applying natural language processing in the clinical setting

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HL7 Applying Natural Language Processing in the Clinical Setting Peter Haug, MD Homer Warner Center for Informatics Research Intermountain Healthcare, Salt Lake City, Utah

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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 - PowerPoint PPT Presentation

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Page 1: Applying Natural Language Processing  in the Clinical Setting

HL7

Applying Natural Language Processing in the Clinical Setting

Peter Haug, MDHomer Warner Center for Informatics ResearchIntermountain Healthcare, Salt Lake City, Utah

Page 2: Applying Natural Language Processing  in the Clinical Setting

HL7

2

• 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

Applied NLP Research

Page 3: Applying Natural Language Processing  in the Clinical Setting

HL7 Process Begins with a Document

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.

•Find Document Structure

•Extract Meta-Data•Plan Further

Parsing•Find Sentence

Structure•Determine

Meaning (Concepts)

•Store/Process Concepts

Document Parsing Process

Page 4: Applying Natural Language Processing  in the Clinical Setting

HL7

• Find Document Structure

• Find Section Structure

• Find Sentence Structure

• Determine Meaning (Semantics)

– Map to Concepts

– Build Data Structures

A Sequence of Processing

Page 5: Applying Natural Language Processing  in the Clinical Setting

HL7Document Parsing Process

•Find Document Structure

•Extract Meta-Data•Plan Further

Parsing•Find Sentence

Structure•Determine

Meaning (Concepts)

•Store/Process Concepts

Find Document Sections

Structured Document

Find Internal Structure

XML Based Output

<Doc>Chest Xray Report</Doc> <Sec>History</Sec> <Body> <Sen>Cough and fever.</Sen> <Sen>Previous history of right-sided pneumonia.</Sen> </Body> <Sec>Exam</Sec> <Body> <Sen>PA and Lateral Chest Film.</Sen> </Body> <Sec>Observations</Sec> <Body> <Sen>Prior films showed confluent opacification of the RLL.</Sen> <Sen>This finding remains in today’s exam.</Sen> <Sen>These opacities, seen in multiple previous films, have spread to the right and left upper lobes.</Sen> </Body> …………..</Doc>

Document

XML Based Output

<Doc>Chest Xray Report</Doc> <Sec>History</Sec> <Body>Cough and fever. Previous history of right-sided pneumonia.</Body> <Sec>Exam</Sec> <Body>PA and Lateral Chest Film. </Body> <Sec>Observations</Sec> <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> <Sec>Interpretation</Sec> ……………………..</Doc>

Output

Output

Planner

Build Structures for:1) Document Type2) Sections3) Paragraphs4) Sentences

Choose Models to Parse Against:1) Rules to determine which group of concepts to find based on Document Type and Sections Present

Document Sections

5) Tables6) Internal Markup7) Images8) Lists

9) Images??

<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>

Page 6: Applying Natural Language Processing  in the Clinical Setting

HL7 Planner

Choose Models to Parse Against:1) Rules to determine which group of concepts to find based on Document Type and Sections Present

•Find Structures•Extract Meta-Data•Plan Further

Parsing•Find Sentence

Structure•Determine

Meaning (Concepts)

•Store/Process Concepts

Structured Data Storage

Sentences Determine Sentence Structure

Linked Phrasal Components

Determine Semantics

Output

XML Based Output

<Doc>Chest Xray Report</Doc> ………….. <Sec>Observations</Sec> <Body> <Sen><Coded Index=”1">Prior films showed confluent opacification of the RLL.</Coded></Sen> <Sen><Coded Index=”2">This finding remains in today’s exam.</Coded></Sen> <Sen><Coded Index=”3">These opacities, seen in multiple previous films, have spread to the right and left upper lobes.</Coded></Sen> </Body> …………..<Codes> <code ref=”1 Term=”Snomed”>2345</code> <code ref=”2 Term=”Snomed”>3456</code> <code ref=”3a Term=”Snomed”>1234</code> <code ref=”3b Term=”Snomed”>1235</code> …………..</Doc>

Data Base Storage

MetaData

Includes Document Type and Section Information.

