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Combined supervised and unsupervised learning to identify subclasses of disease for better prediction
This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonDisease subtyping, which aids in the development of personalised treatments, remains a challenge in data analysis because of the many different ways to group patients based upon their data. However, if I can identify subclasses of disease, this will help to develop better models that are more specific to individuals and should therefore improve prediction and understanding of the underlying characteristics of the disease in question. In addition, patients might suffer from multiple disease complications. Models that are tailored to individuals could improve both prediction of multiple complications and understanding of underlying disease characteristics. However, AI models can become outdated over time due to either sudden changes in the underlying data, such as those caused by new measurement methods, or incremental changes, such as the ageing of the study population. This thesis proposes a new algorithm that integrates consensus clustering methods with classification in order to overcome issues with sample bias. The method was tested on a freely available dataset of real-world breast cancer cases and data from a London hospital on systemic sclerosis, a rare and potentially fatal condition. The results show that nearest consensus clustering classification improves accuracy and prediction significantly when this algorithm is compared with competitive similar methods. In addition, this thesis proposes a new algorithm that integrates latent class models with classification. The new algorithm uses latent class models to cluster patients within groups; this results in improved classification and aids in the understanding of the underlying differences of the discovered groups. The method was tested on data from patients with systemic sclerosis (SSc), a rare and potentially fatal condition, and coronary heart disease. Results show that the latent class multi-label classification (MLC) model improves accuracy when compared with competitive similar methods. Finally, this thesis implemented the updated concept drift method (DDM) to monitor AI models over time and detect drifts when they occur. The method was tested on data from patients with SSc and patients with coronavirus disease (COVID)
Machine learning techniques for arrhythmic risk stratification: a review of the literature
Ventricular arrhythmias (VAs) and sudden cardiac death (SCD) are significant adverse events that affect the morbidity and mortality of both the general population and patients with predisposing cardiovascular risk factors. Currently, conventional disease-specific scores are used for risk stratification purposes. However, these risk scores have several limitations, including variations among validation cohorts, the inclusion of a limited number of predictors while omitting important variables, as well as hidden relationships between predictors. Machine learning (ML) techniques are based on algorithms that describe intervariable relationships. Recent studies have implemented ML techniques to construct models for the prediction of fatal VAs. However, the application of ML study findings is limited by the absence of established frameworks for its implementation, in addition to clinicians’ unfamiliarity with ML techniques. This review, therefore, aims to provide an accessible and easy-to-understand summary of the existing evidence about the use of ML techniques in the prediction of VAs. Our findings suggest that ML algorithms improve arrhythmic prediction performance in different clinical settings. However, it should be emphasized that prospective studies comparing ML algorithms to conventional risk models are needed while a regulatory framework is required prior to their implementation in clinical practice
Temporal-spatial Correlation Attention Network for Clinical Data Analysis in Intensive Care Unit
In recent years, medical information technology has made it possible for
electronic health record (EHR) to store fairly complete clinical data. This has
brought health care into the era of "big data". However, medical data are often
sparse and strongly correlated, which means that medical problems cannot be
solved effectively. With the rapid development of deep learning in recent
years, it has provided opportunities for the use of big data in healthcare. In
this paper, we propose a temporal-saptial correlation attention network (TSCAN)
to handle some clinical characteristic prediction problems, such as predicting
death, predicting length of stay, detecting physiologic decline, and
classifying phenotypes. Based on the design of the attention mechanism model,
our approach can effectively remove irrelevant items in clinical data and
irrelevant nodes in time according to different tasks, so as to obtain more
accurate prediction results. Our method can also find key clinical indicators
of important outcomes that can be used to improve treatment options. Our
experiments use information from the Medical Information Mart for Intensive
Care (MIMIC-IV) database, which is open to the public. Finally, we have
achieved significant performance benefits of 2.0\% (metric) compared to other
SOTA prediction methods. We achieved a staggering 90.7\% on mortality rate,
45.1\% on length of stay. The source code can be find:
\url{https://github.com/yuyuheintju/TSCAN}
Data Science in Healthcare
Data science is an interdisciplinary field that applies numerous techniques, such as machine learning, neural networks, and deep learning, to create value based on extracting knowledge and insights from available data. Advances in data science have a significant impact on healthcare. While advances in the sharing of medical information result in better and earlier diagnoses as well as more patient-tailored treatments, information management is also affected by trends such as increased patient centricity (with shared decision making), self-care (e.g., using wearables), and integrated care delivery. The delivery of health services is being revolutionized through the sharing and integration of health data across organizational boundaries. Via data science, researchers can deliver new approaches to merge, analyze, and process complex data and gain more actionable insights, understanding, and knowledge at the individual and population levels. This Special Issue focuses on how data science is used in healthcare (e.g., through predictive modeling) and on related topics, such as data sharing and data management
QXAI: Explainable AI Framework for Quantitative Analysis in Patient Monitoring Systems
Artificial Intelligence techniques can be used to classify a patient's
physical activities and predict vital signs for remote patient monitoring.
