Explainable AI for Falls Prediction

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

Explainable AI for Falls Prediction

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

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

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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
Publication status	Accepted/In press - 4 Mar 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)

Keywords

Decision Tree
Explainable AI
Classification Algorithm

UN SDGs

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

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Accepted versionAuthor Accepted Manuscript, 505 KB

Cite this

@inproceedings{f09b55042ac5414ebcb1f37e8595ea4c,

title = "Explainable AI 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 = "Decision Tree, Explainable AI, Classification Algorithm",

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

year = "2020",

month = mar,

day = "4",

language = "English",

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

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",

}

TY - GEN

T1 - Explainable AI for Falls Prediction

AU - Lindsay, Leeanne

AU - Kerr, Dermot

AU - Coleman, Sonya

AU - Taylor, Brian

AU - Moorhead, Anne

PY - 2020/3/4

Y1 - 2020/3/4

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 - Decision Tree

KW - Explainable AI

KW - Classification Algorithm

M3 - Conference contribution

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

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

Y2 - 24 April 2020 through 25 April 2020

ER -

Explainable AI for Falls Prediction

Abstract

Conference

Keywords

UN SDGs

Access to Document

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Cite this