Text Mining Of Web Based Medical Content

•Includes Text Mining and Natural Language Processing Methods for extracting information from electronic health records and biomedical literature. •Analyzes text analytic tools for new media such as online forums, social media posts, tweets and video sharing. •Demonstrates how to use speech and audio technologies for improving access to online content for the visually impaired. Text Mining of Web-Based Medical Content examines various approaches to deriving high quality information from online biomedical literature, electronic health records, query search terms, social media posts and tweets. Using some of the latest empirical methods of knowledge extraction, the authors show how online content, generated by both professionals and laypersons, can be mined for valuable information about disease processes, adverse drug reactions not captured during clinical trials, and tropical fever outbreaks. Additionally, the authors show how to perform infromation extraction on a hospital intranet, how to build a social media search engine to glean information about patients' own experiences interacting with healthcare professionals, and how to improve access to online health information. This volume provides a wealth of timely material for health informatic professionals and machine learning, data mining, and natural language researchers. Topics in this book include: •Mining Biomedical Literature and Clinical Narratives •Medication Information Extraction •Machine Learning Techniques for Mining Medical Search Queries •Detecting the Level of Personal Health Information Revealed in Social Media •Curating Layperson’s Personal Experiences with Health Care from Social Media and Twitter •Health Dialogue Systems for Improving Access to Online Content •Crowd-based Audio Clips to Improve Online Video Access for the Visually Impaired •Semantic-based Visual Information Retrieval for Mining Radiographic Image Data •Evaluating the Importance of Medical Terminology in YouTube Video Titles and Descriptions

This open access book 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 difference between rule-based and machine learning-based methods, as well as between supervised and unsupervised machine learning methods, are also explained. Next, ethical concerns regarding the use of sensitive patient records for research purposes are discussed, including methods for de-identifying electronic patient records and safely storing patient records. The book’s closing chapters present a number of applications in clinical text mining and summarise the lessons learned from the previous chapters. The book provides a comprehensive overview of technical issues arising in clinical text mining, and offers a valuable guide for advanced students in health informatics, computational linguistics, and information retrieval, and for researchers entering these fields.

This book explains how can be created information extraction (IE) applications that are able to tap the vast amount of relevant information available in natural language sources: Internet pages, official documents such as laws and regulations, books and newspapers, and social web. Readers are introduced to the problem of IE and its current challenges and limitations, supported with examples. The book discusses the need to fill the gap between documents, data, and people, and provides a broad overview of the technology supporting IE. The authors present a generic architecture for developing systems that are able to learn how to extract relevant information from natural language documents, and illustrate how to implement working systems using state-of-the-art and freely available software tools. The book also discusses concrete applications illustrating IE uses. · Provides an overview of state-of-the-art technology in information extraction (IE), discussing achievements and limitations for the software developer and providing references for specialized literature in the area · Presents a comprehensive list of freely available, high quality software for several subtasks of IE and for several natural languages · Describes a generic architecture that can learn how to extract information for a given application domain

This open access book 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 difference between rule-based and machine learning-based methods, as well as between supervised and unsupervised machine learning methods, are also explained. Next, ethical concerns regarding the use of sensitive patient records for research purposes are discussed, including methods for de-identifying electronic patient records and safely storing patient records. The book's closing chapters present a number of applications in clinical text mining and summarise the lessons learned from the previous chapters.The book provides a comprehensive overview of technical issues arising in clinical text mining, and offers a valuable guide for advanced students in health informatics, computational linguistics, and information retrieval, and for researchers entering these fields. This work was published by Saint Philip Street Press pursuant to a Creative Commons license permitting commercial use. All rights not granted by the work's license are retained by the author or authors.

This book constitutes the refereed proceedings of the 14th IFIP WG 5.5/SOCOLNET Advanced Doctoral Conference on Computing, Electrical and Industrial Systems, DoCEIS 2023, held in Monte da Caparica, Portugal, during July 5-7, 2022. The 22 full papers presented were carefully reviewed and selected from 47 submissions. The papers cover the following topics: energy communities; smart energy and power systems; intelligent manufacturing; health and biomedical information systems; intelligent computational systems; and electronics and communications.

