Summary of Towards System Modelling to Support Diseases Data Extraction From the Electronic Health Records For Physicians Research Activities, by Bushra F. Alsaqer et al.
Towards System Modelling to Support Diseases Data Extraction from the Electronic Health Records for Physicians Research Activities
by Bushra F. Alsaqer, Alaa F. Alsaqer, Amna Asif
First submitted to arxiv on: 1 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Information Retrieval (cs.IR)
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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 The proposed approach aims to utilize Electronic Health Records (EHRs) as a valuable source of patient data, enabling researchers to monitor disease statistics and detect causes. To achieve this, the study focuses on standardizing EHR data formats, converting names of diseases and demographics into one standardized form. This is crucial due to the vast amount of available EHR data. The approach involves pre-processing, annotation, and transforming steps to normalize demographic formats and recognize diseases using machine learning models. The proposed model outperforms a dictionary-based system (MetaMap) in disease recognition, achieving an accuracy of 81% compared to MetaMap’s 67%. This contribution can support research activities by providing a standardized platform for EHR data extraction. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary EHRs are important records that help doctors and researchers understand patient health. This paper wants to make it easier to use this data for research. One problem is that the data is not in one format, but many different ones. To fix this, we need a way to standardize the names of diseases and demographics. We used real EHR data and applied steps like pre-processing, annotation, and transforming to turn the data into a standardized form. The results show that our machine learning model can recognize diseases with 81% accuracy, beating another system called MetaMap. |
Keywords
» Artificial intelligence » Machine learning
