PURPOSE: Time series analysis may be helpful to estimate hematological data on gastric cancer patients who receive S 1, but untreated raw clinical data are not suitable for this approach. Hematological monitoring data interpolated by spline were analyzed by an attractor plot, which is a form of time series analysis. METHODS: Hematological data of three gastric cancer patients were interpolated by cubic spline. The leave-one-out cross validation method was carried out and an attractor plot was adopted to evaluate red blood count (RBC) data. RESULTS: Well-predicted data, such as RBC, changed slightly; however, data with great deviation, such as the white blood count (WBC), were poorly predicted. The reaction of marrow function to chemotherapy was observable by spline interpolation of RBC data. Furthermore, an attractor plot clarified the tendency of the interpolated hematological monitoring data. CONCLUSIONS: It is suggested that spline interpolation is effective as a pretreatment to analyze clinical data from a time series.
PURPOSE: Time series analysis may be helpful to estimate hematological data on gastric cancerpatients who receive S 1, but untreated raw clinical data are not suitable for this approach. Hematological monitoring data interpolated by spline were analyzed by an attractor plot, which is a form of time series analysis. METHODS: Hematological data of three gastric cancerpatients were interpolated by cubic spline. The leave-one-out cross validation method was carried out and an attractor plot was adopted to evaluate red blood count (RBC) data. RESULTS: Well-predicted data, such as RBC, changed slightly; however, data with great deviation, such as the white blood count (WBC), were poorly predicted. The reaction of marrow function to chemotherapy was observable by spline interpolation of RBC data. Furthermore, an attractor plot clarified the tendency of the interpolated hematological monitoring data. CONCLUSIONS: It is suggested that spline interpolation is effective as a pretreatment to analyze clinical data from a time series.
Authors: F Cismondi; L A Celi; A S Fialho; S M Vieira; S R Reti; J M C Sousa; S N Finkelstein Journal: Int J Med Inform Date: 2012-12-28 Impact factor: 4.046