By Vagelis Hristidis
Exploiting the wealthy info present in digital well-being documents (EHRs) can facilitate larger scientific examine and increase the standard of clinical perform. earlier, a trivial volume of analysis has been released at the demanding situations of leveraging this knowledge. Addressing those demanding situations, Information Discovery on digital future health Records explores the know-how to unharness the information saved in EHRs.
Assembling a really interdisciplinary group of specialists, the e-book tackles clinical privateness matters, the shortcoming of standardization for the illustration of EHRs, lacking or unsuitable values, and the supply of a number of wealthy well-being ontologies. It seems to be at the way to seek the EHR assortment given a person question and go back appropriate fragments from the EHRs. It additionally explains easy methods to mine the EHR assortment to extract attention-grabbing styles, team entities to varied periods, or make a decision even if an EHR satisfies a given estate. many of the e-book specializes in textual or numeric info of EHRs, the place extra looking out and mining development has happened. A bankruptcy at the processing of clinical photos can also be included.
Maintaining a uniform type throughout chapters and minimizing technical jargon, this publication offers a few of the how you can extract beneficial wisdom from EHRs. It skillfully discusses how EHR facts should be successfully searched and mined.
Read Online or Download Information Discovery on Electronic Health Records (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series) PDF
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Extra info for Information Discovery on Electronic Health Records (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series)
The LOINC laboratory terms set provides a standard set of universal names and codes for identifying individual laboratory and clinical results. LOINC codes allow users to merge clinical results from many sources into one database for patient care, clinical research, or management. The LOINC database currently contains about 41,000 terms, which include 31,000 observational terms related to laboratory testing. Each record in the LOINC database identifies a clinical observation and contains a formal six-part name, including: the name of the component, the property observed, the timing of the measurement, the type of sample, the scale of measurement, and where relevant, the method of measurement.
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