Assessment of Effective Blood Concentration Readings From Clinical Data on Patients With Heart Failure Diseases After Digoxin Intake: A Projection Based on the Inverse Problem Algorithm

Abstract

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.

Potential risk quantification from multiple biological factors via the inverse problem algorithm as an artificial intelligence tool in clinical diagnosis

“…Noteworthy is that only in the study of Pan et al [1], the rank of constant (rank 14/16) was less than any others, namely, ranks 6/16, 7/29, 5/16, 5/22, and 6/22, respectively in the studies [2][3][4][5][6]. Thus, the optimized loss function, Φ (cf.…”

“…However, a large constant also indicates a minor alignment that can be achieved in optimizing the solution. Noteworthy is that only in the study of Pan et al [ 1 ], the rank of constant (rank 14/16) was less than any others, namely, ranks 6/16, 7/29, 5/16, 5/22, and 6/22, respectively in the studies [ 2 , 3 , 4 , 5 , 6 ]. Thus, the optimized loss function, (cf.…”

“…Table 1 shows the integrated evaluation of the IPA-related papers. As depicted, the expectation value may be the serum creatinine index after contrast administration for the patient with cardiac diagnosis [ 1 ] or digoxin reading after the patient was administered digoxin [ 3 ]. Nevertheless, the expectation values in the same study could be either the maximal ratio of both left-and-right-arterial-to-upper sinuses (LRA/US) or suitable triggered timing of computer tomography angiography (CTA-TT) [ 5 , 6 ].…”

“…The IPA uses mathematical calculations to estimate the expectation value of a specific index according to patient risk factor groups. Furthermore, the contributions of particular risk factors or their cross-interactions can be evaluated and ranked by their importance [ 1 , 2 , 3 , 4 , 5 , 6 ]. Unlike the Taguchi optimization algorithm, which is used to optimize one purpose from a combination of multiple factors [ 7 , 8 , 9 , 10 ], IPA directly estimates the expectation value from a group of data.…”

“…A practical application of IPA usually starts from the expectation value definition, which can be the concentration of a particular drug in the patient’s blood [ 3 ], the severity of coronary artery [ 2 ], carotid stenosis [ 4 ], or even the optimal CTA timing for head and neck scanning [ 5 , 6 ] as well. The IPA is operated according to numerous data of the patient’s biological index.…”