Information Technology in Bio- and Medical Informatics
8th International Conference, ITBAM 2017, Lyon, France, August 28-31, 2017, Proceedings
Published on 11. August 2017
X, 135 pages
978-3-319-64265-9 (ISBN)
Description
This book constitutes the refereed proceedings of the 8th International Conference on Information Technology in Bio- and Medical Informatics, ITBAM 2017, held in Lyon, France, in August 2017.
The 3 revised full papers and 6 poster papers presented were carefully reviewed and selected from 15 submissions. The papers address a broad range of topics in applications of information technology to biomedical engineering and medical informatics.
The 3 revised full papers and 6 poster papers presented were carefully reviewed and selected from 15 submissions. The papers address a broad range of topics in applications of information technology to biomedical engineering and medical informatics.
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Miroslav Bursa | Andreas Holzinger | M. Elena Renda
Information Technology in Bio- and Medical Informatics
8th International Conference, ITBAM 2017, Lyon, France, August 28-31, 2017, Proceedings
Book
07/2017
Springer
€53.49
Shipment within 10-15 days
Content
- Intro
- Preface
- Organization
- Contents
- Editorial
- IT in Biology & Medical Informatics: On the Challenge of Understanding the Data Ecosystem
- 1 Life Science and Medicine as Data Science
- 2 Challenge: Understanding the Data Ecosystem
- 3 Conclusion
- References
- General Track
- A Hybrid Feature Selection Method to Classification and Its Application in Hypertension Diagnosis
- Abstract
- 1 Introduction
- 2 Materials and Methods
- 2.1 Data
- 2.2 Feature Selection
- 3 Experiment and Results
- 3.1 Generation of Target Population
- 3.2 Hybrid Feature Selection
- 3.3 Bayesian Network
- 3.4 Experimental Results
- 4 Conclusion
- Acknowledgment
- References
- Statistical Analysis of Perinatal Risk Factors for Emergency Caesarean Section
- 1 Introduction
- 2 Materials and Methods
- 2.1 Dataset
- 2.2 Population and Inclusion Criteria
- 2.3 Features
- 2.4 Statistical Evaluation
- 3 Results
- 4 Discussion
- 5 Conclusion
- References
- Modelling of Cancer Patient Records: A Structured Approach to Data Mining and Visual Analytics
- Abstract
- 1 Introduction
- 1.1 Background
- 1.2 Case Study - University Hospital Southampton
- 2 Methodology
- 2.1 Process-Driven Framework
- 2.2 Data Warehousing
- 2.3 Data Mining and Modelling
- 2.4 Visualisation
- 3 Clinical Data
- 3.1 Data Sources and Understanding
- 3.2 Data Pre-processing
- 3.3 Multi-dimensional Modelling
- 4 Results
- 4.1 Visual Analytics
- 4.2 Data Mining and Analytics
- 4.3 Discussion
- 5 Conclusion
- Acknowledgements
- References
- Poster Session
- Contextual Decision Making for Cancer Diagnosis
- 1 Introduction
- 2 Model and Decision Analytic Model
- 3 Cancer Diagnosis and Modeling Cancer Diagnosis
- 3.1 Cancer and Cancer Diagnosis
- 3.2 Modeling Cancer Diagnosis
- 4 Computer Aided/Assisted Diagnosis (CAD)
- 5 Context and the Role of Context in Medical Decision Making
- 5.1 Context
- 5.2 Modeling Context for Medical Decision Making
- 6 Critical Discussion and Our Approach
- 6.1 Discussion
- 6.2 Our Contribution
- 7 Conclusion
- References
- A Review of Model Prediction in Diabetes and of Designing Glucose Regulators Based on Model Predictive Control for the Artificial Pancreas
- 1 Introduction
- 2 Datasets
- 3 Evaluation Criteria
- 4 Prediction Models
- 4.1 Linear/Nonlinear Dynamic Models
- 4.2 Neural Network Models
- 5 Results
- 6 Artificial Pancreas - Model Predictive Control
- 7 Discussion
- 8 Conclusion
- References
- Audit Trails in OpenSLEX: Paving the Road for Process Mining in Healthcare
- 1 Motivation
- 2 Background
- 2.1 Standardized Audit Trails
- 2.2 OpenSLEX Meta Model
- 3 Problem
- 4 Approach
- 4.1 Mapping of ATNA Messages to OpenSLEX
- 4.2 Transformation of ATNA Messages
- 5 Conclusion and Future Work
- References
- Riemannian Geometry in Sleep Stage Classification
- 1 Introduction
- 2 Materials and Methods
- 2.1 Covariance Matrix
- 2.2 Riemannian Geometry
- 2.3 Classification in Riemannian Geometry
- 3 Experimental Results
- 3.1 Data Description
- 3.2 Results
- 4 Conclusion and Discussion
- References
- The Use of Convolutional Neural Networks in Biomedical Data Processing
- 1 Introduction
- 1.1 Motivation and Clinical View
- 2 Data and Methodology
- 2.1 Input Dataset Description
- 2.2 Preprocessing and Feature Extraction
- 3 Experimental Part
- 3.1 Methodology and Experiment Design
- 3.2 Evaluation
- 4 Conclusion and Final Results
- References
- Reducing Red Blood Cell Transfusions
- Abstract
- 1 Introduction
- 2 Problem Domain
- 3 Classification
- 4 Implementation Details
- 4.1 Data Implementation
- 4.2 User Implementation
- 5 Analyzing and Improving the Algorithm
- 5.1 Changes from User Acceptance Testing
- 5.2 Supervised Learning
- 5.3 Phase Two
- 6 Results
- 7 Future Work
- 8 Conclusions
- References
- Author Index
