Machine Learning in Bio-Signal Analysis and Diagnostic Imaging

Nilanjan Dey,Surekha Borra,Amira S. Ashour,Fuqian Shi

• Publisher: Academic Press
• Year: 2018
• ISBN:
  9780128160879
• Topic: Ciencias biológicas
• Subtopic: Biotecnología

Machine Learning in Bio-Signal Analysis and Diagnostic Imaging presents original research on the advanced analysis and classification techniques of biomedical signals and images that cover both supervised and unsupervised machine learning models, standards, algorithms, and their applications, along with the difficulties and challenges faced by healthcare professionals in analyzing biomedical signals and diagnostic images. These intelligent recommender systems are designed based on machine learning, soft computing, computer vision, artificial intelligence and data mining techniques. Classification and clustering techniques, such as PCA, SVM, techniques, Naive Bayes, Neural Network, Decision trees, and Association Rule Mining are among the approaches presented.

The design of high accuracy decision support systems assists and eases the job of healthcare practitioners and suits a variety of applications. Integrating Machine Learning (ML) technology with human visual psychometrics helps to meet the demands of radiologists in improving the efficiency and quality of diagnosis in dealing with unique and complex diseases in real time by reducing human errors and allowing fast and rigorous analysis. The book's target audience includes professors and students in biomedical engineering and medical schools, researchers and engineers.

• Examines a variety of machine learning techniques applied to bio-signal analysis and diagnostic imaging
• Discusses various methods of using intelligent systems based on machine learning, soft computing, computer vision, artificial intelligence and data mining
• Covers the most recent research on machine learning in imaging analysis and includes applications to a number of domains

Chapter 1

Ontology-Based Process for Unstructured Medical Report Mapping

Jefferson Tales Oliva^⁎,†; Huei Diana Lee^†; Newton Spolaôr^†; Claudio Saddy Rodrigues Coy^‡; João José Fagundes^‡; Maria de Lourdes Setsuko Ayrizono^‡; Feng Chung Wu^{†,‡ ⁎} Bioinspired Computing Laboratory, Graduate Program in Computer Science and Computational Mathematics, University of São Paulo, São Carlos, Brazil
^† Laboratory of Bioinformatics, Graduate Program in Electrical Engineering and Computer Science, Western Paraná State University, Foz do Iguaçu, Brazil
^‡ Service of Coloproctology, Graduate Program in Medical Sciences, University of Campinas, Campinas, Brazil

Abstract

Hospitals and clinics store an increasing amount of clinical data, such as medical reports. These reports often describe, in natural language, findings from bio-signals, images, and videos collected during a medical procedure. Data mining can explore reports data to find patterns useful to assist experts’ decision making processes and medical procedure development. However, the content of medical reports is rarely organized into an appropriate format. To tackle this issue, we developed the ontology-based Medical Report Mapping Process to represent the content of unstructured reports into a database format. This chapter applied the ontology-based process to map 3654 unstructured upper gastrointestinal endoscopy reports written in Brazilian Portuguese. As a result, a satisfactory mapping performance was achieved. By comparing this result with previous ones in smaller and simpler sets of reports, this chapter suggests that the ontology-based process performs well in sets with different sizes.

Keywords

Text mining; Natural language processing; Data mining; Ontologies; Medical reports

1 Introduction

The human digestive system consists of several anatomical portions in which different abnormalities can occur [1, 2]. In particular, upper alimentary tract like esophagus, stomach, and duodenum, is susceptible to some common diseases and conditions, such as cancer. In Brazil, for example, 7600 and 12,920 new cases of stomach cancer were estimated for the year 2016 in women and men, respectively, and an early and accurate diagnosis becomes imperative in the control and treatment of these diseases [3].

Therefore, upper gastrointestinal endoscopy (UGIE) is an important tool for diagnosis and treatment of lesions in these anatomical regions. It allows experts to capture video and images from the alimentary tract during the UGIE examination. After this medical procedure, the experts usually create textual reports to record findings and information regarding the examination, supplementing the acquired media [4]. In the end, all the medical data regarding UGIE is stored in computers and other equipments and this data is used, for example, to diagnose gastrointestinal disorders.

Data storage capacity is increasing, however, the higher the amount of stored data, the harder the analysis of the data is. This is also the case for medical data in many hospitals and clinics. Human analysis of this data requires valuable time from experts and is susceptible to subjectivity. In this scenario, computational methods have been applied to support analysis of medical video, image, bio-signal, and text [5–9].