The six-volume set LNCS 12742, 12743, 12744, 12745, 12746, and 12747 constitutes the proceedings of the 21st International Conference on Computational Science, ICCS 2021, held in Krakow, Poland, in June 2021.* The total of 260 full papers and 57 short papers presented in this book set were carefully reviewed and selected from 635 submissions. 48 full and 14 short papers were accepted to the main track from 156 submissions; 212 full and 43 short papers were accepted to the workshops/ thematic tracks from 479 submissions. The papers were organized in topical sections named: Part I: ICCS Main Track Part II: Advances in High-Performance Computational Earth Sciences: Applications and Frameworks; Applications of Computational Methods in Artificial Intelligence and Machine Learning; Artificial Intelligence and High-Performance Computing for Advanced Simulations; Biomedical and Bioinformatics Challenges for Computer Science Part III: Classifier Learning from Difficult Data; Computational Analysis of Complex Social Systems; Computational Collective Intelligence; Computational Health Part IV: Computational Methods for Emerging Problems in (dis-)Information Analysis; Computational Methods in Smart Agriculture; Computational Optimization, Modelling and Simulation; Computational Science in IoT and Smart Systems Part V: Computer Graphics, Image Processing and Artificial Intelligence; Data-Driven Computational Sciences; Machine Learning and Data Assimilation for Dynamical Systems; MeshFree Methods and Radial Basis Functions in Computational Sciences; Multiscale Modelling and Simulation Part VI: Quantum Computing Workshop; Simulations of Flow and Transport: Modeling, Algorithms and Computation; Smart Systems: Bringing Together Computer Vision, Sensor Networks and Machine Learning; Software Engineering for Computational Science; Solving Problems with Uncertainty; Teaching Computational Science; Uncertainty Quantification for Computational Models *The conference was held virtually. Chapter “Effective Solution of Ill-posed Inverse Problems with Stabilized Forward Solver” is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.

This book constitutes extended and revised versions of the selected papers from the 13th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2020, held in Valletta, Malta, in February 2020. The 29 revised and extended full papers presented were carefully reviewed and selected from a total of 363 submissions. The papers are organized in topical sections on biomedical electronics and devices; bioimaging; bioinformatics models, methods and algorithms; bio-inspired systems and signal processing; health informatic

Examines various speech technologies deployed in healthcare service robots to maximize the robot's ability to interpret user input. Demonstrates how robot anthropomorphic features and etiquette in behavior promotes user-positive emotions, acceptance of robots, and compliance with robot requests. Analyzes how multimodal medical-service robots and other cyber-physical systems can reduce mistakes and mishaps in the operating room. Evaluates various input methods for improving acceptance of robots in the older adult population. Presents case studies of cognitively and socially engaging robots in the long-term care setting for helping older adults with activities of daily living and in the pediatric setting for helping children with autism spectrum conditions and metabolic disorders. Speech and Automata in Health Care forges new ground by closely analyzing how three separate disciplines - speech technology, robotics, and medical/surgical/assistive care - intersect with one another, resulting in an innovative way of diagnosing and treating both juvenile and adult illnesses and conditions. This includes the use of speech-enabled robotics to help the elderly population cope with common problems associated with aging caused by the diminution in their sensory, auditory and motor capabilities. By examining the emerging nexus of speech, automata, and health care, the authors demonstrate the exciting potential of automata, both speech-driven and multimodal, to affect the healthcare delivery system so that it better meets the needs of the populations it serves. This book provides both empirical research findings and incisive literature reviews that demonstrate some of the more novel uses of speech-enabled and multimodal automata in the operating room, hospital ward, long-term care facility, and in the home. Studies backed by major universities, research institutes, and by EU-funded collaborative projects are debuted in this volume. This volume provides a wealth of timely material for industrial engineers, speech scientists, computational linguists, and for signal processing and intelligent systems design experts. Topics include: Spoken Interaction with Healthcare Robots Service Robot Feature Effects on Patient Acceptance/Emotional Response Designing Embodied and Virtual Agents for the Operating Room The Emerging Role of Robotics for Personal Health Management in the Older-Adult Population Why Input Methods for Robots that Serve the Older Adult Are Critical for Usability Socially and Cognitively Engaging Robots in the Long-Term Care Setting Voice-Enabled Assistive Robots for Managing Autism Spectrum Conditions ASR and TTS for Voice-Controlled Robot Interactions in Treating Children with Metabolic Disorders