Machine learning for detection of lymphedema among breast cancer survivors.

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Machine learning for detection of lymphedema among breast cancer survivors.

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2018 nî lūn-bûn

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2018年の論文

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2018年学术文章

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2018年学术文章

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2018年学术文章

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2018年学术文章

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2018年学术文章

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2018年學術文章

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2018年學術文章

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2018年學術文章

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name

Machine learning for detection of lymphedema among breast cancer survivors.

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Machine learning for detection of lymphedema among breast cancer survivors.

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Item

label

Machine learning for detection of lymphedema among breast cancer survivors.

@en

Machine learning for detection of lymphedema among breast cancer survivors.

@nl

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Machine learning for detection of lymphedema among breast cancer survivors

@en

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Machine learning for detection of lymphedema among breast cancer survivors.

@en

Machine learning for detection of lymphedema among breast cancer survivors.

@nl

P1476

Machine learning for detection of lymphedema among breast cancer survivors

@en

P2093

Bradley E Aouizerat

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Chenge Li

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Gary Yu

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Janet H Van Cleave

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Jeanna M Qiu

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Joan Scagliola

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Judith Haber

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Mei R Fu

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Yao Wang

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Ying Kuen Cheung

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P2860

Psychosocial impact of lymphedema: a systematic review of literature from 2004 to 2011

Class prediction and discovery using gene microarray and proteomics mass spectroscopy data: curses, caveats, cautions.

Predicting breast cancer survivability: a comparison of three data mining methods.

L-dex ratio in detecting breast cancer-related lymphedema: reliability, sensitivity, and specificity.

Minimal limb volume change has a significant impact on breast cancer survivors.

Pre-operative assessment enables early diagnosis and recovery of shoulder function in patients with breast cancer

Proactive approach to lymphedema risk reduction: a prospective study

Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy.

The inconsistency of "optimal" cutpoints obtained using two criteria based on the receiver operating characteristic curve

An integrated approach to an analysis of symptom management in patients with cancer.

P304

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P31

scholarly article

P356

10.21037/MHEALTH.2018.04.02

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P407

English

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P50

Amber A Guth

Deborah Axelrod

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2018-05-29T00:00:00Z

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P698

29963562

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P921

machine learning

P932

5994440

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