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Recognizing Unstructured Emr Electronic by Sheref Fahad (4 results)
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Recognizing unstructured EMR: Recognizing the Electronic Medical Record Data from Unstructured Medical Data Using Visual Text Mining Techniques
Published by LAP LAMBERT Academic Publishing, 2020
ISBN 10: 6202511834 ISBN 13: 9786202511834
Language: English
Condition: New.
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Recognizing unstructured EMR: Recognizing the Electronic Medical Record Data from Unstructured Medical Data Using Visual Text Mining Techniques
Published by LAP LAMBERT Academic Publishing, 2020
ISBN 10: 6202511834 ISBN 13: 9786202511834
Language: English
US$ 107.00
Convert currencyUS$ 8.76 shipping from United Kingdom to U.S.A.Quantity: 4 available
Add to basketCondition: New. Print on Demand.
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Recognizing unstructured EMR: Recognizing the Electronic Medical Record Data from Unstructured Medical Data Using Visual Text Mining Techniques
Published by LAP LAMBERT Academic Publishing, 2020
ISBN 10: 6202511834 ISBN 13: 9786202511834
Language: English
US$ 116.88
Convert currencyUS$ 11.67 shipping from Germany to U.S.A.Quantity: 4 available
Add to basketCondition: New. PRINT ON DEMAND.
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Recognizing unstructured EMR : Recognizing the Electronic Medical Record Data from Unstructured Medical Data Using Visual Text Mining Techniques
Published by LAP LAMBERT Academic Publishing, 2020
ISBN 10: 6202511834 ISBN 13: 9786202511834
Language: English
US$ 75.70
Convert currencyUS$ 71.63 shipping from Germany to U.S.A.Quantity: 1 available
Add to basketTaschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Computer systems and communication technologies made a strong and influential presence in the different fields of medicine. The cornerstone of a functional medical information system is the Electronic Health Records (EHR) management system. EHR implementation and adoption face different barriers that slow down its deployment in different organizations. This research focuses on resolving the most public barriers, which are data entry, unstructured clinical data modifying the physician work flow. This research proposed a solution, which use Text mining and Natural language processing techniques. This solution tested and verified in four real-world clinical organizations. The suggested solution proved correcteness and perciseness with 91.88%.
