The Lookup Table Regression Model for Histogram-Valued Symbolic Data

This paper presents the Lookup Table Regression Model (LTRM) for histogram-valued symbolic data. We first transform the given symbolic data to a numerical data table by the quantile method. Then, under the selected response variable, we apply the Monotone Blocks Segmentation (MBS) to the obtained numerical data table. If the selected response variable and some remained explanatory variable(s) organize a monotone structure, the MBS generates a Lookup Table composed of interval values. For a given object, we search the nearest value of an explanatory variable, then the corresponding value of the response variable becomes the estimated value. If the response variable and the explanatory variable(s) are covariate but they follow to a non-monotonic structure, we need to divide the given data into several monotone substructures. For this purpose, we apply the hierarchical conceptual clustering to the given data, and we obtain Multiple Lookup Tables by applying the MBS to each of substructures. We show the usefulness of the proposed method by using an artificial data set and real data sets

Proceedings of the International Conference on Cognition and Recognition Detection of Monotonic Chain Structures in Mixed Feature Type Multidimensional Data

Symbolic data analysis aims at generalizing some standard statistical methods. Generalization of principal component analysis (PCA) is an interesting and important research problem in symbolic data analysis. A main purpose of PCA is to find a linear structure in multidimensional data. However, a direct extension of PCA is difficult, when each object is described by not only usual quantitative features but also interval valued features and qualitative features. This paper describes a method to detect “monotonic chain structures ” including “linear structure ” based on the Cartesian system model (CSM) which is a mathematical model to manipulate objects described by mixed type feature values. Simple examples are presented to illustrate our approach. 1

Interclass analysis in symbolic pattern classification problems

interclass analysis, feature selection, neighborhood relation, distinguishability, generality, inside view, outside view,

Estimation of Distances to the Red Giant Stars

[Abstract] Based on the Principal Component Analysis and Oort\u27s Galactic rotation model, the distances to the red giant stars are estimated. The 157 red giant stars are devided into two groups, then the distances are estimated as log d(Kpc)=0.471z_2+0.241 for Group 1 and log d(Kpc)=0.446z_2+0.532 for Group 2, where z_2 is the principal component corresponding to the distance

Combination of deep learning and ensemble machine learning using intraoperative video images strongly predicts recovery of urinary continence after robot‐assisted radical prostatectomy

Abstract Background We recently reported the importance of deep learning (DL) of pelvic magnetic resonance imaging in predicting the degree of urinary incontinence (UI) following robot‐assisted radical prostatectomy (RARP). However, our results were limited because the prediction accuracy was approximately 70%. Aim To develop a more precise prediction model that can inform patients about UI recovery post‐RARP surgery using a DL model based on intraoperative video images. Methods and Results The study cohort comprised of 101 patients with localized prostate cancer undergoing RARP. Three snapshots from intraoperative video recordings showing the pelvic cavity (prior to bladder neck incision, immediately following prostate removal, and after vesicourethral anastomosis) were evaluated, including pre‐ and intraoperative parameters. We evaluated the DL model plus simple or ensemble machine learning (ML), and the area under the receiver operating characteristic curve (AUC) was analyzed through sensitivity and specificity. Of 101, 64 and 37 patients demonstrated “early continence (using 0 or 1 safety pad at 3 months post‐RARP)” and “late continence (others),” respectively, at 3 months postoperatively. The combination of DL and simple ML using intraoperative video snapshots with clinicopathological parameters had a notably high performance (AUC, 0.683–0.749) to predict early recovery from UI after surgery. Furthermore, combining DL with ensemble artificial neural network using intraoperative video snapshots had the highest performance (AUC, 0.882; sensitivity, 92.2%; specificity, 78.4%; overall accuracy, 85.3%) to predict early recovery from post‐RARP incontinence, with similar results by internal validation. The addition of clinicopathological parameters showed no additive effects for each analysis using DL, EL and simple ML. Conclusion Our findings suggest that the DL algorithm with intraoperative video imaging is a reliable method for informing patients about the severity of their recovery from UI after RARP, although it is not clear if our methods are reproducible for predicting long‐term UI and pad‐free continence

Functional Outcomes after Selective Clamping in Robot-Assisted Partial Nephrectomy