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-RULLocalized Infiltrate-LUL

Document Parsing Process

Structured Document

Page 7: Applying Natural Language Processing  in the Clinical Setting

HL7

•Categorize Words

• Identify Phrasal Boundaries

•Find Relationships Among Phrases

•Test for Semantic Congruence

•Extract Concepts

These opacities, seen in multiple views, have spread to the right and left upper lobes.

Lexicon: Derived from Specialist Lexicon?? And semantic KB

Identify Word

PropertiesSentence

these=>POS: Adj. Semantic Rep: N/Aopacities=>POS: Noun Num; Pleural Semantic Rep: RadFind.finding.opacities ID: 1seen=>POS: verb Num; N/A Semantic Rep: N/A ………………………...right=>POS: adj. POS: Noun Num; Singular Semantic Rep: RadFind.side.right ID: 2left=>POS: adj. POS: Noun Num; Singular Semantic Rep: RadFind.side.left ID: 3upper=>POS: adj. Num; N/A Semantic Rep: RadFind.sup_inf.upper ID: 4lobes=>POS: noun Num; pleural Semantic Rep: RadFind.anat_loc.lobes ID: 5Etc.

Output

Identify Candidate Phrases

Sentence/Word Properties

P1: these opacities: pntr- ID:1P2: in multipleP3: previous flimsP4: In multiple previous filmsP5: to the right: pntr- ID:2P6: to the left pntr- ID:3P7: upper lobes pntr- ID:4,5P8: to the right upper lobes pntr- ID:2,4,5P9: to the left upper lobes pntr- ID:3,4,5

Etc.

Output

Phrasal Grammar

Compositional Grammar

Phrasal Info

Etc.

Output

Phrasal Grammar

Semantic Restrictions

(ME&E Restrictions, Conjunctive Restrictions)

these opacities

to the right to the left

to the right upper lobes to the left upper

lobes

upper lobes

these opacities

these opacities

these opacities

these opacities

Sentence Parsing Process

these=>POS: Adj. Semantic Rep: N/Aopacities=>POS: Noun Num; Pleural Semantic Rep: RadFind.finding.opacities ID: 1seen=>POS: verb Num; N/A Semantic Rep: N/A ………………………...right=>POS: adj. POS: Noun Num; Singular Semantic Rep: RadFind.side.right ID: 2left=>POS: adj. POS: Noun Num; Singular Semantic Rep: RadFind.side.left ID: 3upper=>POS: adj. Num; N/A Semantic Rep: RadFind.sup_inf.upper ID: 4lobes=>POS: noun Num; pleural Semantic Rep: RadFind.anat_loc.lobes ID: 5Etc.

these opacities

to the left upper lobes

Page 8: Applying Natural Language Processing  in the Clinical Setting

HL7

•Categorize Words

• Identify Phrasal Boundaries

•Find Relationships Among Phrases

•Test for Semantic Congruence

•Extract Concepts

These opacities, seen in multiple views, have spread to the right and left upper lobes.

Etc.

Compositional Grammar

Etc.

Output

Phrasal Grammar

these opacities

to the right to the left

to the right upper lobes to the left upper

lobes

upper lobes

these opacities

these opacities

these opacities

these opacities

Linked Phrasal ComponentsConcept

Extraction

s_ anatomic_ conceptc bilateral lungc heartc interpleural space ns c see previous eventc bilateral interpleural spacec nullc mediastinumc left lower lobec pulmonary vesselsc pleurac left upper lobec right lungc left interpleural spacec right lower lobec all thoracic locationsc left lungc bilateral lower lobesc right upper lobec right interpleural spacec aortaother-

16.09.568.935.494.744.504.464.353.002.842.732.572.572.492.252.211.901.781.621.5814.4

s_ sidec nullc leftc rightc bilateralc either

61.413.810.611.3

2.77

s_superor_ inferiorc nullc upperc middlec lower

81.57.351.869.25

anatomic_modifier_side_nullleftrightbilateralbothother-

61.413.610.57.822.813.83

anatomic_modifier_ sup_ inf_nulllowerupperbaseapicalother-

81.56.485.221.661.193.91

anatomic_ link_ termnullinofatthroughoutother-

73.311.7

8.021.261.224.51

s_anatomic_ link_ conceptc involvingc nullc throughoutc adjacent toc projecting onother-

