Data Mining in Medicine

Beatrice Amico; Carlo Combi; Yuval Shahar

doi:10.1007/978-3-031-24628-9_27

Data Mining in Medicine

Beatrice Amico
, Carlo Combi
, Yuval Shahar

School of Brain Sciences and Cognition

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

Abstract

Clinical databases collect large volumes of information. Relationships and patterns within these data could provide new medical knowledge. Data mining has as major objective the discovery of knowledge from large amounts of data, offers many possibilities for identifying different data features less visible or hidden to common analysis techniques. This chapter focuses on a selection of techniques and illustrates their applicability to medical diagnostic and prognostic problems.

Original language	English
Title of host publication	Machine Learning for Data Science Handbook
Subtitle of host publication	Data Mining and Knowledge Discovery Handbook, Third Edition
Publisher	Springer International Publishing
Pages	607-636
Number of pages	30
ISBN (Electronic)	9783031246289
ISBN (Print)	9783031246272
DOIs	https://doi.org/10.1007/978-3-031-24628-9_27
State	Published - 1 Jan 2023

ASJC Scopus subject areas

General Computer Science
General Mathematics

Access to Document

10.1007/978-3-031-24628-9_27

Cite this

@inbook{117f57bc78614928a9c56512a2073c67,

title = "Data Mining in Medicine",

abstract = "Clinical databases collect large volumes of information. Relationships and patterns within these data could provide new medical knowledge. Data mining has as major objective the discovery of knowledge from large amounts of data, offers many possibilities for identifying different data features less visible or hidden to common analysis techniques. This chapter focuses on a selection of techniques and illustrates their applicability to medical diagnostic and prognostic problems.",

author = "Beatrice Amico and Carlo Combi and Yuval Shahar",

note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2023.",

year = "2023",

month = jan,

day = "1",

doi = "10.1007/978-3-031-24628-9\_27",

language = "English",

isbn = "9783031246272",

pages = "607--636",

booktitle = "Machine Learning for Data Science Handbook",

publisher = "Springer International Publishing",

}

TY - CHAP

T1 - Data Mining in Medicine

AU - Amico, Beatrice

AU - Combi, Carlo

AU - Shahar, Yuval

PY - 2023/1/1

Y1 - 2023/1/1

N2 - Clinical databases collect large volumes of information. Relationships and patterns within these data could provide new medical knowledge. Data mining has as major objective the discovery of knowledge from large amounts of data, offers many possibilities for identifying different data features less visible or hidden to common analysis techniques. This chapter focuses on a selection of techniques and illustrates their applicability to medical diagnostic and prognostic problems.

AB - Clinical databases collect large volumes of information. Relationships and patterns within these data could provide new medical knowledge. Data mining has as major objective the discovery of knowledge from large amounts of data, offers many possibilities for identifying different data features less visible or hidden to common analysis techniques. This chapter focuses on a selection of techniques and illustrates their applicability to medical diagnostic and prognostic problems.

UR - https://www.scopus.com/pages/publications/85195594741

U2 - 10.1007/978-3-031-24628-9_27

DO - 10.1007/978-3-031-24628-9_27

M3 - Chapter

AN - SCOPUS:85195594741

SN - 9783031246272

SP - 607

EP - 636

BT - Machine Learning for Data Science Handbook

PB - Springer International Publishing

ER -

Data Mining in Medicine

Abstract

ASJC Scopus subject areas

Access to Document

Fingerprint

Cite this