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Natural Language Processing for Medical Data
Dr. Anja Pilz discusses the application of natural language processing (NLP) in medical data to support doctors' work, improve documentation efficiency, and enhance billing processes. Key challenges highlighted include ambiguity in medical terminology, the complexity of German expressions, and the need for domain-specific models for entity recognition. The presentation emphasizes the importance of accurate entity linking for effective medical coding and treatment guidance.
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Natural Language Processing for Medical Data
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- 2. Dr. Anja Pilz,ML Conference 2021 About me @anja_pilz aplz ● 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
- 3. Dr. Anja Pilz,ML Conference 2021 Doctors spend more time documenting what they do than with effective treatment ● 70% of work hours dedicated to tasks not performed on the patient (orga & docs) Important as documentation covers symptoms, risk factors, intolerances, treatments, … ● each piece of information is vital for the patient - but can be buried somewhere Not only complex cases quickly become “unscannable” ● Use NLP for Information Extraction: automatically search, analyze, and add structure to these unstructured texts Swiss Medical Journal, 2016;97(1):6–8 Motivation
- 4. Dr. Anja Pilz,ML Conference 2021 Support doctor’s daily work ● create warnings from automatically detected risks and contraindications ● summarize suspected and excluded diagnoses (differential diagnosis) ● add hints to treatment guidelines And much more! Motivation
- 5. Dr. Anja Pilz,ML Conference 2021 Support billing process ● billing process is super complex and needs to be soundproof ● help medical controllers to find relevant information ● automatically find mentions of diseases and treatments ● align with entries from catalogs used for billing (e.g. ICD-10) Motivation Image damedic code
- 6. Dr. Anja Pilz,ML Conference 2021 NLP Tasks for Medical Data filter relevant entities (clinical, billing) Entity Recognition (NER) Entity Linking (NEL/NED) Entity Filtering detect all relevant mentions: ● diagnoses ● procedures ● body parts ● drugs ● measurements ● negations... link to unique concepts: ● entries in (curated) medical ontologies or catalogs ● normalization used for documentation, summarization, & billing
- 7. Dr. Anja Pilz,ML Conference 2021 Challenges: Medical Domain is not News Typical medical texts are very different common NLP data ● super condensed and short, sometimes like an enumeration ● full of abbreviations, acronyms and technical terms ● ambiguity is often resolved through sheer knowledge, not necessarily by the local context Indication: Acute hypoxia. Relapsed AML, GVHD, and renal failure with new hypoxia with clear chest x-ray.
- 8. Dr. Anja Pilz,ML Conference 2021 Abbreviations are used for convenience ● ambiguous ones may cause miscommunication ● potentially jeopardise patient care Entity Linking needs to expand acronyms but must not rely on priors Challenges: Ambiguity TMZ temazepam temozolomide Holper et al., Ambiguous medical abbreviation study: challenges and opportunities, Intern Med J. 2020 LFT liver function test LFT lung function test HWI Harnwegsinfekt Hinterwandinfarkt BCa bladder cancer breast cancer VF Vorhofflimmern Vorhofflattern MS Magensonde Mitralstenose
- 9. Dr. Anja Pilz,ML Conference 2021 Challenges: German Latin origin vs German spelling results in a bunch of variations ● Carcinom, Karcinom, Carzinom, Karzinom, Ca, CA The notorious compound words ● sensory sensation disorder: Schallempfindungsstörung ● occlusion of the central retinal artery: Netzhautarterienverschluss ● detection of Tuberculosis: Tuberkulosenachweis Decompounding is non-trivial and requires profound linguistic knowledge
- 10. Dr. Anja Pilz,ML Conference 2021 ● data is available, e.g. BC5CDR (1500 PubMed articles with annotated chemicals, diseases & their interactions) ● trained models are available ● not “solved” but at a pretty good state of the art Entity Recognition (EN) https://scispacy.apps.allenai.org/
- 11. Dr. Anja Pilz,ML Conference 2021 ● typical off-the-shelf models are not useful for the medical domain ● need to train domain models here Entity Recognition (DE)
- 12. Dr. Anja Pilz,ML Conference 2021 Data? Real patient data ● resides in hospitals and medical practices ● not publicly available Public data ● netdoktor != Dr. B. Oss ● data in layman language does not compare well to real medical texts ● may still help Patient: “Ich habe im Moment keine Blutdruckprobleme” Doctor: “RR gut eingestellt”
- 13. Dr. Anja Pilz,ML Conference 2021 Entity Recognition Get data. Start annotating. ● entities are all concepts of interest: drugs, medical conditions, procedures, body parts, … ● annotation usually requires medical expert knowledge ● super specific vocabulary with lots of abbreviations & acronyms ● good to go after ~1k documents
- 14. Dr. Anja Pilz,ML Conference 2021 Train your own model Entity Recognition + data