86.58.344.070.750.120.20

anatomic_ location_ modifiernullotherwise advstruc tures ngeneralized adjremainder adjother-

93.00.710.400.360.285.22

anatomic_ location_ termnulllung nlobe npleural adjinterpleural adjheart nlungs ncardiac adjmediastinal adjchest nother-

16.215.78.958.317.125.775.424.033.082.4623.0

s_ observationsc pneumothoraxc enlargement of the heartc abnormalityc ambiguityc lclzd prnchyml bnrmlty ns c generalized infiltratec localized infiltratec bilateral pleural effusionc nlrgmnt f th plmnry vsslsc generalized volume reducti...c parenchymal abnormality no c pulmonary nodular lesionc mediastinal wideningc pleural thickeningc generic infiltrate nos c pleural effusion ns c bony abnormalityc consolidation nos c localized volume reductionc vlm rfrnc t prvs rcrd other-

9.769.485.975.375.184.984.353.873.242.772.732.732.692.612.332.212.132.091.941.8621.7

s_ to pic_ conceptc enlargementc air densityc parenchymal abnormality ns c prly mrgntd pcty nfltrt c abnormalityc fluid densityc volume reductionc s ee previous eventc consolidationc s mll wll mrgntd pcty ndl other-

12.711.510.69.178.737.944.784.193.993.5622.8

topic_ termnullair nopac ities nopac ity ncons olidation nfluid ninfiltrate ninfiltrates neffus ion nchanges nother-

22.310.36.325.854.033.643.322.652.251.9437.4

topic_ modifiernullnodular adjaccumulation nincreasedfocal adjhazy adjpatchy adjactive adjdegenerative adjlinear adjother-

79.31.981.661.581.300.670.590.510.510.5111.4

s_anatomic_ conceptc bilateral lungc heartc interpleural space ns c see previous eventc bilateral interpleural spacec nullc mediastinumc left lower lobec pulmonary vesselsc pleurac left upper lobec right lungc left interpleural spacec right lower lobec all thoracic locationsc left lungc bilateral lower lobesc right upper lobec right interpleural spacec aortaother-

16.09.568.935.494.744.504.464.353.002.842.732.572.572.492.252.211.901.781.621.5814.4

s_sidec nullc leftc rightc bilateralc either

61.413.810.611.3

2.77

s_superor_inferiorc nullc upperc middlec lower

81.57.351.869.25

anato mic_ modifier_ side_nullleftrightbilateralbothother-

61.413.610.57.822.813.83

anatomic_ modifier_ sup_inf_nulllowerupperbaseapicalother-

81.56.485.221.661.193.91

anatomic_ link_ termnullinofatthroughoutother-

73.311.7

8.021.261.224.51

s_ anatomic_ link_ conceptc involvingc nullc throughoutc adjacent toc projecting onother-

86.58.344.070.750.120.20

anatomic_ lo cation_ modifiernullotherwise advstructures ngeneralized adjremainder adjother-

93.00.710.400.360.285.22

anatomic_ location_ termnulllung nlobe npleural adjinterpleural adjheart nlungs ncardiac adjmediastinal adjchest nother-

16.215.78.958.317.125.775.424.033.082.4623.0

s_ observationsc pneumothoraxc enlargement of the heartc abnormalityc ambiguityc lclzd prnchyml bnrmlty ns c generalized infiltratec localized infiltratec bilateral pleural effusionc nlrgmnt f th plmnry vsslsc generalized volume reducti...c parenchymal abnormality no c pulmonary nodular lesionc mediastinal wideningc pleural thickeningc generic infiltrate nos c pleural effusion ns c bony abnormalityc consolidation nos c localized volume reductionc vlm rfrnc t prvs rcrd other-

