Intelligent Techniques Inspired by Nature and Used in Biomedical Engineering

Clinical Decision ◽

Patient Specific ◽

Real Problem ◽

History Of ◽

The Right

Managing medical information and knowledge is becoming an increasing problem for healthcare professionals. Medical science that contains ever-increasing amounts of knowledge, such as the medical history of a patient, medical data about diseases, diagnosis and treatment methods, should be necessarily a science of information. The real problem faced by patients and healthcare providers is finding and using relevant knowledge at the right time. In this context, in the middle of 1950s, intelligent computer systems, called clinical decision support systems (CDSS), were introduced as a new concept. CDSS is defined as an active intelligent system that can help medical experts to make decisions by taking specific recommendations. Also, it provides decisions based on resolving patient-specific information and related medical truths. The objective of this chapter is to focus on these systems and explain relations with the field of artificial intelligence methods, approaches, or techniques in this manner.

Extended Clinical Discourse Representation Structure for Controlled Natural Language Clinical Decision Support Systems

International Journal of Reliable and Quality E-Healthcare ◽

10.4018/ijrqeh.2015040101 ◽

2015 ◽

Vol 4 (2) ◽

pp. 1-11 ◽

Cited By ~ 2

Author(s):

David José Murteira Mendes ◽

Irene Pimenta Rodrigues ◽

Carlos F Baeta ◽

Carlos Solano-Rodriguez

Keyword(s):

Medical Science ◽

Clinical History ◽

Clinical Decision ◽

Discourse Representation ◽

Discourse Representation Theory ◽

Single Text ◽

Discourse Representation Structure ◽

History Of ◽

Representation Structure

To support an end to end Question and Answering system to help the clinical practitioners in a cardiovascular healthcare environment, an extended discourse representation structure CIDERS is introduced. This extension of the well-known DRT (Discourse Representation Theory) structures, go beyond single text representation extending them to embrace the general clinical history of a given patient. Introduced is a proposed and developed ontology framework, Ontology for General Clinical Practice, enhancing the currently available state-of-the-art ontologies for medical science and for the cardiovascular specialty, It's shown the scientific and philosophical reasons of its present dual structure with a deeply expressive (SHOIN) terminological base (TBox) and a highly computable (EL++) assertions knowledge base (ABox).

Big Data Analytics in Bioinformatics and Healthcare - Advances in Bioinformatics and Biomedical Engineering ◽

The Benefits of Big Data Analytics in the Healthcare Sector

10.4018/978-1-4666-6611-5.ch018 ◽

2015 ◽

pp. 406-439 ◽

Cited By ~ 3

Author(s):

Andrea Darrel ◽

Margee Hume ◽

Timothy Hardie ◽

Jeffery Soar

Keyword(s):

Big Data ◽

Data Analytics ◽

Healthcare Providers ◽

Big Data Analytics ◽

Clinical Decision ◽

Healthcare Sector ◽

Improved Outcomes ◽

Health Initiatives ◽

Remote Patient

The benefits of big data analytics in the healthcare sector are assumed to be substantial, and early proponents have been very enthusiastic (Chen, Chiang, & Storey, 2012), but little research has been carried out to confirm just what those benefits are, and to whom they accrue (Bollier, 2010). This chapter presents an overview of existing literature that demonstrates quantifiable, measurable benefits of big data analytics, confirmed by researchers across a variety of healthcare disciplines. The chapter examines aspects of clinical operations in healthcare including Cost Effectiveness Research (CER), Clinical Decision Support Systems (CDS), Remote Patient Monitoring (RPM), Personalized Medicine (PM), as well as several public health initiatives. This examination is in the context of searching for the benefits described resulting from the deployment of big data analytics. Results indicate the principle benefits are delivered in terms of improved outcomes for patients and lower costs for healthcare providers.

Global Implications of Emerging Technology Trends - Advances in IT Standards and Standardization Research ◽

Clinical Decision Support Systems Question and Answering

10.4018/978-1-5225-4944-4.ch009 ◽

2018 ◽

pp. 146-157 ◽

Cited By ~ 2

Author(s):

David José Murteira Mendes ◽

Irene Pimenta Rodrigues ◽

César Fonseca

Keyword(s):

Clinical Practice ◽

Decision Support ◽

Decision Support Systems ◽

Support Systems ◽

Medical Science ◽

Clinical Decision ◽

Discourse Representation ◽

Discourse Representation Theory

A question answering system to help clinical practitioners in a cardiovascular healthcare environment to interface clinical decision support systems can be built by using an extended discourse representation structure, CIDERS, and an ontology framework, Ontology for General Clinical Practice. CIDERS is an extension of the well-known DRT (discourse representation theory) structures, intending to go beyond single text representation to embrace the general clinical history of a given patient represented in an ontology. The Ontology for General Clinical Practice improves the currently available state-of-the-art ontologies for medical science and for the cardiovascular specialty. The chapter shows the scientific and philosophical reasons of its present dual structure with a deeply expressive (SHOIN) terminological base (TBox) and a highly computable (EL++) assertions knowledge base (ABox). To be able to use the current reasoning techniques and methodologies, the authors made a thorough inventory of biomedical ontologies currently available in OWL2 format.

