Recognizing unstructured EMR : Recognizing the Electronic Medical Record Data from Unstructured Medical Data Using Visual Text Mining Techniques

Fahad Al Sheref

ISBN 10: 6202511834 / ISBN 13: 9786202511834

Edité par LAP LAMBERT Academic Publishing, 2020

A propos de cet article

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%.

N° de réf. du vendeur 9786202511834

Détails bibliographiques

• Titre: Recognizing unstructured EMR : Recognizing the Electronic Medical Record Data from Unstructured Medical Data Using Visual Text Mining Techniques
• Éditeur: LAP LAMBERT Academic Publishing
• Date de parution: 2020
• Langue: anglais
• ISBN à 10 chiffres: 6202511834
• ISBN à 13 chiffres: 9786202511834
• Reliure: Taschenbuch
• État de l'article: Neu

A propos de ce titre

Synopsis

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%.