الصفحة 1
الصفحة 1
Clinical text mining : Secondary use of electronic patient records
Describes the results of natural language processing and machine learning methods applied to clinical text from electronic patient records. It is divided into twelve chapters. Chapters 1-4 discuss the history and background of the original paper-based patient records, their purpose, and how they are written and structured. These initial chapters do not require any technical or medical background knowledge. The remaining eight chapters are more technical in nature and describe various medical classifications and terminologies such as ICD diagnosis codes, SNOMED CT, MeSH, UMLS, and ATC. Chapters 5-10 cover basic tools for natural language processing and information retrieval, and how to apply them to clinical text. The book’s closing chapters present a number of applications in clinical text mining and summarise the lessons learned from the previous chapters.
Applied Informatics; Third International Conference, ICAI 2020, Ota, Nigeria, October 29–31, 2020, Proceedings
This book constitutes the thoroughly refereed papers of the Second International Conference on Applied Informatics, ICAI 2020, held in Ota, Nigeria, in October 2020. The 35 full papers were carefully reviewed and selected from 101 submissions. The papers are organized in topical sections on artificial intelligence; business process management; cloud computing; data analysis; decision systems; health care information systems; human-computer interaction; image processing; learning management systems; software design engineering.
