Is machine learning prediction of Aβ positivity consistent? An assessment of multiple datasets

Enrico Grisan; E Grecchi; C Buckley; J Wolber

doi:10.1002/alz.040990

Is machine learning prediction of Aβ positivity consistent? An assessment of multiple datasets

Enrico Grisan, E Grecchi, C Buckley, J Wolber

Chemical and Energy Engineering

Research output: Contribution to conference › Poster › peer-review

Abstract

Poster abstract

Original language	English
DOIs	https://doi.org/10.1002/alz.040990
Publication status	Published - 7 Dec 2020

Keywords

Developmental Neuroscience
Clinical Neurology
Health Policy
Psychiatry and Mental health
Cellular and Molecular Neuroscience
Epidemiology
Geriatrics and Gerontology

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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title = "Is machine learning prediction of Aβ positivity consistent? An assessment of multiple datasets",

abstract = "Poster abstract",

keywords = "Developmental Neuroscience, Clinical Neurology, Health Policy, Psychiatry and Mental health, Cellular and Molecular Neuroscience, Epidemiology, Geriatrics and Gerontology",

author = "Enrico Grisan and E Grecchi and C Buckley and J Wolber",

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day = "7",

doi = "10.1002/alz.040990",

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T1 - Is machine learning prediction of Aβ positivity consistent? An assessment of multiple datasets

AU - Grisan, Enrico

AU - Grecchi, E

AU - Buckley, C

AU - Wolber, J

PY - 2020/12/7

Y1 - 2020/12/7

N2 - Poster abstract

AB - Poster abstract

KW - Developmental Neuroscience

KW - Clinical Neurology

KW - Health Policy

KW - Psychiatry and Mental health

KW - Cellular and Molecular Neuroscience

KW - Epidemiology

KW - Geriatrics and Gerontology

UR - https://alz-journals.onlinelibrary.wiley.com/doi/full/10.1002/alz.040990

U2 - 10.1002/alz.040990

DO - 10.1002/alz.040990

M3 - Poster

ER -

Is machine learning prediction of Aβ positivity consistent? An assessment of multiple datasets

Abstract

Keywords

UN SDGs

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