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Artificial Intelligence in Medicine

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Cover of 'Artificial Intelligence in Medicine'

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 32: Connection Between the Parkinson’s Disease Subtypes and Patients’ Symptoms Progression
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Chapter title
Connection Between the Parkinson’s Disease Subtypes and Patients’ Symptoms Progression
Chapter number 32
Book title
Artificial Intelligence in Medicine
Published by
Springer, Cham, May 2019
DOI 10.1007/978-3-030-21642-9_32
Book ISBNs
978-3-03-021641-2, 978-3-03-021642-9

Anita Valmarska, Dragana Miljkovic, Marko Robnik–Šikonja, Nada Lavrač

Twitter Demographics

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

Mendeley readers

The data shown below were compiled from readership statistics for 2 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 2 100%

Demographic breakdown

Readers by professional status Count As %
Professor > Associate Professor 1 50%
Student > Master 1 50%
Readers by discipline Count As %
Nursing and Health Professions 1 50%
Computer Science 1 50%