Pediatric Informatics: Computer Applications in Child Health (Health ...

12 Diagnostic Decision Support 171
that in 73% of cases where GIDEON’s top diagnosis did not match the discharge
diagnosis, the final diagnosis was not contained in GIDEON’s KB, which was
attributed to unusual infections seen primarily in quaternary care hospitals such as
those complicated by invasive lines or surgical hardware. 33
12.5.3 Isabel
Intended function
� Diagnostic decision support in pediatrics, internal medicine and their
subspecialities
Mode by which advice is offered
� Passive: User accesses system when needed
Consultation style
� Consulting model
Underlying decision-making process
� Proprietary pattern recognition software indexing of pediatric and internal
medicine texts. Case findings are matched to the textbook index and relevant
diagnoses are displayed.
Human–computer interaction
� Web-based reminder system based on user text entry
Isabel is a Web-based diagnostic reminder system, with the goal of reducing diagnostic
and decision errors. 34 The system, available since 2002, initially covered
pediatrics and in 2005 was expanded to include adult medicine. The pediatric
content derives from two texts on general pediatrics, one on neonatology and one
on toxicology. A proprietary pattern recognition program extracts concepts from
these texts. 35
Whereas search engines are optimized for keyword searching and retrieval, the
pattern recognition program used in Isabel provides search results optimized for
meaning and context. It is described as “using non-linear adaptive digital signal
processing techniques to find patterns that naturally occur in text based on the
frequency of terms that correspond to concepts” and as using Bayesian inference
and a principle of information theory (the less frequently a word or phrase occurs,
the more information it conveys) to arrive at the probability that a particular document
pertains to a specific subject. Bayesian calculations categorize the context
of the content, and information theory is used to infer its significance. 36 After the
pattern recognition program extracts concepts from the textbook’s documents, they
are linked to diagnoses. According to the developer, synonyms for document terms
are identified and prevalence values for diagnoses are assigned and are adjusted
regionally 37 .
Isabel’s underlying knowledge base (KB) contains textbook information and
proprietary concept extractions on 3,500 diagnoses. The user inputs clinical findings
into a textbox interface. The program then searches the KB text and it matches
documents whose concepts are most similar to the concepts matching the findings
entered by the user. Diagnostic possibilities are displayed, grouped by body