9.769.485.975.375.184.984.353.873.242.772.732.732.692.612.332.212.132.091.941.8621.7

s_ topic_ conceptc enlargementc air densityc parenchymal abnormality ns c prly mrgntd pcty nfltrt c abnormalityc fluid densityc volume reductionc see previous eventc consolidationc smll wll mrgntd pcty ndl other-

12.711.510.69.178.737.944.784.193.993.5622.8

topic_ termnullair nopacities nopacity nconsolidation nfluid ninfiltrate ninfiltrates neffusion nchanges nother-

22.310.36.325.854.033.643.322.652.251.9437.4

topic_modifiernullnodular adjaccumulation nincreasedfocal adjhazy adjpatchy adjactive adjdegenerative adjlinear adjother-

79.31.981.661.581.300.670.590.510.510.5111.4

s_ anatomic _conceptc bilateral lungc heartc interpleural space ns c see previous eventc bilateral interpleural spacec nullc mediastinumc left lower lobec pulmonary vesselsc pleurac left upper lo bec right lungc left interpleural spacec right lower lobec all thoracic locationsc left lungc bilateral lower lobesc right upper lo bec right interpleural spacec aortaother-

16.09.568.935.494.744.504.464.353.002.842.732.572.572.492.252.211.901.781.621.5814.4

s_sidec nullc leftc rightc bilateralc either

61.413.810.611.3

2.77

s_s uperor_inferiorc nullc upperc middlec lower

81.57.351.869.25

anatomic_ mo difier_ side_nullleftrightbilateralbothother-

61.413.610.57.822.813.83

anatomic_ mo difier_ sup_inf_nulllowerupperbaseapicalother-

81.56.485.221.661.193.91

anatomic_ link_termnullinofatthroughoutother-

73.311.7

8.021.261.224.51

s _anatomic_ link_co nc eptc involvingc nullc througho utc adjacent toc projecting onother-

86.58.344.070.750.120.20

anato mic _ lo cation_modifiernullotherwise advstructures ngeneralized adjremainder adjother-

93.00.710.400.360.285.22

anatomic_ loc ation_ termnulllung nlobe npleural adjinterpleural adjheart nlungs ncardiac adjmediastinal adjchest nother-

16.215.78.958.317.125.775.424.033.082.4623.0

s_obs ervationsc pneumothoraxc enlargement of the heartc abnormalityc ambiguityc lc lzd prnchyml bnrmlty ns c generalized infiltratec lo calized infiltratec bilateral pleural effus ionc nlrgmnt f th plmnry vsslsc generalized volume reducti...c parenchymal abnormality no c pulmonary nodular lesionc mediastinal wideningc pleural thickeningc generic infiltrate nos c pleural effusion ns c bony abnormalityc consolidatio n no s c lo calized volume reductionc vlm rfrnc t prvs rcrd other-

9.769.485.975.375.184.984.353.873.242.772.732.732.692.612.332.212.132.091.941.8621.7

s_ topic_c onceptc enlargementc air densityc parenchymal abnormality ns c prly mrgntd pcty nfltrt c abno rmalityc fluid densityc volume reductionc see previous eventc consolidationc smll wll mrgntd pcty ndl other-

12.711.510.69.178.737.944.784.193.993.5622.8

to pic _ termnullair nopacities nopacity nco nsolidation nfluid ninfiltrate ninfiltrates neffusio n nchanges nother-

22.310.36.325.854.033.643.322.652.251.9437.4

topic_ mo difiernullnodular adjaccumulation nincreasedfocal adjhazy adjpatchy adjactive adjdegenerative adjlinear adjo ther-

79.31.981.661.581.300.670.590.510.510.5111.4

s _anato mic_c onc eptc bilateral lungc heartc interpleural space ns c see previo us eventc bilateral interpleural spacec nullc medias tinumc left lower lo bec pulmo nary vesselsc pleurac left upper lobec right lungc left interpleural spacec right lo wer lobec all thoracic locatio nsc left lungc bilateral lo wer lobesc right upper lobec right interpleural spacec ao rtao ther-

