Abdelâali Hassaïne

Abdelâali Hassaïne

University of Oxford

H-index: 19

Europe-United Kingdom

Abdelâali Hassaïne Information

University

University of Oxford

Position

___

Citations(all)

1440

Citations(since 2020)

1069

Cited By

546

hIndex(all)

19

hIndex(since 2020)

16

i10Index(all)

27

i10Index(since 2020)

19

Email

University Profile Page

University of Oxford

Abdelâali Hassaïne Skills & Research Interests

Machine learning

Image processing

Health informatics

Pattern Recognition

Document Image Analysis

Top articles of Abdelâali Hassaïne

A scoping review finds a growing trend in studies validating multimorbidity patterns and identifies five broad types of validation methods

ObjectiveMultimorbidity, the presence of two or more long-term conditions, is a growing public health concern. Many studies use analytical methods to discover multimorbidity patterns from data. We aimed to review approaches used in published literature to validate these patterns.Study Design and SettingWe systematically searched PubMed and Web of Science for studies published between July 2017 and July 2023 that used analytical methods to discover multimorbidity patterns.ResultsOut of 31,617 studies returned by the searches, 172 were included. Of these, 111 studies (64%) conducted validation, the number of studies with validation increased from 53.13% (17 out of 32 studies) to 71.25% (57 out of 80 studies) in 2017-2019 to 2022-2023, respectively. Five types of validation were identified: assessing the association of multimorbidity patterns with clinical outcomes (n = 79), stability across subsamples (n …

Authors

Thamer Ba Dhafari,Alexander Pate,Narges Azadbakht,Rowena Bailey,James Rafferty,Farideh Jalali-Najafabadi,Glen P Martin,Abdelaali Hassaine,Ashley Akbari,Jane Lyons,Alan Watkins,Ronan A Lyons,Niels Peek

Published Date

2023/11/11

Stratification of diabetes in the context of comorbidities, using representation learning and topological data analysis

Diabetes is a heterogenous, multimorbid disorder with a large variation in manifestations, trajectories, and outcomes. The aim of this study is to validate a novel machine learning method for the phenotyping of diabetes in the context of comorbidities. Data from 9967 multimorbid patients with a new diagnosis of diabetes were extracted from Clinical Practice Research Datalink. First, using BEHRT (a transformer-based deep learning architecture), the embeddings corresponding to diabetes were learned. Next, topological data analysis (TDA) was carried out to test how different areas in high-dimensional manifold correspond to different risk profiles. The following endpoints were considered when profiling risk trajectories: major adverse cardiovascular events (MACE), coronary artery disease (CAD), stroke (CVA), heart failure (HF), renal failure (RF), diabetic neuropathy, peripheral arterial disease, reduced visual acuity …

Authors

Malgorzata Wamil,Abdelaali Hassaine,Shishir Rao,Yikuan Li,Mohammad Mamouei,Dexter Canoy,Milad Nazarzadeh,Zeinab Bidel,Emma Copland,Kazem Rahimi,Gholamreza Salimi-Khorshidi

Journal

Scientific Reports

Published Date

2023/7/16

Clinical outcome prediction under hypothetical interventions--a representation learning framework for counterfactual reasoning

Most machine learning (ML) models are developed for prediction only; offering no option for causal interpretation of their predictions or parameters/properties. This can hamper the health systems' ability to employ ML models in clinical decision-making processes, where the need and desire for predicting outcomes under hypothetical investigations (i.e., counterfactual reasoning/explanation) is high. In this research, we introduce a new representation learning framework (i.e., partial concept bottleneck), which considers the provision of counterfactual explanations as an embedded property of the risk model. Despite architectural changes necessary for jointly optimising for prediction accuracy and counterfactual reasoning, the accuracy of our approach is comparable to prediction-only models. Our results suggest that our proposed framework has the potential to help researchers and clinicians improve personalised care (e.g., by investigating the hypothetical differential effects of interventions)

Authors

Yikuan Li,Mohammad Mamouei,Shishir Rao,Abdelaali Hassaine,Dexter Canoy,Thomas Lukasiewicz,Kazem Rahimi,Gholamreza Salimi-Khorshidi

Journal

arXiv preprint arXiv:2205.07234

Published Date

2022/5/15

Blood pressure and risk of venous thromboembolism: a cohort analysis of 5.5 million UK adults and Mendelian randomization studies

