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Clinical Natural Language Processing: Part I Guergana K. Savova, PhD Childrens Hospital Boston and Harvard Medical School.

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Presentation on theme: "Clinical Natural Language Processing: Part I Guergana K. Savova, PhD Childrens Hospital Boston and Harvard Medical School."— Presentation transcript:

1 Clinical Natural Language Processing: Part I Guergana K. Savova, PhD Childrens Hospital Boston and Harvard Medical School

2 Investigators (in alphabetical order)  Childrens Hospital Boston and HMS (site PI: Guergana Savova)  MIT (site PI: Peter Szolovits)  MITRE corporation (site PI: Lynette Hirschman)  Seattle Group Health (site PI: David Carrell)  SUNY Albany (site PI: Ozlem Uzuner)  University of California, San Diego (site PI: Wendy Chapman  University of Colorado (site PI: Martha Palmer)  University of Pittsburg (site PI: Henk Harkema)  University of Utah and Intermountain Healthcare (site PI: Peter Haug)

3 Special Acknowledgement  Our talented super software developers – Vinod Kaggal, lead – Dingcheng Li – Pei Chen – James Masanz

4 Overview  Part 1: –Background and objectives of SHARP 4 cNLP project – Year 1 achievements – Clinical Text Analysis and Knowledge Extraction System (cTAKES) – Year 2 proposed projects – Graphical User Interface to cTAKES: demo  Part 2: – cTAKES: demo

5 Aims  Information extraction (IE): transformation of unstructured text into structured representations and merging clinical data extracted from free text with structured data –Entity and Event discovery –Relation discovery –Normalization template: Clinical Element Model (CEM)  Overarching goal –high-throughput phenotype extraction from clinical free text based on standards and the principles of interoperability –general purpose clinical NLP tool with applications to the majority of all imaginable use cases

6 A 43-year-old woman was diagnosed with type 2 diabetes mellitus by her family physician 3 mpresentation. Her initial blood glucose was 340 mg/dL. Glyburide A 43-year-old woman was diagnosed with type 2 diabetes mellitus by her family physician 3 months before this presentation. Her initial blood glucose was 340 mg/dL. Glyburide A 43-year-old woman was diagnosed with type 2 diabetes mellitus by her family physician 3 months before this presentation. Her initial blood glucose was 340 mg/dL. Glyburide 2.5 mg once daily was prescribed. Since then, self-monitoring of blood glucose (SMBG) showed blood glucose levels of mg/dL. She was referred to an endocrinologist for further evaluation. On examination, she was normotensive and not acutely ill. Her body mass index (BMI) was 18.7 kg/m2 following a recent 10 lb weight loss. Her thyroid was symmetrically enlarged and ankle reflexes absent. Her blood glucose was 272 mg/dL, and her hemoglobin A1c (HbA1c) was 10.3%. A lipid profile showed a total cholesterol of 261 mg/dL, triglyceride level of 321 mg/dL, HDL level of 48 mg/dL, and an LDL of 150 mg/dL. Thyroid function was normal. Urinanalysis showed trace ketones. She adhered to a regular exercise program and vitamin regimen, smoked 2 packs of cigarettes daily for the past 25 years, and limited her alcohol intake to 1 drink daily. Her mother's brother was diabetic. Processing Clinical Notes A 43-year-old woman was diagnosed with type 2 diabetes mellitus by her family physician 3 months before this presentation. Her initial blood glucose was 340 mg/dL. Glyburide 2.5 mg once daily was prescribed. Since then, self-monitoring of blood glucose (SMBG) showed blood glucose levels of mg/dL. She was referred to an endocrinologist for further evaluation. On examination, she was normotensive and not acutely ill. Her body mass index (BMI) was 18.7 kg/m2 following a recent 10 lb weight loss. Her thyroid was symmetrically enlarged and ankle reflexes absent. Her blood glucose was 272 mg/dL, and her hemoglobin A1c (HbA1c) was 10.3%. A lipid profile showed a total cholesterol of 261 mg/dL, triglyceride level of 321 mg/dL, HDL level of 48 mg/dL, and an LDL of 150 mg/dL. Thyroid function was normal. Urinanalysis showed trace ketones. She adhered to a regular exercise program and vitamin regimen, smoked 2 packs of cigarettes daily for the past 25 years, and limited her alcohol intake to 1 drink daily. Her mother's brother was diabetic.