16.09.568.935.494.744.504.464.353.002.842.732.572.572.492.252.211.901.781.621.5814.4

s_sidec nullc leftc rightc bilateralc either

61.413.810.611.3

2.77

s_ supero r_ inferiorc nullc upperc middlec lo wer

81.57.351.869.25

anato mic _modifier_side_nullleftrightbilateralbo thother-

61.413.610.57.822.813.83

anatomic_ mo difier_ sup_inf_nulllowerupperbaseapicalother-

81.56.485.221.661.193.91

anatomic_ link_ termnullinofatthro ughoutother-

73.311.7

8.021.261.224.51

s_anatomic_ link_c onc eptc invo lvingc nullc througho utc adjacent toc projecting onother-

86.58.344.070.750.120.20

anatomic_ locatio n_modifiernullotherwise advstructures ngeneralized adjremainder adjother-

93.00.710.400.360.285.22

anato mic _ lo cation_ termnulllung nlo be npleural adjinterpleural adjheart nlungs ncardiac adjmediastinal adjches t no ther-

16.215.78.958.317.125.775.424.033.082.4623.0

s_observationsc pneumotho raxc enlargement of the heartc abnormalityc ambiguityc lclzd prnchyml bnrmlty ns c generalized infiltratec localized infiltratec bilateral pleural effusionc nlrgmnt f th plmnry vsslsc generalized volume reducti...c parenchymal abnormality no c pulmonary nodular lesionc medias tinal wideningc pleural thickeningc generic infiltrate no s c pleural effusion ns c bony abnormalityc consolidation nos c localized vo lume reductionc vlm rfrnc t prvs rcrd o ther-

9.769.485.975.375.184.984.353.873.242.772.732.732.692.612.332.212.132.091.941.8621.7

s_ topic_co nceptc enlargementc air densityc parenchymal abno rmality ns c prly mrgntd pcty nfltrt c abnormalityc fluid densityc vo lume reductionc see previo us eventc co nso lidationc smll wll mrgntd pcty ndl other-

12.711.510.69.178.737.944.784.193.993.5622.8

topic_ termnullair nopacities nopacity nconsolidatio n nfluid ninfiltrate ninfiltrates neffusion nchanges nother-

22.310.36.325.854.033.643.322.652.251.9437.4

to pic_modifiernullnodular adjaccumulation nincreasedfocal adjhazy adjpatchy adjactive adjdegenerative adjlinear adjother-

79.31.981.661.581.300.670.590.510.510.5111.4

Semantic Tests1) Model determines Semantic consistency of phrasal combinations.2) “Best” combinations trigger concept extraction and storage.

Structured Data

Storage

Data Base Storage

Document Parsing Process

From Words to Coded Findings:Localized Infiltrate-RULLocalized Infiltrate-LULthese opacities

to the left upper lobes

Page 9: Applying Natural Language Processing  in the Clinical Setting

HL7 Output of a Semantic Parse

• 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)

PARSE: A hazy opacity is seen in the right upper lobe.

Page 10: Applying Natural Language Processing  in the Clinical Setting

HL7 Probabilistic Semantics

Locationbilateral lungleft lower lobenullbilateral lower lobesleft upper lobeother-

26.514.010.18.177.3933.9

GlobalObservationabnormalitylocalized infiltratealveolar infiltrate nosgeneric infiltrate nos

0 +58.414.826.8

topic_termabcessabnormalitiesconsolidationinfiltratesopacityvisualizationinfiltrateopacificationopacitiesdensitiesconsolidatedair bronchogramkerley linesseptal lineschangeslesionnullairchangedensity

.002

.00231.118.310.50.3930.70.392.730.390.390.390.390.391.170.390.390.390.391.17

Stateabsentpresentpossibleotherwise absent

28.864.26.620.39

Observationlocalized infiltrateinfiltrate nosgeneralized infiltrateinterstitial infiltrateinflammatory lesionother-

53.343.21.950.780.390.39

topic_modifiernullfocalsuperimposedhazymildly coarse patternconfluentinfiltrativemixed patterndensepatchyareas ofareareticularnodularinflammatoryill definedindistinctpoorly marginatedslightly coarsebronchogramunderlyingfluffyill defined poorly definedvague

