The development of electronic health records and clinical information systems has been progressing at a quickened pace in recent years. With such change, the use of standard vocabulary has aroused much interest. However, classification code sets are developed for their distinctive purposes, no single vocabulary set could satisfy the needs of all specialties; moreover, local customization would be inevitable. This poster describes how a controlled vocabulary with contribution from frontline clinicians was set up in Hong Kong Hospital Authority (HA). The adoption of an electronic maintenance portal with collaboration of representatives from all institutions would also be discussed. The use of such table has helped to maintain the interoperability of captured data between departments, specialties and institutions.
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Database (Oxford)
January 2025
Research and Development Centre, Agriculture and Agri-Food Canada, 960 Carling Avenue, Ottawa, ON CA K1A 0C6, Canada.
It is well-known that the use of vocabulary in phenotype treatments is often inconsistent. An earlier survey of biologists who create or use phenotypic characters revealed that this lack of standardization leads to ambiguities, frustrating both the consumers and producers of phenotypic data. Such ambiguities are challenging for biologists, and more so for Artificial Intelligence, to resolve.
View Article and Find Full Text PDFAm J Reprod Immunol
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Department of gynecology and obstetrics, Huzhou Maternity & Child Health Care Hospital, Huzhou, Zhejiang, China.
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BMC Psychiatry
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West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China.
The current DSM-oriented diagnostic paradigm has introduced the issue of heterogeneity, as it fails to account for the identification of the neurological processes underlying mental illnesses, which affects the precision of treatment. The Research Domain Criteria (RDoC) framework serves as a recognized approach to addressing this heterogeneity, and several assessment and translation techniques have been proposed. Among these methods, transforming RDoC scores from electronic medical records (EMR) using Natural Language Processing (NLP) has emerged as a suitable technique, demonstrating clinical effectiveness.
View Article and Find Full Text PDFSci Rep
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View Article and Find Full Text PDFMol Biol Rep
January 2025
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Background: Litter size in mice is an important fitness and economic feature that is controlled by several genes and influenced by non-genetic factors too. High positive selection pressure in each generation for Litter size at birth (LSB), resulted in the development of high and low prolific lines of inbred Swiss albino mice (SAM). Despite uniform management conditions, these lines showed variability in LSB across the generation.
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