Biomedical Entity Linking - Introduction, approaches, challenges
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This talk gives an introduction to entity linking for biomedical data. It describes the problem to be solved as a three stage task and links to state of the art approaches for these steps. Talk held at the Hamburg Data Science Meetup, Hamburgs largest data event.
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Biomedical Entity Linking - Introduction, approaches, challenges
- 2. About me ● PhD in machine learning & natural language processing from University of Bonn & Fraunhofer IAIS ● Now in industry: AI and data driven products, since 2016 mostly in the medical and healthcare domain ● Main interests: NLP, especially German; information retrieval; recommender systems @anja_pilz aplz
- 3. Outline ● Motivation: why do we need entity linking ○ Ambiguity, use cases ● Entity linking in the biomedical domain ○ Data and ontologies, main challenges ● Technical problem: 3 stage task ○ Approaches for each of these stages (sketches & references) ● Short preview of challenges with German data
- 4. Language is ambiguous “with steroid induced diabetes, I lost a stone in three days, it was grim” Type II diabetes Type I diabetes Gestational diabetes Steroid diabetes gallstones kidney stones the stone (unit) a random stone grim protein, Drosophila variants of steroids
- 5. Why do we care? Need to resolve ambiguity to ● avoid mistakes in patient-doctor communication ○ specialist vs layman vocabulary ● automatically retrieve important information ○ side effects of drugs discussed in online patient fora ● enrich electronic health records (EHR) ○ links to newest research, treatment guidelines or other LOD resources And many more reasons… Entity linking resolves ambiguity by assigning each mention its underlying “sense”.
- 6. Headache Cephalgia Entities: entries in (curated), medical ontologiesMentions: textual references of medical terms like diagnoses, treatments, body parts, drugs, ... Biomedical Entity Linking Migraine Head Pain Cranial Pain Headache (D006261) layman terms EHR specialist vocabulary
- 7. Example: excerpt from a PubMed abstract linked to UMLS (Unified Medical Language System) Biomedical Entity Linking Mohan & Li, MedMentions: A Large Biomedical Corpus Annotated with UMLS Concepts, AKBC 2019 The technique does not require contrast material, so it can safely be used in patients with renal failure.
- 8. Why is that hard? ● Notion of uniqueness: a disease is rendered unique by the person it affects (and the stage) ● Uniqueness heavily affects linkability: which stage of renal failure is meant? ○ candidates “look” super similar ○ might even need additional resources (lab) Acute renal failure: Her baseline Cr is 1.8. On presentation the Cr had increased to 7.7 secondary to the bilateral hydronephrosis. https://icd.who.int/browse11/l-m/en Johnson et al., MIMIC-III, a freely accessible critical care database. Scientific Data 2016
- 9. Given some text document, find all spans of words m that mention some entity e and assign each span to a unique identifier (entry in a KB). Technical Problem Entity Recognition: detect spans to be linked (Sequence Tagging) Candidate Retrieval: find all relevant candidates in a KB (Information Retrieval) Candidate Ranking: decide on the best candidate (Ranking Task) Errorpropagation
- 10. Step 1: Entity Recognition Goal: detect diagnoses, measurements, procedures in the text of the EHR ● supervised: train a sequence tagging model ○ pick a model: lots of literature but mostly sth Bi-LSTM CRF ○ (manually) annotate data ● pro: domain adaptation & custom features ● con: requires training data & medical expertise Roller et al., Detecting Named Entities and Relations in German Clinical Reports, GSCL 2017 Murty et al., Hierarchical Losses and New Resources for Fine-grained Entity Typing and Linking, ACL 2018 Lampe et al., Neural Architectures for Named Entity Recognition. NAACL-HLT 2016 Indication: Acute hypoxia. Relapsed AML, GVHD, and renal failure with new hypoxia with clear chest x-ray.
- 11. Step 1: Entity Recognition Goal: detect diagnoses, measurements, procedures in the text of the EHR ● weakly labeled: keyword matching ○ walk over text and lookup every span in a dictionary ○ keep all spans that have at least one entity candidate ● pro: no need to annotate data ● con: noise, type and recall issues Murty et al., Hierarchical Losses and New Resources for Fine-grained Entity Typing and Linking, ACL 2018 Kolitsas et al., End-to-End Neural Entity Linking, CoNLL 2018 Wiatrak, Iso-Sipilä. Simple Hierarchical Multi-Task Neural End-To-End Entity Linking for Biomedical Text. LOUHI@EMNLP 2020 Indication: Acute hypoxia. Relapsed AML, GVHD, and renal failure with new hypoxia with clear chest x-ray.
- 12. Step 2: Candidate Retrieval Goal: fetch all relevant candidate entities from the ontology ● upper bound on performance: you can’t link what you don’t find GoTo solution: inverted index (lucene) over entity descriptions ● make use of the analyzers coming with lucene for tokenization, stemming, etc ● craft search query from the mention context ● keep top 5, 10, 100 hits as candidates Pilz & Paaß, Collective Search for Concept Disambiguation, COLING 2012
- 13. Step 3: Candidate Ranking Goal: decide on the best candidate as target entity Rank by context similarity ● compare text representations of mention context and entity description (word2vec, topic distributions, etc) ● but: medical ontologies do often not provide extensive descriptions Pilz & Paaß, From names to entities using thematic context distance, CIKM 2011
- 14. Step 3: Candidate Ranking Goal: decide on the best candidate as target entity Add type similarity from hierarchies ● Wikipedia: categories assigned to entities ● UMLS: use semantic types ○ distinguish disease form the gene its caused by ○ LATTE: find boost in linking performance when adding type encoding learned from UMLS types Zhu et al., LATTE: Latent Type Modeling for Biomedical Entity Linking, AAAI 2020 UMLS® Reference Manual
- 15. Step 3: Candidate Ranking Goal: decide on the best candidate as target entity In a nutshell ● find expressive vector representations of mention-candidate pairs ● plug vectors into some function to rank them ○ Ranking SVM, specific loss functions in NN, … ● the information in the vector is more important than the algorithm!
- 16. Challenges with German data ● Data is scarce, nothing comparable to MIMIC-III or MedMentions exists ● Ontologies like UMLS are only available in English ● NLP for German is a tad harder ○ Common nouns look like named entities (upper case) ● … the notorious compound words ○ sensory sensation disorder: Schallempfindungsstörung ○ occlusion of the central retinal artery: Netzhautarterienverschluss Ideas? Let’s discuss!