Aims Evidence for the effect of elevated blood pressure (BP) on the risk of venous thromboembolism (VTE) has been conflicting. We sought to assess the association between systolic BP and the risk of VTE. Methods and results Three complementary studies comprising an observational cohort analysis, a one-sample and two-sample Mendelian randomization were conducted using data from 5 588 280 patients registered in the Clinical Practice Research Datalink (CPRD) dataset and 432 173 UK Biobank participants with valid genetic data. Summary statistics of International Network on Venous Thrombosis genome-wide association meta-analysis was used for two-sample Mendelian randomization. The primary outcome was the first occurrence of VTE event, identified from hospital discharge reports, death registers, and/or primary care records. In the CPRD cohort, 104 017(1.9 …

Authors

Milad Nazarzadeh,Zeinab Bidel,Hamid Mohseni,Dexter Canoy,Ana-Catarina Pinho-Gomes,Abdelaali Hassaine,Abbas Dehghan,David-Alexandre Tregouet,Nicholas L Smith,Kazem Rahimi,INVENT Consortium

Journal

Cardiovascular Research

Published Date

2023/3/1

An explainable Transformer-based deep learning model for the prediction of incident heart failure

Predicting the incidence of complex chronic conditions such as heart failure is challenging. Deep learning models applied to rich electronic health records may improve prediction but remain unexplainable hampering their wider use in medical practice. We aimed to develop a deep-learning framework for accurate and yet explainable prediction of 6-month incident heart failure (HF). Using 100,071 patients from longitudinal linked electronic health records across the U.K., we applied a novel Transformer-based risk model using all community and hospital diagnoses and medications contextualized within the age and calendar year for each patient's clinical encounter. Feature importance was investigated with an ablation analysis to compare model performance when alternatively removing features and by comparing the variability of temporal representations. A post-hoc perturbation technique was conducted to …

Authors

Shishir Rao,Yikuan Li,Rema Ramakrishnan,Abdelaali Hassaine,Dexter Canoy,John Cleland,Thomas Lukasiewicz,Gholamreza Salimi-Khorshidi,Kazem Rahimi

Journal

ieee journal of biomedical and health informatics

Published Date

2022/2/7

COVID-19 trajectories among 57 million adults in England: a cohort study using electronic health records

BackgroundUpdatable estimates of COVID-19 onset, progression, and trajectories underpin pandemic mitigation efforts. To identify and characterise disease trajectories, we aimed to define and validate ten COVID-19 phenotypes from nationwide linked electronic health records (EHR) using an extensible framework.MethodsIn this cohort study, we used eight linked National Health Service (NHS) datasets for people in England alive on Jan 23, 2020. Data on COVID-19 testing, vaccination, primary and secondary care records, and death registrations were collected until Nov 30, 2021. We defined ten COVID-19 phenotypes reflecting clinically relevant stages of disease severity and encompassing five categories: positive SARS-CoV-2 test, primary care diagnosis, hospital admission, ventilation modality (four phenotypes), and death (three phenotypes). We constructed patient trajectories illustrating transition frequency …

