Loading Now

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)

     Abstract of paper      PDF of paper


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
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