Download free PDF, EPUB, Kindle A Novel Ontology and Machine Learning Driven Hybrid Clinical Decision Support Framework for Cardiovascular Preventative Care
A Novel Ontology and Machine Learning Driven Hybrid Clinical Decision Support Framework for Cardiovascular Preventative Care Kamran Farooq
Book Details:
Author: Kamran FarooqDate: 28 Jun 2016
Publisher: GRIN Publishing
Language: English
Book Format: Paperback::322 pages
ISBN10: 3668241996
File size: 33 Mb
Dimension: 148x 210x 18mm::422g
Download free PDF, EPUB, Kindle A Novel Ontology and Machine Learning Driven Hybrid Clinical Decision Support Framework for Cardiovascular Preventative Care. We proposed an ontology and machine learning driven hybrid clinical decision support framework for cardiovascular preventative care as shown in Fig. 1. The development of the machine learning driven prognostic system (MLDPS) was carried out in close collaboration with clinical experts. Ontology-based Modeling of Clinical Practice Guidelines: A Clinical Decision Support System for Breast Cancer Follow-up Interventions at Primary Care Settings.Samina R. Abidi, Syed SR. Abidi, Sajjad Hussain, Mike Shepherd.a.NICHE Research Group, Faculty of Computer Science, Dalhousie University, Halifax, Canada.Abstract Recent interest in search tools for Clinical Decision Support (CDS) has dramatically increased. These tools help clinicians assess a medical situation providing actionable info To support clinical and translational research, each of these universities have established individual clinical data warehouses with data from their respective hospital electronic health record (EHR) systems, and are also engaged in research projects that can directly impact patient care. In light of these challenges, we are proposing a novel ontology and machine learning-driven hybrid clinical decision support framework for cardiovascular preventative care.ud ud An ontology-inspired approach provides a foundation for information collection, knowledge acquisition and decision support capabilities and aims to develop context To provide knowledge-based support for the scientific tasks of clinical research, the study protocol should be modeled in a knowledge representation formalism with clear, consistent and declarative semantics that support drawing clinical conclusions from study observations. The Ontology of Clinical Research (OCRe) is such a model. Therefore, specialized frameworks for large scale cluster computing like Apache Statistical computing and Machine Learning tools like R are used here as well. Production Rule System, Ontology Reasoning, and NLP Assistant: a hybrid clinical decision support application for lung cancer care. New research findings suggest that machine learning may usher in a new era in digital healthcare tools that are able to predict clinical outcomes in patients with known or potential heart problems. These findings are from several studies being presented a Ontology Driven Controlled Natural Language Clinical Decision Support System learning from texts as they summarize the state of the art in natural language processing techniques,statistical and machine learning techniques for ontology extraction. Ogbuji, C. A Framework Ontology for Computer-Based Patient Record Systems. In Proceedings Cancer Care Nova Scotia has developed a breast cancer follow-up Clinical Practice Guideline (CPG) targeting family physicians. In this paper we present a project to computerize and deploy the said CPG in a Breast Cancer Follow-up Decision Support System (BCF-DSS) for use family physicians in a primary care In light of these challenges, we are proposing a novel ontology and machine learning-driven hybrid clinical decision support framework for cardiovascular preventative care. An ontology-inspired approach provides a foundation for information collection, knowledge acquisition and decision support capabilities and aims to develop context sensitive Additional two clinical case studies in the heart disease and breast cancer domains are considered for the development and clinical validation of the machine learning driven prognostic system. The proposed novel ontology and machine learning driven hybrid clinical decision support framework will also be validated in other application areas. A NOVEL HYBRID MEDICAL DIAGNOSIS SYSTEM BASED ON GENETIC DATA ADAPTATION DECISION TREE tree based classifier called Intelligent Agent based Enhanced Multiclass Support Vector Machine (IAEMSVM) for detection rate with less time and low false alarm rate when tested with UCI Machine Learning data set. Keywords: weighted K-Means clustering, Sep 25, 2011 Data Driven Clinical Quality and Decision Support 1. Clinical Quality Improvement Data Driven Decision Support Guest Lecture for Health Information Science HINF 551 University of VictoriaDale 1 An Ontology-based Framework for Authoring and Executing Clinical Practice Guidelines for Clinical Decision Support Systems Sajjad Hussain, Syed Abidi NICHE Research Group, Faculty of Computer Science, Dalhousie University, Canada Correspondence and reprint requests: Syed Sibte Raza Abidi, Professor of Computer Science, Director The novel approach to evidence management implemented in the DIKB enables its prediction accuracy and coverage to be optimized to a particular body of evidence; a feature that is very desirable for clinical decision support. The DIKB is also novel for its computable representation of the conjectures behind a specific application of evidence. An ontology-driven, case-based clinical decision support model for removable partial denture design: We present the initial work toward developing a clinical decision support model for specific design of removable partial dentures (RPDs) in dentistry. This methodology merits further research development to match clinical applications Download scientific diagram | A novel ontology and machine learning-driven hybrid clinical decision support framework for cardiovascular preventative care In this paper, we present a cardiovascular decision support framework based on key ontology engineering principles and a Bayesian Network. The primary objective of this demarcation is to separate domain knowledge (clinical expert s knowledge and clinical The system constructs a clinical decision support system (CDSS) for undergoing surgery based on domain ontology and rules reasoning in the setting of hospitalized diabetic patients. However, the ontology knowledge is built on the experience of clinical practitioners, so it is hard to update these ontologies knowledge when there is a new Composite Ontology-Based Medical Diagnosis Decision Support System Framework Wang & Tanzel Communications of the IIMA 2013 45 2013 Volume 13 Issue 2 methods) requires certain amount of data or cases. On the other hand, model-based (e.g., knowledge-based) approach usually requires a knowledge base and a reasoning component. The Improving Outcomes with Clinical Decision Support: An Implementer's Guide, Second Edition (HIMSS Book Series): implementation has been substantially enhanced with expanded and updated guidance on using CDS interventions to improve care delivery and outcomes. This edition has been reorganized into parts that help readers set up (or refine) a This paper presents a hybrid approach of case-based reasoning and rule-based reasoning, as an alternative to the purely rule-based method, to build a clinical decision support system for ICU. This enables the system to tackle problems like high complexity, low experienced new staff and changing medical conditions. 4.3 A Hybrid Approach for Extracting D P Relationships.guide medical diagnosis and clinical care. Of machine learning and statistics to texts with the goal of finding useful patterns. Applied to this novel representation for knowledge discovery. Classifications such as Human Disease Ontology (DO), Orphanet Rare Performance Analysis of Classification Algorithms on a Novel Unified Clinical Decision Support Model for Predicting Coronary Heart Disease Risks (UCI) Machine learning repository. The performance of algorithms such as Iterative Dichotomiser3(ID3), Naïve Bayes(NB), Support Vector Machine(SVM), k-Nearest search algorithms based systems Back to A Novel Ontology and Machine Learning Driven Hybrid Clinical Decision Support Framework for Cardiovascular Preventative Care. Find in a Library Find A Novel Ontology and Machine Learning Driven Hybrid Clinical Decision Support Framework for Cardiovascular Preventative Care