Summary of Towards Reducing Diagnostic Errors with Interpretable Risk Prediction, by Denis Jered Mcinerney et al.
Towards Reducing Diagnostic Errors with Interpretable Risk Prediction
by Denis Jered McInerney, William Dickinson, Lucy C. Flynn, Andrea C. Young, Geoffrey S. Young, Jan-Willem van de Meent, Byron C. Wallace
First submitted to arxiv on: 15 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); 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 This AI research paper proposes a method using Large Language Models (LLMs) to identify relevant information in patient Electronic Health Records (EHRs), aiming to reduce diagnostic errors. The authors develop a Neural Additive Model to provide individualized risk estimates and mitigate delays in diagnosis. They use LLMs to infer temporally fine-grained retrospective labels of eventual diagnoses, then refine the evidence pool based on correlations learned by the model. An in-depth evaluation is conducted through simulations, demonstrating the usefulness of this approach for clinicians to decide between a list of differential diagnoses. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This AI research paper helps doctors make better decisions by using computers to analyze patient health records. The idea is to use special language models to find important clues in these records that can help diagnose illnesses more accurately and quickly. The researchers developed a new model that looks at patterns in the data and provides personalized risk estimates for each patient. This could reduce mistakes and delays in diagnosis, making healthcare better. |
