Identifying the factors most strongly associated to breathlessness in the general population: an evaluation of 465 different factors using machine learning.

Max Olsson; Magnus Ekström

doi:10.1183/13993003.congress-2023.PA5351

Identifying the factors most strongly associated to breathlessness in the general population: an evaluation of 465 different factors using machine learning.

Research output: Contribution to journal › Published meeting abstract › peer-review

Original language	English
Pages (from-to)	PA5351
Journal	European Respiratory Journal
Volume	62
Issue number	Suppl 67
DOIs	https://doi.org/10.1183/13993003.congress-2023.PA5351
Publication status	Published - 2023 Sept 9
Event	European Respiratory Society (ERS) International Congress - Milano, Italy Duration: 2023 Sept 9 → 2023 Sept 13

Subject classification (UKÄ)

Respiratory Medicine and Allergy

UN SDGs

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

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10.1183/13993003.congress-2023.PA5351Licence: Unspecified

AIR Lund: AIR Lund - Artificially Intelligent use of Registers
Björk, J. (Researcher), Ohlsson, M. (Researcher), Melander, O. (Researcher), Ekelund, U. (Researcher), Larsson, S. (Researcher), Mattsson, T. (Researcher), Mokhtari, A. (Researcher), de Vries, K. (Researcher), Nilsson, A. (Researcher), Lingman, M. (Researcher), Pashami, S. (Researcher), Ottosson, F. (Researcher), Larsson, A. E. (Administrator), Henriksen, A. (Researcher), Högberg, C. (Research student), Olsson, M. (Research student), Nyström, A. (Research student), Olsson de Capretz, P. (Research student), Björkelund, A. (Researcher), Nilsson, A. (Researcher), Åkesson, A. (Research student), Nordberg, A. (Researcher), Ekström, M. (Researcher), Tolestam Heyman, E. (Research student), Lundager Forberg, J. (Researcher), Berg, J. (Research student), Johansson, A. (Research student), Amirahmadi, A. (Research student), Eriksson, T. (Research student), Khoshnood, A. (Researcher), Mäkinen, M. (Research student), Holmberg, P. (Researcher) & Nilsson, T. N. (Research student)
2018/07/01 → 2025/12/31
Project: Research

Cite this

@article{0d15456903624fd195c9d0aed5f1bbdf,

title = "Identifying the factors most strongly associated to breathlessness in the general population: an evaluation of 465 different factors using machine learning.",

author = "Max Olsson and Magnus Ekstr{\"o}m",

year = "2023",

month = sep,

day = "9",

doi = "10.1183/13993003.congress-2023.PA5351",

language = "English",

volume = "62",

pages = "PA5351",

journal = "European Respiratory Journal",

issn = "1399-3003",

publisher = "European Respiratory Society",

number = "Suppl 67",

note = "European Respiratory Society (ERS) International Congress ; Conference date: 09-09-2023 Through 13-09-2023",

}

TY - JOUR

T1 - Identifying the factors most strongly associated to breathlessness in the general population: an evaluation of 465 different factors using machine learning.

AU - Olsson, Max

AU - Ekström, Magnus

PY - 2023/9/9

Y1 - 2023/9/9

U2 - 10.1183/13993003.congress-2023.PA5351

DO - 10.1183/13993003.congress-2023.PA5351

M3 - Published meeting abstract

SN - 1399-3003

VL - 62

SP - PA5351

JO - European Respiratory Journal

JF - European Respiratory Journal

IS - Suppl 67

T2 - European Respiratory Society (ERS) International Congress

Y2 - 9 September 2023 through 13 September 2023

ER -

Identifying the factors most strongly associated to breathlessness in the general population: an evaluation of 465 different factors using machine learning.

Subject classification (UKÄ)

UN SDGs

Access to Document

Projects

AIR Lund: AIR Lund - Artificially Intelligent use of Registers

Cite this