Regression analysis based on non-linear models like deep learning models has
limited explainability due to its black-box nature. This can require
decision-makers to make blind leaps of faith based on non-linear model results,
especially in healthcare applications. In non-invasive monitoring, patient data
from tracking sensors and their predisposing clinical attributes act as input
features for predicting future vital signs. Explaining the contributions of
various features to the overall output of the monitoring application is
critical for a clinician's decision-making. In this study, an Explainable AI
for Quantitative analysis (QXAI) framework is proposed with post-hoc model
explainability and intrinsic explainability for regression and classification
tasks in a supervised learning approach. This was achieved by utilizing the
Shapley values concept and incorporating attention mechanisms in deep learning
models. We adopted the artificial neural networks (ANN) and attention-based
Bidirectional LSTM (BiLSTM) models for the prediction of heart rate and
classification of physical activities based on sensor data. The deep learning
models achieved state-of-the-art results in both prediction and classification
tasks. Global explanation and local explanation were conducted on input data to
understand the feature contribution of various patient data. The proposed QXAI
framework was evaluated using PPG-DaLiA data to predict heart rate and mobile
health (MHEALTH) data to classify physical activities based on sensor data.
Monte Carlo approximation was applied to the framework to overcome the time
complexity and high computation power requirements required for Shapley value
calculations.Comment: This work has been submitted to the ELSEVIER for possible
publication. Copyright may be transferred without notice, after which this
version may no longer be accessibl
Multi Disease Prediction Using HDO Machine Learning Approach
Several machine learning approaches can do predictive analytics on vast volumes of information in various sectors. Predictive analytics in health care is a challenging task. Still, it may ultimately aid physicians in making timely judgments about the health and handling of patients based on vast amounts of information. Breast cancer, diabetes, and heart-related disorders cause numerous fatalities worldwide, yet most of these decreases are attributable to an absence of appropriate screenings. The lack of remedial substructure and a short doctor-to-population proportion contribute to the issue above. Following WHO recommendations, physicians' ratio to affected persons should be in some range; India's doctor-to-public proportion indicates a doctor scarcity. Heart, cancer, and diabetes-related disorders pose a significant danger to humanity if not detected initially. Thus, early detection and identification of these disorders may save many lives. Using classification methods based on machine learning, the focus of this effort is to anticipate dangerous illnesses. Diabetes, heart disease, and breast cancer are discussed in this study. To make this effort easy and accessible to the general community, a web application for therapeutic tests has been developed that use machine learning to create illness predictions. In this study, a web application is created for illness prediction that employs the notion of machine learning-based forecasts for illnesses such as breast cancer, diabetes, and cardiovascular sickness
Machine Learning for the Early Detection of Acute Episodes in Intensive Care Units
In Intensive Care Units (ICUs), mere seconds might define whether a patient lives or dies.
Predictive models capable of detecting acute events in advance may allow for anticipated
interventions, which could mitigate the consequences of those events and promote a
greater number of lives saved.
Several predictive models developed for this purpose have failed to meet the high
requirements of ICUs. This might be due to the complexity of anomaly prediction tasks,
and the inefficient utilization of ICU data. Moreover, some essential intensive care demands,
such as continuous monitoring, are often not considered when developing these
solutions, making them unfit to real contexts.
This work approaches two topics within the mentioned problem: the relevance of
ICU data used to predict acute episodes and the benefits of applying Layered Learning
(LL) techniques to counter the complexity of these tasks. The first topic was undertaken
through a study on the relevance of information retrieved from physiological signals and
clinical data for the early detection of Acute Hypotensive Episodes (AHE) in ICUs. Then,
the potentialities of LL were accessed through an in-depth analysis of the applicability
of a recently proposed approach on the same topic. Furthermore, different optimization
strategies enabled by LL configurations were proposed, including a new approach aimed
at false alarm reduction.
