faculty of computer science © 2006 cmput 605march 3, 2008 concept-based electronic health records:...
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Faculty of Computer Science
CMPUT 605 December 06, 2007March 3,
2008© 2006
Concept-Based Electronic Health Records: Opportunities and Challenges
S. Ebadollahi, S Chang, T. Mahmood, A Coden, A. Amir M. Tanenblatt
14th Annual ACM International Conference on Multimedia (2006)
Amit [email protected]
© 2006
Department of Computing Science
CMPUT 605
Focus
ECG Video: document is not important; behavior of sub-organs like valves, ventricles, myocardium is
ECG –Text Report sub organs, diagnosis
Efficient access to the elements of the content of the data ???
New Paradigm – Concept based Multimedia Medical Records
© 2006
Department of Computing Science
CMPUT 605
Problems with the present system
Electronic Health Records (EHR)
Data—mixed format: HIS for lab reports, ECG’s etc. RIS for reports generated after reviewing medical images, and PACS for diagnostic images.
Different Standards: HL7, DICOM, etc.
Information extraction regarding a single concept of interest (Right Atrium) is difficult
Hence the need for (re)organizing the health records at the information level
© 2006
Department of Computing Science
CMPUT 605
Concept-Based Records Organization: Advantages
Goes beyond dealing with data at the document level
Caters to different categories of users of medical records
– Physicians: Ejection fraction of left ventricle measured while reviewing
the ECG. Ideally system should calculate this using quantification
Algorithms. Should also be able to link it with the diagnosis reports,
textbooks, research papers etc.
– Students: Teaching files with history of medical cases + diagnostic
images + medical journals + textbooks
© 2006
Department of Computing Science
CMPUT 605
Concept-Based Records Organization: Advantages
– Patients: Illustrated version of patient’s disease
– Insurance companies: Prevent misuse of expensive tests (MRI) when not justified by the results of earlier, less expensive tests (EKG)
Timely and decision-enabling information extraction
It entails a better organization of medical records from the
scratch in order to deliver all that is promised …
© 2006
Department of Computing Science
CMPUT 605
Architecture
Analytic Engines
—domain knowledge
Heart Chambers in Video
Parse diagnosis report
Relationships b’n concepts
—ontologies (UMLS)
Is a, spatially/temporally/
functionally related to etc.
© 2006
Department of Computing Science
CMPUT 605
Example
© 2006
Department of Computing Science
CMPUT 605
Addendum
New information may need to be added
Graph Structure with Nodes as concepts and links are
relationships between these concepts
Need federation of Ontologies – different concepts of interest in
different domains
Multimedia content restructuring required – Vision, NLP etc.
Not a new way of analyzing data, but a novel way of organizing
the medical records
© 2006
Department of Computing Science
CMPUT 605
Case Study: Video Content Restructuring
Echocardiography – Imaging of the heart in several planes
Inherent spatio-temporal strcuture
Feature-extraction tools used to target areas of interest
Text snippets extracted from diagnosis report
Undirected graphical models used to learn the spatial
arrangement of cardiac chambers
© 2006
Department of Computing Science
CMPUT 605
Schematic
© 2006
Department of Computing Science
CMPUT 605
Text Analytics for Cancer Pathology Reports
MedTAS (Medical Text Analysis System) was used
Several models – conceptually separate pieces of
knowledge
Pieces of knowledge Disease description,
evaluation procedures etc.
4 sub-models: Tumor model, Specimen model,
Lymph-node model and the disease model
© 2006
Department of Computing Science
CMPUT 605
Text Analytics for Cancer Pathology Reports
Models are annotators (can be institution specific)
MedTAS built on IBMs Ustructured Information
Management Architecture (UIMA) . (Open Source)
© 2006
Department of Computing Science
CMPUT 605
Models
© 2006
Department of Computing Science
CMPUT 605
Potential Avenues
Three main issues
— Determining the unifying architecture
— Determining the concepts that need to be extracted
— Development of robust Analytic engines
Testing & Feedback issues when such records in use
Seamless Integration with existing data
© 2006
Department of Computing Science
CMPUT 605
Thank You For Your Attention!