Understanding of Electronic Medical Records(EMRs) plays a crucial role in improving healthcare outcomes. However, the unstructured nature of EMRs poses several technical challenges for structured information extraction from clinical notes leading to automatic analysis. Natural Language Processing(NLP) techniques developed to process EMRs are effective for variety of tasks, they often fail to preserve the semantics of original information expressed in EMRs, particularly in complex scenarios. This paper illustrates the complexity of the problems involved and deals with conflicts created due to the shortcomings of NLP techniques and demonstrates where domain specific knowledge bases can come to rescue in resolving conflicts that can significantly improve the semantic annotation and structured information extraction. We discuss various insights gained from our study on real world dataset.

1. Challenges in Understanding Clinical Notes:
Why NLP Engines Fall Short and Where
Background Knowledge Can Help
Sujan Perera, Amit Sheth, Krishnaprasad Thirunarayan
Ohio Center of Excellence in Knowledge-enabled Computing (Kno.e.sis)
Wright State University
Suhas Nair, Neil Shah
ezDI LLC

2. Why is it necessary to understand clinical notes?
• 80% of patient data is unstructured1
• Structured data is incomplete and not accurate2,3
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3Comparison of clinical and administrative data sources for hospital coronary artery bypass graft
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3. Why is it necessary to understand clinical notes?
• Key indicators for decision making reside in patient notes
• facilities
• “Holter monitor was ordered by Lisa. She failed to get this
because she did not have transportation”
• non-compliance
• “Atrial fibrillation with poorly controlled ventricular rate due
to noncompliance.”
• financial status
• “The patient mentioned that Bystolic is expensive and cannot
afford it now.”
• family history
• “his father is hypertensive”

4. • ICD10 adaptation – need to understand the relationships
E08 - Diabetes mellitus due to underlying condition
E08.0 - Diabetes mellitus due to underlying condition with hyperosmolarity
E08.00 - without nonketotic hyperglycemic-hyperosmolar coma (NKHHC)
E08.01 - with coma
E08.1 - Diabetes mellitus due to underlying condition with ketoacidosis
E08.10 - without coma
E08.11 – with coma
• The underlying condition can be congenital rubella, Cushing's syndrome, cystic fibrosis,
malignant neoplasm, malnutrition, pancreatitis
Why is it necessary to understand clinical notes?

5. Patient Data Distribution
Structured data
Unstructured data

6. Understanding Clinical
Notes
• What conditions patient has?
• What symptoms patient has?
• What medications with what dosage he/she
taking?
• What are the relationships between entities?
• What is patients medical history?
• What is his/her family history?
• What is patients behavior?
• What are his diet and discharge instructions?

7. Understanding Clinical Notes - Tasks
• Entity identification
• she does not state that the reason for her stopping after two blocks is shortness of
breath or chest discomfort.
• Annotate with standard vocabulary
• Shortness of breath – UMLS concept C0013404
• Chest discomfort – UMLS concept C0235710
• Relationship identification
• Temporal information extraction
• if her symptoms do not change at all, she will go back on the lipitor after two weeks.
• Negation detection
• he has not required any nitroglycerin.
• Certainty detection
• She is not sure if she is just depressed or not.
• Conditioning detection
• if he experiences any chest pain, shortness of breath with exertion or dizziness or
syncopal episodes to let us know.

8. Understanding Linguistic Constructs
• Rule based algorithms are popular
• simplicity
• low computational cost
• Maintains a dictionary of words indicate particular language construct
• Negation – no, not, deny, cannot, don’t etc
• Simple rules deciding applicability
• <negation phrase> * <entity>
• <entity> * <negation phrase>
• Does not associate with the correct phrase of the sentence
• Lead to incorrect output
• he did not make the increase on his metoprolol.
• his weight has not changed and that his edema is primarily at the end
of the day.
• Sometimes even associating to the correct phrase is not enough
• “I do not have an explanation for this dyspnea.”
• “there was no evidence of ischemia."
• Common to other constructs (certainty and conditioning)

9. Leads to Conflicting Instances
• Failure to identify such linguistic constructs leads to conflicting instances
• Document 1:
• Coronary artery disease listed in the current diagnosis list
• “Send for carotid duplex to rule out carotid artery stenosis
given his risk factors and underlying coronary artery dis-
ease.“
• Document 2:
• “Extremities : Warm and dry. No clubbing or cyanosis. No
lower extremity edema.“
• “I have advised the patient on the side effect of potential
lower extremity edema.“
• Document 3
• “He is not having any symptoms of chest pain or exertional syncope or
dizziness.”
• “I advised him that if he experiences chest pain, shortness of breath with
exertion or dizziness or syncopal episodes to let us know and we can do
appropriate workup.”
• 620 instances within 3172 documents

10. Solution
• Our method attempts to resolve the conflicts by understanding other
observations by leveraging coded domain knowledge
Atrial FibrillationSyncope
Is_symptom_of
Warfarin
Atenolol
AspirinIs_medication_for
Symptoms Medication
Medication
Medication
• We used only medication information because extraction algorithms
performs well with its list structure.

11. Knowledge Base
Domains
Cardiology
Orthopedics
Oncology
Neurology
Etc…
Concepts 1008161
Problems(diseases, symptoms) 125778
Procedures 262360
Medicines 298993
Medical Devices 33124
Relationships 77261
is treated with (disease -> medication) 41182
is relevant procedure (procedure -> disease) 3352
is symptom of (symptom -> disease) 8299
contraindicated drug (medication -> disease) 24428

12. Knowledge Base
Statin
simvastatin
zocor
High
Cholesterol
vytorin Pravachol
Pravastatin
• We use medication hierarchy to find the relationships
Is_medication_for
Type_of

13. Evaluation
Predicted Class
Positive Negative
Actual Class Positive 18 6
Negative 3 5
Accuracy = TP + TN
TP + TN + FP + FN
= 71.87%
• 25 Documents
• 32 conflicting instances

14. Observations and Insights
• False Negatives are for common symptoms
• headache, obesity, shortness of breath
• Doctors may not prescribe medications for these symptoms
• False Positives based on common medications
• Aspirin
• Conflicts on major conditions can resolve accurately
• Coronary artery disease, Atrial fibrillation, Peripheral vascular
disease, Ischemia, Cardiomyopathy etc
• Patient should take medications for these conditions

15. Observations and Insights
• The evidences should be ranked
• Metoprolol is strong evidence for hypertension than aspirin
• Insulin is strong evidence for diabetics
• More sophisticated evidence aggregation method should be
used
• Rule-based
• Probabilistic method

16. Beyond Conflicts
• Populate relationships among the entities
• Which medications are associated with which condition
• Derive implicit information in the patient notes
• Patient notes can be incomplete
• Domain experts read the note and can understand beyond
what is written there
• This insights are important for prediction algorithms
• Knowledge driven inferencing can be used to fill this gap

17. Thank You
Perera, Sujan, Amit Sheth, Krishnaprasad Thirunarayan, Suhas Nair, and Neil
Shah. "Challenges in understanding clinical notes: Why nlp engines fall short and
where background knowledge can help." In Proceedings of the 2013
international workshop on Data management & analytics for healthcare, pp. 21-
26. ACM, 2013. PDF