Explainable artificial intelligence for falls prediction

Leeanne Lindsay; Dermot Kerr; Sonya Coleman; Brian Taylor; Anne Moorhead

doi:10.1007/978-981-15-6634-9_8

Explainable artificial intelligence for falls prediction

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

6 Citations (Scopus)

325 Downloads (Pure)

Abstract

With a rapidly ageing population, it is likely that we will en-counter an older adult falling. Falls can cause death, serious injury or harm, loss of confidence and loss of independence. Falling can happen to any of us, however those over 65 years of age can be classified as a group of adults who are more vulnerable and at increased risk of falling. This paper focuses on applying explainable artificial intelligence techniques, in the form of decision trees, to healthcare data in order to predict the risk of falling in older adults. These decision trees could potentially be introduced for health and social care professionals to help aid their judgements when making decisions.

Original language	English
Title of host publication	4th International Conference on Advances in Computing and Data Sciences (ICACDS)-2020
Editors	Mayank Singh, P.K. Gupta, Vipin Tyagi, Jan Flusser, Tuncer Ören, Gianluca Valentino
Publisher	Springer Nature, Singapore
Chapter	8
Pages	76-84
Number of pages	9
ISBN (Electronic)	978-981-15-6634-9
ISBN (Print)	978-981-15-6633-2
DOIs	https://doi.org/10.1007/978-981-15-6634-9_8
Publication status	Published (in print/issue) - 15 Aug 2020
Event	4th International Conference on Advances in Computing and Data Sciences - University of Malta, Valleta, Malta Duration: 24 Apr 2020 → 25 Apr 2020 https://link.springer.com/book/10.1007/978-981-15-6634-9 (Link to conference website)

Conference

Conference	4th International Conference on Advances in Computing and Data Sciences
Abbreviated title	ICACDS 2020
Country/Territory	Malta
City	Valleta
Period	24/04/20 → 25/04/20
Internet address	https://link.springer.com/book/10.1007/978-981-15-6634-9 (Link to conference website)

UN SDGs

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

SDG 3 Good Health and Well-being

Keywords

predictive analytics
health and social care
older people
risk assessment
Human-computer Interfacing
Falls
Risks
Explainable AI
Classification
Decision tree

Access to Document

10.1007/978-981-15-6634-9_8

Accepted versionAuthor Accepted Manuscript, 505 KB

Cite this

Lindsay, L., Kerr, D., Coleman, S., Taylor, B., & Moorhead, A. (2020). Explainable artificial intelligence for falls prediction. In M. Singh, P. K. Gupta, V. Tyagi, J. Flusser, T. Ören, & G. Valentino (Eds.), 4th International Conference on Advances in Computing and Data Sciences (ICACDS)-2020 (pp. 76-84). Springer Nature, Singapore. https://doi.org/10.1007/978-981-15-6634-9_8

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title = "Explainable artificial intelligence for falls prediction",

abstract = "With a rapidly ageing population, it is likely that we will en-counter an older adult falling. Falls can cause death, serious injury or harm, loss of confidence and loss of independence. Falling can happen to any of us, however those over 65 years of age can be classified as a group of adults who are more vulnerable and at increased risk of falling. This paper focuses on applying explainable artificial intelligence techniques, in the form of decision trees, to healthcare data in order to predict the risk of falling in older adults. These decision trees could potentially be introduced for health and social care professionals to help aid their judgements when making decisions.",

keywords = "predictive analytics, health and social care, older people, risk assessment, Human-computer Interfacing, Falls, Risks, Explainable AI, Classification, Decision tree",

author = "Leeanne Lindsay and Dermot Kerr and Sonya Coleman and Brian Taylor and Anne Moorhead",

year = "2020",

month = aug,

day = "15",

doi = "10.1007/978-981-15-6634-9\_8",

language = "English",

isbn = "978-981-15-6633-2",

pages = "76--84",

editor = "Mayank Singh and P.K. Gupta and Vipin Tyagi and Jan Flusser and Tuncer {\"O}ren and Gianluca Valentino",

booktitle = "4th International Conference on Advances in Computing and Data Sciences (ICACDS)-2020",

