Clinical Text Mining : : Secondary Use of Electronic Patient Records.

• Author: Dalianis, Hercules.
• Place / Publishing House: Cham : : Springer International Publishing AG,, 2018.; Ã2018.
• Year of Publication: 2018
• Edition: 1st ed.
• Language: English
• Physical Description: 1 online resource (192 pages)

Table of Contents

• Intro
• Preface
• Objectives
• Organisation of Book Chapters
• Intended Readers
• Limitations
• Book Project During Sabbatical Stay in Sydney
• Aims
• Acknowledgements
• Contents
• 1 Introduction
• 1.1 Early Work and Review Articles
• 2 The History of the Patient Record and the Paper Record
• 2.1 The Egyptians and the Greeks
• 2.2 The Arabs
• 2.3 The Swedes
• 2.4 The Paper Based Patient Record
• 2.5 Greek and Latin Used in the Patient Record
• 2.6 Summary of the History of the Patient Record and the Paper Record
• 3 User Needs: Clinicians, Clinical Researchers and Hospital Management
• 3.1 Reading and Retrieving Efficiency of Patient Records
• 3.2 Natural Language Processing on Clinical Text
• 3.3 Electronic Patient Record System
• 3.4 Different User Groups
• 3.5 Summary
• 4 Characteristics of Patient Records and Clinical Corpora
• 4.1 Patient Records
• 4.2 Pathology Reports
• 4.3 Spelling Errors in Clinical Text
• 4.4 Abbreviations
• 4.5 Acronyms
• 4.6 Assertions
• 4.6.1 Negations
• 4.6.2 Speculation and Factuality
• Levels of Certainty
• Negation and Speculations in Other Languages, Such as Chinese
• 4.7 Clinical Corpora Available
• 4.7.1 English Clinical Corpora Available
• 4.7.2 Swedish Clinical Corpora
• 4.7.3 Clinical Corpora in Other Languages than Swedish
• 4.8 Summary
• 5 Medical Classifications and Terminologies
• 5.1 International Statistical Classification of Diseases and Related Health Problems (ICD)
• 5.1.1 International Classification of Diseases for Oncology (ICD-O-3)
• 5.2 Systematized Nomenclature of Medicine: Clinical Terms (SNOMED CT)
• 5.3 Medical Subject Headings (MeSH)
• 5.4 Unified Medical Language Systems (UMLS)
• 5.5 Anatomical Therapeutic Chemical Classification (ATC)
• 5.6 Different Standards for Interoperability
• 5.6.1 Health Level 7 (HL7).
• Fast Healthcare Interoperability Resources (FHIR)
• 5.6.2 OpenEHR
• 5.6.3 Mapping and Expanding Terminologies
• 5.7 Summary of Medical Classifications and Terminologies
• 6 Evaluation Metrics and Evaluation
• 6.1 Qualitative and Quantitative Evaluation
• 6.2 The Cranfield Paradigm
• 6.3 Metrics
• 6.4 Annotation
• 6.5 Inter-Annotator Agreement (IAA)
• 6.6 Confidence and Statistical Significance Testing
• 6.7 Annotation Tools
• 6.8 Gold Standard
• 6.9 Summary of Evaluation Metrics and Annotation
• 7 Basic Building Blocks for Clinical Text Processing
• 7.1 Definitions
• 7.2 Segmentation and Tokenisation
• 7.3 Morphological Processing
• 7.3.1 Lemmatisation
• 7.3.2 Stemming
• 7.3.3 Compound Splitting (Decompounding)
• 7.3.4 Abbreviation Detection and Expansion
• A Machine Learning Approach for Abbreviation Detection
• 7.3.5 Spell Checking and Spelling Error Correction
• Spell Checking of Clinical Text
• Open Source Spell Checkers
• Search Engines and Spell Checking
• 7.3.6 Part-of-Speech Tagging (POS Tagging)
• 7.4 Syntactical Analysis
• 7.4.1 Shallow Parsing (Chunking)
• 7.4.2 Grammar Tools
• 7.5 Semantic Analysis and Concept Extraction
• 7.5.1 Named Entity Recognition
• Machine Learning for Named Entity Recognition
• 7.5.2 Negation Detection
• Negation Detection Systems
• Negation Trigger Lists
• NegEx for Swedish
• NegEx for French, Spanish and German
• Machine Learning Approaches for Negation Detection
• 7.5.3 Factuality Detection
• 7.5.4 Relative Processing (Family History)
• 7.5.5 Temporal Processing
• TimeML and TIMEX3
• HeidelTime
• i2b2 Temporal Relations Challenge
