By Bonnie Blackburn, Leofranc Holford-Strevens
What are the halcyon days? On what date do the puppy days start? what's Hansel Monday? How do chinese language, Muslim, Mesoamerican, Jewish, and Babylonian calendars range from Christian calendars? The solutions to those and 1000s of alternative exciting questions on the way in which people have marked and measured time over the millennia are available within the Oxford significant other to the Year.
The wish to put aside definite classes of time to mark their value is a transhistorical, transcultural phenomena. nearly all cultures have marked exact days or classes: the banquet day of a saint, the party of a ancient occasion, the turning of a season, a interval of fasting, the birthday of an immense historic determine. round nowadays a wealthy physique of traditions, ideals, and superstitions have grown up, a lot of them basically half-remembered at the present time. Now, for the 1st time, Bonnie Blackburn and Leofranc Holford-Strevens mix this physique of data with a wide-ranging survey of calendars throughout cultures in an authoritative and interesting one-volume reference paintings. the 1st element of The Oxford better half to the 12 months is a daily survey of the calendar 12 months, revealing the historical past, literature, legend, and lore linked to each one season, month, and day. the second one half offers a broader research of time-reckoning: historic and smooth calendars, spiritual and civil, are defined, with convenient tables for the conversion of dates among numerous platforms and a precious index to facilitate fast reference.
The Oxford better half to the 12 months is a distinct and uniquely pleasant reference resource, an crucial reduction for all historians and antiquarians, and a wealthy mine of knowledge and proposal for browsers.
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Signs, symptoms, laboratory and radiographic tests). Maintaining the knowledge base in such systems is the most significant bottleneck in the maintenance of such systems, since the knowledge base needs to be expanded and updated as medical knowledge grows. Input The manner in which clinical information is entered into the CDSS (user interface) varies from system to system, but most diagnostic systems require the user to select terms from its specialized, controlled vocabulary. A. Spooner Comprehension of natural language has been an elusive goal in the development of CDSS.
JAMA 1998;280:1311–1316. 5. Oliveira, J. A shotgun wedding: business decision support meets clinical decision support. J Healthc Inf Manage 2002;16:28–33. 6. DeGruy KB. Healthcare applications of knowledge discovery in databases. J. Healthc Inf Manage 2000;14:59–69. 7. Perreault LE, Metzger JB. A pragmatic framework for understanding clinical decision support. In: Middleton, B, ed. Clinical Decision Support Systems. J Healthc Inf Manage 1999;13:5–21. 8. Metzger J, MacDonald K. Clinical decision support for the independent physician practice.
Again, this conclusion is obvious, but by representing the above syllogism using symbols, where the symbol Low-CO2 represents the state of abnormally low carbon dioxide and the symbol OVERVENTILATED represents the state of an over-ventilated patient, the syllogism looks more computerfriendly: Major Premise: Low-CO2 ⇒ OVERVENTILATED Minor Premise: Low-CO2 Conclusion: OVERVENTILATED Extending this example, suppose we have another statement in our CDSS that over-ventilation should cause a high rate alarm to sound (we can represent this by the symbol HIGH-RATE-ALARM), then we can construct the syllogism: Major Premise: Low-CO2 ⇒ OVERVENTILATED Minor Premise: Over-ventilated ⇒ HIGH-RATE-ALARM Conclusion: Low-CO2 ⇒ HIGH-RATE-ALARM Thus, we have generated a new rule for the system, where the intermediate state of over-ventilation is bypassed.