Literature DB >> 10225344

Selected techniques for data mining in medicine.

N Lavrac1.   

Abstract

Widespread use of medical information systems and explosive growth of medical databases require traditional manual data analysis to be coupled with methods for efficient computer-assisted analysis. This paper presents selected data mining techniques that can be applied in medicine, and in particular some machine learning techniques including the mechanisms that make them better suited for the analysis of medical databases (derivation of symbolic rules, use of background knowledge, sensitivity and specificity of induced descriptions). The importance of the interpretability of results of data analysis is discussed and illustrated on selected medical applications.

Mesh:

Year:  1999        PMID: 10225344     DOI: 10.1016/s0933-3657(98)00062-1

Source DB:  PubMed          Journal:  Artif Intell Med        ISSN: 0933-3657            Impact factor:   5.326


  30 in total

1.  A decision support tool for health service re-design.

Authors:  Eren Demir; Salma Chahed; Thierry Chaussalet; Sam Toffa; Farid Fouladinajed
Journal:  J Med Syst       Date:  2010-05-25       Impact factor: 4.460

2.  Prediction of clinical conditions after coronary bypass surgery using dynamic data analysis.

Authors:  K Van Loon; F Guiza; G Meyfroidt; J-M Aerts; J Ramon; H Blockeel; M Bruynooghe; G Van den Berghe; D Berckmans
Journal:  J Med Syst       Date:  2010-06       Impact factor: 4.460

Review 3.  A Critical Review for Developing Accurate and Dynamic Predictive Models Using Machine Learning Methods in Medicine and Health Care.

Authors:  Hamdan O Alanazi; Abdul Hanan Abdullah; Kashif Naseer Qureshi
Journal:  J Med Syst       Date:  2017-03-11       Impact factor: 4.460

4.  Predictive data mining on monitoring data from the intensive care unit.

Authors:  Fabian Güiza; Jelle Van Eyck; Geert Meyfroidt
Journal:  J Clin Monit Comput       Date:  2012-11-24       Impact factor: 2.502

Review 5.  Mining electronic health records: towards better research applications and clinical care.

Authors:  Peter B Jensen; Lars J Jensen; Søren Brunak
Journal:  Nat Rev Genet       Date:  2012-05-02       Impact factor: 53.242

6.  Predicting metastasis in breast cancer: comparing a decision tree with domain experts.

Authors:  Amir R Razavi; Hans Gill; Hans Ahlfeldt; Nosrat Shahsavar
Journal:  J Med Syst       Date:  2007-08       Impact factor: 4.460

7.  Mining compact predictive pattern sets using classification model.

Authors:  Matteo Mantovani; Carlo Combi; Milos Hauskrecht
Journal:  Artif Intell Med Conf Artif Intell Med (2005-)       Date:  2019-05-30

8.  A clinical decision tool for predicting patient care characteristics: patients returning within 72 hours in the emergency department.

Authors:  Eva K Lee; Fan Yuan; Daniel A Hirsh; Michael D Mallory; Harold K Simon
Journal:  AMIA Annu Symp Proc       Date:  2012-11-03

9.  Comparison of the predictive qualities of three prognostic models of colorectal cancer.

Authors:  Billie Anderson; J Michael Hardin; Dominik D Alexander; William E Grizzle; Sreelatha Meleth; Upender Manne
Journal:  Front Biosci (Elite Ed)       Date:  2010-06-01

10.  Evaluation of the diagnostic power of thermography in breast cancer using Bayesian network classifiers.

Authors:  Cruz-Ramírez Nicandro; Mezura-Montes Efrén; Ameca-Alducin María Yaneli; Martín-Del-Campo-Mena Enrique; Acosta-Mesa Héctor Gabriel; Pérez-Castro Nancy; Guerra-Hernández Alejandro; Hoyos-Rivera Guillermo de Jesús; Barrientos-Martínez Rocío Erandi
Journal:  Comput Math Methods Med       Date:  2013-05-22       Impact factor: 2.238

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