Cargando…

예측모형의 머신러닝 방법론과 통계학적 방법론의 비교: 영상의학 연구에서의 적용

Clinical prediction models has been increasingly published in radiology research. In particular, as a radiomics research is being actively conducted, the prediction model is developed based on the traditional statistical model, as well as machine learning, to account for the high-dimensional data. I...

Descripción completa

Detalles Bibliográficos
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Korean Society of Radiology 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9748465/
https://www.ncbi.nlm.nih.gov/pubmed/36545410
http://dx.doi.org/10.3348/jksr.2022.0111
_version_ 1784849830138347520
collection PubMed
description Clinical prediction models has been increasingly published in radiology research. In particular, as a radiomics research is being actively conducted, the prediction model is developed based on the traditional statistical model, as well as machine learning, to account for the high-dimensional data. In this review, we investigated the statistical and machine learning methods used in clinical prediction model research, and briefly summarized each analytical method for statistical model, machine learning, and statistical learning. Finally, we discussed several considerations for choosing the prediction modeling method.
format Online
Article
Text
id pubmed-9748465
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher The Korean Society of Radiology
record_format MEDLINE/PubMed
spelling pubmed-97484652022-12-20 예측모형의 머신러닝 방법론과 통계학적 방법론의 비교: 영상의학 연구에서의 적용 J Korean Soc Radiol Multi-Task Learning, Prediction Model, and Fast Imaging Algorithm Clinical prediction models has been increasingly published in radiology research. In particular, as a radiomics research is being actively conducted, the prediction model is developed based on the traditional statistical model, as well as machine learning, to account for the high-dimensional data. In this review, we investigated the statistical and machine learning methods used in clinical prediction model research, and briefly summarized each analytical method for statistical model, machine learning, and statistical learning. Finally, we discussed several considerations for choosing the prediction modeling method. The Korean Society of Radiology 2022-11 2022-11-30 /pmc/articles/PMC9748465/ /pubmed/36545410 http://dx.doi.org/10.3348/jksr.2022.0111 Text en Copyrights © 2022 The Korean Society of Radiology https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0 (https://creativecommons.org/licenses/by-nc/4.0/) ) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Multi-Task Learning, Prediction Model, and Fast Imaging Algorithm
예측모형의 머신러닝 방법론과 통계학적 방법론의 비교: 영상의학 연구에서의 적용
title 예측모형의 머신러닝 방법론과 통계학적 방법론의 비교: 영상의학 연구에서의 적용
title_full 예측모형의 머신러닝 방법론과 통계학적 방법론의 비교: 영상의학 연구에서의 적용
title_fullStr 예측모형의 머신러닝 방법론과 통계학적 방법론의 비교: 영상의학 연구에서의 적용
title_full_unstemmed 예측모형의 머신러닝 방법론과 통계학적 방법론의 비교: 영상의학 연구에서의 적용
title_short 예측모형의 머신러닝 방법론과 통계학적 방법론의 비교: 영상의학 연구에서의 적용
title_sort 예측모형의 머신러닝 방법론과 통계학적 방법론의 비교: 영상의학 연구에서의 적용
topic Multi-Task Learning, Prediction Model, and Fast Imaging Algorithm
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9748465/
https://www.ncbi.nlm.nih.gov/pubmed/36545410
http://dx.doi.org/10.3348/jksr.2022.0111
work_keys_str_mv AT yecheugmohyeonguimeosinleoningbangbeoblongwatonggyehagjeogbangbeoblonuibigyoyeongsanguihagyeongueseouijeogyong
AT yecheugmohyeonguimeosinleoningbangbeoblongwatonggyehagjeogbangbeoblonuibigyoyeongsanguihagyeongueseouijeogyong