Papers by Pol Mac Aonghusa
PLOS ONE, 2021
Time analysis of the course of an infectious disease epidemic is a critical way to understand the... more Time analysis of the course of an infectious disease epidemic is a critical way to understand the dynamics of pathogen transmission and the effect of population scale interventions. Computational methods have been applied to the progression of the COVID-19 outbreak in five different countries (Ireland, Germany, UK, South Korea and Iceland) using their reported daily infection data. A Gaussian convolution smoothing function constructed a continuous epidemic line profile that was segmented into longitudinal time series of mathematically fitted individual logistic curves. The time series of fitted curves allowed comparison of disease progression with differences in decreasing daily infection numbers following the epidemic peak being of specific interest. A positive relationship between the rate of declining infections and countries with comprehensive COVID-19 testing regimes existed. Insight into different rates of decline infection numbers following the wave peak was also possible whi...
Practical Perfusion Quantification in Multispectral Endoscopic Video: Using the Minutes after ICG Administration to Assess Tissue Pathology
AMIA ... Annual Symposium proceedings. AMIA Symposium, 2021
The wide availability of near infrared light sources in interventional medical imaging stacks ena... more The wide availability of near infrared light sources in interventional medical imaging stacks enables non-invasive quantification of perfusion by using fluorescent dyes, typically Indocyanine Green (ICG). Due to their often leaky and chaotic vasculatures, intravenously administered ICG perfuses through cancerous tissues differently. We investigate here how a few characteristic values derived from the time series of fluorescence can be used in simple machine learning algorithms to distinguish benign lesions from cancers. These features capture the initial uptake of ICG in the colon, its peak fluorescence, and its early wash-out. By using simple, explainable algorithms we demonstrate, in clinical cases, that sensitivity (specificity) rates of over 95% (95%) for cancer classification can be achieved.
Person-Specific Standardized Vulnerability Assessment in Health and Social Care
Studies in health technology and informatics, 2015
We describe an integrated person-specific standardized vulnerability assessment model designed to... more We describe an integrated person-specific standardized vulnerability assessment model designed to facilitate patient management in health and social care. Such a system is not meant to replace existing health and social assessment models but rather to complement them by providing a holistic picture of the vulnerabilities faced by a given patient. In fact, it should be seen as a screening tool for health and social care workers. One key aspect of the modeling framework is the ability to provide personalized yet standardized multi-dimensional assessments of risk based on incomplete information about the patient status, as is the case in screening situations. Specifically, we integrate a Markov chain model describing the evolution of patients in and out of vulnerable states over time with a Bayesian network that serves to customize the dynamic model. We present an application in the context of elder care.
ArXiv, 2018
Limiting online data collection to the minimum required for specific purposes is mandated by mode... more Limiting online data collection to the minimum required for specific purposes is mandated by modern privacy legislation such as the General Data Protection Regulation (GDPR) and the California Consumer Protection Act. This is particularly true in online services where broad collection of personal information represents an obvious concern for privacy. We challenge the view that broad personal data collection is required to provide personalised services. By first developing formal models of privacy and utility, we show how users can obtain personalised content, while retaining an ability to plausibly deny their interests in topics they regard as sensitive using a system of proxy, group identities we call 3PS. Through extensive experiment on a prototype implementation, using openly accessible data sources, we show that 3PS provides personalised content to individual users over 98% of the time in our tests, while protecting plausible deniability effectively in the face of worst-case thr...
A significant portion of the modern internet is funded by commercial return from customised conte... more A significant portion of the modern internet is funded by commercial return from customised content such as advertising where user interests are learned from users' online behaviour and used to display personalised content. Privacy becomes a concern when personalisation reveals evidence of learning about sensitive topics a user would rather keep private. Examples of potentially sensitive topics we consider include health, finance and sexual orientation. In this thesis we develop novel technologies allowing users to improve control over their personal privacy. We consider three aspects of privacy protection here: i) detecting evidence of unwanted profiling, ii) assessing the potential impact of a threat, and, iii) a flexible framework to help users to take control the flow of information used in personalisation. We model online systems as black-box adversaries with unknown internal workings but with an objective to maximise commercial utility. In a black-box environment absolute ...
Outcome Prediction from Behaviour Change Intervention Evaluations using a Combination of Node and Word Embedding
AMIA ... Annual Symposium proceedings. AMIA Symposium, 2021
Findings from randomized controlled trials (RCTs) of behaviour change interventions encode much o... more Findings from randomized controlled trials (RCTs) of behaviour change interventions encode much of our knowledge on intervention efficacy under defined conditions. Predicting outcomes of novel interventions in novel conditions can be challenging, as can predicting differences in outcomes between different interventions or different conditions. To predict outcomes from RCTs, we propose a generic framework of combining the information from two sources - i) the instances (comprised of surrounding text and their numeric values) of relevant attributes, namely the intervention, setting and population characteristics of a study, and ii) abstract representation of the categories of these attributes themselves. We demonstrate that this way of encoding both the information about an attribute and its value when used as an embedding layer within a standard deep sequence modeling setup improves the outcome prediction effectiveness.
