Structured Extraction of Real World Medical Knowledge using LLMs for Summarization and Search | Paper Citation Record

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This paper cites Towards a definition of knowledge graphs.

Structured Extraction of Real World Medical Knowledge using LLMs for Summarization and Search Towards a definition of knowledge graphs

Reference 1

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This paper cites Industry-scale knowledge graphs: Lessons and challenges.

Structured Extraction of Real World Medical Knowledge using LLMs for Summarization and Search Industry-scale knowledge graphs: Lessons and challenges

Reference 2

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This paper cites Knowledge graphs.

Structured Extraction of Real World Medical Knowledge using LLMs for Summarization and Search Knowledge graphs

Reference 3

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This paper cites A survey on knowledge graphs: Representation, acquisition, and applications.

Structured Extraction of Real World Medical Knowledge using LLMs for Summarization and Search A survey on knowledge graphs: Representation, acquisition, and applications

Reference 4

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This paper cites The unified medical language system (umls): integrating biomedical terminology.

Structured Extraction of Real World Medical Knowledge using LLMs for Summarization and Search The unified medical language system (umls): integrating biomedical terminology

Reference 5

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This paper cites Gene ontology: tool for the unification of biology.

Structured Extraction of Real World Medical Knowledge using LLMs for Summarization and Search Gene ontology: tool for the unification of biology

Reference 6

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This paper cites Disgenet: a comprehensive platform integrating information on human disease-associated genes and variants.

Structured Extraction of Real World Medical Knowledge using LLMs for Summarization and Search Disgenet: a comprehensive platform integrating information on human disease-associated genes and variants

Reference 7

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This paper cites Drugbank: a comprehensive resource for in silico drug discovery and exploration.

Structured Extraction of Real World Medical Knowledge using LLMs for Summarization and Search Drugbank: a comprehensive resource for in silico drug discovery and exploration

Reference 8

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This paper cites Drugbank 5.0: a major update to the drugbank database for 2018.

Structured Extraction of Real World Medical Knowledge using LLMs for Summarization and Search Drugbank 5.0: a major update to the drugbank database for 2018

Reference 9

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This paper cites Drugbank 6.0: the drugbank knowledgebase for 2024.

Structured Extraction of Real World Medical Knowledge using LLMs for Summarization and Search Drugbank 6.0: the drugbank knowledgebase for 2024

Reference 10

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This paper cites Effective mapping of biomedical text to the umls metathesaurus: the metamap program.

Structured Extraction of Real World Medical Knowledge using LLMs for Summarization and Search Effective mapping of biomedical text to the umls metathesaurus: the metamap program

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:01:45.334183Z

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This paper cites Dnorm: dis- ease name normalization with pairwise learning to rank.

Structured Extraction of Real World Medical Knowledge using LLMs for Summarization and Search Dnorm: dis- ease name normalization with pairwise learning to rank

Reference 12

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This paper cites Attention is all you need.

Structured Extraction of Real World Medical Knowledge using LLMs for Summarization and Search Attention is all you need

Reference 13

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This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Structured Extraction of Real World Medical Knowledge using LLMs for Summarization and Search BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 14

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This paper cites Biobert: a pre-trained biomedical language representation model for biomedical text mining.

Structured Extraction of Real World Medical Knowledge using LLMs for Summarization and Search Biobert: a pre-trained biomedical language representation model for biomedical text mining

Reference 15

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This paper cites SciBERT: A Pretrained Language Model for Scientific Text.

Structured Extraction of Real World Medical Knowledge using LLMs for Summarization and Search SciBERT: A Pretrained Language Model for Scientific Text

Reference 16

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This paper cites Publicly Available Clinical BERT Embeddings.

Structured Extraction of Real World Medical Knowledge using LLMs for Summarization and Search Publicly Available Clinical BERT Embeddings

Reference 17

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This paper cites Domain-specific language model pretraining for biomedical natural language processing.

Structured Extraction of Real World Medical Knowledge using LLMs for Summarization and Search Domain-specific language model pretraining for biomedical natural language processing

Reference 18

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This paper cites Biogpt: generative pre-trained transformer for biomedical text generation and mining.

Structured Extraction of Real World Medical Knowledge using LLMs for Summarization and Search Biogpt: generative pre-trained transformer for biomedical text generation and mining

Reference 19

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This paper cites Enhancing phenotype recognition in clinical notes using large language models: Phenobcbert and phenogpt.

Structured Extraction of Real World Medical Knowledge using LLMs for Summarization and Search Enhancing phenotype recognition in clinical notes using large language models: Phenobcbert and phenogpt

Reference 20

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Observation ae4110b7-051f-4220-b9af-5e423bcc7c0e · outbound

This paper cites Large Language Models are Few-Shot Clinical Information Extractors.