Authors

Johan H Thygesen,Christopher Tomlinson,Sam Hollings,Mehrdad A Mizani,Alex Handy,Ashley Akbari,Amitava Banerjee,Jennifer Cooper,Alvina G Lai,Kezhi Li,Bilal A Mateen,Naveed Sattar,Reecha Sofat,Ana Torralbo,Honghan Wu,Angela Wood,Jonathan AC Sterne,Christina Pagel,William N Whiteley,Cathie Sudlow,Harry Hemingway,Spiros Denaxas,Hoda Abbasizanjani,Nida Ahmed,Badar Ahmed,Abdul Qadr Akinoso-Imran,Elias Allara,Freya Allery,Emanuele Di Angelantonio,Mark Ashworth,Vandana Ayyar-Gupta,Sonya Babu-Narayan,Seb Bacon,Steve Ball,Ami Banerjee,Mark Barber,Jessica Barrett,Marion Bennie,Colin Berry,Jennifer Beveridge,Ewan Birney,Lana Bojanić,Thomas Bolton,Anna Bone,Jon Boyle,Tasanee Braithwaite,Ben Bray,Norman Briffa,David Brind,Katherine Brown,Maya Buch,Dexter Canoy,Massimo Caputo,Raymond Carragher,Alan Carson,Genevieve Cezard,Jen-Yu Amy Chang,Kate Cheema,Richard Chin,Yogini Chudasama,Emma Copland,Rebecca Crallan,Rachel Cripps,David Cromwell,Vasa Curcin,Gwenetta Curry,Caroline Dale,John Danesh,Jayati Das-Munshi,Ashkan Dashtban,Alun Davies,Joanna Davies,Gareth Davies,Neil Davies,Joshua Day,Antonella Delmestri,Rachel Denholm,John Dennis,Alastair Denniston,Salil Deo,Baljean Dhillon,Annemarie Docherty,Tim Dong,Abdel Douiri,Johnny Downs,Alexandru Dregan,Elizabeth A Ellins,Martha Elwenspoek,Fabian Falck,Florian Falter,Yat Yi Fan,Joseph Firth,Lorna Fraser,Rocco Friebel,Amir Gavrieli,Moritz Gerstung,Ruth Gilbert,Clare Gillies,Myer Glickman,Ben Goldacre,Raph Goldacre,Felix Greaves,Mark Green,Luca Grieco,Rowena Griffiths,Deepti Gurdasani,Julian Halcox,Nick Hall,Tuankasfee Hama,Anna Hansell,Pia Hardelid,Flavien Hardy,Daniel Harris,Camille Harrison,Katie Harron,Abdelaali Hassaine,Lamiece Hassan,Russell Healey,Angela Henderson,Naomi Herz,Johannes Heyl,Mira Hidajat,Irene Higginson,Rosie Hinchliffe,Julia Hippisley-Cox,Frederick Ho,Mevhibe Hocaoglu,Elsie Horne,David Hughes,Ben Humberstone,Mike Inouye,Samantha Ip,Nazrul Islam,Caroline Jackson,David Jenkins,Xiyun Jiang,Shane Johnson,Umesh Kadam,Costas Kallis,Zainab Karim,Jake Kasan,Michalis Katsoulis,Kim Kavanagh,Frank Kee,Spencer Keene,Seamus Kent,Sara Khalid,Anthony Khawaja,Kamlesh Khunti,Richard Killick

Journal

The Lancet Digital Health

Published Date

2022/7/1

Sparse Principal Component Analysis for Statistical Inference in the Presence of Multicollinearity: A Case Study of Environmental Exposures on All-cause Mortality in UK Biobank

Multicollinearity refers to the presence of collinearity between multiple variables and renders the results of statistical inference erroneous (Type II error). This is particularly important in environmental health research where multicollinearity can hinder inference. To address this, correlated variables are often excluded from the analysis, limiting the discovery of new associations. An alternative approach to address this problem is the use of principal component analysis. This method, combines and projects a group of correlated variables onto a new orthogonal space. While this resolves the multicollinearity problem, it poses another challenge in relation to interpretability of results. Standard hypothesis testing methods can be used to evaluate the association of projected predictors, called principal components, with the outcomes of interest, however, there is no established way to trace the significance of principal components back to individual variables. To address this problem, we investigated the use of sparse principal component analysis which enforces a parsimonious projection. We hypothesise that this parsimony could facilitate the interpretability of findings. To this end, we investigated the association of 20 environmental predictors with all-cause mortality adjusting for demographic, socioeconomic, physiological, and behavioural factors. The study was conducted in a cohort of 379,690 individuals in the UK. During an average follow-up of 8.05 years (3,055,166 total person-years), 14,996 deaths were observed. We used Cox regression models to estimate the hazard ratio (HR) and 95% confidence intervals (CI). The Cox models were fitted to the …

Authors

Mohammad Mamouei,Yajie Zhu,Milad Nazarzadeh,Abdelaali Hassaine,Gholamreza Salimi-Khorshidi,Yutong Cai,Kazem Rahimi

Published Date

2022/2/2

Systolic Blood Pressure and Cardiovascular Risk in Patients With Diabetes: A Prospective Cohort Study

Background Whether the association between systolic blood pressure (SBP) and risk of cardiovascular disease is monotonic or whether there is a nadir of optimal blood pressure remains controversial. We investigated the association between SBP and cardiovascular events in patients with diabetes across the full spectrum of SBP. Methods A cohort of 49 000 individuals with diabetes aged 50 to 90 years between 1990 and 2005 was identified from linked electronic health records in the United Kingdom. Associations between SBP and cardiovascular outcomes (ischemic heart disease, heart failure, stroke, and cardiovascular death) were analyzed using a deep learning approach. Results Over a median follow-up of 7.3 years, 16 378 cardiovascular events were observed. The relationship between SBP and cardiovascular events followed a monotonic pattern, with the group with the lowest baseline SBP of <120 …

