| アイテムタイプ |
会議発表論文 / Conference Paper(1) |
| 公開日 |
2024-04-12 |
| タイトル |
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|
タイトル |
Prediction of BPSD using environmental and vital sensor data |
|
言語 |
en |
| 言語 |
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|
言語 |
eng |
| 資源タイプ |
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|
資源タイプ識別子 |
http://purl.org/coar/resource_type/c_5794 |
|
資源タイプ |
conference paper |
| 著者 |
ONUMA, Hyuta
TOKIWA, Naoya
SHIBATA, Junichi
SUZUKI, Toshikazu
KASHIWAGI, Takehiko
MOE, Tatsuya
KAMURA, Kaito
SHINMI, Tatsunoshin
TANO, Shunichi
MINAMI, Yasuhiro
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| 抄録 |
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内容記述タイプ |
Abstract |
|
内容記述 |
Behavioral and psychological symptoms of dementia (BPSD) that develop in patients in nursing homes or living at home impose a heavy burden on caregivers and family members.In this study, we collect data from environmental and vital sensors at multiple nursing homes. Based on the data, we propose a BPSD prediction method using gradient boosting trees, a machine learning technique. An evaluation experiment, using data gathered over approximately 10 months, achieved an AUC of 0.7217 for the ROC curve and an Average Precision of 0.3961 for the PR curve. The results show the potential of machine learning methods using environmental and vital sensors to predict BPSD. |
|
言語 |
en |
| 書誌情報 |
en : 2024 IEEE First International Conference on Artificial Intelligence for Medicine, Health and Care (AIMHC)
発行日 2024-02-05
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| 出版者 |
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出版者 |
IEEE |
| ISBN |
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識別子タイプ |
ISBN |
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関連識別子 |
9798350371987 |
| DOI |
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|
識別子タイプ |
DOI |
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|
関連識別子 |
https://doi.org/10.1109/AIMHC59811.2024.00017 |
| 権利 |
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|
権利情報 |
(c) 2024 IEEE. Personal use of this material is permitted.Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
| 著者版フラグ |
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出版タイプ |
AM |
|
出版タイプResource |
http://purl.org/coar/version/c_ab4af688f83e57aa |
2024_IEEE_AIMHC_am.pdf (925 KB)
