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.

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.

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.