NLP for Health Informatics: text-mining patient records

School of ComputingFACULTY OF ENGINEERING

NLP for Health Informatics: text-mining patient records

SNOMED CT based semantic tagging of medical narratives

Verbal Autopsy corpus for Machine Learning of Cause of Death

E-Health GATEway to the Clouds

Saman Hina, Sammy Danso, Eric Atwell, Owen Johnson

Natural Language Processing Group


SNOMED CT based semantic tagging of medical narratives --------------------------------------------------------------------- Research Objective Key Challenges Resources Methods

1. Baseline Application2. SNOMED CT Rule-based semantic tagger

Results Conclusion and Future Work


Sample Text Output


Research Objective ---------------------------------------------------------------------To design a novel approach for extraction of semantic information from unstructured medical narratives.

The underlying research hypothesis is that it is possible to annotate natural language medical narratives with high accuracy using SNOMED CT healthcare data standard.

Healthcare Data standards Secure Consistent Authentic sharing among healthcare users with codes.


Key Challenges ---------------------------------------------------------------------

• Clinicians have different ways of expressing one single medical term and do not follow language of healthcare data standards which is a challenge in extracting domain knowledge.

• Not having domain expert. • Use of synonyms, abbreviations , paraphrasing the concepts and different preferred names of a concept increases the complexity of the current research challenge.

• Different patterns of section headers, capitalization of words and content.


Annotations Frequency

Clinical Documents (discharge summaries, progress notes) 1176

Words 965,244

Sentences 51756

SNOMED CT Concepts in the corpus 67575

Corpus-----------------------------------------------------------------------• Corpus from the fourth i2b2/VA 2010 challenge.• Contains discharge summaries and progress notes from four healthcare partners.


Data Standard ------------------------------------------------------------------SNOMED-CT (Systematized Nomenclature of Medicine Clinical Terms) a comprehensive international data standard for clinical terminology. • Number of Concepts from SNOMED CT : 356,432• 16 out of 31Semantic types from SNOMED CT have been used to develop SNOMED CT semantic tagger.

1.Attribute 9. Person2.Body Structure 10. Physical Object3.Disorder 11. Procedure4.Environment 12. Product Or Substance5.Findings 13. Qualifier Value6.Observable Entity 14. Record Artifact 7.Occupation 15. Regime/ Therapy8.Organism 16. Situation


Annotation scheme for Gold Standard Corpus--------------------------------------------------------------------

• Pre annotation of corpus using SNOMED CT dictionary application (Baseline system).

• Reviewing the corpus manually and mark the remaining concepts considering the following language issues; Synonyms, abbreviations, incomplete concepts, paraphrase of concepts and concepts under section headings.

• Concepts which are not identified correctly should be removed.

•In case of non domain user, NCBO bioportal annotator will be used to annotate the gold standard corpus by searching the key words and bigrams of the possible concepts.


• Concepts should be marked up to three levels of granularity.

• Agreement of gold standard is more than 90 %.


Baseline System - SNOMED CT Dictionary Application-----------------------------------------------------------------------•Basic language processing (Tokenize, Sentence Splitting, POS tagging)

•Concepts have been tagged automatically (Dictionary, Lookup).

•SNOMED CT knowledge base was developed by constructing separate dictionaries of 16 semantic types.

•6 out of 16 tags performed well with dictionary application.

1. Disorder 4. Record Artifact

2. Observable Entity 5. Regime/Therapy

3. Person 6. Situation


Optimization of SNOMED CT Knowledgebase---------------------------------------------------------------• Optimizing the concepts in SNOMED CT semantic types to write general rules for semantic tagger.

•Optimization process reduce the size of knowledge base by removing un necessary and ambiguous information.

Entire lung -> LungEar NOS -> Ear

• Long multiword concepts have been transformed into individual concepts to solve paraphrasing problem.

Radiography of chest and lung -> 1. Radiography 2. Chest 3.lung


Rule-based SNOMED CT Semantic tagger --------------------------------------------------------------- This application use the optimized SNOMED CT dictionary as knowledgebase.