Structured Extraction of Real World Medical Knowledge using LLMs for Summarization and Search Large Language Models are Few-Shot Clinical Information Extractors

Reference 21

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This paper cites Large Language Models for Biomedical Knowledge Graph Construction: Information extraction from EMR notes.

Structured Extraction of Real World Medical Knowledge using LLMs for Summarization and Search Large Language Models for Biomedical Knowledge Graph Construction: Information extraction from EMR notes

Reference 22

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This paper cites Automating the expansion of a knowledge graph.

Structured Extraction of Real World Medical Knowledge using LLMs for Summarization and Search Automating the expansion of a knowledge graph

Reference 23

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This paper cites On Faithfulness and Factuality in Abstractive Summarization.

Structured Extraction of Real World Medical Knowledge using LLMs for Summarization and Search On Faithfulness and Factuality in Abstractive Summarization

Reference 24

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This paper cites Graph-constrained Reasoning: Faithful Reasoning on Knowledge Graphs with Large Language Models.

Structured Extraction of Real World Medical Knowledge using LLMs for Summarization and Search Graph-constrained Reasoning: Faithful Reasoning on Knowledge Graphs with Large Language Models

Reference 25

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This paper cites Current procedural terminology: Cpt.

Structured Extraction of Real World Medical Knowledge using LLMs for Summarization and Search Current procedural terminology: Cpt

Reference 26

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This paper cites International Statistical Classification of Diseases and related health problems: Alphabetical index , volume 3.

Structured Extraction of Real World Medical Knowledge using LLMs for Summarization and Search International Statistical Classification of Diseases and related health problems: Alphabetical index , volume 3

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:01:45.209966Z

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Observation 92e513bb-9f7a-4c53-b90a-b8bb557203e0 · outbound

This paper cites Snomed-ct: The advanced terminology and coding system for ehealth.

Structured Extraction of Real World Medical Knowledge using LLMs for Summarization and Search Snomed-ct: The advanced terminology and coding system for ehealth

Reference 28

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This paper cites The human phenotype ontology: a tool for annotating and analyzing human hereditary disease.

Structured Extraction of Real World Medical Knowledge using LLMs for Summarization and Search The human phenotype ontology: a tool for annotating and analyzing human hereditary disease

Reference 29

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This paper cites Normalized names for clinical drugs: Rxnorm at 6 years.

Structured Extraction of Real World Medical Knowledge using LLMs for Summarization and Search Normalized names for clinical drugs: Rxnorm at 6 years

Reference 30

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This paper cites Phenobert: a combined deep learning method for automated recognition of human phenotype ontology.

Structured Extraction of Real World Medical Knowledge using LLMs for Summarization and Search Phenobert: a combined deep learning method for automated recognition of human phenotype ontology

Reference 31

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Observation 4701faa5-b6a2-4105-b7aa-74d613132ca1 · outbound

This paper cites Enriching representation learning using 53 million patient notes through human phenotype ontology embedding.

Structured Extraction of Real World Medical Knowledge using LLMs for Summarization and Search Enriching representation learning using 53 million patient notes through human phenotype ontology embedding

Reference 32

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This paper cites Phenotype annotations for patient notes in the mimic-iii database.

Structured Extraction of Real World Medical Knowledge using LLMs for Summarization and Search Phenotype annotations for patient notes in the mimic-iii database

Reference 33

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Observation a018a836-b460-4c88-b3b3-0250bd147a5c · outbound

This paper cites Respond.health – clinical discovery that responds to the real world.

Structured Extraction of Real World Medical Knowledge using LLMs for Summarization and Search Respond.health – clinical discovery that responds to the real world

Reference 34

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Observation e02eb573-6b03-4fac-a9b9-40bda97ef2ac · outbound

This paper cites Biocreative v cdr task corpus: a resource for chemical disease relation extraction.

Structured Extraction of Real World Medical Knowledge using LLMs for Summarization and Search Biocreative v cdr task corpus: a resource for chemical disease relation extraction

Reference 35

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This paper cites Ncbi disease corpus: a resource for disease name recognition and concept normaliza- tion.

Structured Extraction of Real World Medical Knowledge using LLMs for Summarization and Search Ncbi disease corpus: a resource for disease name recognition and concept normaliza- tion

Reference 36

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This paper cites Named entity recognition and normalization in biomedical literature: a practical case in SARS-CoV-2 literature.

Structured Extraction of Real World Medical Knowledge using LLMs for Summarization and Search Named entity recognition and normalization in biomedical literature: a practical case in SARS-CoV-2 literature

Reference 37

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This paper cites ScispaCy: Fast and Robust Models for Biomedical Natural Language Processing.

