ArticleInternational journal of medical informatics2020
Automatic classification of scanned electronic health record documents.
Article in International journal of medical informatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Bridging the data latency gap: automated extraction of genomic biomarkers from unstructured clinical documents to support real-world oncology data.Cancer causes & control : CCC · 2026Article
- Article
- Automated Multitier Tagging of Chinese Online Health Education Resources Using a Large Language Model: Development and Validation Study.Journal of medical Internet research · 2025Article
- Large language models for clinical decision support in gastroenterology and hepatology.Nature reviews. Gastroenterology & hepatology · 2025Review
- Beyond the Posts: Analyzing Breast Implant Illness Discourse With Natural Language Processing and Deep Learning.Aesthetic surgery journal · 2025Article
- Automated classification of clinical diagnoses in electronic health records using transformer.PloS one · 2025Article
- Medication Extraction and Drug Interaction Chatbot: Generative Pretrained Transformer-Powered Chatbot for Drug-Drug Interaction.Mayo Clinic proceedings. Digital health · 2024Article
- The incremental design of a machine learning framework for medical records processing.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Transforming Hospital Quality Improvement Through Harnessing the Power of Artificial Intelligence.Global journal on quality and safety in healthcare · 2024Article
- Big Data Research on Severe Asthma.Tuberculosis and respiratory diseases · 2024Article
- Enhancing machine learning-based sentiment analysis through feature extraction techniques.PloS one · 2024Article
- Extracting laboratory test information from paper-based reports.BMC medical informatics and decision making · 2023Article
- Impact of word embedding models on text analytics in deep learning environment: a review.Artificial intelligence review · 2023Article
- Extracting Data from the Electronic Health Record of Patients with ADHD Reveals Pediatricians' Discussions of Educational Support and Document Collection.Perspectives in health information managemen · 2023Article
- Improving sentiment classification using a RoBERTa-based hybrid model.Frontiers in human neuroscience · 2023Article
- Deep learning-based NLP data pipeline for EHR-scanned document information extraction.JAMIA open · 2022Article
- Closing the loop: automatically identifying abnormal imaging results in scanned documents.Journal of the American Medical Informatics Association : JAMIA · 2022Article
- A Smartphone App to Increase Immunizations in the Pediatric Solid Organ Transplant Population: Development and Initial Usability Study.JMIR formative research · 2022Article
- Searching the PDF Haystack: Automated Knowledge Discovery in Scanned EHR Documents.Applied clinical informatics · 2021Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
objectivesElectronic Health Records (EHRs) contain scanned documents from a variety of sources such as identification cards, radiology reports, clinical correspondence, and many other document types. We describe the distribution of scanned documents at one health institution and describe the design and evaluation of a system to categorize documents into clinically relevant and non-clinically relevant categories as well as further sub-classifications. Our objective is to demonstrate that text classification systems can accurately classify scanned documents.
methodsWe extracted text using Optical Character Recognition (OCR). We then created and evaluated multiple text classification machine learning models, including both "bag of words" and deep learning approaches. We evaluated the system on three different levels of classification using both the entire document as input, as well as the individual pages of the document. Finally, we compared the effects of different text processing methods.
resultsA deep learning model using ClinicalBERT performed best. This model distinguished between clinically-relevant documents and not clinically-relevant documents with an accuracy of 0.973; between intermediate sub-classifications with an accuracy of 0.949; and between individual classes with an accuracy of 0.913. DISCUSSION: Within the EHR, some document categories such as "external medical records" may contain hundreds of scanned pages without clear document boundaries. Without further sub-classification, clinicians must view every page or risk missing clinically-relevant information. Machine learning can automatically classify these scanned documents to reduce clinician burden.
conclusionUsing machine learning applied to OCR-extracted text has the potential to accurately identify clinically-relevant scanned content within EHRs.
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Registered trials
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