Literature DB >> 9028421

Arden Syntax: the emerging standard language for representing medical knowledge in computer systems.

J Poikonen1.   

Abstract

The Arden Syntax for Medical Logic Modules standard language for knowledge-based computer systems is described. Knowledge systems can use computerized patient data in decision-making. Although they have the potential to reduce adverse drug events and infection rates, improve drug dosing, and decrease the cost of care, knowledge systems have not yet reached the average patient. Arden Syntax for Medical Logic Modules is the standard language for defining clinical decision rules that drive alerts, reminders, clinical guidelines, and data interpretations. Each medical logic module (MLM) in an Arden Syntax knowledge base is designed to make one type of decision. MLMs usually represent either rules that can be encoded, such as generating a warning that the potassium concentration is decreasing in a patient taking digoxin, or complex decision trees for individual patient care plans and clinical protocols. An MLM contains maintenance slots (title, file name, version, originating institution, author, date, specialist, validation information), library slots (stating the MLM's purpose and providing keywords for searching), and knowledge slots (containing the "essence" of the MLM). Arden Syntax is receiving growing support from the medical and information systems communities as the standard language for medical knowledge systems, but legal, ethical, regulatory, and ownership issues remain. If pharmacy is to grow and prosper as a knowledge profession, it should adopt an accepted standard language for representing active, applied knowledge in computer systems.

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Year:  1997        PMID: 9028421     DOI: 10.1093/ajhp/54.3.281

Source DB:  PubMed          Journal:  Am J Health Syst Pharm        ISSN: 1079-2082            Impact factor:   2.637


  2 in total

1.  Knowledge representation forms for data mining methodologies as applied in thoracic surgery.

Authors:  A Kircher; H Granfeldt; A Babic; J Antonsson; U Lönn; H C Ahn
Journal:  Proc AMIA Symp       Date:  2000

2.  Using Arden Syntax to identify registry-eligible very low birth weight neonates from the Electronic Health Record.

Authors:  Indra Neil Sarkar; Elizabeth S Chen; Paul T Rosenau; Matthew B Storer; Beth Anderson; Jeffrey D Horbar
Journal:  AMIA Annu Symp Proc       Date:  2014-11-14
  2 in total

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