This study aimed to assess the risks and benefits of selective clamping in robot-assisted partial nephrectomy (RAPN). We retrospectively analyzed 372 patients who had undergone RAPN at our hospital between July 2010 and March 2021. After propensity score matching between the full and selective clamping groups, perioperative outcomes and postoperative preservation ratio of the estimated glomerular filtration rate (eGFR) were compared at 6 and 12 months of follow-up. After propensity score matching, we evaluated 47 patients from each group. While no significant differences were observed in surgical time, warm ischemia time, or incidence rates of all grades of complications between the two cohorts, the estimated blood loss (EBL) was significantly lower in the full clamping group than in the selective clamping group (30 vs. 60, p = 0.046). However, no significant intergroup differences were observed in the postoperative preservation ratio of eGFR at 6 or 12 months of follow-up (full clamping 94.0% vs. selective clamping 92.7%, p = 0.509, and full clamping 92.0% vs. selective clamping 91.6%, p = 0.476, respectively). Selective clamping resulted in higher EBL rates than did full clamping in RAPN. However, selective clamping provided no renal functional advantage over full clamping in our propensity-score-matched cohort

Comparing pentafecta outcomes between nerve sparing and non nerve sparing robot-assisted radical prostatectomy in a propensity score-matched study

Abstract Pentafecta (continence, potency, cancer control, free surgical margins, and no complications) is an important outcome of prostatectomy. Our objective was to assess the pentafecta achievement between nerve-spring and non-nerve-sparing robot-assisted radical prostatectomy (RARP) in a large single-center cohort. The study included 1674 patients treated with RARP between August 2009 and November 2022 to assess the clinical outcomes. Cox regression analyses were performed to evaluate the prognostic significance of RARP for pentafecta achievement, and 1:1 propensity score matching (PSM) was performed between the nerve-sparing and non-nerve-sparing to test the validity of the results. Pentafecta definition included continence, which was defined as the use of zero pads; potency, which was defined as the ability to achieve and maintain satisfactory erections or ones firm enough for sexual activity and sexual intercourse. The biochemical recurrence rate was defined as two consecutive PSA levels > 0.2 ng/mL after RARP; 90-day Clavien–Dindo complications ≤ 3a; and a negative surgical pathologic margin. The median follow-up period was 61.3 months (IQR 6–159 months). A multivariate Cox regression analysis demonstrated that pentafecta achievement was significantly associated with nerve-sparing (NS) approach (1188 patients) (OR 4.16; 95% CI 2.51–6.9), p < 0.001), unilateral nerve preservation (983 patients) (OR 3.83; 95% CI 2.31–6.37, p < 0.001) and bilateral nerve preservation (205 patients) (OR 7.43; 95% CI 4.14–13.36, p < 0.001). After propensity matching, pentafecta achievement rates in the NS (476 patients) and non-NS (476 patients) groups were 72 (15.1%) and 19 (4%), respectively. (p < 0.001). NS in RARP offers a superior advantage in pentafecta achievement compared with non-NS RARP. This validation study provides the pentafecta outcome after RARP associated with nerve-sparing in clinical practice

Novel Intraoperative Navigation Using Ultra-High-Resolution CT in Robot-Assisted Partial Nephrectomy

To assess the perioperative and short-term functional outcomes of robot-assisted partial nephrectomy (RAPN) with intraoperative navigation using an ultra-high-resolution computed tomography (UHR-CT) scanner, we retrospectively analyzed 323 patients who underwent RAPN using an UHR-CT or area-detector CT (ADCT). Perioperative outcomes and the postoperative preservation ratio of estimated glomerular filtration rate (eGFR) were compared. After the propensity score matching, we evaluated 99 patients in each group. Although the median warm ischemia time (WIT) was less than 25 min in both groups, it was significantly shorter in the UHR-CT group than in the ADCT group (15 min vs. 17 min, p = 0.032). Moreover, the estimated blood loss (EBL) was significantly lower in the UHR-CT group than in the ADCT group (33 mL vs. 50 mL, p = 0.028). However, there were no significant intergroup differences in the postoperative preservation ratio of eGFR at 3 or 6 months of follow-up (ADCT 91.8% vs. UHR-CT 93.5%, p = 0.195; and ADCT 91.7% vs. UHR-CT 94.0%, p = 0.160, respectively). Although no differences in short-term renal function were observed in intraoperative navigation for RAPN in this propensity score&ndash;matched cohort, this study is the first to demonstrate that UHR-CT resulted in a shorter WIT and lower EBL than ADCT