Authors

Shishir Rao,Yikuan Li,Milad Nazarzadeh,Dexter Canoy,Mohammad Mamouei,Abdelaali Hassaine,Gholamreza Salimi-Khorshidi,Kazem Rahimi

Journal

Hypertension

Published Date

2023/3

Uncertainty-Aware Interpretable Deep Learning for Slum Mapping and Monitoring

Over a billion people live in slums, with poor sanitation, education, property rights and working conditions having a direct impact on current residents and future generations. Slum mapping is one of the key problems concerning slums. Policymakers need to delineate slum settlements to make informed decisions about infrastructure development and allocation of aid. A wide variety of machine learning and deep learning methods have been applied to multispectral satellite images to map slums with outstanding performance. Since the physical and visual manifestation of slums significantly varies with geographical region and comprehensive slum maps are rare, it is important to quantify the uncertainty of predictions for reliable and confident application of models to downstream tasks. In this study, we train a U-Net model with Monte Carlo Dropout (MCD) on 13-band Sentinel-2 images, allowing us to calculate pixelwise uncertainty in the predictions. The obtained outcomes show that the proposed model outperforms the previous state-of-the-art model, having both higher AUPRC and lower uncertainty when tested on unseen geographical regions of Mumbai using the regional testing framework introduced in this study. We also use SHapley Additive exPlanations (SHAP) values to investigate how the different features contribute to our model’s predictions which indicate a certain shortwave infrared image band is a powerful feature for determining the locations of slums within images. With our results, we demonstrate the usefulness of including an uncertainty quantification approach in detecting slum area changes over time.

Authors

Thomas Fisher,Harry Gibson,Yunzhe Liu,Moloud Abdar,Marius Posa,Gholamreza Salimi-Khorshidi,Abdelaali Hassaine,Yutong Cai,Kazem Rahimi,Mohammad Mamouei

Journal

Remote Sensing

Published Date

2022/6/26

Validation of risk prediction models applied to longitudinal electronic health record data for the prediction of major cardiovascular events in the presence of data shifts

Aims Deep learning has dominated predictive modelling across different fields, but in medicine it has been met with mixed reception. In clinical practice, simple, statistical models and risk scores continue to inform cardiovascular disease risk predictions. This is due in part to the knowledge gap about how deep learning models perform in practice when they are subject to dynamic data shifts; a key criterion that common internal validation procedures do not address. We evaluated the performance of a novel deep learning model, BEHRT, under data shifts and compared it with several ML-based and established risk models. Methods and results Using linked electronic health records of 1.1 million patients across England aged at least 35 years between 1985 and 2015, we replicated three established statistical models for predicting 5-year risk of incident heart failure, stroke, and …

Authors

Yikuan Li,Gholamreza Salimi-Khorshidi,Shishir Rao,Dexter Canoy,Abdelaali Hassaine,Thomas Lukasiewicz,Kazem Rahimi,Mohammad Mamouei

Journal

European Heart Journal-Digital Health

Published Date

2022/12/22

Targeted-BEHRT: Deep learning for observational causal inference on longitudinal electronic health records

Observational causal inference is useful for decision-making in medicine when randomized clinical trials (RCTs) are infeasible or nongeneralizable. However, traditional approaches do not always deliver unconfounded causal conclusions in practice. The rise of “doubly robust” nonparametric tools coupled with the growth of deep learning for capturing rich representations of multimodal data offers a unique opportunity to develop and test such models for causal inference on comprehensive electronic health records (EHRs). In this article, we investigate causal modeling of an RCT-established causal association: the effect of classes of antihypertensive on incident cancer risk. We develop a transformer-based model, targeted bidirectional EHR transformer (T-BEHRT) coupled with doubly robust estimation to estimate average risk ratio (RR). We compare our model to benchmark statistical and deep learning models for …

Authors

Shishir Rao,Mohammad Mamouei,Gholamreza Salimi-Khorshidi,Yikuan Li,Rema Ramakrishnan,Abdelaali Hassaine,Dexter Canoy,Kazem Rahimi

Journal

IEEE Transactions on Neural Networks and Learning Systems

Published Date

2022/6/23

Investigating the association of environmental exposures and all-cause mortality in the UK Biobank using sparse principal component analysis

