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23rd IEEE International Conference on Machine Learning and Applications, ICMLA 2024

作者： Musto, Henry Stamate, Daniel Stahl, Daniel University of London Data Science and Soft Computing Lab Department of Computing Goldsmiths London United Kingdom School of Health Sciences The University of Manchester United Kingdom Institute of Psychiatry Psychology and Neuroscience Kings College London Department of Biostatistics London United Kingdom

ISBN: (纸本)9798350374889

Alzheimer's Disease (AD) poses significant challenges in research due to limited access to longitudinal patient data caused by privacy constraints. This study uses deep learning, specifically Variational Autoencoders (VAEs), to generate synthetic datasets that replicate real-world AD data while preserving privacy. These datasets were used to train and compare survival analysis models, including Survival XGBoost, Survival Transformers, and the Cox Proportional Hazards model. Results show that deep learning and boosting models outperform the standard Cox model in predicting the progression of AD, achieving a higher degree of accuracy. Our findings suggest that combining synthetic data with advanced machine learning models can improve predictive capabilities in healthcare research, particularly when real patient data is restricted. © 2024 IEEE.

关键词： Adversarial machine learning

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Variational Encoder Based Synthetic Alzheimer's data Generation for Deep Learning, XGBoost and Statistical Survival Analysis

Variational Encoder Based Synthetic Alzheimer's Data Generat...

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International Conference on Machine Learning and Applications (ICMLA)

作者： Henry Musto Daniel Stamate Daniel Stahl Department of Computing Goldsmiths Data Science and Soft Computing Lab University of London London United Kingdom Data Science and Soft Computing Lab Department of Computing Goldsmiths Univerisity of London and School of Health Sciences The University of Manchester United Kingdom Department of Biostatistics Institute of Psychiatry Psychology and Neuroscience Kings College London London United Kingdom

ISBN: (数字)9798350374889

ISBN: (纸本)9798350374896

关键词： Deep learning data privacy Analytical models Accuracy Computational modeling Medical services Predictive models data models Alzheimer's disease Synthetic data

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A Machine Learning Approach for Predicting Deterioration in Alzheimer's Disease

arXiv

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arXiv 2023年

作者： Musto, Henry Stamate, Daniel Pu, Ida Stahl, Daniel Data Science and Soft Computing Lab Department of Computing Goldsmith University of London London United Kingdom Department of Biostatistics and Health Informatics Institute of Psychiatry Psychology Neuroscience Kings College London London United Kingdom

This paper explores deterioration in Alzheimer’s Disease using Machine Learning. Subjects were split into two datasets based on baseline diagnosis (Cognitively Normal, Mild Cognitive Impairment), with outcome of deterioration at final visit (a binomial essentially yes/no categorisation) using data from the Alzheimer’s Disease Neuroimaging Initiative (demographics, genetics, CSF, imaging, and neuropsychological testing etc). Six machine learning models, including gradient boosting, were built, and evaluated on these datasets using a nested cross-validation procedure, with the best performing models being put through repeated nested cross-validation at 100 iterations. We were able to demonstrate good predictive ability using CART predicting which of those in the cognitively normal group deteriorated and received a worse diagnosis (AUC = 0.88). For the mild cognitive impairment group, we were able to achieve good predictive ability for deterioration with Elastic Net (AUC = 0.76). © 2023, CC BY.

关键词： Deterioration

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Balancing Accuracy and Interpretability: An R Package Assessing Complex Relationships Beyond the Cox Model and Applications to Clinical Prediction

SSRN

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SSRN 2024年

作者： Shamsutdinova, Diana Stamate, Daniel Stahl, Daniel Department of Biostatistics and Health Informatics Institute of Psychiatry Psychology and Neuroscience King’s College London London United Kingdom Data Science and Soft Computing Lab Computing Department Goldsmiths University of London United Kingdom School of Health Sciences University of Manchester Manchester United Kingdom

