Summary of Continuous Predictive Modeling Of Clinical Notes and Icd Codes in Patient Health Records, by Mireia Hernandez Caralt et al.
Continuous Predictive Modeling of Clinical Notes and ICD Codes in Patient Health Records
by Mireia Hernandez Caralt, Clarence Boon Liang Ng, Marek Rei
First submitted to arxiv on: 19 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach to predicting diagnoses and treatments in Electronic Health Records (EHRs) is proposed, focusing on assigning International Classification of Diseases (ICD) codes throughout the patient’s stay. Unlike previous research, which concentrated on discharge summaries, this work aims to predict ICD codes at different time points during admission, including before official assignment by clinicians. By developing methods for early prediction, opportunities arise for predictive medicine, such as identifying disease risks, suggesting treatments, and optimizing resource allocation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Predicting diagnoses and treatments in Electronic Health Records (EHRs) is important for improving healthcare. Researchers have been trying to figure out how to do this best. Usually, they focus on the end of a hospital stay, but this paper looks at predicting ICD codes throughout the patient’s stay, even before doctors give their final diagnosis. This could help identify health risks earlier and suggest better treatments. The study shows that predictions can be made just two days after admission, which is important for making healthcare better. |
Keywords
» Artificial intelligence » Classification
