RESUMO
For irregularly measured time-series data, the measurement frequency or interval is as crucial information as measurements are. A well-known time-series visualization such as the line graph is good at showing an overall temporal pattern of change; however, it is not so effective in revealing the measurement frequency/interval while likely giving illusory confidence in values between measurements. In contrast, the bar graph is more effective in showing the frequency/interval, but less effective in showing an overall pattern than the line graph. We integrate the line graph and bar graph in a unified visualization model, called a ripple graph, to take the benefits of both of them with enhanced graphical integrity. Based on the ripple graph, we implemented an interactive time-series data visualization tool, called Stroscope, which facilitates multi-scale visualizations by providing users with a graphical widget to interactively control the integrated visualization model. We evaluated the visualization model (i.e., the ripple graph) through a controlled user study and Stroscope through long-term case studies with neurologists exploring large blood pressure measurement data of stroke patients. Results from our evaluations demonstrate that the ripple graph outperforms existing time-series visualizations, and that Stroscope has the efficacy and potential as an effective visual analysis tool for (irregularly) measured time-series data.
RESUMO
BACKGROUND AND PURPOSE: Thrombolysis is underused in acute ischemic stroke, mainly due to the reluctance of physicians to treat thrombolysis patients. However, a computerized clinical decision support system can help physicians to develop individualized stroke treatments. METHODS: A consecutive series of 958 patients, hospitalized within 12 hours of ischemic stroke onset from a representative clinical center in Korea, was used to establish a prognostic model. Multivariable logistic regression was used to develop the model for global and safety outcomes. An external validation of developed model was performed using 954 patients data obtained from 5 university hospitals or regional stroke centers. RESULTS: Final global outcome predictors were age; previous modified Rankin scale score; initial National Institutes of Health Stroke Scale (NIHSS) score; previous stroke; diabetes; prior use of antiplatelet treatment, antihypertensive drugs, and statins; lacunae; thrombolysis; onset to treatment time; and systolic blood pressure. Final safety outcome predictors were age, initial NIHSS score, thrombolysis, onset to treatment time, systolic blood pressure, and glucose level. The discriminative ability of the prognostic model showed a C-statistic of 0.89 and 0.84 for the global and safety outcomes, respectively. Internal and external validation showed similar C-statistic results. After updating the model, calibration slopes were corrected from 0.68 to 1.0 and from 0.96 to 1.0 for the global and safety outcome models, respectively. CONCLUSIONS: A novel computerized outcome prediction model for thrombolysis after ischemic stroke was developed using large amounts of clinical information. After external validation and updating, the model's performance was deemed clinically satisfactory.