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All Outputs (44)

Understanding health service utilisation patterns for care home residents during the COVID-19 pandemic using routinely collected healthcare data (2024)
Journal Article
Garner, A., Preston, N., Caiado, C., Stubington, E., Hanratty, B., Limb, J., …Knight, J. (2024). Understanding health service utilisation patterns for care home residents during the COVID-19 pandemic using routinely collected healthcare data. BMC Geriatrics, 24(1), Article 449. https://doi.org/10.1186/s12877-024-05062-6

Background: Healthcare in care homes during the COVID-19 pandemic required a balance, providing treatment while minimising exposure risk. Policy for how residents should receive care changed rapidly throughout the pandemic. A lack of accessible data... Read More about Understanding health service utilisation patterns for care home residents during the COVID-19 pandemic using routinely collected healthcare data.

The impact of digital technology in care homes on unplanned secondary care usage and associated costs. (2024)
Journal Article
Garner, A., Lewis, J., Dixon, S., Preston, N., Caiado, C. C. S., Hanratty, B., …Mason, S. M. (2024). The impact of digital technology in care homes on unplanned secondary care usage and associated costs. Age and Ageing, 53(2), 1-7. https://doi.org/10.1093/ageing/afae004

Background A substantial number of Emergency Department (ED) attendances by care home residents are potentially avoidable. Health Call Digital Care Homes is an app-based technology that aims to streamline residents’ care by recording their observati... Read More about The impact of digital technology in care homes on unplanned secondary care usage and associated costs..

PATTERNS OF SOCIAL CARE USE WITHIN THE OLDER POPULATION: WHAT CAN WE LEARN FROM ROUTINELY COLLECTED DATA? (2023)
Journal Article
Brotherhood, K., Hanratty, B., Spiers, G., Caiado, C., & Newton, J. (2023). PATTERNS OF SOCIAL CARE USE WITHIN THE OLDER POPULATION: WHAT CAN WE LEARN FROM ROUTINELY COLLECTED DATA?. Innovation in Aging, 7(Supplement_1), 707-707. https://doi.org/10.1093/geroni/igad104.2294

Research with routinely collected social care data has untapped potential to inform new care delivery approaches and techniques. To identify opportunities for service improvement and enhance our understanding of care pathways experienced by the older... Read More about PATTERNS OF SOCIAL CARE USE WITHIN THE OLDER POPULATION: WHAT CAN WE LEARN FROM ROUTINELY COLLECTED DATA?.

Variations in older people's emergency care use by social care setting: a systematic review of international evidence. (2023)
Journal Article
Brotherhood, K., Searle, B., Spiers, G. F., Caiado, C., & Hanratty, B. (2023). Variations in older people's emergency care use by social care setting: a systematic review of international evidence. British Medical Bulletin, Article ldad033. https://doi.org/10.1093/bmb/ldad033

Older adults' use of social care and their healthcare utilization are closely related. Residents of care homes access emergency care more often than the wider older population; however, less is known about emergency care use across other social care... Read More about Variations in older people's emergency care use by social care setting: a systematic review of international evidence..

Machine learning for determining lateral flow device results for testing of SARS-CoV-2 infection in asymptomatic populations (2022)
Journal Article
Beggs, A. D., Caiado, C. C., Branigan, M., Lewis-Borman, P., Patel, N., Fowler, T., …Banathy, R. (2022). Machine learning for determining lateral flow device results for testing of SARS-CoV-2 infection in asymptomatic populations. Cell Reports Medicine, 3(10), Article 100784. https://doi.org/10.1016/j.xcrm.2022.100784

Rapid antigen tests, in the form of lateral flow devices (LFD) allow testing of a large population for SARS-CoV-2. To reduce the variability seen in device interpretation, we show the design and testing of an AI algorithm based on machine learning. T... Read More about Machine learning for determining lateral flow device results for testing of SARS-CoV-2 infection in asymptomatic populations.

Durham University students’ experiences of asymptomatic COVID-19 testing: a qualitative study (2021)
Journal Article
Jones, L. F., Batteux, E., Bonfield, S., Bhogal, J. K., Taylor, J., Caiado, C., …Weston, D. (2021). Durham University students’ experiences of asymptomatic COVID-19 testing: a qualitative study. BMJ Open, 11(12), https://doi.org/10.1136/bmjopen-2021-055644

Objectives To evaluate the asymptomatic coronavirus testing programme at Durham University by exploring students’ barriers and facilitators to taking part and provide recommendations to improve the programme. Design Qualitative interviews. Setting On... Read More about Durham University students’ experiences of asymptomatic COVID-19 testing: a qualitative study.