publisher = "Springer Nature, Singapore",

note = "4th International Conference on Advances in Computing and Data Sciences, ICACDS 2020 ; Conference date: 24-04-2020 Through 25-04-2020",

url = "https://link.springer.com/book/10.1007/978-981-15-6634-9",

}

Lindsay, L , Kerr, D , Coleman, S , Taylor, B & Moorhead, A 2020, Explainable artificial intelligence for falls prediction. in M Singh, PK Gupta, V Tyagi, J Flusser, T Ören & G Valentino (eds), 4th International Conference on Advances in Computing and Data Sciences (ICACDS)-2020. Springer Nature, Singapore, pp. 76-84, 4th International Conference on Advances in Computing and Data Sciences, Valleta, Malta, 24/04/20. https://doi.org/10.1007/978-981-15-6634-9_8

Explainable artificial intelligence for falls prediction. / Lindsay, Leeanne ; Kerr, Dermot ; Coleman, Sonya et al.
4th International Conference on Advances in Computing and Data Sciences (ICACDS)-2020. ed. / Mayank Singh; P.K. Gupta; Vipin Tyagi; Jan Flusser; Tuncer Ören; Gianluca Valentino. Springer Nature, Singapore, 2020. p. 76-84.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Explainable artificial intelligence for falls prediction

AU - Lindsay, Leeanne

AU - Kerr, Dermot

AU - Coleman, Sonya

AU - Taylor, Brian

AU - Moorhead, Anne

PY - 2020/8/15

Y1 - 2020/8/15

N2 - With a rapidly ageing population, it is likely that we will en-counter an older adult falling. Falls can cause death, serious injury or harm, loss of confidence and loss of independence. Falling can happen to any of us, however those over 65 years of age can be classified as a group of adults who are more vulnerable and at increased risk of falling. This paper focuses on applying explainable artificial intelligence techniques, in the form of decision trees, to healthcare data in order to predict the risk of falling in older adults. These decision trees could potentially be introduced for health and social care professionals to help aid their judgements when making decisions.

AB - With a rapidly ageing population, it is likely that we will en-counter an older adult falling. Falls can cause death, serious injury or harm, loss of confidence and loss of independence. Falling can happen to any of us, however those over 65 years of age can be classified as a group of adults who are more vulnerable and at increased risk of falling. This paper focuses on applying explainable artificial intelligence techniques, in the form of decision trees, to healthcare data in order to predict the risk of falling in older adults. These decision trees could potentially be introduced for health and social care professionals to help aid their judgements when making decisions.

KW - predictive analytics

KW - health and social care

KW - older people

KW - risk assessment

KW - Human-computer Interfacing

KW - Falls

KW - Risks

KW - Explainable AI

KW - Classification

KW - Decision tree

UR - https://www.springer.com/gp/book/9789811566332

UR - https://www.scopus.com/pages/publications/85089246941

UR - https://link.springer.com/book/10.1007/978-981-15-6634-9

U2 - 10.1007/978-981-15-6634-9_8

DO - 10.1007/978-981-15-6634-9_8

M3 - Conference contribution

SN - 978-981-15-6633-2

SP - 76

EP - 84

BT - 4th International Conference on Advances in Computing and Data Sciences (ICACDS)-2020

A2 - Singh, Mayank

A2 - Gupta, P.K.

A2 - Tyagi, Vipin

A2 - Flusser, Jan

A2 - Ören, Tuncer

A2 - Valentino, Gianluca

PB - Springer Nature, Singapore

T2 - 4th International Conference on Advances in Computing and Data Sciences

Y2 - 24 April 2020 through 25 April 2020

ER -

Explainable artificial intelligence for falls prediction

Abstract

Conference

UN SDGs

Keywords

Access to Document

Other files and links

Fingerprint

Algorithmic approaches to measuring and visualising risk of falling by older adults

Cite this

Explainable artificial intelligence for falls prediction

Abstract

Conference

UN SDGs

Keywords

Access to Document

Other files and links

Fingerprint

Student theses

Algorithmic approaches to measuring and visualising risk of falling by older adults

Cite this