Albumin-bilirubin grade as a predictor of survival in hepatocellular carcinoma patients with thrombocytopenia.

BACKGROUND: The models for assessing liver function, mainly the Child-Pugh (CP), albuminbilirubin (ALBI), and platelet-ALBI (PALBI) classifications, have been validated for use in estimating the prognosis of hepatocellular carcinoma (HCC) patients. However, thrombocytopenia is a common finding and may influence the prognostic value of the three models in HCC. AIM: To investigate and compare the prognostic performance of the above three models in thrombocytopenic HCC patients. METHODS: A total of 135 patients with thrombocytopenic HCC who underwent radical surgery were retrospectively analyzed. Preoperative scores on the CP, ALBI and PALBI classifications were estimated accordingly. Kaplan-Meier curves with log-rank tests and Cox regression models were used to explore the significant factors associated with overall survival (OS) and recurrence-free survival (RFS). RESULTS: The preoperative platelet counts were significantly different among the CP, ALBI and PALBI groups. After a median follow-up of 28 mo, 39.3% (53/135) of the patients experienced postoperative recurrence, and 36.3% (49/135) died. Univariate analysis suggested that α-fetoprotein levels, tumor size, vascular invasion, and ALBI grade were significant predictors of OS and RFS. According to the multivariate Cox regression model, ALBI was identified as an independent prognostic factor. However, CP and PALBI grades were not statistically significant prognostic indicators. CONCLUSION: The ALBI grade, rather than CP or PALBI grade, is a significant prognostic indicator for thrombocytopenic HCC patients.

A Fast Weighted Fuzzy C-Medoids Clustering for Time Series Data Based on P-Splines.

The rapid growth of digital information has produced massive amounts of time series data on rich features and most time series data are noisy and contain some outlier samples, which leads to a decline in the clustering effect. To efficiently discover the hidden statistical information about the data, a fast weighted fuzzy C-medoids clustering algorithm based on P-splines (PS-WFCMdd) is proposed for time series datasets in this study. Specifically, the P-spline method is used to fit the functional data related to the original time series data, and the obtained smooth-fitting data is used as the input of the clustering algorithm to enhance the ability to process the data set during the clustering process. Then, we define a new weighted method to further avoid the influence of outlier sample points in the weighted fuzzy C-medoids clustering process, to improve the robustness of our algorithm. We propose using the third version of mueen's algorithm for similarity search (MASS 3) to measure the similarity between time series quickly and accurately, to further improve the clustering efficiency. Our new algorithm is compared with several other time series clustering algorithms, and the performance of the algorithm is evaluated experimentally on different types of time series examples. The experimental results show that our new method can speed up data processing and the comprehensive performance of each clustering evaluation index are relatively good.

Feature Selection Combining Information Theory View and Algebraic View in the Neighborhood Decision System.

Feature selection is one of the core contents of rough set theory and application. Since the reduction ability and classification performance of many feature selection algorithms based on rough set theory and its extensions are not ideal, this paper proposes a feature selection algorithm that combines the information theory view and algebraic view in the neighborhood decision system. First, the neighborhood relationship in the neighborhood rough set model is used to retain the classification information of continuous data, to study some uncertainty measures of neighborhood information entropy. Second, to fully reflect the decision ability and classification performance of the neighborhood system, the neighborhood credibility and neighborhood coverage are defined and introduced into the neighborhood joint entropy. Third, a feature selection algorithm based on neighborhood joint entropy is designed, which improves the disadvantage that most feature selection algorithms only consider information theory definition or algebraic definition. Finally, experiments and statistical analyses on nine data sets prove that the algorithm can effectively select the optimal feature subset, and the selection result can maintain or improve the classification performance of the data set.