Strength of a liberal arts education in preparing future physicians.
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Art↗
Biomedical subjects
D L Ranum
Publications and source records attributed to D L Ranum.
"Pre-medical" informatics.
Many authors have presented arguments in the recent medical literature discussing the premedical preparation of medical students. Much of this work seems to center upon the need to balance the strong science concentration with an increased emphasis on humanities courses. This paper investigates another potential shortcoming of premedical education, that of information literacy. It defines the problem, argues its existence, and proposes the ready availability of a solution.
Computer Literacy↗
Computerized extraction of coded findings from free-text radiologic reports. Work in progress.
A computerized data acquisition tool, the special purpose radiology understanding system (SPRUS), has been implemented as a module in the Health Evaluation through Logical Processing Hospital Information System. This tool uses semantic information from a diagnostic expert system to parse free-text radiology reports and to extract and encode both the findings and the radiologists' interpretations. These coded findings and interpretations are then stored in a clinical data base. The system recognizes both radiologic findings and diagnostic interpretations. Initial tests showed a true-positive rate of 87% for radiographic findings and a bad data rate of 5%. Diagnostic interpretations are recognized at a rate of 95% with a bad data rate of 6%. Testing suggests that these rates can be improved through enhancements to the system's thesaurus and the computerized medical knowledge that drives it. This system holds promise as a tool to obtain coded radiologic data for research, medical audit, and patient care.
Artificial Intelligence↗
Knowledge-based understanding of radiology text.
A data acquisition tool which will extract pertinent diagnostic information from radiology reports has been designed and implemented. Pertinent diagnostic information is defined as that clinical data which is used by the HELP medical expert system. The program uses a memory-based semantic parsing technique to 'understand' the text. Moreover, the memory structures and lexicon necessary to perform this action are automatically generated from the diagnostic knowledge base by using a special purpose compiler. The result is a system where data extraction from free text is directed by an expert system whose goal is diagnosis.
Computer-Assisted Instruction↗