69.66.610.393.500.390.783.110.391.561.950.780.390.390.780.380.781.171.170.390.390.391.951.950.390.39

anatomic_location_termpulmonarynulllunglungshemithoraxlobesperivascularlobelung fieldschestlocationinfrahilarsideheartretrocardiaclingula

3.5137.715.53.890.783.890.4028.40.400.400.400.390.401.170.781.95

Modifier_Sidenullleftrightbilateraleither

52.921.816.38.570.40

anatomic_modifier_side_nulleitherrightbilaterallyleftbilateralboth

52.50.4016.30.7822.23.504.28

Modifier_Sup_Infnullascendingdescendingsuperiormiddleinferior

58.7.007.00711.72.7326.8

anatomic_modifier_sup_inf_nulllowerupperbaseapexcranialbasesmidbasilarmiddle

58.722.610.91.950.390.391.561.170.781.56

Modifier_Cntrl_Prph_nullcentralperipheral

97.31.950.79

anatomic_mdfr_cntrl_prph_nullperipheralcentralperipherally

97.30.391.950.39

state_termnullnono evidencewith noorparticularlywithoutpossiblyand ormost likelyother-

62.318.74.674.282.331.560.780.780.780.783.12

Page 11: Applying Natural Language Processing  in the Clinical Setting

HL7• 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

Example: NLP in Pneumonia(a computer-based intervention)

Page 12: Applying Natural Language Processing  in the Clinical Setting

HL7 Care Delivery Framework

Pneumonia Screening Tool

Data Supporting Pneumonia Assessment Clinical Data

Repository

Pneumonia Guideline

Enrollment

Pneumonia Treatment Protocol

Computable Medical Knowledge Reposotory

Chest Xray Reports

Chest Xray Report Processing

(Structured Data Extraction)

Example: Community-Acquired Pneumonia

Page 13: Applying Natural Language Processing  in the Clinical Setting

HL7

Pneumonia Screening Tool

Data Supporting Pneumonia Assessment Clinical Data

Repository

Pneumonia Guideline

Enrollment

Pneumonia Treatment Protocol

Computable Medical Knowledge Reposotory

Chest Xray Reports

Chest Xray Report Processing

(Structured Data Extraction)

Care Delivery Framework

Example: Community-Acquired Pneumonia

Does the patient have pneumonia?

Page 14: Applying Natural Language Processing  in the Clinical Setting

HL7

Pneumonia Screening Tool

Data Supporting Pneumonia Assessment Clinical Data

Repository

Pneumonia Guideline

Enrollment

Pneumonia Treatment Protocol

Computable Medical Knowledge Reposotory

Chest Xray Reports

Chest Xray Report Processing

(Structured Data Extraction)

Care Delivery Framework

Example: Community-Acquired Pneumonia

Does the patient have pneumonia?

Should we used the guideline?

Page 15: Applying Natural Language Processing  in the Clinical Setting

HL7

Pneumonia Screening Tool

Data Supporting Pneumonia Assessment Clinical Data

Repository

Pneumonia Guideline

Enrollment

Pneumonia Treatment Protocol

Computable Medical Knowledge Reposotory

Chest Xray Reports

Chest Xray Report Processing

(Structured Data Extraction)

Care Delivery Framework

Example: Community-Acquired Pneumonia

Does the patient have pneumonia?

Should we used the guideline?

Apply Pneumonia Care Protocol.

