Summary of Comparison Of Deep Learning and Conventional Methods For Disease Onset Prediction, by Luis H. John et al.
Comparison of deep learning and conventional methods for disease onset prediction
by Luis H. John, Chungsoo Kim, Jan A. Kors, Junhyuk Chang, Hannah Morgan-Cooper, Priya Desai, Chao Pang, Peter R. Rijnbeek, Jenna M. Reps, Egill A. Fridgeirsson
First submitted to arxiv on: 14 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a deep learning approach for predicting disease onset using electronic health records (EHRs). Traditional methods like logistic regression and gradient boosting have been widely used due to their reliability and interpretability. However, these methods may not fully capture the complex patterns in clinical data, leading to limited prediction performance. The proposed method leverages convolutional neural networks (CNNs) for EHR-based disease onset prediction, addressing challenges such as data sparsity and high dimensionality. The authors evaluate their approach using a publicly available dataset, achieving improved prediction accuracy compared to conventional methods. This work demonstrates the potential of deep learning in enhancing disease onset prediction, offering promising insights for personalized healthcare. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper looks at ways to better predict when someone will get sick based on their medical records. Right now, doctors use simple math equations and machine learning algorithms to make predictions. But these methods aren’t perfect because they can’t capture all the subtle patterns in patient data. The authors of this study propose a new way to use special computer networks called convolutional neural networks (CNNs) to analyze electronic health records (EHRs). By using CNNs, doctors might be able to make more accurate predictions about when someone will get sick or develop a particular condition. |
Keywords
» Artificial intelligence » Boosting » Deep learning » Logistic regression » Machine learning
