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Biomedical subjects

Eugene Tseytlin

Publications and source records attributed to Eugene Tseytlin.

3 recordsLinked to original sources

Evaluation of an intelligent tutoring system in pathology: effects of external representation on performance gains, metacognition, and acceptance.

OBJECTIVE: Determine effects of computer-based tutoring on diagnostic performance gains, meta-cognition, and acceptance using two different problem representations. Describe impact of tutoring on spectrum of diagnostic skills required for task performance. Identify key features of student-tutor interaction contributing to learning gains. DESIGN: Prospective, between-subjects study, controlled for participant level of training. Resident physicians in two academic pathology programs spent four hours using one of two interfaces which differed mainly in external problem representation. The case-focused representation provided an open-learning environment in which students were free to explore evidence-hypothesis relationships within a case, but could not visualize the entire diagnostic space. The knowledge-focused representation provided an interactive representation of the entire diagnostic space, which more tightly constrained student actions. MEASUREMENTS: Metrics included results of pretest, post-test and retention-test for multiple choice and case diagnosis tests, ratios of performance to student reported certainty, results of participant survey, learning curves, and interaction behaviors during tutoring. RESULTS: Students had highly significant learning gains after one tutoring session. Learning was retained at one week. There were no differences between the two interfaces in learning gains on post-test or retention test. Only students in the knowledge-focused interface exhibited significant metacognitive gains from pretest to post-test and pretest to retention test. Students rated the knowledge-focused interface significantly higher than the case-focused interface. CONCLUSIONS: Cognitive tutoring is associated with improved diagnostic performance in a complex medical domain. The effect is retained at one-week post-training. Knowledge-focused external problem representation shows an advantage over case-focused representation for metacognitive effects and user acceptance.

Attitude to Computers↗

ReportTutor - an intelligent tutoring system that uses a natural language interface.

ReportTutor is an extension to our work on Intelligent Tutoring Systems for visual diagnosis. ReportTutor combines a virtual microscope and a natural language interface to allow students to visually inspect a virtual slide as they type a diagnostic report on the case. The system monitors both actions in the virtual microscope interface as well as text created by the student in the reporting interface. It provides feedback about the correctness, completeness, and style of the report. ReportTutor uses MMTx with a custom data-source created with the NCI Metathesaurus. A separate ontology of cancer specific concepts is used to structure the domain knowledge needed for evaluation of the student's input including co-reference resolution. As part of the early evaluation of the system, we collected data from 4 pathology residents who typed in their reports without the tutoring aspects of the system, and compared responses to an expert dermatopathologist. We analyzed the resulting reports to (1) identify the error rates and distribution among student reports, (2) determine the performance of the system in identifying features within student reports, and (3) measure the accuracy of the system in distinguishing between correct and incorrect report elements.

Artificial Intelligence↗

Fiswidgets: a graphical computing environment for neuroimaging analysis.

Current fMRI neuroimaging software programs offer the researcher a wealth of analysis methods and tools. However, the incompatibilities in user interface, data format, and computing environment in these tools make it difficult if not impossible for most researchers to take advantage of the full set of resources available for neuroimaging analyses. We describe a graphical computing environment, Functional Imaging Software Widgets (fiswidgets), which was developed to address these interoperability and usability problems. This environment provides a desktop style framework into which 100 subcomponents from a number of widely used fMRI analysis software packages (e.g., AFNI, AIR) are incorporated. It is an open-source, extensible environment available for reuse and modification by other software developers. A discussion of the design criteria (open architecture, modularity, wrapper technology, commercial utilities) that support such loosely integrative computing environments, and the problems entailed in maintaining them (development overhead, distribution logistics, format incompatibilities, graphics vs scripting tradeoffs, and appropriate acknowledgment of software developers) follows.

Computer Graphics↗