7 Clinical Element Model Disorder CEM text: diabetes mellitus code: subject: patient relative temporal context: 3 months ago negation indicator: not negated Disorder CEM text: diabetes mellitus code: subject: family member relative temporal context: negation indicator: not negated Tobacco Use CEM text: smoking code: subject: patient relative temporal context: 25 years negation indicator: not negated Medication CEM text: Glyburide code: subject: patient frequency: once daily negation indicator: not negated strength:2.5 mg A 43-year-old woman was diagnosed with type 2 diabetes mellitus by her family physician 3 months before this presentation. Her initial blood glucose was 340 mg/dL. Glyburide 2.5 mg once daily was prescribed. Since then, self-monitoring of blood glucose (SMBG) showed blood glucose levels of mg/dL. She was referred to an endocrinologist for further evaluation. On examination, she was normotensive and not acutely ill. Her body mass index (BMI) was 18.7 kg/m2 following a recent 10 lb weight loss. Her thyroid was symmetrically enlarged and ankle reflexes absent. Her blood glucose was 272 mg/dL, and her hemoglobin A1c (HbA1c) was 10.3%. A lipid profile showed a total cholesterol of 261 mg/dL, triglyceride level of 321 mg/dL, HDL level of 48 mg/dL, and an LDL of 150 mg/dL. Thyroid function was normal. Urinanalysis showed trace ketones. She adhered to a regular exercise program and vitamin regimen, smoked 2 packs of cigarettes daily for the past 25 years, and limited her alcohol intake to 1 drink daily. Her mother's brother was diabetic. A 43-year-old woman was diagnosed with type 2 diabetes mellitus by her family physician 3 months before this presentation. Her initial blood glucose was 340 mg/dL. Glyburide 2.5 mg once daily was prescribed. Since then, self-monitoring of blood glucose (SMBG) showed blood glucose levels of mg/dL. She was referred to an endocrinologist for further evaluation. On examination, she was normotensive and not acutely ill. Her body mass index (BMI) was 18.7 kg/m2 following a recent 10 lb weight loss. Her thyroid was symmetrically enlarged and ankle reflexes absent. Her blood glucose was 272 mg/dL, and her hemoglobin A1c (HbA1c) was 10.3%. A lipid profile showed a total cholesterol of 261 mg/dL, triglyceride level of 321 mg/dL, HDL level of 48 mg/dL, and an LDL of 150 mg/dL. Thyroid function was normal. Urinanalysis showed trace ketones. She adhered to a regular exercise program and vitamin regimen, smoked 2 packs of cigarettes daily for the past 25 years, and limited her alcohol intake to 1 drink daily. Her mother's brother was diabetic. A 43-year-old woman was diagnosed with type 2 diabetes mellitus by her family physician 3 months before this presentation. Her initial blood glucose was 340 mg/dL. Glyburide 2.5 mg once daily was prescribed. Since then, self-monitoring of blood glucose (SMBG) showed blood glucose levels of mg/dL. She was referred to an endocrinologist for further evaluation. On examination, she was normotensive and not acutely ill. Her body mass index (BMI) was 18.7 kg/m2 following a recent 10 lb weight loss. Her thyroid was symmetrically enlarged and ankle reflexes absent. Her blood glucose was 272 mg/dL, and her hemoglobin A1c (HbA1c) was 10.3%. A lipid profile showed a total cholesterol of 261 mg/dL, triglyceride level of 321 mg/dL, HDL level of 48 mg/dL, and an LDL of 150 mg/dL. Thyroid function was normal. Urinanalysis showed trace ketones. She adhered to a regular exercise program and vitamin regimen, smoked 2 packs of cigarettes daily for the past 25 years, and limited her alcohol intake to 1 drink daily. Her mother's brother was diabetic. A 43-year-old woman was diagnosed with type 2 diabetes mellitus by her family physician 3 months before this presentation. Her initial blood glucose was 340 mg/dL. Glyburide 2.5 mg once daily was prescribed. Since then, self-monitoring of blood glucose (SMBG) showed blood glucose levels of mg/dL. She was referred to an endocrinologist for further evaluation. On examination, she was normotensive and not acutely ill. Her body mass index (BMI) was 18.7 kg/m2 following a recent 10 lb weight loss. Her thyroid was symmetrically enlarged and ankle reflexes absent. Her blood glucose was 272 mg/dL, and her hemoglobin A1c (HbA1c) was 10.3%. A lipid profile showed a total cholesterol of 261 mg/dL, triglyceride level of 321 mg/dL, HDL level of 48 mg/dL, and an LDL of 150 mg/dL. Thyroid function was normal. Urinanalysis showed trace ketones. She adhered to a regular exercise program and vitamin regimen, smoked 2 packs of cigarettes daily for the past 25 years, and limited her alcohol intake to 1 drink daily. Her mother's brother was diabetic.