Multicollinearity refers to the presence of collinearity between multiple variables and renders the results of statistical inference erroneous (Type II error). This is particularly important in environmental health research where multicollinearity can hinder inference. To address this, correlated variables are often excluded from the analysis, limiting the discovery of new associations. An alternative approach to address this problem is the use of principal component analysis. This method, combines and projects a group of correlated variables onto a new orthogonal space. While this resolves the multicollinearity problem, it poses another challenge in relation to interpretability of results. Standard hypothesis testing methods can be used to evaluate the association of projected predictors, called principal components, with the outcomes of interest, however, there is no established way to trace the significance of principal …

Authors

Mohammad Mamouei,Yajie Zhu,Milad Nazarzadeh,Abdelaali Hassaine,Gholamreza Salimi-Khorshidi,Yutong Cai,Kazem Rahimi

Journal

Scientific Reports

Published Date

2022/6/2

Hi-BEHRT: Hierarchical Transformer-based model for accurate prediction of clinical events using multimodal longitudinal electronic health records

Electronic health records (EHR) represent a holistic overview of patients’ trajectories. Their increasing availability has fueled new hopes to leverage them and develop accurate risk prediction models for a wide range of diseases. Given the complex interrelationships of medical records and patient outcomes, deep learning models have shown clear merits in achieving this goal. However, a key limitation of current study remains their capacity in processing long sequences, and long sequence modelling and its application in the context of healthcare and EHR remains unexplored. Capturing the whole history of medical encounters is expected to lead to more accurate predictions, but the inclusion of records collected for decades and from multiple resources can inevitably exceed the receptive field of the most existing deep learning architectures. This can result in missing crucial, long-term dependencies. To address this …

Authors

Yikuan Li,Mohammad Mamouei,Gholamreza Salimi-Khorshidi,Shishir Rao,Abdelaali Hassaine,Dexter Canoy,Thomas Lukasiewicz,Kazem Rahimi

Journal

IEEE journal of biomedical and health informatics

Published Date

2022/11/25

Deep learning with uncertainty quantification for slum mapping using satellite imagery

Over a billion people live in slums, with poor sanitation, education, property rights and working conditions having direct impact on current residents and future generations. A key problem in relation to slums is slum mapping. Without delineations of where all slum settlements are, informed decisions cannot be made by policymakers in order to benefit the most in need. Satellite images have been used in combination with machine learning models to try and fill the gap in data availability of slum locations. Deep learning has been used on RGB images with some success but since labeled satellite images of slums are relatively low quality and the physical/visual manifestation of slums significantly varies within and across countries, it is important to quantify the uncertainty of predictions for reliable application in downstream tasks. Our solution is to train Monte Carlo dropout U-Net models on multispectral 13-band Sentinel-2 images from which we can calculate pixelwise epistemic (model) and aleatoric (data) uncertainty in our predictions. We trained our model on labelled images of Mumbai and verified our epistemic and aleatoric uncertainty quantification approach using altered models trained on modified datasets. We also used SHAP values to investigate how the different features contribute towards the model’s predictions and this showed that certain short-wave infrared and red-edge image bands are powerful features for determining the locations of slums within images. Having created our model with uncertainty quantification, in the future it can be applied to downstream tasks and decision-makers will know where predictions have been made …

Authors

Thomas Fisher,Harry Gibson,Gholamreza Salimi-Khorshidi,Abdelaali Hassaine,Yutong Cai,Kazem Rahimi,Mohammad Mamouei

Published Date

2021/8/9

Transfer Learning in Electronic Health Records through Clinical Concept Embedding

Deep learning models have shown tremendous potential in learning representations, which are able to capture some key properties of the data. This makes them great candidates for transfer learning: Exploiting commonalities between different learning tasks to transfer knowledge from one task to another. Electronic health records (EHR) research is one of the domains that has witnessed a growing number of deep learning techniques employed for learning clinically-meaningful representations of medical concepts (such as diseases and medications). Despite this growth, the approaches to benchmark and assess such learned representations (or, embeddings) is under-investigated; this can be a big issue when such embeddings are shared to facilitate transfer learning. In this study, we aim to (1) train some of the most prominent disease embedding techniques on a comprehensive EHR data from 3.1 million patients, (2) employ qualitative and quantitative evaluation techniques to assess these embeddings, and (3) provide pre-trained disease embeddings for transfer learning. This study can be the first comprehensive approach for clinical concept embedding evaluation and can be applied to any embedding techniques and for any EHR concept.