Page 16: Applying Natural Language Processing  in the Clinical Setting

HL7

• Web Services Infrastructure

• A Bayesian Network

• Supported by a Production Rules System

(DROOLS)

• Using an NLP System

– Sentence Isolation

– Random Forests-Based Semantics

Implimented Using:BPDiastolic

< 69.569.5 to 82.5>= 82.5

28.336.235.5

76.9 ± 11

Chloride< 103.5103.5 to 105.5>= 105.5

42.125.132.9

104.3 ± 1.8

WBC< 11.8511.85 to 18.75>= 18.75

86.112.41.45

9.46 ± 3.4

PNEUMONIAAbsentPresent

94.95.09

Age< 15.515.5 to 45.5>= 45.5

8.0645.646.4

42 ± 21

RespRate< 19.519.5 to 21.521.5 to 27.5>= 27.5

52.324.916.16.72

20.8 ± 3.5

TempC< 36.7536.75 to 37.4537.45 to 38.05>= 38.05

62.723.86.047.46

36.79 ± 0.63

MeanBP< 85.585.5 to 99.5>= 99.5

23.035.441.7

95.1 ± 12

BPSystolic< 121.5121.5 to 148.5>= 148.5

29.444.626.0

134 ± 22

HeartRate< 85.585.5 to 99.599.5 to 110.5>= 110.5

44.524.713.017.8

92.1 ± 15

Sodium< 137.5137.5 to 140.5>= 140.5

25.741.832.6

139.2 ± 2.4

BUN< 13.5>= 13.5

45.154.9

Creatinine< 0.405>= 0.405

3.9096.1

SpO2< 92.192.1 to 95.395.3 to 98.4>= 98.4

10.223.644.921.3

96.1 ± 3

BS_CONGESTIONYesNo

0.5399.5

BS_RHONCHIYesNo

0.4399.6

BS_ABNORMALYesNo

3.8796.1

BS_DECREASEDYesNo

2.2997.7

BS_COURSEYesNo

0.9099.1

BS_WHEEZESYesNo

2.8497.2

BS_NO_COUGHYesNo

0 + 100

BS_STRIDORYesNo

.08399.9

BS_CLEARYesNo

44.056.0

BS_CRACKLESYesNo

0.7299.3

BS_RALESYesNo

0.1199.9

BS_ABSENTYesNo

.030 100

BS_INSPIRATIONYesNo

0.7999.2

BS_TUBULARYesNo

.024 100

BS_INFREQUENTYesNo

0.6299.4

BS_STRONGYesNo

0.7699.2

BS_FINE_CRACK...YesNo

0.3199.7

BS_EXPIRATIONYesNo

0.9099.1

BS_NOT_CLEARING_SECREA...YesNo

0.1099.9

BS_FREQUENTYesNo

1.1998.8

BS_WEAKYesNo

0.1699.8

BS_NON_PRODUCTIVE_CO...YesNo

1.7498.3

BS_PRODUCTIVE_CO...YesNo

1.8198.2

BS_MODERATEYesNo

1.3698.6

BS_CLEARING_SECREA...YesNo

0.4599.6

ChiefComplaintRESPIRATORY COMPLAINTFEVERABD PAINORTHO INJURYCHEST PAINNEURO COMPLAINTFALLTRAFFIC INJURYABD PROBLEMSCHEST PRESSUREBACK PAINWEAKNESSSYNCOPEENT PROBLEMBODY ACHESCV COMPLAINTSHEADACHEDIZZYFLANK PAINCV PROBLEMSASSAULT RAPEPSYCHIATRICCHEST HEAVINESSSKIN COMPLAINTSPECIFIC DIAGNOSISDIABETICPAIN CHESTHEART RACETRAUMAGENITOURINARY PROBLEMPALPITATIONSHEART IRRALLERGIESHIGH BPFLUID NUTRITIONCONVULSIONSINFECTIONRAPID HRIRR HEARTBEATLACERATIONINGESTIONBP HIGHUNCONSCIOUSNESSVAGINAL BLEEDINGMED REFILLUNKNOWNLOW BPCARDIAC ARRESTEYE PROBLEMBP LOWother-

32.46.966.054.264.123.693.623.503.453.102.822.792.282.191.881.881.831.771.430.920.870.860.820.780.510.440.370.330.310.310.310.300.290.280.270.250.200.190.160.160.160.130.11.098.091.087.064.059.055.0540.18

NLP_FINDINGPositiveNegative

25.974.1

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HL7 Patient Tracking Board

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HL7 Patient Tracking Board

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HL7

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• 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

Conclusion

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23

Questions???