Documents containing narratives

Tokenizer Sentence Splitter

Part Of Speech Tagger

Morphological Analyzer

SNOMED CT knowledge base

Rules

Colour coded SNOMED CT Semantic types

Extracting concepts and

plural concepts


SNOMED CT Semantic

Types

Baseline Semantic Tagger

SNOMED CT Semantic Tagger

Recall(%)

Precision

(%)

F-Score(%)

Recall

(%)

Precision

(%)

F-Score(%)

Body Structure 47 89 61 74 74 74

Disorder 82 97 89 78 94 85

Environment 51 96 67 80 63 70

Observable Entity

85 89 87 82 87 85

Occupation 40 100 57 80 73 77

Person 92 100 96 93 100 96

Procedure 44 96 60 87 73 80

Record Artifact 100 77 87 100 77 87

Regime/Therapy

96 96 96 96 93 95

Situation 80 95 87 72 95 82

Average 61 96 75 82 79 81


Conclusion and Future Work---------------------------------------------------------------

•Corpus containing long multiword concepts has been automatically extracted and tagged with 10 out 16 SNOMED semantic types.•Annotation of unseen test corpus will be completed by domain users to test SNOMED CT semantic tagger.•Optimization of the remaining SNOMED CT semantic types to construct general rules.•Corpus annotations will be contributed to the users through i2b2 organizers.

Samuel Danso1,3, Eric Atwell1, Owen Johnson1, Guus ten Asbroek2, Seyi Soromekun2, Karen Edmond2, Chris Hurt4, Lisa Hurt2, Charles Zandoh3, Charlotte Tawiah3, Zelee Hill2, Justin

Fenty2, Seeba Amenga Etego3, Seth Owusu Agyei2,3, and Betty R Kirkwood2.1 University of Leeds 2 London School of Hygiene and Tropical Medicine 3 Kintampo Health Research Centre, Ghana4 University of Cardiff

Presented BySamuel Danso

PhD Student - NLP Research Group, University of [email protected]

21st July 2011

Causes of Death Information – The global picture

• About 57 million people die each year

• Cause of Death Information is vitally important to health planners and policy makers at all levels.

• How do we find out the 67% ?– Verbal Autopsy

Who What

WHO and national/ international bodiesglobal and national cause-specific mortality estimates; ICD coding

local public health managers top-ranking causes of death and public health priorities

epidemiologists and health services researchers relating to specific populations and sub-groups

institutional managers and clinical auditorspatterns for deaths within institutions and health care systems

medical and legal practitioners individual causes for particular cases

Use of CoD Information

Source Byass et al, 2007

• basically, a narrative of an account of an incident that led to the death of a person.

• An idea from the 17th Century used in the UK and other developed countries. Now recommended by WHO as the standard approach used in the developing countries.

Coded part

Sample of VA Data for Infant Death I

5.2.6. Health worker measured the blood pressure and told you it was high…… 1. Yes 2 No 8. NKDHIGHBP

5.2.7. Convulsions like in children…………………………….………………1. Yes 2 No 8. NK

DCONVULSE

5.2.8. Fever during labour………………………………….……..…………… 1. Yes 2 No 8. NK DFEVER

5.2.9. Umbilical cord delivered before the baby………………………………. 1. Yes 2. No 8. NK DPROLAPSE

5.2.10. Umbilical cord around the baby’s neck…………………………………. 1. Yes 2. No 8. NK DCORDNECK

5.2.11. Heavy bleeding during labour or after delivery………………………… 1. Yes 2. No 8. NK DBLEED

5.2.12. Did somebody put their hand inside the womb to remove the placenta?.. 1. Yes 2. No 8. NK DRETPLAC

5.2.13. Other: 1. Yes 2. No 8. NK DOTHER

free text partCan you tell me something about your pregnancy?I was never ill though out my pregnancy. I started ANC in the 5th month of my pregnancy at Dwenewoho and then continued every month. I did not start ANC earlier because I was not ill and not also attended to ANC because I was well.Can you tell me something about your labourlabour started me on Monday early dawn when I experienced waist, stomach pains and the break of water. I visited Kintampo hospital on Tuesday and was given one drip of water. I was also given blood.Can you tell me something about the baby?the baby was nice.Can you tell me what happened after delivery?something was done because it could not cry immediately after delivery. So enema pump 'Bentua' was used to it something on the nose before it was able to breath. The nurses said the baby's time was not due Any signs and symptoms before the death of the child ?

I did not know what kill the baby. According to the nurses its time was not due so it was kept in an incubator and it died the next day.

Sample of VA Data for Infant Death II

Characteristics of corpus

• Data sparseness and imbalance - 46 categories

Characteristics of corpus: free text

Some Statistics

Average number of word tokens per document

150

Estimated number of word tokens ≈ 1.5 Million

Number of documents- infant ≈ 8000

Number of documents– adult women

≈ 2500

Diagnosis information 10500

Characteristics of corpus: free text

“WHEN THE CHILD WAS SIXTEEN (16) DAYS OLD SHE FELL SICK WHICH LAUTED FOR THREE (3) DAYS BEFORE SHE DIED. THE CHILD HAVING DIFFICULT BREATING. ANY TIME, SHE BREATHS, YOU SEE A HOLE IN THE CHEST, AND ALSO MAKING NOISE IN THE CHEST. SHE HAD CONVULSION WHEN SHE WAS SEVERTEEN (17) DAYS OLD BEFORE SHE DIED THE FOLLOWING DAY. SHE ALSO HAD A BULGING FONTENED AND SEVERE HOT BODY WHICH LASTED

FOR TWO (2) DAYS BEFORE SHE DIED. THE CHILD ALSO HAD A FIT WHICH SHE COULD NOT OPEN HER MOUTH.”