Research Anthology on Decision Support Systems and Decision Management in Healthcare, Business, and Engineering ◽

Clinical Decision Support Systems Question and Answering

10.4018/978-1-7998-9023-2.ch026 ◽

2021 ◽

pp. 543-554

Author(s):

David José Murteira Mendes ◽

Irene Pimenta Rodrigues ◽

César Fonseca

Keyword(s):

Clinical Practice ◽

Decision Support ◽

Decision Support Systems ◽

Support Systems ◽

Medical Science ◽

Clinical Decision ◽

Discourse Representation ◽

Discourse Representation Theory

A question answering system to help clinical practitioners in a cardiovascular healthcare environment to interface clinical decision support systems can be built by using an extended discourse representation structure, CIDERS, and an ontology framework, Ontology for General Clinical Practice. CIDERS is an extension of the well-known DRT (discourse representation theory) structures, intending to go beyond single text representation to embrace the general clinical history of a given patient represented in an ontology. The Ontology for General Clinical Practice improves the currently available state-of-the-art ontologies for medical science and for the cardiovascular specialty. The chapter shows the scientific and philosophical reasons of its present dual structure with a deeply expressive (SHOIN) terminological base (TBox) and a highly computable (EL++) assertions knowledge base (ABox). To be able to use the current reasoning techniques and methodologies, the authors made a thorough inventory of biomedical ontologies currently available in OWL2 format.

The measurement of process and the role of information technology

Proceedings of the Royal Society of Edinburgh Section B Biological Sciences ◽

10.1017/s0269727000005650 ◽

1993 ◽

Vol 101 ◽

pp. 31-53

Author(s):

M. P. Ryan ◽

W. Dodd

Keyword(s):

Information Technology ◽

Quality Assurance ◽

Clinical Decision ◽

Data Sets ◽

Process Of Care ◽

Operational Systems ◽

History Of ◽

Computer Based ◽

English Speaking

SynopsisProcess of care is the most immediate, relevant and susceptible to improvement of Donabedian's three elements of quality assurance. To place the study of process in context, the history of quality assurance in English-speaking countries is reviewed, with particular emphasis on the U.S.A. A range of methods are examined in detail and examples are provided to illustrate their strengths and weaknesses. Particular attention is paid to the use of explicit and implicit criteria. The importance of information technology in monitoring the process of care cannot be over-stated. Limited audit is possible with manual records but all substantial projects require computer support. The value of capturing data from operational systems rather than from dedicated projects is emphasised. Attention is drawn to the key importance of structured records and minimum data sets; these allow information to be pooled and process studies to be generalised. Examples are given of quality assurance projects which have used information technology. Finally potential future developments are reviewed with particular reference to clinical guidelines and computer-based clinical decision support systems.

An Overview of Efforts to Bring Clinical Knowledge to the Point of Care

Clinical Knowledge Management ◽

10.4018/978-1-59140-300-5.ch016 ◽

2005 ◽

pp. 285-296

Author(s):

Dean F. Sittig

Keyword(s):

Decision Support ◽

Decision Support Systems ◽

Support Systems ◽

Point Of Care ◽

Clinical Information ◽

Clinical Decision ◽

Clinical Knowledge ◽

The Right

By bringing people the right information in the right format at the right time and place, state of the art clinical information systems with imbedded clinical knowledge can help people make the right clinical decisions. This chapter provides an overview of the efforts to develop systems capable of delivering such information at the point of care. The first section focuses on “library-type” applications that enable a clinician to look-up information in an electronic document. The second section describes a myriad of “real-time clinical decision support systems.” These systems generally deliver clinical guidance at the point of care within the clinical information system (CIS). The third section describes several “hybrid” systems, which combine aspects of real-time clinical decision support systems with library-type information. Finally, section four provides a brief look at various attempts to bring clinical knowledge, in the form of computable guidelines, to the point of care.be sufficiently expressive to explicitly capture the design rational (process and outcome intentions) of the guideline’s author, while leaving flexibility at application time to the attending physician and their own preferred methods.” (Shahar, 2001)

Towards an Intelligent Integrated Approach for Clinical Decision Support

Big Data ◽

10.4018/978-1-4666-9840-6.ch092 ◽

2016 ◽

pp. 1987-2005

Author(s):

