Summary of Nlice: Synthetic Medical Record Generation For Effective Primary Healthcare Differential Diagnosis, by Zaid Al-ars et al.
NLICE: Synthetic Medical Record Generation for Effective Primary Healthcare Differential Diagnosis
by Zaid Al-Ars, Obinna Agba, Zhuoran Guo, Christiaan Boerkamp, Ziyaad Jaber, Tareq Jaber
First submitted to arxiv on: 24 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 systematic method for creating patient records grounded in medical knowledge is proposed, enabling activities involving differential diagnosis. The approach combines a public disease-symptom data source (SymCat) with Synthea to construct synthetic patient records. To increase expressiveness, medically-standardized symptom modeling via NLICE is used, adding contextual information for each condition. Evaluations of Naive Bayes and Random Forest models on the synthetic data show increased accuracy when using the NLICE-based dataset. The proposed approach addresses a major barrier to AI application in healthcare, solving issues with incomplete and insufficient information. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps create medical records that doctors can use to figure out what’s wrong with patients. It uses special computer tools to make fake patient data that’s based on real symptoms. This lets doctors train computers to make good diagnoses. The new way of making fake data is better than the old way, and it makes it easier for computers to learn from mistakes. |
Keywords
* Artificial intelligence * Naive bayes * Random forest * Synthetic data