BackgroundAccurate and interpretable models are essential for clinical decision-making, where predictions can directly impact patient care. Machine learning (ML) survival methods can handle complex multidimensional data and achieve high accuracy but require post-hoc explanations. Traditional models such as the Cox Proportional Hazards Model (Cox-PH) are less flexible, but fast, stable, and intrinsically transparent. Moreover, ML does not always outperform Cox-PH in clinical settings, warranting a diligent model validation. We aimed to develop a set of R functions to help explore the limits of Cox-PH compared to the tree-based and deep learning survival models for clinical prediction modelling, employing ensemble learning and nested *** developed a set of R functions, publicly available as the package "survcompare". It supports Cox-PH are Cox-Lasso, and Survival Random Forest (SRF) and DeepHit are the ML alternatives, along with the ensemble methods integrating Cox-PH with SRF or DeepHit designed to isolate the marginal value of ML. The package performs a repeated nested cross-validation and tests for statistical significance of the ML’s superiority using the survival-specific performance metrics, the concordance index, time-dependent AUC-ROC and calibration *** get practical insights, we applied this methodology to clinical and simulated datasets with varying complexities and *** simulated data with non-linearities or interactions, ML models outperformed Cox-PH at sample sizes ≥500. ML superiority was also observed in imaging and high-dimensional clinical data. However, for tabular clinical data, the performance gains of ML were minimal;in some cases, regularised Cox-Lasso recovered much of the ML’s performance advantage with significantly faster computations. Ensemble methods combining Cox-PH and ML predictions were instrumental in quantifying Cox-PH’s limits and improving ML calibration. Traditional models like Cox-PH or Cox

关键词： Prediction models

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Predicting risk of dementia with machine learning and survival models using routine primary care records

Predicting risk of dementia with machine learning and surviv...

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2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Langham, John Stamate, Daniel Wu, Charlotte A. Murtagh, Fionn Morgan, Catharine Reeves, David Ashcroft, Darren Kontopantelis, Evan McMillan, Brian University of London Data Science and Soft Computing Lab Department of Computing Goldsmiths London United Kingdom The University of Manchester NIHR School for Primary Care Research Division of Population Health Health Services Research Primary Care School of Health Sciences Manchester United Kingdom

ISBN: (纸本)9781665401265

Worldwide, it is forecasted that 131.5 million people will suffer from dementia by 2050, and the annual cost of care will increase from 818 billion USD in 2016 to 2 trillion USD by 2030, with burgeoning social consequences. Given a timely prediction of a dementia outcome in patients, appropriate mitigating interventions can be applied to reduce risk. However such prediction facilities need to be made available to wider populations, and these facilities cannot rely on specialised, costly and invasive testing (such as neuroimaging, cerebrospinal fluid collection, etc which constitute important instruments used in diagnosis), for interventions to have a meaningful quantitative impact. Hence an emerging need exists for the wider application of prognostic measures which can be deployed using lower cost data sources such as longitudinal records routinely collected by general practices. This paper proposes an efficient prediction modelling approach to the risk of dementia, using CPRD data collected from GP practices in UK, and based on machine learning in particular the Gradient Boosting Machines model combined with a survival model such as the Cox Proportional Hazard, encapsulated in a semi-supervised learning and model calibration methodology. © 2021 IEEE.

关键词： Machine learning

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Predicting Alzheimer’s Disease Diagnosis Risk over Time with Survival Machine Learning on the ADNI Cohort

arXiv

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arXiv 2023年

作者： Musto, Henry Stamate, Daniel Pu, Ida Stahl, Daniel Data Science & Soft Computing Lab Computing Department Goldsmiths College University of London United Kingdom Division of Population Health Health Services Research & Primary Care School of Health Sciences University of Manchester United Kingdom Institute of Psychiatry Psychology and Neuroscience Biostatistics and Health Info-matics Department King’s College London London United Kingdom

The rise of Alzheimer’s Disease worldwide has prompted a search for efficient tools which can be used to predict deterioration in cognitive decline leading to dementia. In this paper, we explore the potential of survival machine learning as such a tool for building models capable of predicting not only deterioration but also the likely time to deterioration. We demonstrate good predictive ability (0.86 C-Index), lending support to its use in clinical investigation and prediction of Alzheimer’s Disease risk. © 2023, CC BY.

关键词： Machine learning

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Predicting Risk of Dementia with Survival Machine Learning and Statistical Methods: Results on the English Longitudinal Study of Ageing Cohort

arXiv

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arXiv 2023年

作者： Stamate, Daniel Musto, Henry Ajnakina, Olesya Stahl, Daniel Data Science & Soft Computing Lab Computing Department Goldsmiths College University of London United Kingdom Division of Population Health Health Services Research & Primary Care School of Health Sciences University of Manchester United Kingdom Institute of Psychiatry Psychology and Neuroscience Biostatistics and Health Informatics Department King’s College London United Kingdom Department of Behavioural Science and Health Institute of Epidemiology and Health Care University College London United Kingdom

Machine learning models that aim to predict dementia onset usually follow the classification methodology ignoring the time until an event happens. This study presents an alternative, using survival analysis within the context of machine learning techniques. Two survival method extensions based on machine learning algorithms of Random Forest and Elastic Net are applied to train, optimise, and validate predictive models based on the English Longitudinal Study of Ageing – ELSA cohort. The two survival machine learning models are compared with the conventional statistical Cox proportional hazard model, proving their superior predictive capability and stability on the ELSA data, as demonstrated by computationally intensive procedures such as nested cross-validation and Monte Carlo validation. This study is the first to apply survival machine learning to the ELSA data, and demonstrates in this case the superiority of AI based predictive modelling approaches over the widely employed Cox statistical approach in survival analysis. Implications, methodological considerations, and future research directions are discussed. © 2023, CC BY.

