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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:DSS-Figure-1.PNG|240px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig2 Berezin PLoSCompBio23 19-12.png|240px]]</div>
A '''[[clinical decision support system]]''' ('''CDSS''') is a "computer [system] designed to impact clinician decision making about individual patients at the point in time these decisions are made." As such, it can be viewed as a knowledge management tool used to further clinical advice for patient care based on multiple items of patient data. In the early days, CDSSs were conceived of as being used to literally make decisions for the clinician. The clinician would input the information and wait for the CDSS to output the "right" choice, and the clinician would simply act on that output. However, the modern methodology involves the clinician interacting with the CDSS at the point of care, utilizing both their own knowledge and the CDSS to produce the best diagnosis from the test data. Typically, a CDSS suggests avenues for the physician to explore, and the physician is expected to use their own knowledge and judgement to narrow down possibilities.
'''"[[Journal:Ten simple rules for managing laboratory information|Ten simple rules for managing laboratory information]]"'''


CDSSs can be roughly divided into two types: those with knowledge bases and those without. The knowledge-based approach typically covers the diagnosis of many different diseases, while the non-knowledge-based approach often focuses on a narrow list of symptoms, such as symptoms for a single disease. ('''[[Clinical decision support system|Full article...]]''')<br />
[[Information]] is the cornerstone of [[research]], from experimental data/[[metadata]] and computational processes to complex inventories of reagents and equipment. These 10 simple rules discuss best practices for leveraging [[laboratory information management system]]s (LIMS) to transform this large information load into useful scientific findings. The development of [[mathematical model]]s that can predict the properties of biological systems is the holy grail of [[computational biology]]. Such models can be used to test biological hypotheses, guide the development of biomanufactured products, engineer new systems meeting user-defined specifications, and much more ... ('''[[Journal:Ten simple rules for managing laboratory information|Full article...]]''')<br />


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''Recently featured'':
''Recently featured'': [[Medical practice management system]], [[Biodiversity informatics]], [[American Society of Crime Laboratory Directors/Laboratory Accreditation Board]]
{{flowlist |
* [[Journal:Hierarchical AI enables global interpretation of culture plates in the era of digital microbiology|Hierarchical AI enables global interpretation of culture plates in the era of digital microbiology]]
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}}

Revision as of 18:03, 10 June 2024

Fig2 Berezin PLoSCompBio23 19-12.png

"Ten simple rules for managing laboratory information"

Information is the cornerstone of research, from experimental data/metadata and computational processes to complex inventories of reagents and equipment. These 10 simple rules discuss best practices for leveraging laboratory information management systems (LIMS) to transform this large information load into useful scientific findings. The development of mathematical models that can predict the properties of biological systems is the holy grail of computational biology. Such models can be used to test biological hypotheses, guide the development of biomanufactured products, engineer new systems meeting user-defined specifications, and much more ... (Full article...)

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