A Large-Scale CNN Ensemble for Medication Safety Analysis

Akhtyamova, Liliya; Ignatov, Andrey; Cardiff, John

doi:10.1007/978-3-319-59569-6_29

Cited by 26 publications

(3 citation statements)

References 7 publications

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“…Evaluation metrics, including accuracy, precision, and recall, would have been used to assess the ensemble's performance in predicting bankruptcy and creditworthiness. Akhtyamova, Ignatov, Cardiff (2017) [5] : Large-Scale CNN Ensemble for Medication Safety Analysis Although not part of the previously mentioned studies, Akhtyamova et al's work on a large-scale CNN ensemble for medication safety analysis is relevant. The methodology likely included the creation of a massive dataset related to medication safety, potentially from healthcare forums or databases.…”

Section: Methodology Reviewmentioning

confidence: 99%

Ensemble learning in machine learning: Integrating multiple models for improved predictions

Sharma,

Garg,

Kumar

2018

Int. J. Appl. Res.

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In the rapidly evolving landscape of machine learning, the pursuit of enhancing predictive accuracy and robustness has led to the prominence of ensemble learning methodologies. This paper explores the paradigm of ensemble learning, a sophisticated approach that amalgamates diverse predictive models to yield superior and more reliable outcomes than individual models. The fundamental premise behind ensemble learning lies in the synergistic integration of multiple models, each possessing unique strengths and perspectives, to collectively outperform the limitations inherent in standalone models. Ensemble learning operates on the principle that the aggregation of predictions from diverse models can mitigate individual biases and uncertainties, ultimately resulting in a more accurate and stable predictive framework. This paper provides a comprehensive overview of various ensemble learning techniques, including bagging, boosting, and stacking, elucidating their underlying mechanisms and showcasing their efficacy in different scenarios. The discussion encompasses the theoretical underpinnings of ensemble methods, shedding light on how diversity among constituent models contributes to the overall ensemble's superior predictive performance. The exploration extends to real-world applications of ensemble learning across diverse domains, emphasizing instances where conventional single-model approaches fall short. Through case studies and empirical evidence, the paper underscores the versatility of ensemble learning in addressing complex challenges, from classification and regression problems to outlier detection and pattern recognition. Moreover, the paper delves into the intricacies of model selection, highlighting the importance of carefully curating an ensemble of models with complementary strengths. The concept of model diversity, both in terms of algorithmic variance and data representation, emerges as a pivotal factor influencing the success of ensemble learning endeavors.

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Section: Methodology Reviewmentioning

confidence: 99%

Ensemble learning in machine learning: Integrating multiple models for improved predictions

Sharma,

Garg,

Kumar

2018

Int. J. Appl. Res.

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“…In this study, a well-known deep learning algorithm CNN is used. CNN algorithms are applied in many different areas such as natural language processing (NLP) [13,14], biomedical processing [15,16], especially image [17,18] and audio processing [19,20]. It is a deep learning algorithm that has the best classification success, especially in the field of image processing.…”

Section: Classificationmentioning

confidence: 99%

Deep Learning Method for Handwriting Recognition

Erdoğan

Tümer

2021

MANAS Journal of Engineering

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The advancement of technology nowadays resulted into documents, such as forms and petitions, being filled out in computer and digital environment. Yet in some cases, documents are still preserved in traditional style, on print. Due to its distinct proportions, however, its storage, sharing and filing has become a complication. The relocation of these written documents to digital environment is therefore of great significance. In this view, this study aims to explore methodologies of digitizing handwritten documents. In this study, the documents converted to image format were pre-processed using image processing methods. These operations include dividing lines of the document into image format, dividing into words which then divided into characters, and finally, a classification operation on the characters. As classification phase, one of the deep learning methods is the Convolution Neural Network method is used in image recognition. The model was trained using the EMNIST dataset, and in the character, dataset created from the documents at hand. The dataset created had a success rate of 87.81%. Characters classified as finishers are sequentially combined and the document is transferred to the computer afterwards.

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“…It should be mentioned here about convolutional neural networks (CNN) having successful applications in opinion mining health social networks, see e.g. [9,10]. However, they dealt with the traditional 5-star ratings rather than the combined classes used in this paper.…”

Section: Problem Settingmentioning

confidence: 99%

Opinion Mining on Small and Noisy Samples of Health-Related Texts

Akhtyamova

Alexandrov

Cardiff

et al. 2018

Advances in Intelligent Systems and Computing

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The topic of people's health has always attracted the attention of public and private structures, the patients themselves and, therefore, researchers. Social networks provide an immense amount of data for analysis of health-related issues; however it is not always the case that researchers have enough data to build sophisticated models. In the paper, we artificially create this limitation to test performance and stability of different popular algorithms on small samples of texts. There are two specificities in this research apart from the size of a sample: (a) here, instead of usual 5-star classification, we use combined classes reflecting a more practical view on medicines and treatments; (b) we consider both original and noisy data. The experiments were carried out using data extracted from the popular forum AskaPatient. For tuning parameters, GridSearchCV technique was used. The results show that in dealing with small and noisy data samples, GMDH Shell is superior to other methods. The work has a practical orientation.

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A Large-Scale CNN Ensemble for Medication Safety Analysis

Cited by 26 publications

References 7 publications

Ensemble learning in machine learning: Integrating multiple models for improved predictions

Ensemble learning in machine learning: Integrating multiple models for improved predictions

Deep Learning Method for Handwriting Recognition

Opinion Mining on Small and Noisy Samples of Health-Related Texts

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