Predicting long-term outcome after acute ischemic stroke: a simple index works in patients from controlled clinical trials

Background and Purpose—An early and reliable prognosis for recovery in stroke patients is important for initiation of individual treatment and for informing patients and relatives. We recently developed and validated models for predicting survival and functional independence within 3 months after acute stroke, based on age and the National Institutes of Health Stroke Scale score assessed within 6 hours after stroke. Herein we demonstrate the applicability of our models in an independent sample of patients from controlled clinical trials. Methods—The prognostic models were used to predict survival and functional recovery in 5419 patients from the Virtual International Stroke Trials Archive (VISTA). Furthermore, we tried to improve the accuracy by adapting intercepts and estimating new model parameters. Results—The original models were able to correctly classify 70.4% (survival) and 72.9% (functional recovery) of patients. Because the prediction was slightly pessimistic for patients in the controlled trials, adapting the intercept improved the accuracy to 74.8% (survival) and 74.0% (functional recovery). Novel estimation of parameters, however, yielded no relevant further improvement. Conclusions—For acute ischemic stroke patients included in controlled trials, our easy-to-apply prognostic models based on age and National Institutes of Health Stroke Scale score correctly predicted survival and functional recovery after 3 months. Furthermore, a simple adaptation helps to adjust for a different prognosis and is recommended if a large data set is available. (Stroke. 2008;39:000-000.

Recurrent Stroke Prediction using Machine Learning Algorithms with Clinical Public Datasets: An Empirical Performance Evaluation

غالبًا ما تكون السكتة الدماغية المتكررة مدمرة وقادرة على التسبب في إعاقة شديدة أو الوفاة. ومع ذلك ، فإن ما يقرب من 90 ٪ من أسباب السكتة الدماغية المتكررة قابلة للتغير ، مما يعني أنه يمكن تجنب السكتات الدماغية المتكررة عن طريق التحكم في عوامل الخطر ، والتي هي في الأساس سلوكية واستقلابية بطبيعتها. وبالتالي ، يتضح من الأعمال السابقة أن نموذج التنبؤ بالسكتة الدماغية المتكررة يمكن أن يساعد في تقليل احتمالية الإصابة بسكتة دماغية متكررة. أظهرت الأعمال السابقة نتائج واعدة في التنبؤ بحالات السكتة الدماغية لأول مرة باستخدام أساليب التعلم الآلي. ومع ذلك ، هناك أعمال محدودة للتنبؤ بالسكتة الدماغية المتكررة باستخدام أساليب التعلم الآلي. ومن ثم ، تم اقتراح هذا العمل لإجراء تحليل تجريبي والتحقيق في خوارزميات التعلم الآلي المطبقة في نماذج التنبؤ بالسكتة الدماغية المتكررة. يهدف هذا البحث إلى التحقيق في أداء خوارزميات التعلم الآلي ومقارنتها باستخدام مجموعات البيانات السريرية العامة للسكتة الدماغية المتكررة. في هذه الدراسة ، تم استخدام الشبكة العصبية الاصطناعية (ANN) وآلة المتجهات الداعمة (SVM) وقائمة قواعد بايزي (BRL) ومقارنة أدائها في مجال نموذج التنبؤ بالسكتة الدماغية المتكررة. تظهر نتيجة التجارب التجريبية أن ANN سجلت أعلى دقة عند 80.00٪ ، تليها BRL بنسبة 75.91٪ و SVM بنسبة 60.45٪.Recurrent strokes can be devastating, often resulting in severe disability or death. However, nearly 90% of the causes of recurrent stroke are modifiable, which means recurrent strokes can be averted by controlling risk factors, which are mainly behavioral and metabolic in nature. Thus, it shows that from the previous works that recurrent stroke prediction model could help in minimizing the possibility of getting recurrent stroke. Previous works have shown promising results in predicting first-time stroke cases with machine learning approaches. However, there are limited works on recurrent stroke prediction using machine learning methods. Hence, this work is proposed to perform an empirical analysis and to investigate machine learning algorithms implementation in the recurrent stroke prediction models. This research aims to investigate and compare the performance of machine learning algorithms using recurrent stroke clinical public datasets. In this study, Artificial Neural Network (ANN), Support Vector Machine (SVM) and Bayesian Rule List (BRL) are used and compared their performance in the domain of recurrent stroke prediction model. The result of the empirical experiments shows that ANN scores the highest accuracy at 80.00%, follows by BRL with 75.91% and SVM with 60.45%

Modeling Stroke Diagnosis with the Use of Intelligent Techniques

The purpose of this work is to test the efficiency of specific intelligent classification algorithms when dealing with the domain of stroke medical diagnosis. The dataset consists of patient records of the ”Acute Stroke Unit”, Alexandra Hospital, Athens, Greece, describing patients suffering one of 5 different stroke types diagnosed by 127 diagnostic attributes / symptoms collected during the first hours of the emergency stroke situation as well as during the hospitalization and recovery phase of the patients. Prior to the application of the intelligent classifier the dimensionality of the dataset is further reduced using a variety of classic and state of the art dimensionality reductions techniques so as to capture the intrinsic dimensionality of the data. The results obtained indicate that the proposed methodology achieves prediction accuracy levels that are comparable to those obtained by intelligent classifiers trained on the original feature space

An Integrative Paradigm for Enhanced Stroke Prediction: Synergizing XGBoost and xDeepFM Algorithms