- 15. Dr. Anja Pilz,ML Conference 2021 Most work in research: link entity mentions to concepts in medical thesaurus UMLS ● higher level metadata enrichment ● index new publications by topic & keywords ● hot topic and a bunch of publications exists Why not? ● no German version (yet) ● concepts are sometimes not specific enough Entity Linking 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 Mohan & Li, MedMentions: A Large Biomedical Corpus Annotated with UMLS Concepts, AKBC 2019
- 16. Dr. Anja Pilz,ML Conference 2021 ICD-10 Linking ICD: International Statistical Classification of Diseases and Related Health Problems ● catalogs mental and physical disorders in most specific and precise form ● global standard for clinical documentation and billing ● published yearly by the WHO https://icd.who.int/browse10/2019/en
- 17. Dr. Anja Pilz,ML Conference 2021 ICD-10 Linking ICD: International Statistical Classification of Diseases and Related Health Problems ● catalogs mental and physical disorders in most specific and precise form ● global standard for clinical documentation and billing ● published yearly by the WHO ● … comes with German modification ICD-10-GM (BfArM) https://www.dimdi.de/static/de/klassifikationen/icd/icd-10-gm
- 18. Dr. Anja Pilz,ML Conference 2021 Higher clinical relevance ● support doctors: can’t get much more specific than with a diagnosis code ● support medical controllers: ICD codes are the items used in billing, not UMLS concepts Requires entity filtering to avoid false positives ● excluded or suspected diagnoses ● “state after diseases”: clinically but not be billing relevant ICD-10 Linking EHR Keine Hinweis auf intrazerebrale Blutung. Z.n. Hysterektomie, 2006
- 19. Dr. Anja Pilz,ML Conference 2021 Most mentions may be clinically relevant, but not coding relevant. Need relation extraction approaches here.. Entity Filtering for primary coding Prostatacarcinom in der Vorgeschichte Vorbekannte Osteochondrose Z.n. mehrfachem Apoplexen, zuletzt 2006 Mamma-Ca wurde ausgeschlossen. Keine Hinweis auf intrazerebrale Blutung. Die BWK 9-Fraktur zeigte sich mit fehlender knöcherner Durchbauung im Sinne einer Pseudarthrose. Intrazerebrale Blutung konnte nicht bestätigt werden. Verdacht auf arterielle Hypertonie.
- 20. Dr. Anja Pilz,ML Conference 2021 Toy example. Typical cases are much more complex.
- 21. Dr. Anja Pilz,ML Conference 2021 To be really useful, the link must be super specific ● “some renal failure” (N17*) is not good enough Specificity relates to the stage of the disease ● hugely affects treatment complexity and care intensity ● treatment complexity directly corresponds to the hospital’s bill send to the insurance company ICD-10 Linking https://www.dimdi.de/static/de/klassifikationen/icd/icd-10-gm
- 22. Dr. Anja Pilz,ML Conference 2021 Specificity To describe a disease in a certain stage or manifestation, the catalog is super specific ● 40 entries for different instances of Diabetes Mellitus, Type 1 and Type 2 each ● there are even more forms of Diabetes... Difference is sometimes only one word ● “nicht” or “mit/ohne”: usual stopwords are dangerous here! https://www.dimdi.de/static/de/klassifikationen/icd/icd-10-gm
- 23. Dr. Anja Pilz,ML Conference 2021 Precision vs Context ICD is completely different from Wikipedia ● catalog entries are precise descriptions without further context ● descriptions are not the most commonly used names ● descriptions tend to be very long: median number of words is 5, maximum is 28 ● typically not used in this form by the doctors: low character overlap, low similarity ... RR 150/90... ... rezidiv. Bluthochdruck mit Schwächegefühl...
- 24. Dr. Anja Pilz,ML Conference 2021 About Context.. Disambiguating information need not be located the discharge letter ● can even be in a completely different data format, e.g. lab measurements ● N18*: multiple measurements of a specific lab value (Creatinine) ● not an NLP task anymore, time series analysis? https://www.dimdi.de/static/de/klassifikationen/icd/icd-10-gm
- 25. Dr. Anja Pilz,ML Conference 2021 Entity Linking in Practice GoTo solution for candidate retrieval: inverted index over catalog descriptions ● basically a vector space model with cosine similarity over (query, entry) ● make use of the analyzers coming with lucene for tokenization, stemming, etc Secret sauce ● add medical knowledge and extend the descriptions (e.g. synonyms) ● hand craft search query from the mention context Gist: aim for high recall, you can’t link what you don’t find... Pilz & Paaß, Collective Search for Concept Disambiguation, COLING 2012
- 26. Dr. Anja Pilz,ML Conference 2021 Can handle typos and spelling variations. Query: “diabetes meltus” fetches all codes for Diabetes mellitus. Demo
- 27. Dr. Anja Pilz,ML Conference 2021 Can handle alternative names like synonyms or acronyms. Query “ANV 3” fetches all “Akutes Nierenversagen ... Stadium 3” codes But which one is it? Can not decide on the best candidate... Demo
- 28. Dr. Anja Pilz,ML Conference 2021 Best Candidate? Recipe: rank by context similarity to decide on best candidate ● find expressive vector representations of mention-candidate pairs ○ word2vec ○ topic distributions (LDA) ○ graphical similarity … ● plug vectors into some ranking model ○ Ranking SVM ○ specific loss functions in Neural Networks (Hamming) But we have seen: catalog does not provide extensive descriptions, so... Next time! Pilz & Paaß, From names to entities using thematic context distance, CIKM 2011
- 29. Dr. Anja Pilz,ML Conference 2021 Thanks! Questions? Say Hi!