Improving and benchmarking of algorithms for Γ-maximin, Γ-maximax and interval dominance (2021)
Journal Article
Nakharutai, N., Troffaes, M. C., & Caiado, C. C. (2021). Improving and benchmarking of algorithms for Γ-maximin, Γ-maximax and interval dominance. International Journal of Approximate Reasoning: Uncertainty in Intelligent Systems, 133, 95-115. https://doi.org/10.1016/j.ijar.2021.03.005

Γ-maximin, Γ-maximax and interval dominance are familiar decision criteria for making decisions under severe uncertainty, when probability distributions can only be partially identified. One can apply these three criteria by solving sequences of line... Read More about Improving and benchmarking of algorithms for Γ-maximin, Γ-maximax and interval dominance.

Justified Stories with Agent-Based Modelling for Local COVID-19 Planning (2021)
Journal Article
Badham, J., Barbrook-Johnson, P., Caiado, C., & Castellani, B. (2021). Justified Stories with Agent-Based Modelling for Local COVID-19 Planning. Journal of Artificial Societies and Social Simulation, 24(1), Article 8. https://doi.org/10.18564/jasss.4532

This paper presents JuSt-Social, an agent-based model of the COVID-19 epidemic with a range of potential social policy interventions. It was developed to support local authorities in North East England who are making decisions in a fast moving crisis... Read More about Justified Stories with Agent-Based Modelling for Local COVID-19 Planning.

Accounting for Model Discrepancy in Uncertainty Analysis by Combining Numerical Simulation and Bayesian Emulation Techniques (2020)
Conference Proceeding
Formentin, H. N., Vernon, I., Goldstein, M., Caiado, C., Avansi, G., & Schiozer, D. (2020). Accounting for Model Discrepancy in Uncertainty Analysis by Combining Numerical Simulation and Bayesian Emulation Techniques. . https://doi.org/10.3997/2214-4609.202035095

Model discrepancy specifies unavoidable differences between a physical system and its corresponding computer model. Incomplete information, simplifications and lack of knowledge about the physical state originate model discrepancy. Misevaluation of m... Read More about Accounting for Model Discrepancy in Uncertainty Analysis by Combining Numerical Simulation and Bayesian Emulation Techniques.

A Novel Patient-Specific Model for Predicting Severe Oliguria; Development and Comparison With Kidney Disease: Improving Global Outcomes Acute Kidney Injury Classification (2020)
Journal Article
Howitt, S. H., Oakley, J., Caiado, C., Goldstein, M., Malagon, I., McCollum, C., & Grant, S. W. (2020). A Novel Patient-Specific Model for Predicting Severe Oliguria; Development and Comparison With Kidney Disease: Improving Global Outcomes Acute Kidney Injury Classification. Critical Care Medicine, 48(1), e18-e25. https://doi.org/10.1097/ccm.0000000000004074

Objectives: The Kidney Disease: Improving Global Outcomes urine output criteria for acute kidney injury lack specificity for identifying patients at risk of adverse renal outcomes. The objective was to develop a model that analyses hourly urine outpu... Read More about A Novel Patient-Specific Model for Predicting Severe Oliguria; Development and Comparison With Kidney Disease: Improving Global Outcomes Acute Kidney Injury Classification.

Efficient Selection of Reservoir Model Outputs within an Emulation-Based Bayesian History Matching Uncertainty Analysis (2019)
Journal Article
Ferreira, C., Vernon, I., Caiado, C., Formentin, H., Avansi, G., Goldstein, M., & Schiozer, D. (2019). Efficient Selection of Reservoir Model Outputs within an Emulation-Based Bayesian History Matching Uncertainty Analysis. SPE Journal, OTC-29801. https://doi.org/10.4043/29801-ms

When performing classic uncertainty reduction based on dynamic data, a large number of reservoir simulations need to be evaluated at high computational cost. As an alternative, we construct Bayesian emulators that mimic the dominant behaviour of the... Read More about Efficient Selection of Reservoir Model Outputs within an Emulation-Based Bayesian History Matching Uncertainty Analysis.

Improving and benchmarking of algorithms for decision making with lower previsions (2019)
Journal Article
Nakharutai, N., Troffaes, M. C., & Caiado, C. (2019). Improving and benchmarking of algorithms for decision making with lower previsions. International Journal of Approximate Reasoning: Uncertainty in Intelligent Systems, 113, 91-105. https://doi.org/10.1016/j.ijar.2019.06.008

Maximality, interval dominance, and E-admissibility are three well-known criteria for decision making under severe uncertainty using lower previsions. We present a new fast algorithm for nding maximal gambles. We compare its performance to existing a... Read More about Improving and benchmarking of algorithms for decision making with lower previsions.

