By Vagelis Hristidis
Exploiting the wealthy details present in digital health and wellbeing documents (EHRs) can facilitate greater clinical study and enhance the standard of scientific perform. beforehand, a trivial volume of study has been released at the demanding situations of leveraging this knowledge. Addressing those demanding situations, details Discovery on digital health and wellbeing files explores the know-how to unharness the information saved in EHRs. Assembling a very interdisciplinary crew of specialists, the publication tackles scientific privateness matters, the inability of standardization for the illustration of EHRs, lacking or unsuitable values, and the supply of a number of wealthy future health ontologies. It appears to be like at easy methods to seek the EHR assortment given a consumer question and go back proper fragments from the EHRs. It additionally explains tips on how to mine the EHR assortment to extract fascinating styles, team entities to numerous periods, or make a decision no matter if an EHR satisfies a given estate. many of the publication makes a speciality of textual or numeric info of EHRs, the place extra looking and mining development has happened. A bankruptcy at the processing of clinical photographs can be integrated. conserving a uniform kind throughout chapters and minimizing technical jargon, this ebook offers some of the how you can extract invaluable wisdom from EHRs. It skillfully discusses how EHR facts may be successfully searched and mined.
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Extra info for Information Discovery on Electronic Health Records (Chapman & Hall CRC Data Mining and Knowledge Discovery Series)
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. In addition, each record has a unique name for tests, identifying code with check digits, synonyms, and other useful information. MB Property Observed Timing Type of Sample Scale SCNC PT SER/PLAS QN TIME PT PPP^CONTROL QN MCNC PT SER/PLAS Method TILT TUBE Note: SCNC, substance concentration; MCNC, mass concentration; SER/PLAS, serum/ plasma; PPP^CONTROL, platelet poor plasma; PT, point in time; QN, quantitative.
We have reviewed the XML syntax, data model, and semantic aspects of its definition. We reviewed some related work, both in industry and academia, which are based in XML or extend XML in new and more powerful directions. We also compared XML and HTML, outlining the differences in approach and syntax of these two languages. We talked about XML storage, parsing, and querying, and based on this we identified the advantages and disadvantages of this metalanguage, which has become the lingua franca of the World Wide Web.
EHRs have several advantages of over conventional paper records. The costs of physically storing paper (and other media) records and centralizing them in a convenient location are highly minimized when an EHR solution is implemented. The poor legibility and medical errors induced by handwritten medical records is also minimized by the extensive use of terminologies, dictionaries, and abbreviations in EHRs. Other benefits of EHRs are the improved interaction between physicians and patients, flagging of potentially harmful drug interactions, and the ability to exploit these electronic records in decision support systems and information discovery systems.
Information Discovery on Electronic Health Records (Chapman & Hall CRC Data Mining and Knowledge Discovery Series) by Vagelis Hristidis