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Artificial Intelligence in Medicine

Overview of attention for book
Artificial Intelligence in Medicine
Springer International Publishing

Table of Contents

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    Book Overview
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    Chapter 1 Common Misconceptions and Future Directions for AI in Medicine: A Physician-Data Scientist Perspective
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    Chapter 2 AI in Healthcare: Ethical and Privacy Challenges
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    Chapter 3 Recent Context-Aware LSTM for Clinical Event Time-Series Prediction
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    Chapter 4 Deep Learning Approach for Pathogen Detection Through Shotgun Metagenomics Sequence Classification
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    Chapter 5 Fully Interactive Lungs Auscultation with AI Enabled Digital Stethoscope
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    Chapter 6 An Automated Fall Detection System Using Recurrent Neural Networks
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    Chapter 7 A Semi-supervised Learning Approach for Pan-Cancer Somatic Genomic Variant Classification
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    Chapter 8 ChronoNet: A Deep Recurrent Neural Network for Abnormal EEG Identification
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    Chapter 9 Convolutional Recurrent Neural Network for Bubble Detection in a Portable Continuous Bladder Irrigation Monitor
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    Chapter 10 Mammogram Classification with Ordered Loss
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    Chapter 11 Agent-Based Models and Spatial Enablement: A Simulation Tool to Improve Health and Wellbeing in Big Cities
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    Chapter 12 Towards Health 4.0: e-Hospital Proposal Based Industry 4.0 and Artificial Intelligence Concepts
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    Chapter 13 MitPlan: A Planning Approach to Mitigating Concurrently Applied Clinical Practice Guidelines
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    Chapter 14 Automatic Alignment of Surgical Videos Using Kinematic Data
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    Chapter 15 A Rule-Based Expert System for Automatic Implementation of Somatic Variant Clinical Interpretation Guidelines
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    Chapter 16 Considering Temporal Preferences and Probabilities in Guideline Interaction Analysis
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    Chapter 17 Predicting Patient’s Diagnoses and Diagnostic Categories from Clinical-Events in EHR Data
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    Chapter 18 Assessing the Effectiveness of Sequences of Treatments Using Sequential Patterns
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    Chapter 19 Bayesian Network vs. Cox’s Proportional Hazard Model of PAH Risk: A Comparison
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    Chapter 20 Pursuing Optimal Prediction of Discharge Time in ICUs with Machine Learning Methods
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    Chapter 21 Towards the Economic Evaluation of Two Mini-invasive Surgical Techniques for Head&Neck Cancer: A Customizable Model for Different Populations
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    Chapter 22 Gated Hidden Markov Models for Early Prediction of Outcome of Internet-Based Cognitive Behavioral Therapy
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    Chapter 23 A Data-Driven Exploration of Hypotheses on Disease Dynamics
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    Chapter 24 On Predicting the Outcomes of Chemotherapy Treatments in Breast Cancer
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    Chapter 25 External Validation of a “Black-Box” Clinical Predictive Model in Nephrology: Can Interpretability Methods Help Illuminate Performance Differences?
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    Chapter 26 Classifying Small Volumes of Tissue for Real-Time Monitoring Radiofrequency Ablation
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    Chapter 27 Mobile Indoor Localization with Bluetooth Beacons in a Pediatric Emergency Department Using Clustering, Rule-Based Classification and High-Level Heuristics
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    Chapter 28 NONCADO: A System to Prevent Falls by Encouraging Healthy Habits in Elderly People
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    Chapter 29 The Minimum Sampling Rate and Sampling Duration When Applying Geolocation Data Technology to Human Activity Monitoring
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    Chapter 30 AI-Driven Pathology Laboratory Utilization Management via Data- and Knowledge-Based Analytics
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    Chapter 31 Using Cluster Ensembles to Identify Psychiatric Patient Subgroups
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    Chapter 32 Connection Between the Parkinson’s Disease Subtypes and Patients’ Symptoms Progression
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    Chapter 33 Interpretable Patient Subgrouping Using Trace-Based Clustering
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    Chapter 34 Extracting Food-Drug Interactions from Scientific Literature: Tackling Unspecified Relation
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    Chapter 35 Bayesian Clustering for HIV1 Protease Inhibitor Contact Maps
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    Chapter 36 Time-to-Birth Prediction Models and the Influence of Expert Opinions
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    Chapter 37 Modelling the Impact of AI for Clinical Decision Support
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    Chapter 38 Generating Positive Psychosis Symptom Keywords from Electronic Health Records
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    Chapter 39 Text-to-Movie Authoring of Anatomy Lessons
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    Chapter 40 Machine Learning and Feature Selection for the Classification of Mental Disorders from Methylation Data
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    Chapter 41 A Machine Learning Approach to Predict Diabetes Using Short Recorded Photoplethysmography and Physiological Characteristics
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    Chapter 42 Identification of Patient Prescribing Predicting Cancer Diagnosis Using Boosted Decision Trees
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    Chapter 43 Automatic Image-Derived Estimation of the Arterial Whole-Blood Input Function from Dynamic Cerebral PET with $$^{18}$$ F-Choline
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    Chapter 44 A Semi-supervised Approach to Segment Retinal Blood Vessels in Color Fundus Photographs
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    Chapter 45 A Critical Look at Studies Applying Over-Sampling on the TPEHGDB Dataset
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    Chapter 46 Perspectives on Assurance Case Development for Retinal Disease Diagnosis Using Deep Learning
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    Chapter 47 Rapid Detection of Heart Rate Fragmentation and Cardiac Arrhythmias: Cycle-by-Cycle rr Analysis, Supervised Machine Learning Model and Novel Insights
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    Chapter 48 Evaluation of Random Forest and Ensemble Methods at Predicting Complications Following Cardiac Surgery
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    Chapter 49 Mining Compact Predictive Pattern Sets Using Classification Model
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    Chapter 50 Inferring Temporal Phenotypes with Topological Data Analysis and Pseudo Time-Series
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    Chapter 51 Unsupervised Mitral Valve Segmentation in Echocardiography with Neural Network Matrix Factorization
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    Chapter 52 Unsupervised Learning from Motion Sensor Data to Assess the Condition of Patients with Parkinson’s Disease
Attention for Chapter 42: Identification of Patient Prescribing Predicting Cancer Diagnosis Using Boosted Decision Trees
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Chapter title
Identification of Patient Prescribing Predicting Cancer Diagnosis Using Boosted Decision Trees
Chapter number 42
Book title
Artificial Intelligence in Medicine
Published by
Springer, Cham, May 2019
DOI 10.1007/978-3-030-21642-9_42
Book ISBNs
978-3-03-021641-2, 978-3-03-021642-9
Authors

Josephine French, Cong Chen, Katherine Henson, Brian Shand, Patrick Ferris, Josh Pencheon, Sally Vernon, Meena Rafiq, David Howe, Georgios Lyratzopoulos, Jem Rashbass, French, Josephine, Chen, Cong, Henson, Katherine, Shand, Brian, Ferris, Patrick, Pencheon, Josh, Vernon, Sally, Rafiq, Meena, Howe, David, Lyratzopoulos, Georgios, Rashbass, Jem

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The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 16 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 16 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 3 19%
Researcher 3 19%
Student > Ph. D. Student 2 13%
Professor 1 6%
Unknown 7 44%
Readers by discipline Count As %
Medicine and Dentistry 3 19%
Nursing and Health Professions 2 13%
Computer Science 1 6%
Philosophy 1 6%
Social Sciences 1 6%
Other 1 6%
Unknown 7 44%

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