Artificial Intelligence for Healthcare Applications and Managementintroduces application domains of various AI algorithms across healthcare management. Instead of discussing AI first and then exploring its applications in healthcare afterward, the authors attack the problems in context directly, in order to accelerate the path of an interested reader toward building industrial-strength healthcare applications. Readers will be introduced to a wide spectrum of AI applications supporting all stages of patient flow in a healthcare facility. The authors explain how AI supports patients throughout a healthcare facility, including diagnosis and treatment recommendations needed to get patients from the point of admission to the point of discharge while maintaining quality, patient safety, and patient/provider satisfaction.
AI methods are expected to decrease the burden on physicians, improve the quality of patient care, and decrease overall treatment costs. Current conditions affected by COVID-19 pose new challenges for healthcare management and learning how to apply AI will be important for a broad spectrum of students and mature professionals working in medical informatics. This book focuses on predictive analytics, health text processing, data aggregation, management of patients, and other fields which have all turned out to be bottlenecks for the efficient management of coronavirus patients.
Author(s): Boris Galitsky, Saveli Goldberg
Publisher: Academic Press
Year: 2022
Language: English
Pages: 525
City: London
Front Matter
Copyright
Contributors
Introduction
The issues of ML in medicine this book is solving
AI for diagnosis and treatment
Health discourse
Acknowledgments
Supplementary data sets
References
Multi-case-based reasoning by syntactic-semantic alignment and discourse analysis
Introduction
Multi-case-based reasoning in the medical field
Mixed illness description
Probabilistic ontology
Mapping a patient record to identified cases
Learning semantic similarity
Discourse analysis and discourse trees
Alignment of linguistic graphs
Abstract meaning representation
Aligning AMR
Alignment algorithm
Case-based reasoning in health
Mining cases from health forum threads
Discourse disentanglement
Building a repository of labeled cases and diagnoses
An example of navigating an extended discourse tree for three documents
Constructing extended DTs
System architecture
Evaluation
Datasets
Evaluation of text matching
Overall assessment of the Symptom Checker engine
Diagnosing forum data
Related work
Conclusions
Supplementary data sets
References
Obtaining supported decision trees from text for health system applications
Introduction
Supported decision trees for an expert system
Obtaining supported decision trees from text
From a discourse tree to its supported decision tree
System architecture of construction of a supported decision tree
Evaluation
Decision trees in health
Defining decision tree as a supervised learning task
Decision trees and COVID-19
Expert system for health management
Basic expert systems and their values in health domain
Backward chaining inference
Expert system and health management
Clinical use of expert systems
Expert system lifecycle
Learning ES rules
Dynamics of ES usage
Conclusions
Supplementary data sets
References
Search and prevention of errors in medical databases
Introduction
Data entry errors when transferring information from the initial medical documentation to the studied database
Analyzed databases
Impossible/internally inconsistent data
Externally inconsistent data
Impossible/internally inconsistent data entry in B and S databases
Specific type of the errors ``omitted data´´
Errors in initial medical information
Measurement errors ``bodyweight´´ as an indicator of the quality of the initial information
Algorithm
Errors in EMR data
User history of previous errors
Physicians vs non-physicians
Effect of practice location on weight error rates
Error rates over time
Error rates and user experience
Error reduction
Detection errors in datasets
Alarm system in data entry process
``Follow-up summary´´ as a method of error prevention
``Follow-up summary´´ description
``Follow-up summary´´ implementation
``Follow-up summary´´ utilization
``Follow-up summary´´ effectiveness
Conclusions
Supplementary data sets
References
Overcoming AI applications challenges in health: Decision system DINAR2
Introduction
Problems of introducing medical AI applications
Domain overfitting
Terminology problems
Cognitive bias
Integration of AI into clinical practice
Integrated decision support system at the regional consultative Center for Intensive Pediatrics (DINAR2)
Idea and problems of the regional consultative Center for Intensive Pediatrics
History of DINAR2 development
Methods
Considering data provided by a doctor as input for the operation of DINAR2 as fuzzy sets
Construction of diagnostic rules
An assessment of a patients state of severity
A definition of the leading pathological syndrome
Methods of stimulation of intellectual activity of a CIP consultant
DINAR2s operation in extreme situations
Freedom of a consultants actions within DINAR2 limits
Organization of a database of local hospitals
Stimulating doctors to improve DINAR2
DINAR2 efficiency
Conclusions
Supplementary data sets
References
Formulating critical questions to the user in the course of decision-making
Introduction
Reasoning patterns and formulating critical questions
Automated building of reasoning chains
Questions as relative complement of linguistic representations
Generating text from AMR graph fragment
Deriving critical questions via anti-unification
Question-generation system architecture
Chatbot implementation
Data collection
Evaluation
Syntactic and semantic generalizations
Semantic generalization
Attribute-based generalization
Building questions via generalization of instances
Discussion and conclusions
Supplementary data sets
References
Relying on discourse analysis to answer complex questions by neural machine reading comprehension
