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

166 M.J. Feldman
important design goal was the system’s ability to explain its reasoning. A user can
obtain “Evidence for Dx” for any of the diseases shown on DXplain’s list (see
Figs. 12.1 and 12.2). 21 The system will then display the entered findings that support
the diagnosis, as well as other important findings associated with the diagnosis.
DXplain’s structure and heuristics were influenced by previous work (Internist-I
and QMR. 14 ) in which the functional unit is the “disease profile.” In DXplain, each
disease profile contains an average of 53 clinical findings (10 to 100 +). Each finding
is related to one or more diseases by two parameters:
Term Frequency (TF) indicates how often a finding is seen in a disease and is
analogous to sensitivity.
Evoking Strength (ES) answers the question “Given this finding, how strongly
should I consider that disease?” and is analogous to positive predictive value
(PPV).
Term Importance (TI), is disease independent, and indicates the significance of a
particular clinical finding, ranging from 1 (unimportant) to 5 (must be explained
(e.g. “coma”) ).
There are approximately 244,000 (2 × 53 × 2,300) data points describing diseasefinding
relationships in the KB. 22
Factors that contribute to scoring diseases in DXplain include heuristic approximations
of PPV, prevalence, TI, and sensitivity. 18 Pediatric ages (such as a child
or infant) will exclude adult diseases (e.g. Alzheimer Disease), and some diseases
(hypothyroidism, gastroesophageal reflux disease) may contain separate profiles
Fig. 12.1 DXplain: Case Analysis of a newborn with cyanosis, tachypnea, and retractions