ReviewHealthcare (Basel, Switzerland)2023
A Review on Electronic Health Record Text-Mining for Biomedical Name Entity Recognition in Healthcare Domain.
Review in Healthcare (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 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
12 citing papers in PubMed, 43 citations in OpenAlex.
- A framework for extraction of clinical information from radiological mammography reports using large language models and retrieval augmented generation.BMC medical informatics and decision making · 2026Article
- Literature-informed gene extraction and ranking for multimodal data fusion.Briefings in bioinformatics · 2026Article
- Machine Learning-Based Frailty Prediction and Classification in Community-Dwelling Older Adults: A Systematic Review of Validation, Explainability, and Implementation Readiness.Healthcare (Basel, Switzerland) · 2026Review
- Multi-modal deep-attention-BiLSTM based early detection of mental health issues using social media posts.Scientific reports · 2025Article
- Intelligent classification and prediction of students' mental health in online learning environments using boosting algorithm and LIWC features.Scientific reports · 2025Article
- Leveraging large language models to mimic domain expert labeling in unstructured text-based electronic healthcare records in non-english languages.BMC medical informatics and decision making · 2025Article
- Biomedical named entity recognition using improved green anaconda-assisted Bi-GRU-based hierarchical ResNet model.BMC bioinformatics · 2025Article
- Evaluating Medical Entity Recognition in Health Care: Entity Model Quantitative Study.JMIR medical informatics · 2024Article
- CPMI-ChatGLM: parameter-efficient fine-tuning ChatGLM with Chinese patent medicine instructions.Scientific reports · 2024Article
- Sequential lexicon enhanced bidirectional encoder representations from transformers: Chinese named entity recognition using sequential lexicon enhanced BERT.PeerJ. Computer science · 2024Article
- Enhancing diagnostic accuracy in symptom-based health checkers: a comprehensive machine learning approach with clinical vignettes and benchmarking.Frontiers in artificial intelligence · 2024Article
- BIR: Biomedical Information Retrieval System for Cancer Treatment in Electronic Health Record Using Transformers.Sensors (Basel, Switzerland) · 2023Article
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 at 2 institutions in 2 countries.
Funding
Abstract
Biomedical-named entity recognition (bNER) is critical in biomedical informatics. It identifies biomedical entities with special meanings, such as people, places, and organizations, as predefined semantic types in electronic health records (EHR). bNER is essential for discovering novel knowledge using computational methods and Information Technology. Early bNER systems were configured manually to include domain-specific features and rules. However, these systems were limited in handling the complexity of the biomedical text. Recent advances in deep learning (DL) have led to the development of more powerful bNER systems. DL-based bNER systems can learn the patterns of biomedical text automatically, making them more robust and efficient than traditional rule-based systems. This paper reviews the healthcare domain of bNER, using DL techniques and artificial intelligence in clinical records, for mining treatment prediction. bNER-based tools are categorized systematically and represent the distribution of input, context, and tag (encoder/decoder). Furthermore, to create a labeled dataset for our machine learning sentiment analyzer to analyze the sentiment of a set of tweets, we used a manual coding approach and the multi-task learning method to bias the training signals with domain knowledge inductively. To conclude, we discuss the challenges facing bNER systems and future directions in the healthcare field.
Indexed as
Identifiers
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.