8 Comparative Effectiveness Disorder CEM text: diabetes mellitus code: subject: patient relative temporal context: 3 months ago negation indicator: not negated Disorder CEM text: diabetes mellitus code: subject: family member relative temporal context: negation indicator: not negated Tobacco Use CEM text: smoking code: subject: patient relative temporal context: 25 years negation indicator: not negated Medication CEM text: Glyburide code: subject: patient frequency: once daily negation indicator: not negated strength:2.5 mg Compare the effectiveness of different treatment strategies (e.g., modifying target levels for glucose, lipid, or blood pressure) in reducing cardiovascular complications in newly diagnosed adolescents and adults with type 2 diabetes. Compare the effectiveness of traditional behavioral interventions versus economic incentives in motivating behavior changes (e.g., weight loss, smoking cessation, avoiding alcohol and substance abuse) in children and adults.

9 Meaningful Use Disorder CEM text: diabetes mellitus code: subject: patient relative temporal context: 3 months ago negation indicator: not negated Disorder CEM text: diabetes mellitus code: subject: family member relative temporal context: negation indicator: not negated Tobacco Use CEM text: smoking code: subject: patient relative temporal context: 25 years negation indicator: not negated Medication CEM text: Glyburide code: subject: patient frequency: once daily negation indicator: not negated strength:2.5 mg Maintain problem list Maintain active med list Record smoking status Provide clinical summaries for each office visit Generate patient lists for specific conditions Submit syndromic surveillance data

10 Clinical Practice Disorder CEM text: diabetes mellitus code: subject: patient relative temporal context: 3 months ago negation indicator: not negated Medication CEM text: Glyburide code: subject: patient frequency: once daily negation indicator: not negated strength:2.5 mg Provide problem list and meds from the visit

11 Applications  Meaningful use of the EMR  Comparative effectiveness  Clinical investigation –Patient cohort identification –Phenotype extraction  Epidemiology  Clinical practice  …..

12 How does NLP fit?  Demo pipeline, v1 –All medications in Mayo dataset extracted with cTAKES (NLP method) –Processed 360,452 notes for 10,000 patients –3,442,000 CEMs were created –Processing time: 1.6 sec/doc

13 Year 1

14 Y1 Technical and Scientific Activities  Gold standard corpus development: – corpus creation methodology – de-id and PHI surrogate generation tools – seed corpus generation (PAD, pneumonia, breast cancer) – annotation schema development based on CEM normalization target – annotation guidelines and pilot annotations – gold standard annotations are in progress  Type System for software development  Development of Evaluation workbench  Methods development – entity and event discovery – relation discovery

15 Y1 Software Deliverables (cTAKES modules) JULAUGSEPOCTNOVDECJANFEBMARAPRMAYJUN Dependency Parser Drug Profile Module Smoking Status Classifier CEM ‘orderMedAmb’ Population Full-Cycle Pipeline v1

16 SHARP Security Roundtable for Cloud-Deployed cNLP  May 23-24, 2011  Participants: SHARP 1, SHARP 4, health care organizations, the Veterans Administration, industry, and other research institutions  Providing guidance to institutions seeking to use cloud technologies to support development and application of cNLP tools  A set of recommendations for the novel legal and governance issues regarding the proper stewardship and use of clinical data

17 SHARP Collaborations  SHARP 1: –Around security in a cloud computing environment  SHARP 3 (SMaRT): –Around extraction of data from the clinical narrative –I2b2 database for data persistence?

18 Partnerships  NCBC-funded initiatives –Integrating Informatics and Biology to the Bedside (i2b2) –Integrating Data for Analysis, Anonymization and Sharing (iDASH) –Ontology Development and Information Extraction (ODIE)  Veterans Administration  R01s –Shared annotated lexical resource –Temporal relation discovery for the clinical domain –Milti-source integrated platform for answering clinical questions  University of York (UK), University of Trento (Italy), Brandeis University (USA)  eMERGE, PGRN (Pharmacogenomics Research Network)

19 clinical Text Analysis and Knowledge Extraction System (cTAKES)

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21 Overview Goal: Phenotype extraction Generic – to be used for a variety of retrievals and use cases Expandable – at the information model level and methods Modular Cutting edge technologies – best methods combining existing practices and novel research with rapid technology transfer Terminology agnostic: able to plug in any terminology Best software practices (80M+ notes) Stand-alone tool easily pluggable within other platforms/toolsets Apache v2.0 license Commitment to both R and D in R&D

22 cTAKES Adoption  May, 2011: –2306 downloads*  i2b2 NLP cell integration; relevance to CTSAs  eMERGE (SGH, NW)  PGRN (HMS, NW)  Extensions: Yale (YTEX), MITRE * Source:

