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Articles 1 - 2 of 2
Full-Text Articles in Physical Sciences and Mathematics
Reducing Symptom Distress In Patients With Advanced Cancer Using An E-Alert System For Caregivers: Pooled Analysis Of Two Randomized Clinical Trials, David H. Gustafson, Lori L. Dubenske, Amy K. Atwood, Ming-Yuan Chih, Roberta A. Johnson, Fiona Mctavish, Andrew Quanbeck, Roger L. Brown, James F. Cleary, Dhavan Shah
Reducing Symptom Distress In Patients With Advanced Cancer Using An E-Alert System For Caregivers: Pooled Analysis Of Two Randomized Clinical Trials, David H. Gustafson, Lori L. Dubenske, Amy K. Atwood, Ming-Yuan Chih, Roberta A. Johnson, Fiona Mctavish, Andrew Quanbeck, Roger L. Brown, James F. Cleary, Dhavan Shah
Health and Clinical Sciences Faculty Publications
Background: Symptom distress in patients toward the end of life can change rapidly. Family caregivers have the potential to help patients manage those symptoms, as well as their own stress, if they are equipped with the proper resources. Electronic health (eHealth) systems may be able to provide those resources. Very sick patients may not be able to use such systems themselves to report their symptoms but family caregivers could.
Objective: The aim of this paper was to assess the effects on cancer patient symptom distress of an eHealth system that alerts clinicians to significant changes in the patient’s symptoms, as …
Mining Non-Lattice Subgraphs For Detecting Missing Hierarchical Relations And Concepts In Snomed Ct, Licong Cui, Wei Zhu, Shiqiang Tao, James T. Case, Olivier Bodenreider, Guo-Qiang Zhang
Mining Non-Lattice Subgraphs For Detecting Missing Hierarchical Relations And Concepts In Snomed Ct, Licong Cui, Wei Zhu, Shiqiang Tao, James T. Case, Olivier Bodenreider, Guo-Qiang Zhang
Computer Science Faculty Publications
Objective: Quality assurance of large ontological systems such as SNOMED CT is an indispensable part of the terminology management lifecycle. We introduce a hybrid structural-lexical method for scalable and systematic discovery of missing hierarchical relations and concepts in SNOMED CT.
Material and Methods: All non-lattice subgraphs (the structural part) in SNOMED CT are exhaustively extracted using a scalable MapReduce algorithm. Four lexical patterns (the lexical part) are identified among the extracted non-lattice subgraphs. Non-lattice subgraphs exhibiting such lexical patterns are often indicative of missing hierarchical relations or concepts. Each lexical pattern is associated with a potential specific type of error. …