The development of electronic health records (EHR) systems has enabled the collection of a vast amount of digitized patient data. However, utilizing EHR data for predictive modeling presents several challenges due to ...
ISBN:
(纸本)9781956792041
The development of electronic health records (EHR) systems has enabled the collection of a vast amount of digitized patient data. However, utilizing EHR data for predictive modeling presents several challenges due to its unique characteristics. With the advancements in machinelearning techniques, deep learning has demonstrated its superiority in various applications, including healthcare. This survey systematically reviews recent advances in deep learning-based predictive models using EHR data. Specifically, we introduce the background of EHR data and provide a mathematical definition of the predictive modeling task. We then categorize and summarize predictive deep models from multiple perspectives. Furthermore, we present benchmarks and toolkits relevant to predictive modeling in healthcare. Finally, we conclude this survey by discussing open challenges and suggesting promising directions for future research.
Feature fusion is an important machinelearning technique because it gives classifiers more powerful capabilities in capturing important characteristics of data. Ensemble learning is a technique in designing machine l...
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Drought is a severe natural event that affects agriculture greatly, particularly in regions where rain-fed agriculture is the main source of food. It has a major detrimental impact on society, the environment, and the...
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This research extends previous studies on the effects of climate change on agricultural yield predictions by integrating advanced machinelearning (ML) methods with real time environmental sensing data. It builds on t...
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The advancement in digital image processing and machinelearning technologies has paved the way for innovative applications in the field of botany and pharmacology, particularly in the identification and characterizat...
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machinelearning, Deep learning and cyber security have an enormous multidisciplinary convergence. Recent advances in deep learning have indicated a tremendous increase in cyber security and intrusion detection. Throu...
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For predicting water evaporation (WE), machinelearning (ML) is a crucial decision support tool. Several machinelearning techniques (MLT) have been utilized to help with the forecast of WE. This review article has in...
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Cardiovascular diseases are still among the leading causes of deaths worldwide, thus calling for early detection and precise risk assessment. Traditional methods of diagnosing cardiovascular disease often rely on stan...
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Recent advances in machinelearning (ML) show that Neural machine Translation (NMT) models can mock the program behavior when trained on input-output pairs. Such models can mock the functionality of existing programs ...
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ISBN:
(纸本)9798350322637
Recent advances in machinelearning (ML) show that Neural machine Translation (NMT) models can mock the program behavior when trained on input-output pairs. Such models can mock the functionality of existing programs and serve as quick-to-deploy reverse engineering tools. Still, the problem of automatically learning such predictive and reversible models from programs needs to be solved. This work introduces a generic approach for automated and reversible program behavior modeling. It achieves 94% of overall accuracy in the conversion of Markdown-to-HTML and HTML-to-Markdown markups.
The proceedings contain 200 papers. The topics discussed include: automated brain tumor detection and classification through deep learning analysis of MRI scans;Tri-UnityNet: a multifaceted ensemble model for pneumoni...
ISBN:
(纸本)9798350364828
The proceedings contain 200 papers. The topics discussed include: automated brain tumor detection and classification through deep learning analysis of MRI scans;Tri-UnityNet: a multifaceted ensemble model for pneumonia detection;breast cancer detection using neural networks;electric vehicle battery health monitoring system;early detection of cardiovascular disorders using enhanced ANN model;Healthbot analytics: optimizing healthcare efficiency through intelligent integration;driver drowsiness detection using Mobilenetv2 with transfer learning approach;identification of uterine cervical cancer using CNN compared to ANFIS Approach On MRI Images;violence detection through surveillance videos using combination of VGG16 And LSTM;and OLFV: harnessing the power of enhanced deep learning model to recognize fingerprints using optimization and classification principles.
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