ArticleJournal of pathology informatics2020
Artificial Intelligence-Driven Structurization of Diagnostic Information in Free-Text Pathology Reports.
Article in Journal of pathology informatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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
9 citing papers in PubMed.
- Role of artificial intelligence in haematolymphoid diagnostics.Histopathology · 2025Review
- Synoptic reporting by summarizing cancer pathology reports using large language models.npj health systems · 2025Article
- Digital Information Ecosystems in Modern Care Coordination and Patient Care Pathways and the Challenges and Opportunities for AI Solutions.Journal of medical Internet research · 2024Article
- GPT-Driven Radiology Report Generation with Fine-Tuned Llama 3.Bioengineering (Basel, Switzerland) · 2024Article
- Applications of cutting-edge artificial intelligence technologies in biomedical literature and document mining.Medical review (2021) · 2023Article
- An accessible, efficient, and accurate natural language processing method for extracting diagnostic data from pathology reports.Journal of pathology informatics · 2022Article
- Empowering digital pathology applications through explainable knowledge extraction tools.Journal of pathology informatics · 2022Article
- Searching Full-Text Anatomic Pathology Reports Using Business Intelligence Software.Journal of pathology informatics · 2022Article
- Validation of deep learning natural language processing algorithm for keyword extraction from pathology reports in electronic health records.Scientific reports · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundFree-text sections of pathology reports contain the most important information from a diagnostic standpoint. However, this information is largely underutilized for computer-based analytics. The vast majority of NLP-based methods lack a capacity to accurately extract complex diagnostic entities and relationships among them as well as to provide an adequate knowledge representation for downstream data-mining applications.
methodsIn this paper, we introduce a novel informatics pipeline that extends open information extraction (openIE) techniques with artificial intelligence (AI) based modeling to extract and transform complex diagnostic entities and relationships among them into Knowledge Graphs (KGs) of relational triples (RTs).
resultsEvaluation studies have demonstrated that the pipeline's output significantly differs from a random process. The semantic similarity with original reports is high (Mean Weighted Overlap of 0.83). The
conclusionThe results demonstrated important properties of the pipeline such as
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.