Rajendra Akerkar

Keyword(s):

Big Data ◽

Decision Support ◽

Medical Information ◽

Integrated Approach ◽

Clinical Decision ◽

Research Direction ◽

Integrated System ◽

Intelligent Integrated System

Nowadays, making use of big data is becoming mainstream in different enterprises and industry sectors. The medical sector is no exception. Specifically, medical services, which generate and process enormous volumes of medical information and medical device data, have been quickening big data utilization. In this chapter, we present a concept of an intelligent integrated system for direct support of decision making of physicians. This is a work in progress and the focus is on decision support for pharmacogenomics, which is the study of the relationship between a specific person's genetic makeup and his or her response to drug treatment. Further, we discuss a research direction considering the current shortcomings of clinical decision support systems.

Differential Diagnosis and Treatment in a Patient With Posterior Upper Thoracic Pain

Physical Therapy ◽

10.1093/ptj/86.2.254 ◽

2006 ◽

Vol 86 (2) ◽

pp. 254-268 ◽

Cited By ~ 4

Author(s):

Stacie J Fruth

Keyword(s):

Differential Diagnosis ◽

Clinical Decision Making ◽

Clinical Decision ◽

Trigger Points ◽

Diagnosis And Treatment ◽

Thoracic Pain ◽

Patient Reported ◽

History Of ◽

The Right ◽

Upper Thoracic

Background and Purpose. Determining the source of a patient's pain in the upper thoracic region can be difficult. Costovertebral (CV) and costotransverse (CT) joint hypomobility and active trigger points (TrPs) are possible sources of upper thoracic pain. This case report describes the clinical decision-making process for a patient with posterior upper thoracic pain. Case Description. The patient had a 4-month history of pain; limited cervical, trunk, and shoulder active range of motion; limited and painful mobility of the right CV /CT joints of ribs 3 through 6; and periscapular TrPs. Interventions included CV / CT joint mobilizations, TrP release, and flexibility and postural exercises. Outcomes. The patient reported intermittent mild discomfort after 7 physical therapy sessions. Examination findings were normal, and he was able to resume all preinjury activities. Discussion. This case suggests that CV /CT mobilizations and active TrP release may have been beneficial in reducing pain and restoring function in this patient. [Fruth SJ. Differential diagnosis and treatment in a patient with posterior upper thoracic pain. Phys Ther. 2006;86:254-268.]

РОЗРОБКА ПЛАТФОРМИ СИСТЕМИ ПІДТРИМКИ ПРИЙНЯТТЯ РІШЕННЯ ДЛЯ МЕДИЧНОЇ ІНФОРМАЦІЙНОЇ СИСТЕМИ З ВІДКРИТИМ КОДОМ OPENEMR

Medical Informatics and Engineering ◽

10.11603/mie.1996-1960.2015.3.4999 ◽

2015 ◽

Author(s):

A. V. Semenets ◽

V. P. Martsenyuk

Keyword(s):

Decision Making ◽

Open Source ◽

Medical Information ◽

Clinical Decision ◽

Medical Decision ◽

Decision Making Process ◽

Model View Controller ◽

Platform Development ◽

Developer Tools

<h3>Introduction</h3><p class="1415">An importance of Medical Information Systems (MIS) in medical practice and education is displayed. The wide usage of the Electronic Medical Records (EMR) software is signed.</p><h3>Current state. The open source EMR-systems usage</h3><p class="1415">The importance and alternative approaches to the implementation of the MIS in the Ukraine healthcare system are discussed. The situation on the MIS development in Ukraine is presented. The benefits of the open-source MIS usage are shown.</p><p class="1415">Effectiveness of the Clinical Decision Support Systems (CDSS) application in the medical decision making process are introduced. The CDSS capabilities in diagnostic of the pregnancy pathologies are considered.</p><h3>The CDSS platform development implementation</h3><p class="1415">The step-by-step results of the CDSS platform development as the plugin for the open-source MIS OpenEMR are presented.</p><p class="1415">The open-source MIS OpenEMR developer tools and software API are reviewed.</p><p class="1415">The information model of the CDSS database was proposed and developed. The Model-View-Controller (MVC) based approach to the CDSS architecture is proposed. The CDSS dialog subsystem implementation according to the MIS OpenEMR programming API is developed. The administrative module of the CDSS platform based on the Yii2 php-framework is created.</p><p class="1415">An approach to the decision making process which is based on the decision tree algorithm usage is proposed. An implementation of the given above approach as separate web-service based won the Google App Engine (GAE) capabilities is presented. The data exchanging formats and methods which OpenEMR CDSS module and DecissionTree GAE service interaction establish are developed.</p>