C-RECS （Creative Repository of Electro-Communications)
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Heteroatom doped fullerenes as a novel electrocatalyst material for CO reduction reaction
Get PDF電気通信大学博士（工学）2024doctoral thesi
Analyses of Polarizability and Raman Intensity of π-Conjugated Hydrocarbons Using Natural Perturbation Orbitals
Get PDF電気通信大学博士（理学）2024doctoral thesi
Analysis of BPSD onset time to improve BPSD prediction performance with environmental and vital sensor data
Get PDF認知症患者が発症する行動・心理症状（BPSD）は，介護者の大きな負担となるだけでなく，患者本人の生活の質にも影響を与えている．BPSDを事前に予測し，症状へ対処が可能となれば介護者の負担を軽減できる．先行研究として複数の介護施設から収集した環境・バイタルセンサデータに基づき，機械学習を利用したBPSD予測を行った．しかし未だPR曲線のAverage Precisionが低い．そこで本研究では，BPSD予測の高精度化に向けてデータ解析を行なった．解析結果から特定の症状には24時間周期があることを確認した．この結果は，機械学習手法によるBPSD発症予測の可能性を示すものである．Behavioral and psychological symptoms of dementia (BPSD) that develop in patients with dementia not only impose a heavy burden on caregivers, but also affect the quality of life of the patients themselves. If BPSD can be predicted in advance and symptoms can be dealt with, the burden on caregivers can be reduced. In a preliminary experiment, we predicted BPSD using machine learning based on environmental and vital sensor data collected from multiple nursing homes. However, the Average Precision of the PR curve is still low. In this study, we analyzed data to improve the accuracy of BPSD prediction.The data analysis confirms that certain symptoms have a 24-hour cycle. The results show the possibility of predicting the onset of BPSD using machine learning methods.journal articl
