Matches in SemOpenAlex for { <https://semopenalex.org/work/W2339974927> ?p ?o ?g. }
- W2339974927 endingPage "26" @default.
- W2339974927 startingPage "17" @default.
- W2339974927 abstract "Entering medical encounter data by hand is time-consuming. In addition, data are often not entered into the database in a timely enough fashion to enable their use for subsequent mission planning. The Patient Informatics Processing Software semi-automates the data collection process onboard ships. Then data within these images are captured and used to populate a database, after which multiple ship databases are used for reporting and analysis. In this paper, we used the Patient Informatics Processing Software Hybrid Hadoop Hive to orchestrate database processing via various ships, by marshaling the distributed servers, running the various tasks in parallel, managing all of the communications and data transfers between the various parts of the system, and providing for redundancy and fault tolerance. Then we employed the Apache Hive as a data warehouse infrastructure built on top of Hadoop for data summarization, query, and analysis to identify traumatic brain injury (TBI) as well as other injury cases. Finally, a proposed Misdiagnosis Minimization Approach method was used for data analysis. We collected data on three ship variables (Byrd, Boxer, Kearsage) and injuries to four body regions (head, torso, extremities, and abrasions) to determine how the set of collected variables relates to the body injuries. Two dimensions or canonical variables (survival vs. mortality) were necessary to understand the association between the two sets of variables. Our method improved data classification and showed that survival, mortality, and morbidity rates can be derived from the superset of Medical Operations data and used for future decision-making and planning. We suggest that an awareness of procedural errors as well as methods to reduce misclassification should be incorporated into all TBI clinical trials." @default.
- W2339974927 created "2016-06-24" @default.
- W2339974927 creator A5033260657 @default.
- W2339974927 date "2015-01-01" @default.
- W2339974927 modified "2023-10-09" @default.
- W2339974927 title "Discovery of medical Big Data analytics: Improving the prediction of traumatic brain injury survival rates by data mining Patient Informatics Processing Software Hybrid Hadoop Hive" @default.
- W2339974927 cites W1504975562 @default.
- W2339974927 cites W1543768025 @default.
- W2339974927 cites W1543907397 @default.
- W2339974927 cites W185077138 @default.
- W2339974927 cites W1963886662 @default.
- W2339974927 cites W1964701642 @default.
- W2339974927 cites W1970636524 @default.
- W2339974927 cites W1980965129 @default.
- W2339974927 cites W1984234640 @default.
- W2339974927 cites W1985824646 @default.
- W2339974927 cites W1986906265 @default.
- W2339974927 cites W1987710675 @default.
- W2339974927 cites W1990514899 @default.
- W2339974927 cites W1990833375 @default.
- W2339974927 cites W1993527150 @default.
- W2339974927 cites W1994638866 @default.
- W2339974927 cites W2001619934 @default.
- W2339974927 cites W2004977911 @default.
- W2339974927 cites W2014344617 @default.
- W2339974927 cites W2017773008 @default.
- W2339974927 cites W2017937731 @default.
- W2339974927 cites W2018811232 @default.
- W2339974927 cites W2023495162 @default.
- W2339974927 cites W2025065899 @default.
- W2339974927 cites W2025341678 @default.
- W2339974927 cites W2027434940 @default.
- W2339974927 cites W2036551686 @default.
- W2339974927 cites W2043306527 @default.
- W2339974927 cites W2045703052 @default.
- W2339974927 cites W2046003946 @default.
- W2339974927 cites W2049623605 @default.
- W2339974927 cites W2054233483 @default.
- W2339974927 cites W2056130715 @default.
- W2339974927 cites W2059515884 @default.
- W2339974927 cites W2066425341 @default.
- W2339974927 cites W2070322568 @default.
- W2339974927 cites W2081907387 @default.
- W2339974927 cites W2082269894 @default.
- W2339974927 cites W2084333448 @default.
- W2339974927 cites W2090695191 @default.
- W2339974927 cites W2092022777 @default.
- W2339974927 cites W2094189737 @default.
- W2339974927 cites W2102218344 @default.
- W2339974927 cites W2126863528 @default.
- W2339974927 cites W2147645183 @default.
- W2339974927 cites W2151159184 @default.
- W2339974927 cites W2169828275 @default.
- W2339974927 cites W4254211255 @default.
- W2339974927 doi "https://doi.org/10.1016/j.imu.2016.01.002" @default.
- W2339974927 hasPublicationYear "2015" @default.
- W2339974927 type Work @default.
- W2339974927 sameAs 2339974927 @default.
- W2339974927 citedByCount "55" @default.
- W2339974927 countsByYear W23399749272016 @default.
- W2339974927 countsByYear W23399749272017 @default.
- W2339974927 countsByYear W23399749272018 @default.
- W2339974927 countsByYear W23399749272019 @default.
- W2339974927 countsByYear W23399749272020 @default.
- W2339974927 countsByYear W23399749272021 @default.
- W2339974927 countsByYear W23399749272022 @default.
- W2339974927 countsByYear W23399749272023 @default.
- W2339974927 crossrefType "journal-article" @default.
- W2339974927 hasAuthorship W2339974927A5033260657 @default.
- W2339974927 hasBestOaLocation W23399749271 @default.
- W2339974927 hasConcept C119599485 @default.
- W2339974927 hasConcept C124101348 @default.
- W2339974927 hasConcept C127413603 @default.
- W2339974927 hasConcept C135572916 @default.
- W2339974927 hasConcept C138816342 @default.
- W2339974927 hasConcept C145642194 @default.
- W2339974927 hasConcept C154945302 @default.
- W2339974927 hasConcept C159110408 @default.
- W2339974927 hasConcept C170858558 @default.
- W2339974927 hasConcept C191630685 @default.
- W2339974927 hasConcept C199360897 @default.
- W2339974927 hasConcept C2522767166 @default.
- W2339974927 hasConcept C2777904410 @default.
- W2339974927 hasConcept C41008148 @default.
- W2339974927 hasConcept C71924100 @default.
- W2339974927 hasConcept C75684735 @default.
- W2339974927 hasConcept C77088390 @default.
- W2339974927 hasConcept C79158427 @default.
- W2339974927 hasConceptScore W2339974927C119599485 @default.
- W2339974927 hasConceptScore W2339974927C124101348 @default.
- W2339974927 hasConceptScore W2339974927C127413603 @default.
- W2339974927 hasConceptScore W2339974927C135572916 @default.
- W2339974927 hasConceptScore W2339974927C138816342 @default.
- W2339974927 hasConceptScore W2339974927C145642194 @default.
- W2339974927 hasConceptScore W2339974927C154945302 @default.
- W2339974927 hasConceptScore W2339974927C159110408 @default.
- W2339974927 hasConceptScore W2339974927C170858558 @default.
- W2339974927 hasConceptScore W2339974927C191630685 @default.