ArticleHealthcare informatics research2018
HEDEA: A Python Tool for Extracting and Analysing Semi-structured Information from Medical Records.
Article in Healthcare informatics research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- A Situational Analysis of the Impact of the COVID-19 Pandemic on Digital Health Research Initiatives in South Asia.Cureus · 2023Review
- ANNO: A General Annotation Tool for Bilingual Clinical Note Information Extraction.Healthcare informatics research · 2022Article
- Extracting Structured Genotype Information from Free-Text HLA Reports Using a Rule-Based Approach.Journal of Korean medical science · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesOne of the most important functions for a medical practitioner while treating a patient is to study the patient's complete medical history by going through all records, from test results to doctor's notes. With the increasing use of technology in medicine, these records are mostly digital, alleviating the problem of looking through a stack of papers, which are easily misplaced, but some of these are in an unstructured form. Large parts of clinical reports are in written text form and are tedious to use directly without appropriate pre-processing. In medical research, such health records may be a good, convenient source of medical data; however, lack of structure means that the data is unfit for statistical evaluation. In this paper, we introduce a system to extract, store, retrieve, and analyse information from health records, with a focus on the Indian healthcare scene.
methodsA Python-based tool, Healthcare Data Extraction and Analysis (HEDEA), has been designed to extract structured information from various medical records using a regular expression-based approach.
resultsThe HEDEA system is working, covering a large set of formats, to extract and analyse health information.
conclusionsThis tool can be used to generate analysis report and charts using the central database. This information is only provided after prior approval has been received from the patient for medical research purposes.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.