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Challenges in Understanding Clinical Notes:
Why NLP Engines Fall Short and Where
Background Knowledge Can Help
Sujan Perer...
Why is it necessary to understand clinical notes?
• 80% of patient data is unstructured1
• Structured data is incomplete a...
Why is it necessary to understand clinical notes?
• Key indicators for decision making reside in patient notes
• facilitie...
• ICD10 adaptation – need to understand the relationships
E08 - Diabetes mellitus due to underlying condition
E08.0 - Diab...
Patient Data Distribution
Structured data
Unstructured data
Understanding Clinical
Notes
• What conditions patient has?
• What symptoms patient has?
• What medications with what dosa...
Understanding Clinical Notes - Tasks
• Entity identification
• she does not state that the reason for her stopping after t...
Understanding Linguistic Constructs
• Rule based algorithms are popular
• simplicity
• low computational cost
• Maintains ...
Leads to Conflicting Instances
• Failure to identify such linguistic constructs leads to conflicting instances
• Document ...
Solution
• Our method attempts to resolve the conflicts by understanding other
observations by leveraging coded domain kno...
Knowledge Base
Domains
Cardiology
Orthopedics
Oncology
Neurology
Etc…
Concepts 1008161
Problems(diseases, symptoms) 125778...
Knowledge Base
Statin
simvastatin
zocor
High
Cholesterol
vytorin Pravachol
Pravastatin
• We use medication hierarchy to fi...
Evaluation
Predicted Class
Positive Negative
Actual Class Positive 18 6
Negative 3 5
Accuracy = TP + TN
TP + TN + FP + FN
...
Observations and Insights
• False Negatives are for common symptoms
• headache, obesity, shortness of breath
• Doctors may...
Observations and Insights
• The evidences should be ranked
• Metoprolol is strong evidence for hypertension than aspirin
•...
Beyond Conflicts
• Populate relationships among the entities
• Which medications are associated with which condition
• Der...
Thank You
Perera, Sujan, Amit Sheth, Krishnaprasad Thirunarayan, Suhas Nair, and Neil
Shah. "Challenges in understanding c...
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Challenges in understanding clinical notes: Why NLP engines fall short and where background knowledge can help.

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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.

Publié dans : Technologie
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Challenges in understanding clinical notes: Why NLP engines fall short and where background knowledge can help.

  1. 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. 2. Why is it necessary to understand clinical notes? • 80% of patient data is unstructured1 • Structured data is incomplete and not accurate2,3 1goo.gl/abqYYn 2Strengths and Limitations of CMS Administrative Data in Research 3Comparison of clinical and administrative data sources for hospital coronary artery bypass graft surgery report cards
  3. 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. 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. 5. Patient Data Distribution Structured data Unstructured data
  6. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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

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