Cardiovascular disease risk prediction via machine learning using mental health data.

Mohsen Dorraki; Zhibin Liao; D. Abbott; Peter J. Psaltis; E Baker; Niranjan Bidargaddi; Anton van den Hengel; J Narula; J. W. Verjans

doi:10.1093/ehjdh/ztac076.2784

Cardiovascular disease risk prediction via machine learning using mental health data.

Mohsen Dorraki, Zhibin Liao, D. Abbott, Peter J. Psaltis, E Baker, Niranjan Bidargaddi, Anton van den Hengel, J Narula, J. W. Verjans

College of Medicine and Public Health

Research output: Contribution to journal › Meeting Abstract › peer-review

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Abstract

Robust and accurate risk prediction models are much needed in cardiovascular disease. It is well-known that mental health is associated with the risk of developing cardiovascular disease. It is unknown whether mental health markers can enhance existing risk prediction models for cardiovascular disease.

Original language	English
Pages (from-to)	2784
Number of pages	1
Journal	European Heart Journal
Volume	3
Issue number	4
DOIs	https://doi.org/10.1093/ehjdh/ztac076.2784
Publication status	Published - Dec 2022
Event	ESC Congress 2022 - Barcelona, Spain Duration: 26 Aug 2022 → 29 Aug 2022

Keywords

Risk prediction model
cardiovascular disease
mental health data
machine learning

UN SDGs

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

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10.1093/ehjdh/ztac076.2784Licence: CC BY-NC

Published versionFinal published version, 598 KBLicence: CC BY-NC

Cite this

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title = "Cardiovascular disease risk prediction via machine learning using mental health data.",

abstract = "Robust and accurate risk prediction models are much needed in cardiovascular disease. It is well-known that mental health is associated with the risk of developing cardiovascular disease. It is unknown whether mental health markers can enhance existing risk prediction models for cardiovascular disease.",

keywords = "Risk prediction model, cardiovascular disease, mental health data, machine learning",

author = "Mohsen Dorraki and Zhibin Liao and D. Abbott and Psaltis, {Peter J.} and E Baker and Niranjan Bidargaddi and {van den Hengel}, Anton and J Narula and Verjans, {J. W.}",

year = "2022",

month = dec,

doi = "10.1093/ehjdh/ztac076.2784",

language = "English",

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pages = "2784",

journal = "European Heart Journal",

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T1 - Cardiovascular disease risk prediction via machine learning using mental health data.

AU - Dorraki, Mohsen

AU - Liao, Zhibin

AU - Abbott, D.

AU - Psaltis, Peter J.

AU - Baker, E

AU - Bidargaddi, Niranjan

AU - van den Hengel, Anton

AU - Narula, J

AU - Verjans, J. W.

PY - 2022/12

Y1 - 2022/12

N2 - Robust and accurate risk prediction models are much needed in cardiovascular disease. It is well-known that mental health is associated with the risk of developing cardiovascular disease. It is unknown whether mental health markers can enhance existing risk prediction models for cardiovascular disease.

AB - Robust and accurate risk prediction models are much needed in cardiovascular disease. It is well-known that mental health is associated with the risk of developing cardiovascular disease. It is unknown whether mental health markers can enhance existing risk prediction models for cardiovascular disease.

KW - Risk prediction model

KW - cardiovascular disease

KW - mental health data

KW - machine learning

U2 - 10.1093/ehjdh/ztac076.2784

DO - 10.1093/ehjdh/ztac076.2784

M3 - Meeting Abstract

SN - 0195-668X

VL - 3

SP - 2784

JO - European Heart Journal

JF - European Heart Journal

IS - 4

T2 - ESC Congress 2022

Y2 - 26 August 2022 through 29 August 2022

ER -

Cardiovascular disease risk prediction via machine learning using mental health data.

Abstract

Keywords

UN SDGs

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