Novelty detection algorithms for behavioural analysis in healthcare

用于医疗保健行为分析的新颖性检测算法

• 批准号: 1953881
• 金额: --
• 依托单位: University of Bristol
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2017
• 资助国家: 英国
• 起止时间: 2017 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-1953881
• 关键词: Novelty; detection; algorithms; behavioural; analysis

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.

数字卫生的主要挑战之一是对人类行为的理解。为此，近年来使用基于机器学习的家庭传感器的活动识别已被证明是成功的。使用信号处理技术从原始数据计算的标准特征包括时域特征和频域特征，以及基于专家知识的特征。这些活动（如睡觉、锻炼、打扫卫生）很好地反映了患者一段时间以来的生活方式特征。在许多情况下，目标不仅仅是识别特定的行为，而是检测异常行为，在这种情况下，对标准行为模式的偏离是感兴趣的。该项目的目的是自动识别生活方式的变化，这些变化可以作为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。这些算法旨在区分正类和其他类别。覆盖所有可能的正常行为的区域的定义是困难的。此外，将正实例与异常分开的边界是不精确的&因此，靠近它的观测结果很容易被错误分类。此外，编制患有常见疾病的患者的数据集相对容易，但对于阴性类别来说就不一样了。除非对患者进行检测，否则不能假定患者为阴性病例。这就是博士阶段的动机，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，并且观测到的时间序列通常是单变量的或固定维的。相比之下，医疗保健数据需要新的新颖检测方法，这些方法能够检测渐进和平滑的变化，处理多个异构数据流，并在对患者进行监测时执行在线分析，而不会引起任何误报。这还需要设计适应特定信号特征的新检索模型，例如采样不均匀、数据缺失、非高斯性、非平稳过程的存在或非i.i.d。关系。此外，该项目的第二个挑战将是纳入一个额外的抽象层次，以严格的方式分析复杂的活动序列和它们之间的相互作用。这些活动通常以等级的方式组织，从基本的姿势/行走到日常生活活动。因此，考虑到这种固有结构的新的分层新颖性检测算法将被设计成这样一种方式，即模式可以在给定的层次结构中被归类为新颖性，而不一定是所有的。第三，该项目将需要改进从活动时间序列中获得意义的算法，从单个活动的简单检测转向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进行定制，因为并不是所有的新奇事物都可能对每个临床医生都有信息或有用。为了解释这一点，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显示令人惊讶的信息和模式的可视化感兴趣。也就是说，用户感兴趣的是寻找补充或反驳他们先前期望的模式，而不是那些证实他们期望的模式。该项目将研究考虑到临床医生先前期望的方法，以返回最相关的新颖性。最后，数字健康数据的一个关键属性是数据的大规模性质。在这种情况下，由于新数据源的可用性不断增加，数据管理变得更具挑战性。

项目成果

Active Learning with Label Proportions

使用标签比例进行主动学习

• 发表时间: 2019
• 作者: Poyiadzi R

LABEL PROPAGATION FOR LEARNING WITH LABEL PROPORTIONS

• DOI: 10.1109/mlsp.2018.8517083
• 发表时间: 2018-09
• 期刊: 2018 IEEE 28th International Workshop on Machine Learning for Signal Processing (MLSP)
• 作者: Rafael Poyiadzi;Raúl Santos-Rodríguez;N. Twomey