Novelty detection algorithms for behavioural analysis in healthcare

数字健康领域的关键挑战之一是对人类行为的理解。为此，近年来基于机器学习的家庭传感器活动识别被证明是成功的。利用信号处理技术从原始数据计算出的标准特征既包括时域特征和频域特征，也包括基于专家知识的特征。这些活动(如睡觉、锻炼、清洁)可以很好地反映患者随时间推移的生活方式。在许多这样的情况下，目标不仅是识别特定的行为，而且是检测异常行为，在这种情况下，偏离标准行为模式是有意义的。这个项目的目的是自动识别生活方式的变化，这些变化可以用作疾病存在或进展的证据。医疗保健的许多方面使得开箱即用的新奇检测技术的应用具有挑战性。这些算法的目的是区分积极类别和其他所有类别。涵盖所有可能的正常行为的区域的定义是困难的。此外，区分阳性实例和异常的边界也是不精确的&因此，靠近该边界的观测很容易被错误分类。此外，编辑常见疾病患者的数据集相对容易，但对于阴性类则不同。除非经过检测，否则患者不能被认为是阴性病例。这些是博士学位四个阶段的动机，总结如下。在新颖性检测文献中，假设变化是突然的和稀疏的，并且(观察到的时间序列通常是单变量的或固定维度的。相比之下，医疗数据需要新的新奇检测方法，能够检测到渐进和平稳的变化，处理多个异类数据流，并在对患者的监控正在进行时执行在线分析，而不会导致任何错误警报。这也将需要设计适应特定信号特征的新的检索模型，例如不均匀采样、数据缺失、非高斯性、存在非平稳过程或非身份识别。关系。此外，该项目的第二个挑战将是纳入额外的抽象水平，以严格的方式分析复杂的活动序列及其之间的相互作用。这些活动通常是按等级方式组织的，从基本姿势/步态到日常生活活动。因此，考虑到这种固有结构的新的层级新颖性检测算法将被设计成这样一种方式，即模式可以被归类为在层级的给定级别上的新颖性，而不一定是所有的新颖性。第三，该项目将需要推进从活动的时间序列中获得意义的算法，从简单的个人活动检测转移到对活动序列的正确理解。这些将专门针对临床医生感兴趣的行为模式而定制，因为并不是所有的新奇事物对每个临床医生都有同样的信息量或用处。为了说明这一点，该项目将纳入探索性数据分析的最新结果，其中用户通常对突出令人惊讶的信息和模式的可视化感兴趣。也就是说，用户感兴趣的是找到与他们先前的期望互补或矛盾的模式，而不是那些确认他们的模式。该项目将研究考虑临床医生先前预期的方法，以便返回最相关的新奇事物。最后，数字健康数据的一个关键属性是数据的规模性。在这种情况下，由于新数据源的可用性不断增加，数据管理变得更加具有挑战性。

One of the key challenges in digital health is the understanding of human behaviour. To this end, using activity recognition from home sensors based on machine learning has been proven successful in recent years. The standard features computed from raw data using signal processing techniques include both time domain features & frequency domain features, as well as features based on expert knowledge. These activities (e.g.sleeping, exercising, cleaning) provide a good proxy for the characterisation of the patient's lifestyle over time. In many of these cases, the objective will be not just to recognise a particular behaviour but the detection of anomalous behaviour, in which case the deviation from standard behavioural patterns is of interest.The aim of this project is to automatically identify changes in the lifestyle that could be used as evidence of the presence or progress of a disease. Many aspects of healthcare make the application of out-of-the-box novelty detection techniques challenging. The algorithms aim to distinguish between the positive class& everything else. The definition of the region that covers all possible normal behaviours is difficult. Also, the boundary separating the positive instances from anomalies is imprecise & thus observations that lie close to it can be easily misclassified. Moreover, it is relatively easy to compile a dataset with patients with a common disease, but this is not the same for the negative class. Patients cannot be assumed to be negative instances unless they have been tested. These are the motivation for the 4 phases of the PhD summarised below. In the novelty detection literature changes are assumed to be abrupt and sparse, and (the observed time series is often univariate or of fixed dimensions. In contrast, healthcare data calls for new novelty detection methodologies, which are able to detect gradual and smooth changes, handle multiple heterogeneous data streams, & perform online analysis as the monitoring of the patients is on-going, without causingany false alarm. This will also require the design of new retrieval models adapted to the particular signal characteristics, e.g. uneven-sampling, missing data, non-Gaussianity, presence of non-stationary processes, or non-i.i.d. relations. Additionally, a 2nd challenge of this project will be to incorporate an additional levelof abstraction to analyse complex sequences of activities and the interplay between them in a rigorous manner. These activities are usually organised in a hierarchical fashion, from basic posture/ambulation to Activities of Daily Living. Thus, new hierarchical novelty detection algorithms that account for this inherent structure will be designed in such a way that patterns can be categorised as novelties at a given level of the hierarchy, not necessarily at all of them. In 3rd place, the project will need to advance algorithms that derive meaning from the time series of activities, moving from a simple detection of individual activities to a proper understanding of sequences of activities. These will be specifically tailored to the behavioural patterns of interest to clinicians as not all novelties may be as informative or useful for each clinician. In order to account for this, the project will incorporate recent results from exploratory data analysis, where users are typically interested in visualisations that highlight surprising information & patterns. That is, users are interested in finding patterns that complement or contradict their prior expectations, rather than those that confirm them. The project will study methods that take into account the clinician's prior expectations in order to return the most relevant novelties. Finally, a key property of digital health's data is the large-scale nature of the data. In this case, data management becomes more challenging due to the increasing availability of new data sources.