Distance Metrics in Clustering and Weighted Scoring Algorithm

Collateral mechanism in the Electricity Market ensures the payments are executed on a timely manner; thus maintains the continuous cash flow. In order to value collaterals, Takasbank, the authorized central settlement bank, creates segments of the market participants by considering their short-term and long-term debt/credit information arising from all market activities. In this study, the data regarding participants’ daily and monthly debt payment and penalty behaviors is analyzed with the aim of discovering high-risk participants that fail to clear their debts on-time frequently. Different clustering techniques along with different distance metrics are considered to obtain the best clustering. Moreover, data preprocessing techniques along with Recency, Frequency, Monetary Value (RFM) scoring have been used to determine the best representation of the data. The results show that Agglomerative Clustering with cosine distance achieves the best separated clustering when the non-normalized dataset is used; this is also acknowledged by a domain expert.

Download Full-text

Polyethylene glycol versus lactulose in the treatment of hepatic encephalopathy: a systematic review and meta-analysis

BMJ Open Gastroenterology ◽

10.1136/bmjgast-2021-000648 ◽

2021 ◽

Vol 8 (1) ◽

pp. e000648

Author(s):

Gilles Jadd Hoilat ◽

Mohamad Fekredeen Ayas ◽

Judie Noemie Hoilat ◽

Ahmed Abu-Zaid ◽

Ceren Durer ◽

...

Keyword(s):

Polyethylene Glycol ◽

Hepatic Encephalopathy ◽

Web Of Science ◽

Meta Analysis ◽

Standard Of Care ◽

Brain Dysfunction ◽

Cochrane Library ◽

Continuous Data ◽

Inverse Variance ◽

Scoring Algorithm

BackgroundHepatic encephalopathy (HE) is defined as brain dysfunction that occurs because of acute liver failure or liver cirrhosis and is associated with significant morbidity and mortality. Lactulose is the standard of care till this date; however, polyethylene glycol (PEG) has gained the attention of multiple investigators.MethodsWe screened five databases namely PubMed, Scopus, Web of Science, Cochrane Library and Embase from inception to 10 February 2021. Dichotomous and continuous data were analysed using the Mantel-Haenszel and inverse variance methods, respectively, which yielded a meta-analysis comparing PEG versus lactulose in the treatment of HE.ResultsFour trials with 229 patients were included. Compared with lactulose, the pooled effect size demonstrated a significantly lower average HE Scoring Algorithm (HESA) Score at 24 hours (Mean difference (MD)=−0.68, 95% CI (−1.05 to –0.31), p<0.001), a higher proportion of patients with reduction of HESA Score by ≥1 grade at 24 hours (risk ratio (RR)=1.40, 95% CI (1.17 to 1.67), p<0.001), a higher proportion of patients with a HESA Score of grade 0 at 24 hours (RR=4.33, 95% CI (2.27 to 8.28), p<0.0010) and a shorter time to resolution of HE group (MD=−1.45, 95% CI (−1.72 to –1.18), p<0.001) in favour of patients treated with PEG.ConclusionPEG leads to a higher drop in the HESA Score and thus leads to a faster resolution of HE compared with lactulose.

Download Full-text

Automated tumor proportion scoring for PD-L1 expression based on multistage ensemble strategy in non-small cell lung cancer

Journal of Translational Medicine ◽

10.1186/s12967-021-02898-z ◽

2021 ◽

Vol 19 (1) ◽

Author(s):

Boju Pan ◽

Yuxin Kang ◽

Yan Jin ◽

Lin Yang ◽

Yushuang Zheng ◽

...

Keyword(s):

Lung Cancer ◽

Image Analysis ◽

Small Cell Lung Cancer ◽

Cell Lung Cancer ◽

Small Cell ◽

Automated Image Analysis ◽

Small Cell Lung ◽

Cutoff Values ◽

Scoring Algorithm ◽

Better Than

Abstract Introduction Programmed cell death ligand-1 (PD-L1) expression is a promising biomarker for identifying treatment related to non-small cell lung cancer (NSCLC). Automated image analysis served as an aided PD-L1 scoring tool for pathologists to reduce inter- and intrareader variability. We developed a novel automated tumor proportion scoring (TPS) algorithm, and evaluated the concordance of this image analysis algorithm with pathologist scores. Methods We included 230 NSCLC samples prepared and stained using the PD-L1(SP263) and PD-L1(22C3) antibodies separately. The scoring algorithm was based on regional segmentation and cellular detection. We used 30 PD-L1(SP263) slides for algorithm training and validation. Results Overall, 192 SP263 samples and 117 22C3 samples were amenable to image analysis scoring. Automated image analysis and pathologist scores were highly concordant [intraclass correlation coefficient (ICC) = 0.873 and 0.737]. Concordances at moderate and high cutoff values were better than at low cutoff values significantly. For SP263 and 22C3, the concordances in squamous cell carcinomas were better than adenocarcinomas (SP263 ICC = 0.884 vs 0.783; 22C3 ICC = 0.782 vs 0.500). In addition, our automated immune cell proportion scoring (IPS) scores achieved high positive correlation with the pathologists TPS scores. Conclusions The novel automated image analysis scoring algorithm permitted quantitative comparison with existing PD-L1 diagnostic assays and demonstrated effectiveness by combining cellular and regional information for image algorithm training. Meanwhile, the fact that concordances vary in different subtypes of NSCLC samples, which should be considered in algorithm development.

Download Full-text