In this study, a projection of effective blood concentration (EBC) readings of digoxin is made using the inverse problem algorithm based on clinical data for patients with heart failure diseases. Seven factors, including body surface area (BSA), blood urine nitrogen (BUN), creatinine, sodium (Na), potassium (K), magnesium (Mg) ion readings, and mean arterial pressure (MAP) were compiled with nonlinear regression fit to develop a projection function having 29 terms obtained from an inverse problem algorithm via the default function run in STATISTICA. Accordingly, data collected from the clinical 168 heart failure patients were normalized to be included in same domain range ([Formula: see text]1 to +1), and then calculated by the specific algorithm to optimize the numerical solution to evaluate EBC readings of digoxin. The evaluated first-order regression fit owned an optimal loss function ([Formula: see text]) coupled with correlation coefficient [Formula: see text] = 0.892 and variance of 89.20%. Furthermore, 45 patients having similar clinical syndromes were also adopted to verify the projection and implied with high agreement. The BUN factor dominated the projection and defined as the most significant coefficient in the analysis, and K ion, MAP, BSA, and Mg ion factors exhibited minor contributions to the projection. The repeated trials to lower number of factors from seven to a smaller number (namely 6, 5, 4, 3, 2, and 1) for simplifying method but resulting with unaccepted outcomes, with high loss function values and low linearity. However, the algorithm held its accuracy to handle the verified data that were out of the original bounds. The proposed algorithm demonstrated a useful analysis to handle the drug administration in pharmaceutical field.

This study optimized the ultrasound image of carotid artery stenosis using Taguchi dynamic analysis and an indigenous water phantom. Eighteen combinations of seven essential factors of the ultrasound scan facility were organized according to Taguchi’s L18 orthogonal array. The seven factors were assigned as follows: (1) angle of probe; (2) signal gain; (3) resolution vs. speed; (4) dynamic range; (5) XRES; (6) zoom; (7) time gain compensation. An indigenous water phantom was customized to satisfy the quantified need in Taguchi’s analysis. Unlike the conventional dynamic Taguchi analysis, an innovative quantified index, the figure of merit (FOM), was proposed to integrate four specific quality characteristics, namely (i) average difference between the practical scan and theoretically preset area (78.5, 50.2 and 12.6 mm2) of stenosis, (ii) standard deviation of the average, (iii) practical scan’s sensitivity β to various stenosis diameters (10, 8, and 4 mm), and (iv) correlation coefficient r2 of the linear regressed sensitivity curve. The highest value (FOM = 0.413) was furnished by the optimal combination of factors on 18 groups under study, yielding high r2 and low β or standard deviation values and the best quality of ultrasound images for the further clinical diagnosis. The comparison between FOM and the conventional signal-to-noise (S/N) ratio in Taguchi’s analysis revealed that FOM compiled more quality characteristics that were superior by nature to fulfill the practical need in clinical diagnosis. The alternative choice in ultrasound scan optimization can be based on stenosis diameter variation from a different perspective to be explored in the follow-up study.