Stroke prediction plays a crucial role in preventing and managing this debilitating condition. In this study, we address the challenge of stroke prediction using a comprehensive dataset, and propose an ensemble model that combines the power of XGBoost and xDeepFM algorithms. Our work aims to improve upon existing stroke prediction models by achieving higher accuracy and robustness. Through rigorous experimentation, we validate the effectiveness of our ensemble model using the AUC metric. Through comparing our findings with those of other models in the field, we gain valuable insights into the merits and drawbacks of various approaches. This, in turn, contributes significantly to the progress of machine learning and deep learning techniques specifically in the domain of stroke prediction

Applying machine learning algorithms to medical knowledge

Dissertação de mestrado integrado em Engenharia InformáticaAchieving great and undeniable success in a great variety of industries and businesses has made the term Big Data very popular among the scientific community. Big Data (BD) refers to the ever fast-growing research area in Computer Science (CS) that comprises many work areas across the world. The healthcare sector is widely known to be highly proficient in the production of big quantities of data. It can go from health information, such as the patient’s blood pressure and cholesterol levels, to more private and sensitive data, such as the medical procedures history or the report of ongoing diseases. The application of sophisticated techniques enables a profound and rigorous analysis of data, something a human cannot do in real-time. However, a machine is capable of rapidly collect, group, storage and examine vast amounts of data and extract unknown and possi bly interesting knowledge from it. The algorithms used can discover hidden relationships between attributes that prove to be very useful for a corporation’s work. Buried structures within the produced data can also be detected by these techniques. Machine Learning (ML) methods can be adjusted and modelled to different input representations - this adaptability is one of the factors that contributes to its blooming prosperity. The main goal is to make predictions on data, by building utterly efficient models that can accurately take in the data and thus predict a certain outcome. This is especially important to the healthcare industry since it can considerably improve the lives of many patients. Everything from detecting a type of disease, predicting the chance of morbidity after a hospital stay, to aid in the decision making of treatment strategies are vital to patients as well as to clinicians. Any improvement over established methods that have been previously studied, tested and published are an asset that will improve the patient’s satisfaction about the healthcare performance in medical institutions. This can be achieved by refining those algorithms or implementing new approaches that will make better predictions on the given data. The main objective of this dissertation is to propose ML approaches having acknowledged and evaluated the existent methods used in clinical data. In order to fulfill this goal, an analysis of the state of the art of medical knowledge repositories and scientific papers published related to the selected keywords selected was performed. In this line of work, it is crucial to understand, compare and discuss the results obtained to those previously published. Thus, one of the goals is to suggest new ways of solving those problems and measuring them up against the existent ones.Obter um sucesso enorme e inegável numa grande variedade de indústrias e companhias, tomou o termo Big Data (BD) muito popular entre a comunidade científica. Big Data refere-se à área de investigação em Engenharia Informática que revela um crescimento rápido e está envolvida em várias áreas em todo o mundo. O setor da saúde é universalmente con-hecido por ser altamente frutífero na produção de grandes quantidades de dados. Podem variar desde dados de saúde, tais como, o valor da pressão sanguínea e nível de coles-terol do paciente, até dados mais confidenciais, como o histórico de cirurgias realizadas e doenças diagnosticadas. A aplicação de técnicas sofisticadas permite uma análise profunda e rigorosa dos dados -algo que um ser humano não consegue fazer em tempo real. No entanto, uma máquina não tem dificuldades em recolher, agrupar, armazenar e analisar rapidamente grandes quanti-dades de dados e extrair deles conhecimento que era desconhecido e, possivelmente, interessante. Os algoritmos usados podem ser usados para descobrir relações desconhecidas entre os vários atributos, que se podem revelar bastante úteis para o dia-a-dia de uma empresa. Estruturas e padrões escondidos nos dados podem ser também detetados através das mesmas técnicas. Os métodos de Machine Learning (ML) podem ser ajustados e modela-dos de forma a aceitar diferentes representações de dados de entrada - esta adaptabilidade é um dos fatores mais proeminentes que contribui para a sua prosperidade. O principal objetivo é fazer previsões sobre os dados, de modo a construir modelos totalmente eficientes que possam analisar os dados de forma precisa, e, assim, prever um determinado resultado. Isto é especialmente importante para o setor da saúde, uma vez que pode melhorar consideravelmente a vida de muitos pacientes. Tudo, desde a deteção de um certo tipo de doença, prever a probabilidade de morbilidade após um internamento até a auxiliar na tomada de decisão em relação a estratégias de tratamento, é vital para os pacientes, bem como para os médicos. Portanto, qualquer melhoria em relação a métodos já estabelecidos que foram previamente estudados, testados e publicados é uma mais-valia que melhorará a satisfação do paciente em relação à sua experiência com os serviços de saúde. Tal pode ser alcançado refinando esses algoritmos ou mesmo implementando novas abordagens que farão melhores previsões sobre os dados. O principal objetivo desta dissertação é propor abordagens de ML, fazendo um reconhecimento e avaliando os métodos existentes utilizados em dados médicos. Desta forma, foi posta em prática uma análise ao estado da arte de repositórios de conhecimento médico, bem como a artigos científicos relacionados com esses conjuntos de dados. Assim, é fundamental compreender, comparar e discutir os resultados obtidos com os publicados anteriormente. Portanto, um dos objetivos é sugerir novas formas de resolver os problemas, tecendo uma comparação com os existentes