Uncertainty-aware learning from sparse, unlabelled, and out-of-distribution time series
type: CreativeWork
description: Nowadays, machine learning is increasingly popular in the analysis of healthcare time series, as it can support improved diagnostics, personalised monitoring, and effective performance tracking. The ever-increasing availability of datasets from wearable sensors, mobile devices, and continuous monitoring technologies has created new opportunities for real-world applications, as they can offer a rich and continuous picture of an individual's health and fitness in everyday environments as well as in clinical or supervised healthcare settings. Despite the potential that machine learning models offer for healthcare time series, they still face notable challenges. Sensor-based datasets are frequently sparse (with missing values) if acquired outside controlled environments, while a substantial proportion remain unlabelled or only partially labelled. Further, models often exhibit limited generalisability under out-of-distribution conditions, which arise due to heterogeneity across hospitals, sensors, and patient populations. Such factors limit the use of real-world time series in deep learning applications, and are consequential in healthcare as unreliable models may not only yield poor performance but also produce overconfident metrics. In this context, this thesis moves beyond the narrow settings of conventional models through uncertainty-aware learning. Effectively incorporating uncertainty enhances training by addressing key challenges, as it informs the handling of missing data, prioritises the most informative samples for annotation, and detects distribution shifts in out-of-distribution data to guide fine-tuning. By leveraging uncertainty, this thesis makes models more data-efficient, adaptive, and generalisable across diverse healthcare use cases. The first contribution focuses on incorporating uncertainty-aware sequence-to-sequence predictions on sparse time series. These are often sparsely-sampled due to missed recordings, device malfunctions, or diverse sensing c…
abstract: Nowadays, machine learning is increasingly popular in the analysis of healthcare time series, as it can support improved diagnostics, personalised monitoring, and effective performance tracking. The ever-increasing availability of datasets from wearable sensors, mobile devices, and continuous monitoring technologies has created new opportunities for real-world applications, as they can offer a rich and continuous picture of an individual's health and fitness in everyday environments as well as in clinical or supervised healthcare settings. Despite the potential that machine learning models offer for healthcare time series, they still face notable challenges. Sensor-based datasets are frequently sparse (with missing values) if acquired outside controlled environments, while a substantial proportion remain unlabelled or only partially labelled. Further, models often exhibit limited generalisability under out-of-distribution conditions, which arise due to heterogeneity across hospitals, sensors, and patient populations. Such factors limit the use of real-world time series in deep learning applications, and are consequential in healthcare as unreliable models may not only yield poor performance but also produce overconfident metrics. In this context, this thesis moves beyond the narrow settings of conventional models through uncertainty-aware learning. Effectively incorporating uncertainty enhances training by addressing key challenges, as it informs the handling of missing data, prioritises the most informative samples for annotation, and detects distribution shifts in out-of-distribution data to guide fine-tuning. By leveraging uncertainty, this thesis makes models more data-efficient, adaptive, and generalisable across diverse healthcare use cases. The first contribution focuses on incorporating uncertainty-aware sequence-to-sequence predictions on sparse time series. These are often sparsely-sampled due to missed recordings, device malfunctions, or diverse sensing c…
author: Vavaroutas, Sotirios
language: eng
keywords: Artificial Intelligence, Machine Learning, Uncertainty-Aware Learning, Sparse Data, Unlabelled Data, Out-of-Distribution Data, Time Series
license: https://www.repository.cam.ac.uk/bitstreams/98d1fc07-efcb-4175-acfc-88330924f467/download
license: https://creativecommons.org/licenses/by/4.0/
date published: 2025-12-02
date modified: 2026-07-24T01:33:13Z
provider: Cambridge University
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: University of Cambridge

PropertyValue
property ID: DOI
value: 10.17863/CAM.129978