Introduction
Examples where discourse analysis is essential for MRC
Discourse dataset
Discourse parsing
Incorporating syntax into model
Attention mechanism for the sequence of tokens
Enabling attention mechanism with syntactic features
Including discourse structure into the model
Pre-trained language models and their semantic extensions
Encoding and alignment with BERT
Direct similarity-based question answering
Correcting an MRC answer
System architecture
Evaluation
Discussion and conclusions
Supplementary data sets
References
Machine reading between the lines (RBL) of medical complaints
Introduction
RBL, machine reading comprehension, and inference
RBL and common sense
RBL as generalization and web mining
Patient repeats what he wants to say
Reading deep between the lines
RBL in storytelling
Extracting RBL results from text
Difficult RBL cases
RBL in a dialogue
Question formation and diversification
System architecture
Statistical model of RBL
RBL and NLI
NLI and semantic fragments
Reinforcement learning approach
Language models
Storytelling discourse approach
Evaluation
Meaningfulness of generated RBLs
Search recall improvement
Discussions
Conclusions
Supplementary data sets
References
Discourse means for maintaining a proper rhetorical flow
Introduction
Medical dialogue systems
Discourse tree of a dialogue
Response selection
Speech acts and communicative actions
A dialogue with doubt
Further extending the set of rhetorical relations toward dialogue
Computing rhetorical relation of entailment
Dialogue generation as language modeling
Strategies for informative conversations
Rhetorical agreement between questions and answers
Discourse parsing of a dialogue
Constructing a dialogue from text
Building a dialogue based on a DT
Constructing questions
System architecture
Evaluation
Discussions and conclusions
Supplementary data sets
References
Dialogue management based on forcing a user through a discourse tree of a text
Introduction
Keeping a learner focused on a text
Navigating discourse tree in conversation
The dialogue flow
Managing user intents
Handling epistemic states
User intent recognizer
Nearest neighbor-based learning for user intent recognition
System architecture
Evaluation
Evaluation setting
Assessment of navigation algorithm
Related work
Personalization in health chatbots
Interaction in the mental space
Persuasive dialogue
Conclusions
Supplementary data sets
References
Building medical ontologies relying on communicative discourse trees
Introduction
Ontology extraction from text
Text mining
Introducing discourse features
Discourse-level support for ontology construction
Issues associated with not using discourse information for ontology entry extraction
Annotating events
Informative and uninformative parts of text
Informative and uninformative parts of an answer
How a discourse tree indicates what to index and what not to index
How rhetorical relations determine indexing rules
Designing ontologies
Systematized nomenclature of medicine-Clinical terms
Relation extractor based on syntactic parsing
Conceptualization process
Neural dictionary manager
Phrase aggregator
Ontologies supporting reasoning
Entity grid helps to extract relationships
Validating ontology
Specific ontology types in bioinformatics
Spatial taxonomy
Supporting search
System architecture
Evaluation
Datasets
Assessment of ontology consistency
An assessment of search improvement due to ontology
Conclusions
Supplementary data sets
References
Explanation in medical decision support systems
Introduction
Models of machine learning explanation
Interpretable models
Black-box models
Explanation based on comparison of the local case with the closest case with an alternative ML solution
Finding the closest point to a local case
A bi-directional adversarial meta-agent between user and ML system
Meta-agent behavior
Steps of the meta-agent
Discussion
Conclusions
Supplementary data sets
References
Passive decision support for patient management
Introduction
Dr. Watson-type systems
Principles of Dr. Watson-type systems
Dr. Watson-type system formalization
Patient management system (SAGe)
Requirements and subsystems
Information import
Diagnostics
Treatment effectiveness
Treatment adequacy
Discontinuation of observation
Integral assessment of patients in the department
Features of Dr. Watson-type system presented in SAGe
Discovery of contradictions and omissions
Attempts to direct physicians towards alternative solutions
Encouragement and motivation
Conclusions
Supplementary data sets
References
Multimodal discourse trees for health management and security
Introduction
Forensic linguistics
Extended discourse trees
Victims right and state responsibility to investigate
Discourse analysis of health and security-related scenarios
Discourse of a reasonable doubt
Discourse analysis of a scenario
Multimodal discourse representation
Multimodal discourse tree for a crime report
Multimodal data sources and references between them
Manipulation with discourse trees
Extended discourse tree
Mobile location data and COVID-19
Call detail records and COVID-19
Automatic number plate recognition
Reasoning about a cause and effect of data records
Representing causal links by R-C framework
Reasoning with arguments extracted from text
System architecture
Evaluation
Discussions and conclusions
Supplementary data sets
References
Improving open domain content generation by text mining and alignment
Introduction
Content generation in health care
Content generation for personalization
Natural language generation in intensive care
Processing raw natural language generation results
Alignment of raw and true content
Fact-checking of deep learning generation
Personalized drug recommendation
Discourse structure deviation of the corrected content
System architecture
Deep learning subsystem
Raw content correction
Probabilistic text merging
Graph-based fact-checking
Entity substitution
Evaluation
Discussions
Conclusions
Supplementary data sets
References
