ReviewJournal of healthcare engineering2018
Data Processing and Text Mining Technologies on Electronic Medical Records: A Review.
Review in Journal of healthcare engineering, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 82 papers, 2 of them syntheses that pooled it.
What it found
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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.
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Who cites it
82 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Electronic health record-based prediction models for dementia detection: a systematic review of model performance and quality.Journal of the American Medical Informatics Association : JAMIA · 2026Pooled it
- Pooled it
- Evaluating the Effectiveness of Robotic Process Automation for Cancer Registry Data Abstraction in a Production EHR Environment.Journal of clinical medicine · 2026Article
- Benchmarking Large Language Models for MIMIC-IV Clinical Note Summarization.Journal of healthcare informatics research · 2026Article
- Portability of a text mining algorithm for detecting adverse drug reactions in electronic health records across diverse patient groups in two Dutch hospitals.PLOS digital health · 2026Article
- Comparison of local large language models for extraction of signs and symptoms data from electronic health records.PloS one · 2026Article
- HypKG: Hypergraph-based Knowledge Graph Contextualization for Precision Healthcare.The semantic Web--ISWC ... : ... International Semantic Web Conference ... proceedings. International Semantic Web Conference · 2026Article
- Association between complete blood count-derived hematological inflammatory ratios and nutritional risk in elderly patients with acute ischemic stroke.Frontiers in neurology · 2026Observational
- A Systematic Review of Drug-Related InteractionsUtilizing Deep Learning and LLMs for Prediction and Mitigation.ACS omega · 2025Review
- A Multiagent Summarization and Auto-Evaluation Framework for Medical Text: Development and Evaluation Study.JMIR AI · 2025Article
- Identifying Common Disease Trajectories of Progressive Supranuclear Palsy with Electronic Health Records.Movement disorders clinical practice · 2025Article
- Automating the extraction of otology symptoms from clinic letters: a methodological study using natural language processing.BMC medical informatics and decision making · 2025Article
- Natural Language Processing andJMIR medical informatics · 2025Article
- Generalizing machine learning models from clinical free text.Scientific reports · 2025Article
- Applying text-mining to clinical notes: the identification of patient characteristics from electronic health records (EHRs).BMC medical informatics and decision making · 2025Article
- Leveraging BERT for embedding ICD codes from large scale cardiovascular EMR data to understand patient diagnostic patterns.BMC medical informatics and decision making · 2025Article
- Enhancing medical text classification with GAN-based data augmentation and multi-task learning in BERT.Scientific reports · 2025Article
- Machine Learning-Driven Prediction of Vitamin D Deficiency Severity with Hybrid Optimization.Bioengineering (Basel, Switzerland) · 2025Article
- Development and validation of interpretable machine learning models for triage patients admitted to the intensive care unit.PloS one · 2025Article
- Pharmacist-Led Assessment of Drug-Induced Liver Injury in Hospitalized Patients.The Canadian journal of hospital pharmacy · 2025Article
22 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Currently, medical institutes generally use EMR to record patient's condition, including diagnostic information, procedures performed, and treatment results. EMR has been recognized as a valuable resource for large-scale analysis. However, EMR has the characteristics of diversity, incompleteness, redundancy, and privacy, which make it difficult to carry out data mining and analysis directly. Therefore, it is necessary to preprocess the source data in order to improve data quality and improve the data mining results. Different types of data require different processing technologies. Most structured data commonly needs classic preprocessing technologies, including data cleansing, data integration, data transformation, and data reduction. For semistructured or unstructured data, such as medical text, containing more health information, it requires more complex and challenging processing methods. The task of information extraction for medical texts mainly includes NER (named-entity recognition) and RE (relation extraction). This paper focuses on the process of EMR processing and emphatically analyzes the key techniques. In addition, we make an in-depth study on the applications developed based on text mining together with the open challenges and research issues for future work.
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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.