A Neuro-Ontology for the Neurological Examination

Background: The Use of Clinical Data in Electronic Health Records for Machine-Learning or Data Analytics Depends on the Conversion of Free Text into Machine-Readable Codes. We Have Examined the Feasibility of Capturing the Neurological Examination as Machine-Readable Codes based on UMLS Metathesaurus Concepts. Methods: We Created a Target Ontology for Capturing the Neurological Examination using 1100 Concepts from the UMLS Metathesaurus. We Created a Dataset of 2386 Test-Phrases based on 419 Published Neurological Cases. We Then Mapped the Test-Phrases to the Target Ontology. Results: We Were Able to Map All of the 2386 Test-Phrases to 601 Unique UMLS Concepts. a Neurological Examination Ontology with 1100 Concepts Has Sufficient Breadth and Depth of Coverage to Encode All of the Neurologic Concepts Derived from the 419 Test Cases. using Only Pre-Coordinated Concepts, Component Ontologies of the UMLS, Such as HPO, SNOMED CT, and OMIM, Do Not Have Adequate Depth and Breadth of Coverage to Encode the Complexity of the Neurological Examination. Conclusion: An Ontology based on a Subset of UMLS Has Sufficient Breadth and Depth of Coverage to Convert Deficits from the Neurological Examination into Machine-Readable Codes using Pre-Coordinated Concepts. the Use of a Small Subset of UMLS Concepts for a Neurological Examination Ontology Offers the Advantage of Improved Manageability as Well as the Opportunity to Curate the Hierarchy and Subsumption Relationships

The Display of Photographic-Quality Images on the Web: A Comparison of Two Technologies

Downloading Medical Images on the Web Creates Certain Compromises. the Tradeoff is between Higher Resolution and Faster Download Times. as Resolution Increases, Download Times Increase. High-resolution (Photographic Quality) Electronic Images Can Potentially Play a Key Role in Medical Education and Patient Care. on the Internet, Images Are Typically Formatted as Graphics Interchange Format (GIF) or the Joint Photographic Experts Group (JPEG) Flies. However, These Formats Are Associated with Considerable Data Loss in Both Color Depth and Image Resolution. Furthermore, These Images Are Available in a Single Resolution and Have No Capability of Allowing the User to Adjust Resolution as Needed. Images in the Photo Compact Disc (PCD) Format Have Higher Resolutions Than GIF or JPEG, But Suffer the Disadvantage of Large File Sizes Leading to Long Download Times on the Web. Furthermore, Native Web Browsers Are Not Currently Able to Read PCD Flies. the FlashPix Format (FPX) Offers Distinct Advantages over the PCD, GIF, and JPEG Formats for Display of High-Resolution Images on the Web. a Java Applet Can Be Easily Downloaded for Viewing FPX Images. FPX Images Are Higher Resolution Than JPEG and GIF Images. FPX Images Offer Rich Resolutions Comparable to PCD Images with Shorter Download Times. © 1999 IEEE

Making the Neurology Clerkship More Effective: Can E-Textbook Facilitate Learning?

Objective: in 1998, the 4-Week Neurology Elective Clerkship Was Converted into a 2-Week Required Neurology Rotation at the University of Illinois at Chicago. We Hypothesized that the Interactive E-Textbook, a Computer-Assisted Learning Tool, Could Successfully Replace a Paper-Based Syllabus and a Traditional Neurology Textbook during a 2-Week Rotation, While Incorporating Department Teaching Conferences to Replace the Medical Student Lecture Series. Methods: We Created an E-Textbook and Made It Available Simultaneously in a CD-ROM Format and on a Password-Protected Website. the Online Quiz and Course Assessment Were Administered by the Blackboard Web Server. Results: after Implementation of the E-Textbook over 6 Years, the Feedback Shows High Student Satisfaction, and Student Evaluations of the Neurology Clerkship Have Risen. Creation of an E-Textbook for the Neurology Clerkship Made Our Faculty More Productive While Increasing Student Satisfaction and Facilitating Learning Efficacy. Discussion: The Results Show that the E-Textbook is an Appropriate Alternative to Facilitate Learning of Basic and Clinical Neurology during a 2-Week Rotation. the Students Demonstrated Successful Learning in a Computerized Environment. © 2005 W. S. Maney & Son Ltd

MCA Flow Asymmetry is a Marker for Cerebrovascular Disease

Utilizing the UIC Vascular Laboratory Registry, We Retrospectively Analysed the Significance of Side to Side Middle Cerebral Artery (MCA) Flow Velocity Differences. Side to Side Differences \u3e 15% Measured by Transcranial Doppler Were Considered Asymmetric. Asymmetric Subjects Had 5 Times Greater Chance of Having Significant Cervical Carotid Narrowing on Either Side on Duplex Doppler and a 3.7 Times Greater Chance of Having a Stroke on Brain CT or MRI. MCA Flow Velocity Asymmetry is a Marker for Underlying Carotid Disease and Stroke

Evaluation of Standard and Semantically-Augmented Distance Metrics for Neurology Patients

Background: Patient distances can be calculated based on signs and symptoms derived from an ontological hierarchy. There is controversy as to whether patient distance metrics that consider the semantic similarity between concepts can outperform standard patient distance metrics that are agnostic to concept similarity. The choice of distance metric can dominate the performance of classification or clustering algorithms. Our objective was to determine if semantically augmented distance metrics would outperform standard metrics on machine learning tasks. Methods: We converted the neurological findings from 382 published neurology cases into sets of concepts with corresponding machine-readable codes. We calculated patient distances by four different metrics (cosine distance, a semantically augmented cosine distance, Jaccard distance, and a semantically augmented bipartite distance). Semantic augmentation for two of the metrics depended on concept similarities from a hierarchical neuro-ontology. For machine learning algorithms, we used the patient diagnosis as the ground truth label and patient findings as machine learning features. We assessed classification accuracy for four classifiers and cluster quality for two clustering algorithms for each of the distance metrics. Results: Inter-patient distances were smaller when the distance metric was semantically augmented. Classification accuracy and cluster quality were not significantly different by distance metric. Conclusion: Although semantic augmentation reduced inter-patient distances, we did not find improved classification accuracy or improved cluster quality with semantically augmented patient distance metrics when applied to a dataset of neurology patients. Further work is needed to assess the utility of semantically augmented patient distances