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Using Multivariate Statistics

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Anomaly detection is an important problem that has been researched within diverse research areas and application domains. Many anomaly detection techniques have been specifically developed for certain application domains, while others are more generic. This survey tries to provide a structured and comprehensive overview of the research on anomaly detection. We have grouped existing techniques into different categories based on the underlying approach adopted by each technique. For each category we have identified key assumptions, which are used by the techniques to differentiate between normal and anomalous behavior. When applying a given technique to a particular domain, these assumptions can be used as guidelines to assess the effectiveness of the technique in that domain. For each category, we provide a basic anomaly detection technique, and then show how the different existing techniques in that category are variants of the basic technique. This template provides an easier and more succinct understanding of the techniques belonging to each category. Further, for each category, we identify the advantages and disadvantages of the techniques in that category. We also provide a discussion on the computational complexity of the techniques since it is an important issue in real application domains. We hope that this survey will provide a better understanding of the different directions in which research has been done on this topic, and how techniques developed in one area can be applied in domains for which they were not intended to begin with.

/pdf/anomaly-detection-a-survey-1x33g9m0a3.pdf

Anomaly detection: A survey

Brain-computer interfaces for communication and control.

Optical coherence tomography (OCT) has developed rapidly since its first realisation in medicine and is currently an emerging technology in the diagnosis of skin disease. OCT is an interferometric technique that detects reflected and backscattered light from tissue and is often described as the optical analogue to ultrasound. The inherent safety of the technology allows for in vivo use of OCT in patients. The main strength of OCT is the depth resolution. In dermatology, most OCT research has turned on non-melanoma skin cancer (NMSC) and non-invasive monitoring of morphological changes in a number of skin diseases based on pattern recognition, and studies have found good agreement between OCT images and histopathological architecture. OCT has shown high accuracy in distinguishing lesions from normal skin, which is of great importance in identifying tumour borders or residual neoplastic tissue after therapy. The OCT images provide an advantageous combination of resolution and penetration depth, but specific studies of diagnostic sensitivity and specificity in dermatology are sparse. In order to improve OCT image quality and expand the potential of OCT, technical developments are necessary. It is suggested that the technology will be of particular interest to the routine follow-up of patients undergoing non-invasive therapy of malignant or premalignant keratinocyte tumours. It is speculated that the continued technological development can propel the method to a greater level of dermatological use.

https://link.springer.com/content/pdf/bfm%3A978-3-319-06419-2%2F1.pdf

Optical Coherence Tomography

Recognition of complex trajectories in multivariate time-series data requires effective models and representations for the analysis and matching of functional data. In this work, we introduce a new representation that allows for matching of noisy, and unevenly-sampled trajectories, and we explore whether this representation may be used to characterise the state of health of a patient based on vital-sign data. We model the evolution of each vital-sign trajectory using multivariate Gaussian process regression, and we introduce a similarity measurement for the comparison of latent functions based on the local likelihood of the points in each trajectory. The similarity measurement is then used for recognising known trajectories and identifying unknown trajectories as would be required for identifying “abnormal” vital-sign time-series. We test our approach using a dataset that contains vital-sign observational data collected from a cohort of 154 patients who are recovering from gastrointestinal surgery. We show that our approach is able to discriminate between abnormal patient trajectories corresponding to those who deteriorated physiologically and were admitted to a higher level of care, from those belonging to patients who had no clinically relevant events.

Gaussian process clustering for the functional characterisation of vital-sign trajectories

We investigated the feasibility of a mobile-phone based system for patients with type 2 diabetes who had recently commenced insulin therapy but remained poorly controlled. The system was evaluated in a feasibility study in a general practice setting with 23 patients over six months. A total of 22 patients successfully completed the study and used the system for a mean of 217 days (range 162-376). Blood glucose control improved, as reflected by a mean decrease in HbA(1c) of 0.66% (P = 0.05), with the mean insulin dose increasing by 17 units (P = 0.006). Blood glucose monitoring compliance was high, with readings available for 6.2 days per week, although use of the mobile phone decreased during the study. On average, the mobile phone diary was used for 3.5 days per week. Insulin dose adjustments were made throughout the study by all patients, but not as frequently as would be expected for the degree of hyperglycaemia observed.

Telemedicine-supported insulin optimisation in primary care:

Manual centile-based early warning scores derived from statistical distributions of observational vital-sign data.

Health informatics is a field in which the disciplines of software engineering and machine learning necessarily co-exist. This discussion paper considers the interaction of software engineering and machine learning, set within the context of health informatics, where the scale of clinical practice requires new engineering approaches from both disciplines. We introduce applications implemented in large on-going research programmes undertaken between the Departments of Engineering Science and Computer Science at Oxford University, the Oxford University Hospitals NHS Trust, and the Guy's and St Thomas' NHS Foundation Trust, London.

/pdf/machine-learning-and-software-engineering-in-health-2mi8wwrlmh.pdf

Machine learning and software engineering in health informatics

Most pulse oximeters automatically alter the intensity of their light-emitting diodes (LEDs) according to the absorption of the finger, toe or earlobe to which they are attached. This paper investigates the effect of changing LED intensity on pulse oximeter accuracy. Our results show that the peak wavelength of a red LED typically increases by 8 nm as its intensity is increased ten-fold. To determine whether this shift introduces a significant error, a simple theoretical model based on the Beer–Lambert law is used. The model predicts that a 10:1 change in LED intensity results in a 2·5% error at 50% arterial oxygen saturation (SpO2). At high saturations (SpO2 ≥ 85%) the model predicts little loss of accuracy and thus any effect due to changes in LED intensity will be apparent only at low saturations.

Lionel Tarassenko

Papers

Gaussian process clustering for the functional characterisation of vital-sign trajectories

Telemedicine-supported insulin optimisation in primary care:

Manual centile-based early warning scores derived from statistical distributions of observational vital-sign data.

Machine learning and software engineering in health informatics

The effect of varying LED intensity on pulse oximeter accuracy