• Temporal Processing for Swedish Clinical Text
• Temporal Processing for French Clinical Text
• Temporal Processing for Portuguese Clinical Text
• 7.5.6 Relation Extraction
• 2010 i2b2/VA Challenge Relation Classification Task.
• Other Approaches for Relation Extraction
• 7.5.7 Anaphora Resolution
• i2b2 Challenge in Coreference Resolution for Electronic Medical Records
• 7.6 Summary of Basic Building Blocks for Clinical Text Processing
• 8 Computational Methods for Text Analysis and Text Classification
• 8.1 Rule-Based Methods
• 8.1.1 Regular Expressions
• 8.2 Machine Learning-Based Methods
• 8.2.1 Features and Feature Selection
• Term Frequency-Inverse Document Frequency, tf-idf
• Vector Space Model
• 8.2.2 Active Learning
• 8.2.3 Pre-Annotation with Revision or Machine Assisted Annotation
• 8.2.4 Clustering
• 8.2.5 Topic Modelling
• 8.2.6 Distributional Semantics
• 8.2.7 Association Rules
• 8.3 Explaining and Understanding the Results Produced
• 8.4 Computational Linguistic Modules for Clinical Text Processing
• 8.5 NLP Tools: UIMA, GATE, NLTK etc
• 8.6 Summary of Computational Methods for Text Analysis and Text Classification
• 9 Ethics and Privacy of Patient Records for Clinical Text Mining Research
• 9.1 Ethical Permission
• 9.2 Social Security Number
• 9.3 Safe Storage
• 9.4 Automatic De-Identification of Patient Records
• 9.4.1 Density of PHI in Electronic Patient Record Text
• 9.4.2 Pseudonymisation of Electronic Patient Records
• 9.4.3 Re-Identification and Privacy
• Black Box Approach
• 9.5 Summary of Ethics and Privacy of Patient Records for Clinical Text Mining Research
• 10 Applications of Clinical Text Mining
• 10.1 Detection and Prediction of Healthcare Associated Infections (HAIs)
• 10.1.1 Healthcare Associated Infections (HAIs)
• 10.1.2 Detecting and Predicting HAI
• 10.1.3 Commercial HAI Surveillance Systems and Systems in Practical Use
• 10.2 Detection of Adverse Drug Events (ADEs)
• 10.2.1 Adverse Drug Events (ADEs)
• 10.2.2 Resources for Adverse Drug Event Detection
• 10.2.3 Passive Surveillance of ADEs.
• 10.2.4 Active Surveillance of ADEs
• 10.2.5 Approaches for ADE Detection
• An Approach for Swedish Clinical Text
• An Approach for Spanish Clinical Text
• A Joint Approach for Spanish and Swedish Clinical Text
• 10.3 Suicide Prevention by Mining Electronic Patient Records
• 10.4 Mining Pathology Reports for Diagnostic Tests
• 10.4.1 The Case of the Cancer Registry of Norway
• 10.4.2 The Medical Text Extraction (Medtex) System
• 10.5 Mining for Cancer Symptoms
• 10.6 Text Summarisation and Translation of Patient Record
• 10.6.1 Summarising the Patient Record
• 10.6.2 Other Approaches in Summarising the Patient Record
• 10.6.3 Summarising Medical Scientific Text
• 10.6.4 Simplification of the Patient Record for Laypeople
• 10.7 ICD-10 Diagnosis Code Assignment and Validation
• 10.7.1 Natural Language Generation from SNOMED CT
• 10.8 Search Cohort Selection and Similar Patient Cases
• 10.8.1 Comorbidities
• 10.8.2 Information Retrieval from Electronic Patient Records
• 10.8.3 Search Engine Solr
• 10.8.4 Supporting the Clinician in an Emergency Department with the Radiology Report
• 10.8.5 Incident Reporting
• 10.8.6 Hypothesis Generation
• 10.8.7 Practical Use of SNOMED CT
• 10.8.8 ICD-10 and SNOMED CT Code Mapping
• 10.8.9 Analysing the Patient's Speech
• 10.8.10 MYCIN and Clinical Decision Support
• 10.8.11 IBM Watson Health
• 10.9 Summary of Applications of Clinical Text Mining
• 11 Networks and Shared Tasks in Clinical Text Mining
• 11.1 Conferences, Workshops and Journals
• 11.2 Summary of Networks and Shared Tasks in Clinical Text Mining
• 12 Conclusions and Outlook
• 12.1 Outcomes
• References
• Index.