Authors

Jose Roberto Ayala Solares,Yajie Zhu,Abdelaali Hassaine,Shishir Rao,Yikuan Li,Mohammad Mamouei,Dexter Canoy,Kazem Rahimi,Gholamreza Salimi-Khorshidi

Journal

arXiv preprint arXiv:2107.12919

Published Date

2021/7/27

Risk factor identification for incident heart failure using neural network distillation and variable selection

Recent evidence shows that deep learning models trained on electronic health records from millions of patients can deliver substantially more accurate predictions of risk compared to their statistical counterparts. While this provides an important opportunity for improving clinical decision-making, the lack of interpretability is a major barrier to the incorporation of these black-box models in routine care, limiting their trustworthiness and preventing further hypothesis-testing investigations. In this study, we propose two methods, namely, model distillation and variable selection, to untangle hidden patterns learned by an established deep learning model (BEHRT) for risk association identification. Due to the clinical importance and diversity of heart failure as a phenotype, it was used to showcase the merits of the proposed methods. A cohort with 788,880 (8.3% incident heart failure) patients was considered for the study. Model distillation identified 598 and 379 diseases that were associated and dissociated with heart failure at the population level, respectively. While the associations were broadly consistent with prior knowledge, our method also highlighted several less appreciated links that are worth further investigation. In addition to these important population-level insights, we developed an approach to individual-level interpretation to take account of varying manifestation of heart failure in clinical practice. This was achieved through variable selection by detecting a minimal set of encounters that can maximally preserve the accuracy of prediction for individuals. Our proposed work provides a discovery-enabling tool to identify risk factors in both …

Authors

Yikuan Li,Shishir Rao,Mohammad Mamouei,Gholamreza Salimi-Khorshidi,Dexter Canoy,Abdelaali Hassaine,Thomas Lukasiewicz,Kazem Rahimi

Journal

arXiv preprint arXiv:2102.12936

Published Date

2021/2/17

A Robust Method for Text, Line, and Word Segmentation for Historical Arabic Manuscripts

The segmentation of old documents is a crucial phase for reading and understanding the content of a document automatically. Also, the extraction of words and phrases in a document needs segmentation of each line and word. But, the variations of text lines directions throughout the same document and overlapping characters between two or more text lines, especially in Arabic manuscripts, are the problems that usually found in such documents. For that, this chapter proposes an approach for text segmentation as well as line and word for historical Arabic manuscripts. First, text segmentation is realized using an encoder-decoder deep model to segment the main text and side text in the image. The model has been trained on two Arabic manuscripts dataset including Bukhari and RASM2018 datasets. Then, the segmentation of lines using a smoothing approach followed by thresholding determined …

Authors

Omar Elharrouss,Somaya Al-Maadeed,Jihad Mohamad Alja’am,Abdelaali Hassaine

Journal

Data Analytics for Cultural Heritage: Current Trends and Concepts

Published Date

2021

Association between cardiometabolic disease multimorbidity and all-cause mortality in 2 million women and men registered in UK general practices

Background Myocardial infarction (MI), stroke and diabetes share underlying risk factors and commonalities in clinical management. We examined if their combined impact on mortality is proportional, amplified or less than the expected risk separately of each disease and whether the excess risk is explained by their associated comorbidities. Methods Using large-scale electronic health records, we identified 2,007,731 eligible patients (51% women) and registered with general practices in the UK and extracted clinical information including diagnosis of myocardial infarction (MI), stroke, diabetes and 53 other long-term conditions before 2005 (study baseline). We used Cox regression to determine the risk of all-cause mortality with age as the underlying time variable and tested for excess risk due to interaction between cardiometabolic conditions …

Authors

Dexter Canoy,Jenny Tran,Mariagrazia Zottoli,Rema Ramakrishnan,Abdelaali Hassaine,Shishir Rao,Yikuan Li,Gholamreza Salimi-Khorshidi,Robyn Norton,Kazem Rahimi

Journal

BMC medicine

Published Date

2021/12

Deep Bayesian Gaussian processes for uncertainty estimation in electronic health records

One major impediment to the wider use of deep learning for clinical decision making is the difficulty of assigning a level of confidence to model predictions. Currently, deep Bayesian neural networks and sparse Gaussian processes are the main two scalable uncertainty estimation methods. However, deep Bayesian neural networks suffer from lack of expressiveness, and more expressive models such as deep kernel learning, which is an extension of sparse Gaussian process, captures only the uncertainty from the higher-level latent space. Therefore, the deep learning model under it lacks interpretability and ignores uncertainty from the raw data. In this paper, we merge features of the deep Bayesian learning framework with deep kernel learning to leverage the strengths of both methods for a more comprehensive uncertainty estimation. Through a series of experiments on predicting the first incidence of heart failure …

