Behavioural symptoms and urinary tract infections (UTI) are among the most common problems faced by people with dementia. One of the key challenges in the management of these conditions is early detection and timely intervention in order to reduce distress and avoid unplanned hospital admissions. Using in-home sensing technologies and machine learning models for sensor data integration and analysis provides opportunities to detect and predict clinically significant events and changes in health status. We have developed an integrated platform to collect in-home sensor data and performed an observational study to apply machine learning models for agitation and UTI risk analysis. We collected a large dataset from 88 participants with a mean age of 82 and a standard deviation of 6.5 (47 females and 41 males) to evaluate a new deep learning model that utilises attention and rational mechanism. The proposed solution can process a large volume of data over a period of time and extract significant patterns in a time-series data (i.e. attention) and use the extracted features and patterns to train risk analysis models (i.e. rational). The proposed model can explain the predictions by indicating which time-steps and features are used in a long series of time-series data. The model provides a recall of 91\% and precision of 83\% in detecting the risk of agitation and UTIs. This model can be used for early detection of conditions such as UTIs and managing of neuropsychiatric symptoms such as agitation in association with initial treatment and early intervention approaches. In our study we have developed a set of clinical pathways for early interventions using the alerts generated by the proposed model and a clinical monitoring team has been set up to use the platform and respond to the alerts according to the created intervention plans.
翻译:患有痴呆症者面临的最常见的问题之一是早期发现和及时干预,以减少危难和避免意外住院入院。使用家庭内遥感技术和机器学习模型进行传感器数据整合和分析,为检测和预测临床重大事件和健康状况变化提供了机会。我们开发了一个综合平台,以收集家庭内传感器数据,并进行了观察研究,以应用机器学习模型来进行焦虑和尿道风险分析。我们从88名参与者那里收集了庞大的数据集,平均年龄82岁,标准戒备率为6.5(47名女性和41名男性),以评价新的深层次学习模型,利用关注和合理机制。拟议的解决方案可以在一段时间内处理大量数据,在时间序列数据(即注意)中提取重要模式和健康状况变化。我们开发了一个综合平台,利用抽取的特征和模式来培训风险分析模型(即理性 ) 拟议的模型可以解释预测值,通过在时间-时间-时间-时间-时间-时间-警报系列中使用什么(47名女性和41名男性),以评价新的深层次学习模式,用以进行时间-时间-时间-时间-时间-时间-时间-时间-状态处理,用以检测。这个模型可以回顾模型,用来进行这种模型,用来评估,用来评估。这个模型可以回顾-时间-11早期-11的模型用于检测,用于测测测测测测测测测测测测。这个模型,用于测测测测测测。