The results regarding data relevance might contribute to a shift in paradigm in terms
of information retrieved for AHE prediction. It was found that most of the information
commonly used in the literature might be wrongly perceived as valuable, since only three
features related to blood pressure measures presented actual distinctive traits. On another
note, the different LL-based strategies developed confirm the versatile possibilities
offered by this paradigm. Although these methodologies did not promote significant
performance improvements in this specific context, they can be further explored and
adapted to other domains.Em Unidades de Cuidados Intensivos (UCIs), meros segundos podem ser o fator determinante
entre a vida e a morte de um paciente. Modelos preditivos para a previsão de
eventos adversos podem promover intervenções antecipadas, com vista à mitigação das
consequências destes eventos, e traduzir-se num maior número de vidas salvas.
Múltiplos modelos desenvolvidos para este propósito não corresponderam às exigências
das UCIs. Isto pode dever-se à complexidade de tarefas de previsão de anomalias e
à ineficiência no uso da informação gerada em UCIs. Além disto, algumas necessidades
inerentes à provisão de cuidados intensivos, tais como a monitorização contínua, são muitas
vezes ignoradas no desenvolvimento destas soluções, tornando-as desadequadas para
contextos reais.
Este projeto aborda dois tópicos dentro da problemática introduzida, nomeadamente
a relevância da informação usada para prever episódios agudos, e os benefícios de técnicas
de Aprendizagem em Camadas (AC) para contrariar a complexidade destas tarefas. Numa
primeira fase, foi conduzido um estudo sobre o impacto de diversos sinais fisiológicos
e dados clínicos no contexto da previsão de episódios agudos de hipotensão. As potencialidades
do paradigma de AC foram avaliadas através da análise de uma abordagem
proposta recentemente para o mesmo caso de estudo. Nesta segunda fase, diversas estratégias
de otimização compatíveis com configurações em camadas foram desenvolvidas,
incluindo um modelo para reduzir falsos alarmes.
Os resultados relativos à relevância da informação podem contribuir para uma mudança
de paradigma em termos da informação usada para treinar estes modelos. A maior
parte da informação poderá estar a ser erroneamente considerada como importante, uma
vez que apenas três variáveis, deduzidas dos valores de pressão arterial, foram identificadas
como realmente impactantes. Por outro lado, as diferentes estratégias baseadas
em AC confirmaram a versatilidade oferecida por este paradigma. Apesar de não terem
promovido melhorias significativas neste contexto, estes métodos podem ser adaptados a
outros domínios
Time-Series Embedded Feature Selection Using Deep Learning: Data Mining Electronic Health Records for Novel Biomarkers
As health information technologies continue to advance, routine collection and digitisation of patient health records in the form of electronic health records present as an ideal opportunity for data-mining and exploratory analysis of biomarkers and risk factors indicative of a potentially diverse domain of patient outcomes. Patient records have continually become more widely available through various initiatives enabling open access whilst maintaining critical patient privacy. In spite of such progress, health records remain not widely adopted within the current clinical statistical analysis domain due to challenging issues derived from such “big data”.Deep learning based temporal modelling approaches present an ideal solution to health record challenges through automated self-optimisation of representation learning, able to man-ageably compose the high-dimensional domain of patient records into data representations able to model complex data associations. Such representations can serve to condense and reduce dimensionality to emphasise feature sparsity and importance through novel embedded feature selection approaches. Accordingly, application towards patient records enable complex mod-elling and analysis of the full domain of clinical features to select biomarkers of predictive relevance.Firstly, we propose a novel entropy regularised neural network ensemble able to highlight risk factors associated with hospitalisation risk of individuals with dementia. The application of which, was able to reduce a large domain of unique medical events to a small set of relevant risk factors able to maintain hospitalisation discrimination.Following on, we continue our work on ensemble architecture approaches with a novel cas-cading LSTM ensembles to predict severe sepsis onset within critical patients in an ICU critical care centre. We demonstrate state-of-the-art performance capabilities able to outperform that of current related literature.Finally, we propose a novel embedded feature selection application dubbed 1D convolu-tion feature selection using sparsity regularisation. Said methodology was evaluated on both domains of dementia and sepsis prediction objectives to highlight model capability and generalisability. We further report a selection of potential biomarkers for the aforementioned case study objectives highlighting clinical relevance and potential novelty value for future clinical analysis.Accordingly, we demonstrate the effective capability of embedded feature selection ap-proaches through the application of temporal based deep learning architectures in the discovery of effective biomarkers across a variety of challenging clinical applications
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