
Publikováno v:
Diabetes. 71
Introduction: Screening for previously undiagnosed diabetes in the emergency department (ED) can enhance early detection, which may particularly benefit patients with barriers to accessing primary care. To optimize use of limited ED resources, there
Autor:
Nicole Alexander-Scott, Brandon D.L. Marshall, Jennifer Ahern, Benjamin D Hallowell, Claire Pratty, Maxwell S. Krieger, Bennett Allen, Jesse L. Yedinak, Magdalena Cerdá, Daniel B. Neill, Yu Li, William C. Goedel, Robert C. Schell
Publikováno v:
Addiction
BACKGROUND AND AIMS: In light of the accelerating drug overdose epidemic in North America, new strategies are needed to identify communities most at risk to prioritize geographically the existing public health resources (e.g. street outreach, naloxon
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ba5a2beea2a7290d1c4ee3bcdd0ee417
https://europepmc.org/articles/PMC8904285/
https://europepmc.org/articles/PMC8904285/
Autor:
Robert C Schell, Bennett Allen, William C Goedel, Benjamin D Hallowell, Rachel Scagos, Yu Li, Maxwell S Krieger, Daniel B Neill, Brandon D L Marshall, Magdalena Cerda, Jennifer Ahern
Publikováno v:
Am J Epidemiol
Predictors of opioid overdose death in neighborhoods are important to identify, both to understand characteristics of high-risk areas and to prioritize limited prevention and intervention resources. Machine learning methods could serve as a valuable
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::875a6a13e8d4ae305302c6a6f06a7568
https://europepmc.org/articles/PMC9214774/
https://europepmc.org/articles/PMC9214774/
Autor:
Konstantin Klemmer, Daniel B. Neill
Publikováno v:
SIGSPATIAL/GIS
Machine learning is gaining popularity in a broad range of areas working with geographic data, such as ecology or atmospheric sciences. Here, data often exhibit spatial effects, which can be difficult to learn for neural networks. In this study, we p
Publikováno v:
Annual Review of Criminology. 2:473-491
Predictive analytics in policing is a data-driven approach to ( a) characterizing crime patterns across time and space and ( b) leveraging this knowledge for the prevention of crime and disorder. This article outlines the current state of the field,
Autor:
Jeffery B. Alvarez, Jean-Emmanuel Bibault, Anita Burgun, Jinzheng Cai, Zhidong Cao, Ken Chang, Jonathan H. Chen, William C. Chen, Mildred Cho, Peter Jaeho Cho, Toby C. Cornish, Anthony Costa, Andre Dekker, Karen Drukker, Jessilyn Dunn, Okyaz Eminaga, Bradley J. Erickson, Laure Fournier, Sanjiv Sam Gambhir, Efstathios D. Gennatas, Maryellen L. Giger, Iva Halilaj, Adam P. Harrison, Bryan He, Julian C. Hong, Dakai Jin, Michael C. Jin, Arthur Jochems, Jayashree Kalpathy-Cramer, Daniel S. Kapp, Mehran Karimzadeh, William Karnes, Philippe Lambin, Curtis P. Langlotz, Joonsang Lee, Hui Li, Joseph C. Liao, Anthony L. Lin, Rebecca Y. Lin, Yun Liu, Le Lu, David Magnus, Chris McIntosh, Shun Miao, James K. Min, Daniel B. Neill, Eric Karl Oermann, David Ouyang, Lily Peng, Sonia Phene, Maarten G. Poirot, Jennifer L. Quon, Daniel Ranti, Arvind Rao, Ramesh Raskar, Christopher Rombaoa, Daniel L. Rubin, Jason Samarasena, Jayne Seekins, Karthik Seetharam, Emily Shearer, Adam Sibley, Karnika Singh, Praveer Singh, Margarita Sordo, Duminda Suraweera, Aly Al-Amyn Valliani, Yvonka van Wijk, Praneeth Vepakomma, Bo Wang, Ge Wang, Nicholas Wang, Yirui Wang, Elisa Warner, Mattea Welch, Kimberly Wong, Zhenqin Wu, Fuyong Xing, Lei Xing, Ke Yan, Pingkun Yan, Lu Yang, Kristen W. Yeom, Robin Zachariah, Daniel Zeng, Lin Zhang, Ling Zhang, Xuhong Zhang, Li Zhou, James Zou
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::aa320ae1c368b21aa83be70ba954784f
https://doi.org/10.1016/b978-0-12-821259-2.00035-1
https://doi.org/10.1016/b978-0-12-821259-2.00035-1
Artificial intelligence (AI) techniques have been widely applied to infectious disease outbreak detection and early warning, trend prediction, and public health response modeling and assessment. Such public health surveillance and response tasks of m
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6b4c5564d99054a079b064a58c474848
https://doi.org/10.1016/b978-0-12-821259-2.00022-3
https://doi.org/10.1016/b978-0-12-821259-2.00022-3
Publikováno v:
Health Equity
Big data is both a product and a function of technology and the ever-growing analytic and computational power. The potential impact of big data in health care innovation cannot be ignored. The technology-mediated transformative potential of big data