Authors

Yikuan Li,Shishir Rao,Abdelaali Hassaine,Rema Ramakrishnan,Dexter Canoy,Gholamreza Salimi-Khorshidi,Mohammad Mamouei,Thomas Lukasiewicz,Kazem Rahimi

Journal

Scientific reports

Published Date

2021/10/19

BEHRT-HF: an interpretable transformer-based, deep learning model for prediction of incident heart failure

Background/Introduction Predicting incident heart failure has been challenging. Deep learning models when applied to rich electronic health records (EHR) offer some theoretical advantages. However, empirical evidence for their superior performance is limited and they remain commonly uninterpretable, hampering their wider use in medical practice. Purpose We developed a deep learning framework for more accurate and yet interpretable prediction of incident heart failure. Methods We used longitudinally linked EHR from practices across England, involving 100,071 patients, 13% of whom had been diagnosed with incident heart failure during follow-up. We investigated the predictive performance of a novel transformer deep learning model, “Transformer for Heart Failure” (BEHRT-HF), and validated it using both an external held-out dataset and an …

Authors

S Rao,Y Li,R Ramakrishnan,A Hassaine,D Canoy,Y Zhu,G Salimi-Khorshidi,K Rahimi

Journal

European Heart Journal

Published Date

2020/11

Learning multimorbidity patterns from electronic health records using non-negative matrix factorisation

Multimorbidity, or the presence of several medical conditions in the same individual, has been increasing in the population — both in absolute and relative terms. Nevertheless, multimorbidity remains poorly understood, and the evidence from existing research to describe its burden, determinants and consequences has been limited. Previous studies attempting to understand multimorbidity patterns are often cross-sectional and do not explicitly account for multimorbidity patterns’ evolution over time; some of them are based on small datasets and/or use arbitrary and narrow age ranges; and those that employed advanced models, usually lack appropriate benchmarking and validations. In this study, we (1) introduce a novel approach for using Non-negative Matrix Factorisation (NMF) for temporal phenotyping (i.e., simultaneously mining disease clusters and their trajectories); (2) provide quantitative metrics for the …

Authors

Abdelaali Hassaine,Dexter Canoy,Jose Roberto Ayala Solares,Yajie Zhu,Shishir Rao,Yikuan Li,Mariagrazia Zottoli,Kazem Rahimi,Gholamreza Salimi-Khorshidi

Journal

Journal of Biomedical Informatics

Published Date

2020/12/1

Plasma lipids and risk of aortic valve stenosis: a Mendelian randomization study

Aims Aortic valve stenosis is commonly considered a degenerative disorder with no recommended preventive intervention, with only valve replacement surgery or catheter intervention as treatment options. We sought to assess the causal association between exposure to lipid levels and risk of aortic stenosis. Methods and results Causality of association was assessed using two-sample Mendelian randomization framework through different statistical methods. We retrieved summary estimations of 157 genetic variants that have been shown to be associated with plasma lipid levels in the Global Lipids Genetics Consortium that included 188 577 participants, mostly European ancestry, and genetic association with aortic stenosis as the main outcome from a total of 432 173 participants in the UK Biobank. Secondary negative control outcomes included aortic regurgitation and …

Authors

Milad Nazarzadeh,Ana-Catarina Pinho-Gomes,Zeinab Bidel,Abbas Dehghan,Dexter Canoy,Abdelaali Hassaine,Jose Roberto Ayala Solares,Gholamreza Salimi-Khorshidi,George Davey Smith,Catherine M Otto,Kazem Rahimi

Journal

European heart journal

Published Date

2020/10/21

Untangling the complexity of multimorbidity with machine learning

The prevalence of multimorbidity has been increasing in recent years, posing a major burden for health care delivery and service. Understanding its determinants and impact is proving to be a challenge yet it offers new opportunities for research to go beyond the study of diseases in isolation. In this paper, we review how the field of machine learning provides many tools for addressing research challenges in multimorbidity. We highlight recent advances in promising methods such as matrix factorisation, deep learning, and topological data analysis and how these can take multimorbidity research beyond cross-sectional, expert-driven or confirmatory approaches to gain a better understanding of evolving patterns of multimorbidity. We discuss the challenges and opportunities of machine learning to identify likely causal links between previously poorly understood disease associations while giving an estimate of the …

