Deep Learning Prediction Model for Patient Survival Outcomes in Palliative Care Using Actigraphy Data and Clinical Information

Huang, Yu-Wen; Roy, Nidita; Dhar, Eshita; Upadhyay, Umashankar; Kabir, Muhammad Ashad; Uddin, Mohy; Tseng, Ching Li; Syed-Abdul, Shabbir

doi:10.3390/cancers15082232

Cited by 5 publications

(2 citation statements)

References 45 publications

(74 reference statements)

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“…The paper discusses the potential of deep learning in automating the analysis of stone carving art and highlights its contribution to the understanding of cultural heritage. In [14] explores the temporal evolution of ancient Chinese stone carving decoration using data mining techniques. The study analyzes a large dataset of stone carving images from different periods and applies clustering and association rule mining algorithms to identify temporal patterns and stylistic changes.…”

Section: Related Workmentioning

confidence: 99%

Evolution and Stylistic Characteristics of Ancient Chinese Stone Carving Decoration LSTM-DL Approach with Image Visualization

Tian

2023

IJRITCC

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In recent years, advancements in data analysis techniques and deep learning algorithms have revolutionized the field of art and cultural studies. Ancient Chinese stone carving decoration holds significant historical and cultural value, reflecting the artistic and stylistic evolution of different periods. This paper explored the Weighted Long Short-Term Memory Deep Learning (WLSTM – DL) evolution and stylistic characteristics of ancient Chinese stone carving decoration through the application of image visualization techniques combined with a Long Short-Term Memory (LSTM) time-series deep learning architecture. The WLSTM-DL model uses the optimized feature selection with the grasshopper optimization for the feature extraction and selection. By analyzing a comprehensive dataset of stone carving images from different periods, the WLSTM-DL model captures the temporal relationships and patterns in the evolution of stone carving decoration. The model utilizes LSTM, a specialized deep-learning architecture for time-series data, to uncover stylistic characteristics and identify significant changes over time. The findings of this study provide valuable insights into the evolution and stylistic development of ancient Chinese stone carving decoration. The application of image visualization techniques and the WLSTM-DL model showcase the potential of data analysis and deep learning in uncovering hidden narratives and understanding the intricate details of ancient artworks.

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Section: Related Workmentioning

confidence: 99%

Evolution and Stylistic Characteristics of Ancient Chinese Stone Carving Decoration LSTM-DL Approach with Image Visualization

Tian

2023

IJRITCC

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“…Machine learning (ML) methods have been used to solve several problems recently, such as diagnosing cancer [9], COVID-19 [10], autism [11,12], meningitis, diabetes, and heart disease. Recent research suggests that ML can summarize patient characteristics and predict T2DM risk [13][14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

A Comparison of Machine Learning Techniques for the Detection of Type-2 Diabetes Mellitus: Experiences from Bangladesh

et al. 2023

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Diabetes is a chronic disease caused by a persistently high blood sugar level, causing other chronic diseases, including cardiovascular, kidney, eye, and nerve damage. Prompt detection plays a vital role in reducing the risk and severity associated with diabetes, and identifying key risk factors can help individuals become more mindful of their lifestyles. In this study, we conducted a questionnaire-based survey utilizing standard diabetes risk variables to examine the prevalence of diabetes in Bangladesh. To enable prompt detection of diabetes, we compared different machine learning techniques and proposed an ensemble-based machine learning framework that incorporated algorithms such as decision tree, random forest, and extreme gradient boost algorithms. In order to address class imbalance within the dataset, we initially applied the synthetic minority oversampling technique (SMOTE) and random oversampling (ROS) techniques. We evaluated the performance of various classifiers, including decision tree (DT), logistic regression (LR), support vector machine (SVM), gradient boost (GB), extreme gradient boost (XGBoost), random forest (RF), and ensemble technique (ET), on our diabetes datasets. Our experimental results showed that the ET outperformed other classifiers; to further enhance its effectiveness, we fine-tuned and evaluated the hyperparameters of the ET. Using statistical and machine learning techniques, we also ranked features and identified that age, extreme thirst, and diabetes in the family are significant features that prove instrumental in the detection of diabetes patients. This method has great potential for clinicians to effectively identify individuals at risk of diabetes, facilitating timely intervention and care.

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Detection and prediction of future massive black hole mergers with machine learning and truncated waveforms

Houba,

Strub,

Ferraioli

et al. 2024

Phys. Rev. D

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Deep Learning Prediction Model for Patient Survival Outcomes in Palliative Care Using Actigraphy Data and Clinical Information

Cited by 5 publications

References 45 publications

Evolution and Stylistic Characteristics of Ancient Chinese Stone Carving Decoration LSTM-DL Approach with Image Visualization

Evolution and Stylistic Characteristics of Ancient Chinese Stone Carving Decoration LSTM-DL Approach with Image Visualization

A Comparison of Machine Learning Techniques for the Detection of Type-2 Diabetes Mellitus: Experiences from Bangladesh

Detection and prediction of future massive black hole mergers with machine learning and truncated waveforms

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