Computational prediction of diagnosis and feature selection on mesothelioma patient health records | Health & Environmental Research Online (HERO)

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Tags

HERO ID

6876146

Reference Type

Journal Article

Title

Computational prediction of diagnosis and feature selection on mesothelioma patient health records

Author(s)

Chicco, D; Rovelli, C; ,

Year

2019

Is Peer Reviewed?

Journal

PLoS ONE
EISSN: 1932-6203

Publisher

PUBLIC LIBRARY SCIENCE

Location

SAN FRANCISCO

Volume

Issue

Language

English

PMID

30629589

DOI

10.1371/journal.pone.0208737

Web of Science Id

WOS:000455483000018

URL

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85059802073&doi=10.1371%2fjournal.pone.0208737&partnerID=40&md5=3d1bd89ea8e750551afbb5d36c319a3e
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Abstract

Mesothelioma is a lung cancer that kills thousands of people worldwide annually, especially those with exposure to asbestos. Diagnosis of mesothelioma in patients often requires time-consuming imaging techniques and biopsies. Machine learning can provide for a more effective, cheaper, and faster patient diagnosis and feature selection from clinical data in patient records.