An integrated machine learning predictive scheme for longitudinal laboratory data to evaluate the factors determining renal function changes in patients with different chronic kidney disease stages

Tsai, Ming-Hsien; Jhou, Mao-Jhen; Liu, Tzu-Chi; Fang, Yu-Wei; Lu, Chi-Jie

doi:10.3389/fmed.2023.1155426

Cited by 2 publications

(2 citation statements)

References 83 publications

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“…Following this, a backward elimination strategy is implemented, where these basic functions are systematically removed. The aim of this backward phase is to refine and optimize the combinations of the remaining knots, ensuring that the most effective and representative model is achieved [45].…”

Section: Marsmentioning

confidence: 99%

Development of a Machine Learning Algorithm to Correlate Lumbar Disc Height on X-rays with Disc Bulging or Herniation

Lin,

Chang,

Hsiao

et al. 2024

Diagnostics

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Lumbar disc bulging or herniation (LDBH) is one of the major causes of spinal stenosis and related nerve compression, and its severity is the major determinant for spine surgery. MRI of the spine is the most important diagnostic tool for evaluating the need for surgical intervention in patients with LDBH. However, MRI utilization is limited by its low accessibility. Spinal X-rays can rapidly provide information on the bony structure of the patient. Our study aimed to identify the factors associated with LDBH, including disc height, and establish a clinical diagnostic tool to support its diagnosis based on lumbar X-ray findings. In this study, a total of 458 patients were used for analysis and 13 clinical and imaging variables were collected. Five machine-learning (ML) methods, including LASSO regression, MARS, decision tree, random forest, and extreme gradient boosting, were applied and integrated to identify important variables for predicting LDBH from lumbar spine X-rays. The results showed L4-5 posterior disc height, age, and L1-2 anterior disc height to be the top predictors, and a decision tree algorithm was constructed to support clinical decision-making. Our study highlights the potential of ML-based decision tools for surgeons and emphasizes the importance of L1-2 disc height in relation to LDBH. Future research will expand on these findings to develop a more comprehensive decision-supporting model.

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Section: Marsmentioning

confidence: 99%

Development of a Machine Learning Algorithm to Correlate Lumbar Disc Height on X-rays with Disc Bulging or Herniation

Lin,

Chang,

Hsiao

et al. 2024

Diagnostics

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“…Finally, studying the local characteristics and differentiated development strategies of rural areas can provide useful experience and inspiration for the practice of rural ecological construction and carbon neutrality. Successful cases and experiences in different regions can learn from each other and promote the sustainable development of rural areas Tsai et al (2023). In addition, research can also provide reference for government policy-making and planning, promoting the implementation of rural ecological civilization construction and carbon neutrality goals.…”

Section: Local Characteristics and Differentiated Developmentmentioning

confidence: 99%

Coordinated development of rural ecological construction and carbon neutrality: a deep learning approach for enhanced sustainability

Li,

Feng

2024

Front. Ecol. Evol.

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IntroductionIn recent years, the world has faced increasingly severe climate change and ecological environmental problems. As an important part of the ecological system, rural areas also face many challenges. Rural ecological construction and carbon neutrality, as a solution, have attracted widespread attention. However, achieving the coordinated development of rural ecological construction and carbon neutrality requires more in-depth research and effective methods.MethodsThis study aims to explore how to promote the coordinated development of rural ecological construction and carbon neutrality through the combination of a Transformer-RNN model and cross-attention mechanism. We propose a deep learning framework that combines the parallelism and global dependency capturing capabilities of the Transformer model with the temporal information handling capabilities of the RNN model. By integrating these two models, we leverage their respective strengths to improve the performance of the model. Furthermore, we introduce a cross-attention mechanism that enables the model to simultaneously focus on the relationship between rural ecological construction and carbon neutrality. Through cross-attention, the model accurately captures the impact of rural ecological construction measures on carbon neutrality and the feedback effect of carbon neutrality on the rural ecological environment. In our experiments, we collected relevant data on rural ecological construction and carbon neutrality, including environmental indicators, socio-economic factors, land use patterns, energy consumption, and carbon emissions.Results and discussionWe preprocess the data and train the combined Transformer-RNN model with the cross-attention mechanism. The trained model demonstrates promising results in capturing the complex dependencies and relationships between rural ecological construction and carbon neutrality. The significance of this study lies in deepening the understanding of the coordinated development relationship between rural ecological construction and carbon neutrality and providing a novel deep learning-based method to solve related problems. By introducing the Transformer-RNN model with a cross-attention mechanism, we provide decision-makers with more scientific and accurate decision support, promoting the improvement of the rural ecological environment and the achievement of carbon neutrality goals.

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An integrated machine learning predictive scheme for longitudinal laboratory data to evaluate the factors determining renal function changes in patients with different chronic kidney disease stages

Cited by 2 publications

References 83 publications

Development of a Machine Learning Algorithm to Correlate Lumbar Disc Height on X-rays with Disc Bulging or Herniation

Development of a Machine Learning Algorithm to Correlate Lumbar Disc Height on X-rays with Disc Bulging or Herniation

Coordinated development of rural ecological construction and carbon neutrality: a deep learning approach for enhanced sustainability

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