Medical knowledge extraction from big data

著者

    • Koutsojannis, Constantinos

書誌事項

Medical knowledge extraction from big data

Constantinos M. Koutsojannis, editor

（Computer science, technology and applications）

Nova science publishers, c2020

大学図書館所蔵 件 / 全2件

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注記

Includes bibliographical references and index

Summary: "Data mining refers to the activity of going through big data sets to look for relevant information. As human health care data are the most difficult of all data to collect and their primary direction is the treatment of patients, and secondarily dealing with research, almost the only vindication for collecting medical data is to benefit the disease. All data miners should take into account that Medical Knowledge Extraction is internally connected with the Evidence-Based Medical approach because it uses data for already treated or not patients and there are times that opposites to Guideline Based medical practice. Additonally all researchers should be aware when are dealing with medical databases they may face the possibility that their work will never be accepted or even used from health care professionals if all these obligations will not be correctly addressed from the early beginning. In the present book, one can find after the three introductory chapters, a number of successfully evaluated appli

収録内容

  • Chapter 1. Medical Big Data (Anthi Malliori, PhD, Department of Medical Physics, University Patras, Patras, Greece)
  • Chapter 2. Data Mining in Health Sciences (Andreas Andrikopoulos, PhD, Laboratory of Health Physics and Computational Intelligence, University of Patras, Patras, Greece)
  • Chapter 3. Medical Knowledge Extraction: Particular Difficulties and Obligations (Constantinos Koutsojannis, PhD, Health Physics and Computational Intelligence Lab, University of Patras, Patras, Greece)
  • Chapter 4. An Intelligent System for Histological Outcome Prediction of Parathyroid Hyperplasia Based on CT Imaging (Kalogeropoulou Christina, PhD, Zampakis Petros, PhD, Hatzilygeroudis Ioannis, PhD, and Constantinos Koutsojannis, PhD Tsimara Maria, Department of Radiology, University of Patras, Patras, Greece, and others)
  • Chapter 5. Knowledge Extraction Algorithms for Endoleak Prediction into the Abdominal Aortic Aneurysm (Maria-Konstantina Chasapi, Zampakis Petros, MD, and Constantinos Koutsojannis, PhD, Laboratory of Health Physics and Computational Intelligence, University Patras, Patras, Greece, and others)
  • Chapter 6. Intelligent Evaluation of Morphological Factors Which Affect the Manifest or Not of Stroke in Patients with Carotid Disease by Using Data Mining Algorithms (Chasapi Lamprini, Zampakis Petros and Constantinos Koutsojannis, PhD, Laboratory of Health Physics and Computational Intelligence, University Patras, Patras, Greece, and others)
  • Chapter 7. General Knowledge Modified from Expert's Clinical Experience for Precise Depression Diagnosis in a Mobile Intelligent System (Andreas Andrikopoulos, PhD, Constantinos Koutsojannis, PhD, and Ioannis Hatzilygeroudis, PhD, Laboratory of Health Physics and Computational Intelligence, University of Patras, Patras, Greece, and others)
  • Chapter 8. A Hybrid Intelligent System for Diagnosis of Male Impotence with the Use of Direct Knowledge Representation (Constantinos Koutsojannis, PhD, Grigorios Beligiannis, PhD, Perimenis Petros, PhD, and Ioannis Hatzilygeroudis, PhD, Laboratory of Health Physics & Computational Intelligence, University of Patras, Patras, Greece, and others)
  • Chapter 9. Big Data Mining for Analysing and Processing Brain Signals Harvested with SQUID Based MEG (Adamantios-Ioannis Statyris, Constantinos Koutsojannis, PhD, and Ioannis Hatzilygeroudis, PhD, Laboratory of Health Physics and Computational Intelligence, University of Patras, Patras, Greece, and others)

内容説明・目次

内容説明

Data mining refers to the activity of going through big data sets to look for relevant information. As human health care data are the most difficult of all data to collect and their primary direction is the treatment of patients, and secondarily dealing with research, almost the only vindication for collecting medical data is to benefit the disease. All data miners should take into account that Medical Knowledge Extraction is internally connected with the Evidence-Based Medical approach because it uses data for already treated or not patients and there are times that opposites to Guideline Based medical practice. Additonally all researchers should be aware when are dealing with medical databases they may face the possibility that their work will never be accepted or even used from health care professionals if all these obligations will not be correctly addressed from the early beginning. In the present book, one can find after the three introductory chapters, a number of successfully evaluated applications that have been developed after mining approaches in Big or smaller amount (according to the application) of medical Data in different fields of every day clinical practice from teams of experts. The challenging adventure of Medical Knowledge Extraction can be followed by ambitious researchers finally resulting in a successful decision support system, that some times is so novel that it will provide new directions for basic or clinical research further that the existed. At least this procedure will save the experience of the best doctors on duty and will help young residents to be better and better.

目次

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