By focusing on text, one can note that this media type is essential, for example, in medical reports and records regarding UGIE or other medical procedures. To analyze this data with the assistance of computers, text mining methods are a relevant alternative. The idea is to identify, retrieve and analyze patterns in unstructured text written in natural language [10]. As a result, medical experts can obtain relevant content from a large amount of reports and textual records.

Although text mining methods are powerful tools to find data patterns, they are susceptible to different textual issues, such as mistypings, synonyms variability, irrelevant words and missing information [11]. In this scenario, we developed, in collaboration with medical and computer experts, the ontology-based medical report mapping process (OMRMP) method and its implementation as a computational tool [12, 13]. This proposal stands out due to its ability to transform content from unstructured medical reports into a format similar to a database (DB) table. To do so, a domain specific ontology is considered as an alternative to represent mapping rules that transform sets of relevant terms into values for database attributes. In particular, the ontology—a structure with classes, instances and relations [14]—integrates experts’ knowledge and links reports words with database table values. OMRMP yields an attribute value table that is useful as an input for experts’ studies, analyses, and decision making processes. The table is also compatible with data mining, machine learning, and other computational intelligence approaches that extract knowledge from structured data [15]. Another difference from conventional text mining methods is that OMRMP ontology and auxiliary structures deal with textual issues usual in reports from medical domain.

This chapter aims to apply the OMRMP method and tool to map a large set of 3654 artificial reports with valid terms usual in real UGIE medical reports. As a result, unstructured information from these reports, written in natural language, was successfully structured. Moreover, satisfactory results were achieved in terms of reduction of phrases and words, and percentage of report terms mapped. These achievements are competitive with the ones from previous work with smaller sets of UGIE reports, with less textual patters to map.

This chapter is organized as follows: Section 2 presents some pieces of related chapter. Section 3 describes the OMRMP method and its computational implementation. Section 4 details the experimental setup conducted in this chapter and Section 5 reports and discusses the results obtained by applying OMRMP in 3654 textual reports. Section 6 concludes this chapter with final highlights.

2 Related Work

Some concepts used in related work are named-entity recognition (NER) and the unified Medical language system (UMLS) [16], which can be computed in applications related to natural language processing (NLP) and text mining. The former concept is an alternative to extract meaningful terms, such as the names of diseases and gens, from unstructured biomedical text [17, 18]. UMLS, in turn, consists in a collection of ontologies and vocabularies from distinct domains that can be used to support text processing methods [19].

Lee et al. [20] develops a method that illustrates NER application to process a corpus composed of MEDLINE abstracts [21]. After finding terms that delimit named entities, the proposal categorizes them into semantic classes described in an ontology. An important component of this method, a machine learning algorithm named Support Vector Machines [15], is used to identify entities boundaries and to classify the entities found.

Another piece of related work performs two additional steps before NER application [22]. In particular, the first step uses natural language processing techniques to split pieces of text into sentences before tagging its words with appropriate parts of speech. Nouns are then submitted to the second step to create groups of words representing noun phrases. Afterwards, NER matches the obtained phrases with concepts of an ontology to yield named entities.

NER and UMLS were combin...

Table of contents

• Cover image
• Title page
• Table of Contents
• Copyright
• Contributors
• Preface
• Chapter 1: Ontology-Based Process for Unstructured Medical Report Mapping
• Chapter 2: A Computer-Aided Diagnoses System for Detecting Multiple Ocular Diseases Using Color Retinal Fundus Images
• Chapter 3: A DEFS Based System for Differential Diagnosis Between Severe Fatty Liver and Cirrhotic Liver Using Ultrasound Images
• Chapter 4: Infrared Thermography and Soft Computing for Diabetic Foot Assessment
• Chapter 5: Automated Classification of Hypertension and Coronary Artery Disease Patients by PNN, KNN, and SVM Classifiers Using HRV Analysis
• Chapter 6: Optimization of ROI Size for Development of Computer Assisted Framework for Breast Tissue Pattern Characterization Using Digitized Screen Film Mammograms
• Chapter 7: Optimization of ANN Architecture: A Review on Nature-Inspired Techniques
• Chapter 8: Ensemble Learning Approach to Motor Imagery EEG Signal Classification
• Chapter 9: Medical Images Analysis Based on Multilabel Classification
• Chapter 10: Figure Retrieval From Biomedical Literature: An Overview of Techniques, Tools, and Challenges
• Chapter 11: Application of Machine Learning Algorithms for Classification and Security of Diagnostic Images
• Chapter 12: Robotics in Healthcare: An Internet of Medical Robotic Things (IoMRT) Perspective
• Index