Gaining more understanding about reservoir behavior through assimilation of breakthrough time and productivity deviation in the history matching process (2019)
Journal Article
Formentin, H. N., Almeida, F. L. R., Avansi, G. D., Maschio, C., Schiozer, D. J., Caiado, C., …Goldstein, M. (2019). Gaining more understanding about reservoir behavior through assimilation of breakthrough time and productivity deviation in the history matching process. Journal of Petroleum Science and Engineering, 173, 1080-1096. https://doi.org/10.1016/j.petrol.2018.10.045

History matching (HM) is an inverse problem where uncertainties in attributes are reduced by comparison with observed dynamic data. Typically, normalized misfit summarizes dissimilarities between observed and simulation data. Especially for long-time... Read More about Gaining more understanding about reservoir behavior through assimilation of breakthrough time and productivity deviation in the history matching process.

Evaluating betting odds and free coupons using desirability (2019)
Journal Article
Nakharutai, N., Caiado, C. C., & Troffaes, M. C. (2019). Evaluating betting odds and free coupons using desirability. International Journal of Approximate Reasoning: Uncertainty in Intelligent Systems, 106, 128-145. https://doi.org/10.1016/j.ijar.2019.01.002

In the UK betting market, bookmakers often offer a free coupon to new customers. These free coupons allow the customer to place extra bets, at lower risk, in combination with the usual betting odds. We are interested in whether a customer can exploit... Read More about Evaluating betting odds and free coupons using desirability.

Monte Carlo sampling for error propagation in linear regression and applications in isochron geochronology (2018)
Journal Article
Li, Y., Zhang, S., Hobbs, R., Caiado, C., Sproson, A., Selby, D., & Rooney, A. (2019). Monte Carlo sampling for error propagation in linear regression and applications in isochron geochronology. Science Bulletin, 64(3), 189-197. https://doi.org/10.1016/j.scib.2018.12.019

Geochronology is essential for understanding Earth’s history. The availability of precise and accurate isotopic data is increasing; hence it is crucial to develop transparent and accessible data reduction techniques and tools to transform raw mass sp... Read More about Monte Carlo sampling for error propagation in linear regression and applications in isochron geochronology.

Improved linear programming methods for checking avoiding sure loss (2018)
Journal Article
Nakharutai, N., Troffaes, M. C., & Caiado, C. C. (2018). Improved linear programming methods for checking avoiding sure loss. International Journal of Approximate Reasoning: Uncertainty in Intelligent Systems, 101, 293-310. https://doi.org/10.1016/j.ijar.2018.07.013

We review the simplex method and two interior-point methods (the affine scaling and the primal-dual) for solving linear programming problems for checking avoiding sure loss, and propose novel improvements. We exploit the structure of these problems t... Read More about Improved linear programming methods for checking avoiding sure loss.

The KDIGO acute kidney injury guidelines for cardiac surgery patients in critical care: a validation study (2018)
Journal Article
Howitt, S. H., Grant, S. W., Caiado, C., Carlson, E., Kwon, D., Dimarakis, I., …McCollum, C. (2018). The KDIGO acute kidney injury guidelines for cardiac surgery patients in critical care: a validation study. BMC Nephrology, 19(1), Article 149. https://doi.org/10.1186/s12882-018-0946-x

Background The Kidney Disease: Improving Global Outcomes (KDIGO) Acute Kidney Injury (AKI) guidelines assign the same stage of AKI to patients whether they fulfil urine output criteria, serum creatinine criteria or both criteria for that stage. This... Read More about The KDIGO acute kidney injury guidelines for cardiac surgery patients in critical care: a validation study.

Emulation of reservoir production forecast considering variation in petrophysical properties (2018)
Journal Article
Moreno, R., Avansi, G., Schiozer, D., Vernon, I., Goldstein, M., & Caiado, C. (2018). Emulation of reservoir production forecast considering variation in petrophysical properties. Journal of Petroleum Science and Engineering, 165, 711-725. https://doi.org/10.1016/j.petrol.2018.02.056

Implementation of proxy models, such as emulators might reduce the computational time required in a variety of reservoir simulation studies. By definition, an emulator uses reservoir properties as input parameters in a statistical model constructed f... Read More about Emulation of reservoir production forecast considering variation in petrophysical properties.

Validation of Three Postoperative Risk Prediction Models for Intensive Care Unit Mortality after Cardiac Surgery (2018)
Journal Article
Grant, S., Venkateswaran, R., Malagon, I., Goldstein, M., McCollum, C., Caiado, C., & Howitt, S. (2018). Validation of Three Postoperative Risk Prediction Models for Intensive Care Unit Mortality after Cardiac Surgery. Thoracic and Cardiovascular Surgeon, 66(8), 651-660. https://doi.org/10.1055/s-0037-1608897

Background: Several cardiac surgery risk prediction models based on postoperative data have been developed. However, unlike preoperative cardiac surgery risk prediction models, postoperative models are rarely externally validated or utilized by clini... Read More about Validation of Three Postoperative Risk Prediction Models for Intensive Care Unit Mortality after Cardiac Surgery.