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Applying Natural Language Processing in the Clinical Setting

Applying Natural Language Processing in the Clinical Setting. Peter Haug, MD Homer Warner Center for Informatics Research Intermountain Healthcare, Salt Lake City, Utah. Applied NLP Research. Goals : Affect Care Delivery Extract Clinical Data from Medical Documents

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Applying Natural Language Processing in the Clinical Setting

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  1. Applying Natural Language Processing in the Clinical Setting Peter Haug, MD Homer Warner Center for Informatics Research Intermountain Healthcare, Salt Lake City, Utah

  2. Applied NLP Research • Goals: • Affect Care Delivery • Extract Clinical Data from Medical Documents • Use Extracted Data to Alter Care • Improve Documentation • Identify Necessary Data • Identify Eligible Patients • Support Clinical Research • Data Extraction • Phenotype Recognition • Research Alerting • Improve Administrative Data • Improve Data for Business Planning • Improve Data for Billing • Examples: • Support for Diagnostic Systems • Screening for Disease • Assess Risk • Triggering Orders • Activating Clinical Protocols • Encode Admit Diagnoses • Identify Patients for Trauma Registry • Complete Problem List • Identify Patients for Research Recruitment

  3. Process Begins with a Document Document Parsing Process • Find Document Structure • Extract Meta-Data • Plan Further Parsing • Find Sentence Structure • Determine Meaning (Concepts) • Store/Process Concepts Chest Xray Report History: Cough and fever. Previous history of right-sided pneumonia. Exam: PA and Lateral Chest Film. Observations: Prior films showed confluent opacification of the RLL. This finding remains in today’s exam. These opacities, seen in multiple previous films, have spread to the right and left upper lobes. Interpretation: Extension of previously diagnosed pneumonia to right and left upper lobes.

  4. A Sequence of Processing • Find Document Structure • Find Section Structure • Find Sentence Structure • Determine Meaning (Semantics) • Map to Concepts • Build Data Structures

  5. Document Parsing Process • Find Document Structure • Extract Meta-Data • Plan Further Parsing • Find Sentence Structure • Determine Meaning (Concepts) • Store/Process Concepts <Body>Prior films showed confluent opacification of the RLL. This finding remains in today’s exam. These opacities, seen in multiple previous films, have spread to the right and left upper lobes.</Body> <Sen>These opacities, seen in multiple previous films, have spread to the right and left upper lobes.</Sen>

  6. Document Parsing Process • Find Structures • Extract Meta-Data • Plan Further Parsing • Find Sentence Structure • Determine Meaning (Concepts) • Store/Process Concepts Sen><Coded Index=”3">These opacities, seen in multiple previous films, have spread to the right and left upper lobes.</Coded></Sen> <code ref=”3a” Terminology=”TermX?”>1234</code> <code ref=”3b” Terminology=”TermX?”>1235</code> Coded Findings: Localized Infiltrate-RUL Localized Infiltrate-LUL

  7. Sentence Parsing Process These opacities, seen in multiple views, have spread to the right and left upper lobes. these=>POS: Adj. Semantic Rep: N/A opacities=>POS: Noun Num; Pleural Semantic Rep: RadFind.finding.opacities ID: 1 seen=>POS: verb Num; N/A Semantic Rep: N/A ………………………... right=>POS: adj. POS: Noun Num; Singular Semantic Rep: RadFind.side.right ID: 2 left=>POS: adj. POS: Noun Num; Singular Semantic Rep: RadFind.side.left ID: 3 upper=>POS: adj. Num; N/A Semantic Rep: RadFind.sup_inf.upper ID: 4 lobes=>POS: noun Num; pleural Semantic Rep: RadFind.anat_loc.lobes ID: 5 Etc. • Categorize Words • Identify Phrasal Boundaries • Find Relationships Among Phrases • Test for Semantic Congruence • Extract Concepts these opacities to the left upper lobes

  8. Document Parsing Process These opacities, seen in multiple views, have spread to the right and left upper lobes. From Words to Coded Findings: Localized Infiltrate-RUL Localized Infiltrate-LUL • Categorize Words • Identify Phrasal Boundaries • Find Relationships Among Phrases • Test for Semantic Congruence • Extract Concepts these opacities to the left upper lobes

  9. Output of a Semantic Parse PARSE: A hazy opacity is seen in the right upper lobe. • Instantiated Event: • 1001 *Overall Concept : *localized infiltrate (0.998669) • 1002 *State Concept : *present (0.780993) • 1003 Presence Term : null (0.779583) • 1004 *Topic Concept : *poorly-marginated opacity (infiltrate) (1.0) • 1005 Topic Term : opacity~n (1.0) • 1006 Topic Modifier Term: hazy~adj. (1.0) • 1008 Topographic Location Term : null (0.588844) • 1009 *Severity Concept : *null (0.969009) • 1010 Severity Term : null (0.962739) • 1011 *Link Concept : involving (0.686011) • 1012 Topic Location Link Term : in (1.0) • 1013 *Anatomic Concept : *right upper lobe (1.0) • 1014 Anatomic Location Mod : null (0.9375) • 1015 Anatomic Location : lobe~n (1.0) • 1016 Anatomic Location Mod1 : right (1.0) • 1017 Anatomic Location Mod2 : upper (1.0) • 1018 Anatomic Location Mod3 : null (1.0) • 1019 Anatomic Location Mod4 : null (1.0) • 1020 Anatomic Location Mod5 : null (1.0)

  10. Probabilistic Semantics

  11. Example: NLP in Pneumonia(a computer-based intervention) • Goal: • Identify Pneumonia Patients in the ED Rapidly • Assess Risk • Suggest Intervention • Approach: • Use Probabilistic System to Identify Patients • Suggest Enrollment in Pneumonia Protocol • Provide Therapeutic Suggestions • Requires Data Extracted from the X-ray Report

  12. Care Delivery Framework Example: Community-Acquired Pneumonia

  13. Care Delivery Framework Example: Community-Acquired Pneumonia Does the patient have pneumonia?

  14. Care Delivery Framework Example: Community-Acquired Pneumonia Does the patient have pneumonia? Should we used the guideline?

  15. Care Delivery Framework Example: Community-Acquired Pneumonia Does the patient have pneumonia? Should we used the guideline? Apply Pneumonia Care Protocol.

  16. Implimented Using: • Web Services Infrastructure • A Bayesian Network • Supported by a Production Rules System (DROOLS) • Using an NLP System • Sentence Isolation • Random Forests-Based Semantics

  17. Patient Tracking Board

  18. Patient Tracking Board

  19. Conclusion • Natural Language Processing Does Play A Role In Patient Care • Useful Applications Will Blend NLP-Derived Data With Structured Data From The EHR • Radiology Reports Are A Data-rich Target For NLP

  20. Questions???

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