Structured Extraction of Real World Medical Knowledge using LLMs for Summarization and Search ScispaCy: Fast and Robust Models for Biomedical Natural Language Processing

Reference 38

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This paper cites Language Models are Few-Shot Learners.

Structured Extraction of Real World Medical Knowledge using LLMs for Summarization and Search Language Models are Few-Shot Learners

Reference 39

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This paper cites Helpsteer2- preference: Complementing ratings with preferences, 2024.

Structured Extraction of Real World Medical Knowledge using LLMs for Summarization and Search Helpsteer2- preference: Complementing ratings with preferences, 2024

Reference 40

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This paper cites Qwen2.5: A party of foundation models, September 2024.

Structured Extraction of Real World Medical Knowledge using LLMs for Summarization and Search Qwen2.5: A party of foundation models, September 2024

Reference 41

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This paper cites mGTE: Generalized Long-Context Text Representation and Reranking Models for Multilingual Text Retrieval.

Structured Extraction of Real World Medical Knowledge using LLMs for Summarization and Search mGTE: Generalized Long-Context Text Representation and Reranking Models for Multilingual Text Retrieval

Reference 42

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This paper cites Physiobank, physiotoolkit, and physionet: components of a new research resource for complex physiologic signals.

Structured Extraction of Real World Medical Knowledge using LLMs for Summarization and Search Physiobank, physiotoolkit, and physionet: components of a new research resource for complex physiologic signals

Reference 43

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This paper cites Mimic-iii, a freely accessible critical care database.

Structured Extraction of Real World Medical Knowledge using LLMs for Summarization and Search Mimic-iii, a freely accessible critical care database

Reference 44

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This paper cites Comparing deep learning and concept ex- traction based methods for patient phenotyping from clinical narratives.

Structured Extraction of Real World Medical Knowledge using LLMs for Summarization and Search Comparing deep learning and concept ex- traction based methods for patient phenotyping from clinical narratives

Reference 45

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This paper cites Less annotating, more classifying: Addressing the data scarcity issue of supervised machine learning with deep transfer learning and bert-nli.

Structured Extraction of Real World Medical Knowledge using LLMs for Summarization and Search Less annotating, more classifying: Addressing the data scarcity issue of supervised machine learning with deep transfer learning and bert-nli

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:01:44.993094Z

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Observation 2c43f371-c032-45bb-b0dd-842fc8a26974 · outbound

This paper cites Responsible use of mimic data with online services like gpt,.

Structured Extraction of Real World Medical Knowledge using LLMs for Summarization and Search Responsible use of mimic data with online services like gpt,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:01:44.977951Z

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Observation a4528bcf-8dc0-480f-a7f4-f006422ec49e · outbound

This paper cites Phenorerank: A re-ranking model for phenotypic concept recognition pre-trained on human phenotype ontology.

Structured Extraction of Real World Medical Knowledge using LLMs for Summarization and Search Phenorerank: A re-ranking model for phenotypic concept recognition pre-trained on human phenotype ontology

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:01:44.948937Z

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This paper cites Doc2hpo: a web application for efficient and accurate hpo concept curation.

Structured Extraction of Real World Medical Knowledge using LLMs for Summarization and Search Doc2hpo: a web application for efficient and accurate hpo concept curation

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:01:44.933223Z

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Observation 9e03d03c-6073-4939-a2d1-6f93987af2df · outbound

This paper cites Clinphen extracts and prioritizes patient phenotypes directly from medical records to expedite genetic disease diagnosis.

Structured Extraction of Real World Medical Knowledge using LLMs for Summarization and Search Clinphen extracts and prioritizes patient phenotypes directly from medical records to expedite genetic disease diagnosis

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:01:44.917246Z

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Observation d148a9e4-d13d-4a15-b4dd-e02961e746d1 · outbound

This paper cites Phenotagger: a hybrid method for phenotype concept recognition using human phenotype ontology.

Structured Extraction of Real World Medical Knowledge using LLMs for Summarization and Search Phenotagger: a hybrid method for phenotype concept recognition using human phenotype ontology

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:01:44.901206Z

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Observation bdb401eb-05c5-438d-8de8-a0c542115081 · outbound

This paper cites From Local to Global: A Graph RAG Approach to Query-Focused Summarization.

Structured Extraction of Real World Medical Knowledge using LLMs for Summarization and Search From Local to Global: A Graph RAG Approach to Query-Focused Summarization

Reference 52

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Observation 1146a842-cae1-4529-b16f-f0641bed6d4c · outbound

This paper cites an unresolved cited work.

Structured Extraction of Real World Medical Knowledge using LLMs for Summarization and Search Unresolved cited work

Reference 2023

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