Authors

Abdelaali Hassaine,Gholamreza Salimi-Khorshidi,Dexter Canoy,Kazem Rahimi

Published Date

2020/9/1

BEHRT: transformer for electronic health records

Today, despite decades of developments in medicine and the growing interest in precision healthcare, vast majority of diagnoses happen once patients begin to show noticeable signs of illness. Early indication and detection of diseases, however, can provide patients and carers with the chance of early intervention, better disease management, and efficient allocation of healthcare resources. The latest developments in machine learning (including deep learning) provides a great opportunity to address this unmet need. In this study, we introduce BEHRT: A deep neural sequence transduction model for electronic health records (EHR), capable of simultaneously predicting the likelihood of 301 conditions in one’s future visits. When trained and evaluated on the data from nearly 1.6 million individuals, BEHRT shows a striking improvement of 8.0–13.2% (in terms of average precision scores for different tasks), over the …

Authors

Yikuan Li,Shishir Rao,José Roberto Ayala Solares,Abdelaali Hassaine,Rema Ramakrishnan,Dexter Canoy,Yajie Zhu,Kazem Rahimi,Gholamreza Salimi-Khorshidi

Journal

Scientific reports

Published Date

2020/4/28

An interpretable model for incident heart failure prediction with uncertainty estimation

Background Forecasting incident heart failure is a critical demand for prevention. Recent research suggested the superior performance of deep learning models on the prediction tasks using electronic health records. However, even with a relatively accurate predictive performance, the major impediments to the wider use of deep learning models for clinical decision making are the difficulties of assigning a level of confidence to model predictions and the interpretability of predictions. Purpose We aimed to develop a deep learning framework for more accurate incident heart failure prediction, with provision of measures of uncertainty and interpretability. Methods We used a longitudinal linked electronic health records dataset, Clinical Practice Research Datalink, involving 788,880 patients, 8.3% of whom had an incident heart failure diagnosis. To embed the …

Authors

Y Li,S Rao,A Hassaine,R Ramakrishnan,Y Zhu,D Canoy,T Lukasiewicz,G Salimi-Khorshidi,K Rahimi

Journal

European Heart Journal

Published Date

2020/11

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Abdelâali Hassaïne FAQs

What is Abdelâali Hassaïne's h-index at University of Oxford?

The h-index of Abdelâali Hassaïne has been 16 since 2020 and 19 in total.

What are Abdelâali Hassaïne's top articles?

The articles with the titles of

A scoping review finds a growing trend in studies validating multimorbidity patterns and identifies five broad types of validation methods

Stratification of diabetes in the context of comorbidities, using representation learning and topological data analysis

Clinical outcome prediction under hypothetical interventions--a representation learning framework for counterfactual reasoning

Blood pressure and risk of venous thromboembolism: a cohort analysis of 5.5 million UK adults and Mendelian randomization studies

An explainable Transformer-based deep learning model for the prediction of incident heart failure

COVID-19 trajectories among 57 million adults in England: a cohort study using electronic health records

Sparse Principal Component Analysis for Statistical Inference in the Presence of Multicollinearity: A Case Study of Environmental Exposures on All-cause Mortality in UK Biobank

Systolic Blood Pressure and Cardiovascular Risk in Patients With Diabetes: A Prospective Cohort Study

...

are the top articles of Abdelâali Hassaïne at University of Oxford.

What are Abdelâali Hassaïne's research interests?

The research interests of Abdelâali Hassaïne are: Machine learning, Image processing, Health informatics, Pattern Recognition, Document Image Analysis

What is Abdelâali Hassaïne's total number of citations?

Abdelâali Hassaïne has 1,440 citations in total.

What are the co-authors of Abdelâali Hassaïne?

The co-authors of Abdelâali Hassaïne are Kazem Rahimi, Thomas Lukasiewicz, Abbes Amira, Ahmed Bouridane, Somaya Al-maadeed, Reza Khorshidi (Gholamreza Salimi-Khorshidi).

    Co-Authors

    H-index: 80
    Kazem Rahimi

    Kazem Rahimi

    University of Oxford

    H-index: 53
    Thomas Lukasiewicz

    Thomas Lukasiewicz

    University of Oxford

    H-index: 48
    Abbes Amira

    Abbes Amira

    De Montfort University

    H-index: 46
    Ahmed Bouridane

    Ahmed Bouridane

    Northumbria University

    H-index: 43
    Somaya Al-maadeed

    Somaya Al-maadeed

    Qatar University

    H-index: 33
    Reza Khorshidi (Gholamreza Salimi-Khorshidi)

    Reza Khorshidi (Gholamreza Salimi-Khorshidi)

    University of Oxford

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