Extracting, Visualizing, and Learning from Dynamic Data: Perfusion in Surgical Video for Tissue Characterization
2022 IEEE International Conference on Digital Health (ICDH)
Annals of Behavioral Medicine, 2020
Background Artificial Intelligence (AI) is transforming the process of scientific research. AI, c... more Background Artificial Intelligence (AI) is transforming the process of scientific research. AI, coupled with availability of large datasets and increasing computational power, is accelerating progress in areas such as genetics, climate change and astronomy [NeurIPS 2019 Workshop Tackling Climate Change with Machine Learning, Vancouver, Canada; Hausen R, Robertson BE. Morpheus: A deep learning framework for the pixel-level analysis of astronomical image data. Astrophys J Suppl Ser. 2020;248:20; Dias R, Torkamani A. AI in clinical and genomic diagnostics. Genome Med. 2019;11:70.]. The application of AI in behavioral science is still in its infancy and realizing the promise of AI requires adapting current practices. Purposes By using AI to synthesize and interpret behavior change intervention evaluation report findings at a scale beyond human capability, the HBCP seeks to improve the efficiency and effectiveness of research activities. We explore challenges facing AI adoption in behavi...
Scientific Advisory Board (SAB) Meetings
Other approaches in this domain have several limitations: content portals (e.g. twc open gov data... more Other approaches in this domain have several limitations: content portals (e.g. twc open gov data portal, data.gov.uk, IOGDC (Ding et al. 2011)) lack sufficient capabilities to explore, navigate and query across collections of multi-domain data; enterprise data integration platforms require significant technical expertise and effort from the user; technical tools for data cleaning and integration (e.g. in (Gonzalez et al. 2010), (Huynh and Mazzocchi )) require some technical skills and lack semantic depth to be able to answer complicated queries. QuerioCity uses and augments technologies from the fields of Linked Data and Semantics-based Integration. We are tapping on research output from the fields of Pay-asyou-go data integration (based on non-expert input) (Bizer et al. 2009), (Madhavan et al. 2007), RDF stores, Information provenance and Anonymization.
Behaviour change is essential to improve population health, the selfmanagement of illness, chroni... more Behaviour change is essential to improve population health, the selfmanagement of illness, chronic conditions and health professional practice. Evidence about behaviour change interventions is currently being produced at such a rate that manual systems for evidence review and synthesis cannot keep up. Neither can they account for all the relevant features of interventions. The Human Behaviour-Change Project (HBCP) aims to bring together behavioural scientists, computer scientists and system architects to advance progress in behaviour change. It aims to answer variants of the ‘big question’ of behaviour change: ‘What works, compared with what, how well, with what exposure, with what behaviours (for how long), for whom, in what settings, and why?’ The main outputs will be: 1) an ontology of behaviour change interventions; 2) an AI system capable of extracting and interpreting evidence from published literature and making predictions; 3) an interface allowing users (researchers, policy...
Link 2 Outcome : Coordinating Social Care and Healthcare using Semantic Web Technologies
ArXiv, 2017
The explosion in volume and variety of data offers enormous potential for research and commercial... more The explosion in volume and variety of data offers enormous potential for research and commercial use. Increased availability of personal data is of particular interest in enabling highly customised services tuned to individual needs. Preserving the privacy of individuals against reidentification attacks in this fast-moving ecosystem poses significant challenges for a one-size fits all approach to anonymisation. In this paper we present (k,)-anonymisation, an approach that combines the k-anonymisation and-differential privacy models into a single coherent framework, providing privacy guarantees at least as strong as those offered by the individual models. Linking risks of less than 5% are observed in experimental results, even with modest values of k and. Our approach is shown to address well-known limitations of k-anonymity and-differential privacy and is validated in an extensive experimental campaign using openly available datasets.
arXiv: Cryptography and Security, 2017
The explosion in volume and variety of data offers enormous potential for research and commercial... more The explosion in volume and variety of data offers enormous potential for research and commercial use. Increased availability of personal data is of particular interest in enabling highly customised services tuned to individual needs. Preserving the privacy of individuals against reidentification attacks in this fast-moving ecosystem poses significant challenges for a one-size fits all approach to anonymisation. In this paper we present ($k$,$\epsilon$)-anonymisation, an approach that combines the $k$-anonymisation and $\epsilon$-differential privacy models into a single coherent framework, providing privacy guarantees at least as strong as those offered by the individual models. Linking risks of less than 5\% are observed in experimental results, even with modest values of $k$ and $\epsilon$. Our approach is shown to address well-known limitations of $k$-anonymity and $\epsilon$-differential privacy and is validated in an extensive experimental campaign using openly available datas...
Due to the fast pace at which research reports in behaviour change are published, researchers, co... more Due to the fast pace at which research reports in behaviour change are published, researchers, consultants and policymakers would benefit from more automatic ways to process these reports. Automatic extraction of the reports’ intervention content, population, settings and their results etc. are essential in synthesising and summarising the literature. However, to the best of our knowledge, no unique resource exists at the moment to facilitate this synthesis. In this paper, we describe the construction of a corpus of published behaviour change intervention evaluation reports aimed at smoking cessation. We also describe and release the annotation of 57 entities, that can be used as an off-the-shelf data resource for tasks such as entity recognition, etc. Both the corpus and the annotation dataset are being made available to the community.
Wellcome Open Research, 2020
Changing behaviour is necessary to address many of the threats facing human populations. However... more Changing behaviour is necessary to address many of the threats facing human populations. However, identifying behaviour change interventions likely to be effective in particular contexts as a basis for improving them presents a major challenge. The Human Behaviour-Change Project harnesses the power of artificial intelligence and behavioural science to organise global evidence about behaviour change to predict outcomes in common and unknown behaviour change scenarios.
Scientific Advisory Board (SAB)
Biophysics inspired artificial intelligence for colorectal cancer characterization
Artificial Intelligence in Gastroenterology, 2021
Human Behaviour-Change Project
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Papers by Pol Mac Aonghusa