ArticleBioMed research international
Natural Language Processing Based Instrument for Classification of Free Text Medical Records.
Article in BioMed research international. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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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.
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Who cites it
7 citing papers in PubMed.
- Automated Detection of Substance-Use Status and Related Information from Clinical Text.Sensors (Basel, Switzerland) · 2022Article
- The Application of Projection Word Embeddings on Medical Records Scoring System.Healthcare (Basel, Switzerland) · 2021Article
- Medical Text Classification Using Hybrid Deep Learning Models with Multihead Attention.Computational intelligence and neuroscience · 2021Article
- Review
- Projection Word Embedding Model With Hybrid Sampling Training for Classifying ICD-10-CM Codes: Longitudinal Observational Study.JMIR medical informatics · 2019Article
- Clinical Natural Language Processing in languages other than English: opportunities and challenges.Journal of biomedical semantics · 2018Review
- Artificial Intelligence Learning Semantics via External Resources for Classifying Diagnosis Codes in Discharge Notes.Journal of medical Internet research · 2017Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
According to the Ministry of Labor, Health and Social Affairs of Georgia a new health management system has to be introduced in the nearest future. In this context arises the problem of structuring and classifying documents containing all the history of medical services provided. The present work introduces the instrument for classification of medical records based on the Georgian language. It is the first attempt of such classification of the Georgian language based medical records. On the whole 24.855 examination records have been studied. The documents were classified into three main groups (ultrasonography, endoscopy, and X-ray) and 13 subgroups using two well-known methods: Support Vector Machine (SVM) and
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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.