关键词： Machine learning

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A Machine Learning Approach for Predicting Deterioration in Alzheimer’s Disease

A Machine Learning Approach for Predicting Deterioration in ...

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International Conference on Machine Learning and Applications (ICMLA)

作者： Henry Musto Daniel Stamate Ida Pu Daniel Stahl Data Science and Soft Computing Lab University of London London United Kingdom Institute of Psychiatry Psychology and Neuroscience Kings College London London United Kingdom

ISBN: (纸本)9781665443388

关键词： Neuroimaging Conferences Imaging Genetics Boosting Alzheimer's disease Diseases

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Applying Deep Learning to Predicting Dementia and Mild Cognitive Impairment 16th

Applying Deep Learning to Predicting Dementia and Mild Cogni...

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16th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations, AIAI 2020

作者： Stamate, Daniel Smith, Richard Tsygancov, Ruslan Vorobev, Rostislav Langham, John Stahl, Daniel Reeves, David Data Science & Soft Computing Lab London United Kingdom Computing Department Goldsmiths University of London London United Kingdom Department of Biostatistics and Health Informatics King’s College London London United Kingdom Division of Population Health Health Services Research and Primary Care University of Manchester Manchester United Kingdom

ISBN: (纸本)9783030491857

Dementia has a large negative impact on the global healthcare and society. Diagnosis is rather challenging as there is no standardised test. The purpose of this paper is to conduct an analysis on ADNI data and determine its effectiveness for building classification models to differentiate the categories Cognitively Normal (CN), Mild Cognitive Impairment (MCI), and Dementia (DEM), based on tuning three Deep Learning models: two Multi-Layer Perceptron (MLP1 and MLP2) models and a Convolutional Bidirectional Long Short-Term Memory (ConvBLSTM) model. The results show that the MLP1 and MLP2 models accurately distinguish the DEM, MCI and CN classes, with accuracies as high as 0.86 (SD 0.01). The ConvBLSTM model was slightly less accurate but was explored in view of comparisons with the MLP models, and for future extensions of this work that will take advantage of time-related information. Although the performance of ConvBLSTM model was negatively impacted by a lack of visit code data, opportunities were identified for improvement, particularly in terms of pre-processing. © 2020, IFIP International Federation for Information Processing.

关键词： Multilayer neural networks

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A new machine learning framework for understanding the link etween cannabis use and first-episode psychosis 12

A new machine learning framework for understanding the link ...

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12th Annual Conference on Health Informatics Meets eHealth, eHealth 2018

作者： Alghamdi, Wajdi Stamate, Daniel Stahl, Daniel Zamyatin, Alexander Murray, Robin Di Forti, Marta Data Science and Soft Computing Lab Department of Computing Goldsmiths University of London United Kingdom Department of Biostatistics and Health Informatics Institute of Psychiatry Psychology and Neuroscience King's College London United Kingdom Faculty of Informatics Department of Applied Informatics National Research Tomsk State University Russia Department of Psychosis Studies Institute of Psychiatry Psychology and Neuroscience King's College London United Kingdom MRC Social Genetic and Developmental Psychiatry Centre Institute of Psychiatry Psychology and Neuroscience King's College London United Kingdom

ISBN: (纸本)9781614998570

Lately, several studies started to investigate the existence of links between cannabis use and psychotic disorders. This work proposes a refined Machine Learning framework for understanding the links between cannabis use and 1st episode psychosis. The novel framework concerns extracting predictive patterns from clinical data using optimised and post-processed models based on Gaussian Processes, Support Vector Machines, and Neural Networks algorithms. The cannabis use attributes' predictive power is investigated, and we demonstrate statistically and with ROC analysis that their presence in the dataset enhances the prediction performance of the models with respect to models built on data without these specific attributes. © 2018 The authors and IOS Press.

关键词： eHealth First-episode psychosis Gaussian processes Machine learning Neural networks Support vector machine

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