ReviewYearbook of medical informatics2020
Medical Information Extraction in the Age of Deep Learning.
Review in Yearbook of medical informatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 1 of them a synthesis that pooled it.
What it found
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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
35 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Use of unstructured text in prognostic clinical prediction models: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2022Pooled it
- A Sentence Classification-Based Medical Status Extraction Pipeline for Electronic Health Records: Institutional Case Study.JMIR medical informatics · 2026Article
- SynthEHR-eviction: enhancing eviction SDoH detection with LLM-augmented synthetic EHR data.NPJ digital medicine · 2026Article
- A FAIR gold-standard benchmark for geographic entity recognition in cancer genomics literature for biomedical NLP.Bioinformatics advances · 2026Article
- SimSUM - simulated benchmark with structured and unstructured medical records.Journal of biomedical semantics · 2025Article
- Information Extraction of Doctoral Theses Using Two Different Large Language Models vs Health Services Researchers: Development and Usability Study.JMIR formative research · 2025Article
- A review on knowledge graphs for healthcare: Resources, applications, and promises.Journal of biomedical informatics · 2025Review
- Using Open-Source Large Language Models to Identify Access to Germline Genetic Testing in Veterans With Breast Cancer From Unstructured Text.JCO clinical cancer informatics · 2025Article
- A non-invasive diagnostic approach for neuroblastoma utilizing preoperative enhanced computed tomography and deep learning techniques.Scientific reports · 2025Article
- Ethical challenges and evolving strategies in the integration of artificial intelligence into clinical practice.PLOS digital health · 2025Article
- Knowledge-guided generative artificial intelligence for automated taxonomy learning from drug labels.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Augmented non-hallucinating large language models as medical information curators.NPJ digital medicine · 2024Article
- GPAD: a natural language processing-based application to extract the gene-disease association discovery information from OMIM.BMC bioinformatics · 2024Article
- Information extraction from weakly structured radiological reports with natural language queries.European radiology · 2024Article
- A review of research on eligibility criteria for clinical trials.Clinical and experimental medicine · 2023Review
- Novel method for predicting nonvisible symptoms using machine learning in cancer palliative care.Scientific reports · 2023Article
- Contextualized medication information extraction using Transformer-based deep learning architectures.Journal of biomedical informatics · 2023Article
- CareD: Caregiver's Experience with Cognitive Decline in Reddit Posts.Proceedings. IEEE International Conference on Healthcare Informatics · 2023Article
- A distributable German clinical corpus containing cardiovascular clinical routine doctor's letters.Scientific data · 2023Article
- Linguistic and ontological challenges of multiple domains contributing to transformed health ecosystems.Frontiers in medicine · 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
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesWe survey recent developments in medical Information Extraction (IE) as reported in the literature from the past three years. Our focus is on the fundamental methodological paradigm shift from standard Machine Learning (ML) techniques to Deep Neural Networks (DNNs). We describe applications of this new paradigm concentrating on two basic IE tasks, named entity recognition and relation extraction, for two selected semantic classes-diseases and drugs (or medications)-and relations between them.
methodsFor the time period from 2017 to early 2020, we searched for relevant publications from three major scientific communities: medicine and medical informatics, natural language processing, as well as neural networks and artificial intelligence.
resultsIn the past decade, the field of Natural Language Processing (NLP) has undergone a profound methodological shift from symbolic to distributed representations based on the paradigm of Deep Learning (DL). Meanwhile, this trend is, although with some delay, also reflected in the medical NLP community. In the reporting period, overwhelming experimental evidence has been gathered, as illustrated in this survey for medical IE, that DL-based approaches outperform non-DL ones by often large margins. Still, small-sized and access-limited corpora create intrinsic problems for data-greedy DL as do special linguistic phenomena of medical sublanguages that have to be overcome by adaptive learning strategies.
conclusionsThe paradigm shift from (feature-engineered) ML to DNNs changes the fundamental methodological rules of the game for medical NLP. This change is by no means restricted to medical IE but should also deeply influence other areas of medical informatics, either NLP- or non-NLP-based.
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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.