Reference health
Generative Artificial Intelligence-Based Medical Entity Data Extractor Using Large Language Models
30/30 checkable references clean · checked 2026-08-07
Every reference with a DOI in the deposited reference list resolved to a known work in Crossref or DataCite at the dated check, and none carried a retraction, withdrawal, or removal notice.
34 without a DOI — not checked. A reference deposited without a DOI is never matched by title or guessed at; it stays outside the checked set, and this line discloses that.
The 30 checked references that resolve
resolves10.1055/s-0038-1638592
Extracting Information from Textual Documents in the Electronic Health Record: A Review of Recent Research
resolves10.1038/s41591-018-0335-9
Evaluation and accurate diagnoses of pediatric diseases using artificial intelligence
resolves10.1001/jamanetworkopen.2021.26337
Assessing the Prognostic Significance of Tumor-Infiltrating Lymphocytes in Patients With Melanoma Using Pathologic Features Identified by Natural Language Processing
resolves10.1016/j.jbi.2011.08.006
Coreference resolution: A review of general methodologies and applications in the clinical domain
resolves10.1016/j.jhsa.2017.03.023
Electronic Communication of Protected Health Information: Privacy, Security, and HIPAA Compliance
resolves10.1002/hcs2.61
Large language models in health care: Development, applications, and challenges
resolves10.1007/s10439-023-03297-9
Need an AI-Enabled, Next-Generation, Advanced ChatGPT or Large Language Models (LLMs) for Error-Free and Accurate Medical Information
resolves10.1609/aaai.v35i16.17665
Entity Structure Within and Throughout: Modeling Mention Dependencies for Document-Level Relation Extraction
resolves10.1609/aaai.v34i05.6282
TANDA: Transfer and Adapt Pre-Trained Transformer Models for Answer Sentence Selection
resolves10.1109/ICEIEC49280.2020.9152257
Using a Pre-Trained Language Model for Medical Named Entity Extraction in Chinese Clinic Text
resolves10.1093/jamia/ocz200
Deep learning in clinical natural language processing: a methodical review
resolves10.1145/3560815
Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing
resolves10.1093/jamia/ocz144
Identifying relations of medications with adverse drug events using recurrent convolutional neural networks and gradient boosting
resolves10.1007/s40264-018-0761-0
MADEx: A System for Detecting Medications, Adverse Drug Events, and Their Relations from Clinical Notes
resolves10.1186/s12911-019-0935-4
A study of deep learning methods for de-identification of clinical notes in cross-institute settings
resolves10.4338/ACI-2016-12-RA-0211
The effects of natural language processing on cross-institutional portability of influenza case detection for disease surveillance
resolves10.1093/jamia/ocad107
Clinical concept and relation extraction using prompt-based machine reading comprehension
resolves10.1016/j.jbi.2023.104370
Contextualized medication information extraction using Transformer-based deep learning architectures
resolves10.1093/jamia/ocz166
2018 n2c2 shared task on adverse drug events and medication extraction in electronic health records
resolves10.1093/jamia/ocad012
The 2022 n2c2/UW shared task on extracting social determinants of health
resolves10.1016/j.jksuci.2024.101933
MED-Prompt: A novel prompt engineering framework for medicine prediction on free-text clinical notes
The 34 references without a DOI — listed, not checked
no DOI — not checkedStructured data entry in the electronic medical record: perspectives of pediatric specialty physicians and surgeons
no DOI — not checkedEvaluating large language models for use in healthcare: A framework for translational value assessment
no DOI — not checkedref10
no DOI — not checkedDeep patient representation of clinical notes via multitask learning for mortality prediction
no DOI — not checkedNatural language processing (almost) from scratch
no DOI — not checkedref15
no DOI — not checkedAttention is all you need
no DOI — not checkedref17
no DOI — not checkedref18
no DOI — not checkedref19
no DOI — not checkedref20
no DOI — not checkedref21
no DOI — not checkedExploring the limits of transfer learning with a unified text-to-text transformer
no DOI — not checkedref24
no DOI — not checkedGeneralized autoregressive pretraining for language understanding
no DOI — not checkedref27
no DOI — not checkedref33
no DOI — not checkedref34
no DOI — not checkedDomain-specific language model pretraining for biomedical natural language processing
no DOI — not checkedref40
no DOI — not checkedref42
no DOI — not checkedref43
no DOI — not checkedref44
no DOI — not checkedref45
no DOI — not checkedref54
no DOI — not checkedTraining language models to follow instructions with human feedback
no DOI — not checkedref56
no DOI — not checkedref58
no DOI — not checkedref59
no DOI — not checkedref60
no DOI — not checkedVisualizing data using t-sne
no DOI — not checkedref62
no DOI — not checkedref63
no DOI — not checkedAe-gpt: Using large language models to extract adverse events from surveillance reports-a use case with influenza vaccine adverse events
What this badge says. CiteStamped means the CHECKABLE references of this
work were clean at the dated check: each resolved to a known work in a public registry, and none
carried a retraction notice at that time. It says nothing about the quality, findings,
or importance of the work itself, and nothing about references deposited without a DOI.
checked 2026-08-07 — re-checked daily as this page is visited; titles and statuses come from Crossref and DataCite and are not part of the signed record
Embed this badge
Both snippets point at the live badge image and link back to this page. The badge re-renders from the daily check, so an embed never goes stale by more than a day of visits.
<a href="https://citestamp.com/citestamped/10.2139/ssrn.5063216"><img src="https://citestamp.com/citestamped/10.2139/ssrn.5063216/badge.svg" alt="CiteStamped reference-health badge" width="460" height="64"></a>
[](https://citestamp.com/citestamped/10.2139/ssrn.5063216)