“WHEN THE CHILD WAS SIXTEEN (16) DAYS OLD SHE FELL SICK WHICH LAUTED FOR THREE (3) DAYS BEFORE SHE DIED. THE CHILD HAVING DIFFICULT BREATING. ANY TIME, SHE BREATHS, YOU SEE A HOLE IN THE CHEST, AND ALSO MAKING NOISE IN THE CHEST. SHE HAD CONVULSION WHEN SHE WAS SEVERTEEN (17) DAYS OLD BEFORE SHE DIED THE FOLLOWING DAY. SHE ALSO HAD A BULGING FONTENED AND SEVERE HOT BODY WHICH LASTED

FOR TWO (2) DAYS BEFORE SHE DIED. THE CHILD ALSO HAD A FIT WHICH SHE COULD NOT OPEN HER MOUTH.”

Misspellings Misspellings

Inappropriate use of punctuation marks

Inappropriate use of punctuation marks

Grammatical errorGrammatical error

• Spelling and grammatical mistakes posing parsing problems

• Different ways of expressing the same concept.• Baby came out• Baby landed• Gave birth

• Local words– “– ‘afam’– ‘bentoa’

• Abbreviations – ANC = Antenatal Clinic– TBA = Traditional Birth Antendant

• Fuzzy expression of clinical concepts. Sometimes no clinical concept expressed at all. (..” I visited Kintampo hospital on Tuesday and was given one drip of water. ..”)

Delivery

• Missing values (-)• 215 variables• Entries are coded

– sex = 1, 2, 8 or 9– Weight= 1.45, 9.99 or 8.88

• Continues revision of questionnaire resulting in blank values for some variables

Results: 46 categories - combined dataset

Results: 6 categories – combined dataset

Results: 46 categories – time of death

Results: 6 categories – time of death

• Key lessons– CRISP-DM is the appropriate methodology for this project

– It is feasible to use machine learning techniques to classify CoD in Verbal Autopsies

– Split of dataset by clinical definitions into homogenous sets improves classifier performance

– Classification at top level of hierarchy of CoD could lead to increase in performance across classifiers due to number of classes (46 to 6) and instances per class.

Discussion and Conclusion

Discussion and ConclusionCan you tell me something about your pregnancy?I was never ill though out my pregnancy. I started ANC in the 5th month of my pregnancy at Dwenewoho and then continued every month. I did not start ANC earlier because I was not ill and not also attended to ANC because I was well.Can you tell me something about your labourlabour started me on Monday early dawn when I experienced waist, stomach pains and the break of water. I visited Kintampo hospital on Tuesday and was given one drip of water. I was also given blood.Can you tell me something about the baby?the baby was nice.Can you tell me what happened after delivery?something was done because it could not cry immediately after delivery. So enema pump 'Bentua' was used to it something on the nose before it was able to breath. The nurses said the baby's time was not due Any signs and symptoms before the death of the child ?

I did not know what kill the baby. According to the nurses its time was not due so it was kept in an incubator and it died the next day.

Other Uses of corpus?

e-Health GATEway to the Clouds

http://www.comp.leeds.ac.uk/nlp/e-health• WP1: Clouds on the White Rose Grid VRE• Deliverables: A secure cloud-based VRE on the White Rose Grid

(Month 2), e-health records from TPP stored (Month 3), access and research support tools (Month 3). Iterative refinement (Month 3-5).

• WP2: GATEway component • Deliverables: A GATE plug-in module capable of securely

pseudonymising the free text elements of the example e-health records (Month 3). Iterative refinement (Month 3-5).

• WP3: Evaluation and Sustainability • Deliverables: Evaluation of WP1 and WP2 combined into a cohesive

e-health VRE (Month 5), sustainability plan (Month 4), dissemination as a case study (mid Month 5), hand-over to ongoing support by YCHI (Month 6)

http://www.comp.leeds.ac.uk/nlp/e-health


We welcome e-Health MSc / PhD Projects

SNOMED CT based semantic tagging of medical narratives

Verbal Autopsy corpus for Machine Learning of Cause of Death

E-Health GATEway to the Clouds

Saman Hina, Sammy Danso, Eric Atwell, Owen Johnson

Natural Language Processing Group

NLP for Health Informatics: text-mining patient records

Documents

Transcript of NLP for Health Informatics: text-mining patient records