23 cTAKES Technical Details Open source Apache v2.0 license Java 1.5 Framework IBM’s Unstructured Information Management Architecture (UIMA) open source framework, Apache project Methods Natural Language Processing methods (NLP) Based on standards and conventions to foster interoperability Application High-throughput system

24 cTAKES: Components Sentence boundary detection (OpenNLP technology) Tokenization (rule-based) Morphologic normalization (NLM’s LVG) POS tagging (OpenNLP technology) Shallow parsing (OpenNLP technology) Named Entity Recognition Dictionary mapping (lookup algorithm) Machine learning (MAWUI) types: diseases/disorders, signs/symptoms, anatomical sites, procedures, medications Negation and context identification (NegEx) Dependency parser Drug Profile module Smoking status classifier CEM normalization module

25 Output Example: Drug Object “Tamoxifen 20 mg po daily started on March 1, 2005.” Drug Text: Tamoxifen Associated code: C Strength: 20 mg Start date: March 1, 2005 End date: null Dosage: 1.0 Frequency: 1.0 Frequency unit: daily Duration: null Route: Enteral Oral Form: null Status: current Change Status: no change Certainty: null

26 Conversion to CEMs CASTransformCEM Freemarker Transform Template jCAS Consumer cTAKES Drug NER

27 Year 2 and Forward

28 AgentLoc the patient will complete his thiotepa dose today, and he will return tomorrow for the last dose of his thiotepa. His donor completed stem-cell collection yesterday The patient returns to the outpatient clinic today for follow-up Courtesy of Martha Palmer

29 Agent LocTheme, and he will return tomorrow for the last dose of his thiotepa. His donor completed stem-cell collection yesterday The patient returns to the outpatient clinic today for follow-up the patient will complete his thiotepa dose today Courtesy of Martha Palmer

30 Agent LocTheme Agent Purpose His donor completed stem-cell collection yesterday The patient returns to the outpatient clinic today for follow- up the patient will complete his thiotepa dose today, and he will return tomorrow for the last dose of his thiotepa. Courtesy of Martha Palmer

31 AgentAction Agent LocTheme Agent Purpose Coreference: “patient’s donor” The patient returns to the outpatient clinic today for follow-up the patient will complete his thiotepa dose today, and he will return tomorrow for the last dose of his thiotepa. His donor completed stem-cell collection yesterday Courtesy of Martha Palmer

32 AgentAction Agent LocTheme Agent TERMINATESOVERLAP Purpose Coreference: “patient’s donor” The patient returns to the outpatient clinic today for follow-up the patient will complete his thiotepa dose today, and he will return tomorrow for the last dose of his thiotepa. His donor completed stem-cell collection yesterday Courtesy of Martha Palmer

33 The patient returns to the outpatient clinic today for follow-up the patient will complete his thiotepa dose today, and he will return tomorrow for the last dose of his thiotepa. His donor completed stem-cell collection yesterday Courtesy of Martha Palmer

34 Y2 Proposed Deliverables  Release of a library of de-identification tools (Sept, 2011) –MIST –MIT/SUNY  Evaluation workbench (Sept, 2011)  cTAKES Side Effects module (Aug, 2011)  Modules for relation extraction (Dec, 2011) –Semantic role labeler –Relation classifier –Integration of CLEAR-TK (University of Colorado)  End-to-end tool, v2 (cTAKES v2) (April, 2012) –NLP to populate CEMs for Diseases, Sign/Symptoms, Procedures, Labs, Anatomical sites –Integration of LexGrid/LexEVS services

35 Development Challenges and Opportunities  Open source strategy  Release early release often  Test driven development with continuous integration  All milestones measured by what we can get IRB and DUA approved and deployed with real or de- identified clinical data

36 Courtesy of David Carrell

37 Partnerships  Strengthen existing SHARP collaborations –Initiate collaborations with SHARP 2 around usability –SHARP 1: methods for data security in a cloud deployed framework –I2b2: the glue between SHARP 3 and SHARP 4  Non-SHARP collaborations

38 Graphical User Interface (GUI) to cTAKES: a Prototype Pei Chen Childrens Hospital Boston

39 cTAKES as a Service  Objectives 1.Demo cTAKES prototype web application  Empower End Users to leverage cTAKES 2.Gather feedback for future cTAKES GUI 3.Potential system integrations with other applications (i.e. i2b2, ARC, Web Annotator)  Developed within i2b2 to integrate cTAKES in the i2b2 NLP cell

40 cTAKES Web Application

41 Single clinical note

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53 Technologies Front-End Web GUI –ExtJS –JavaScript Back-End cTAKES –JAVA –UIMA Middleware Web Services JAVA Apache CXF JSON

54 Deployment Considerations  Deployment Model  Security  Performance  Licensing (UMLS, Apache, GPL v.3)

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