mlsanityData to 7 Oct 2026

Research on Disease Prediction Model Construction Based on Computer AI deep Learning Technology

arXiv 2406.16982 ↗First posted 23 Jun 2024cs.LG cs.AIcited by 6

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Abstract (arXiv)

The prediction of disease risk factors can screen vulnerable groups for effective prevention and treatment, so as to reduce their morbidity and mortality. Machine learning has a great demand for high-quality labeling information, and labeling noise in medical big data poses a great challenge to efficient disease risk warning methods. Therefore, this project intends to study the robust learning algorithm and apply it to the early warning of infectious disease risk. A dynamic truncated loss model is proposed, which combines the traditional mutual entropy implicit weight feature with the mean variation feature. It is robust to label noise. A lower bound on training loss is constructed, and a method based on sampling rate is proposed to reduce the gradient of suspected samples to reduce the influence of noise on training results. The effectiveness of this method under different types of noise was verified by using a stroke screening data set as an example. This method enables robust learning of data containing label noise.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 8 of its 29 references that are in the map. First posted on arXiv 23 Jun 2024.

Builds on

8 of 29 references.

References · 8

  1. 2020Med-BERT: pre-trained contextualized embeddings on large-scale structured electronic health records for disease predictioncited by 368
  2. 2023NDC-Scene: Boost Monocular 3D Semantic Scene Completion in Normalized Device Coordinates Spacecited by 88
  3. 2024Survival Prediction Across Diverse Cancer Types Using Neural Networkscited by 80
  4. 2023Deception Detection from Linguistic and Physiological Data Streams Using Bimodal Convolutional Neural Networkscited by 54
  5. 2023Improving Depth Gradient Continuity in Transformers: A Comparative Study on Monocular Depth Estimation with CNNcited by 35
  6. 2024Regional Style and Color Transfercited by 30
  7. 2019Constrained Attractor Selection Using Deep Reinforcement Learningcited by 17
  8. 2020Attractor Selection in Nonlinear Energy Harvesting Using Deep Reinforcement Learningcited by 15

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Cited by year

Citing papers per year: 2024: 6.

6 papers.

  1. 2024Adaptive Friction in Deep Learning: Enhancing Optimizers with Sigmoid and Tanh Functioncited by 16
  2. 2024Dynamic Hypergraph-Enhanced Prediction of Sequential Medical Visitscited by 12
  3. 2024Text classification optimization algorithm based on graph neural networkcited by 8
  4. 2024Convolutional Neural Networks for Predictive Modeling of Lung Diseasecited by 7
  5. 2024Reducing Bias in Deep Learning Optimization: The RSGDM Approachcited by 7
  6. 2024Stock Type Prediction Model Based on Hierarchical Graph Neural Networkcited by 5