Optimized classification predictions with a new index combining machine learning algorithms

Voting is a commonly used ensemble method aiming to optimize classification predictions by combining results from individual base classifiers. However, the selection of appropriate classifiers to participate in voting algorithm is currently an open issue. In this study we developed a novel Dissimilarity-Performance (DP) index which incorporates two important criteria for the selection of base classifiers to participate in voting: their differential response in classification (dissimilarity) when combined in triads and their individual performance. To develop this empirical index we firstly used a range of different datasets to evaluate the relationship between voting results and measures of dissimilarity among classifiers of different types (rules, trees, lazy classifiers, functions and Bayes). Secondly, we computed the combined effect on voting performance of classifiers with different individual performance and/or diverse results in the voting performance. Our DP index was able to rank the classifier combinations according to their voting performance and thus to suggest the optimal combination. The proposed index is recommended for individual machine learning users as a preliminary tool to identify which classifiers to combine in order to achieve more accurate classification predictions avoiding computer intensive and time-consuming search

mRECIST criteria and contrast-enhanced US for the assessment of the response of hepatocellular carcinoma to transarterial chemoembolization

PURPOSEWe aimed to evaluate the combination of the modified Response Evaluation Criteria In Solid Tumors (mRECIST) and contrast-enhanced ultrasonography (CEUS) as a tool for the assessment of hepatocellular carcinoma treated with transarterial chemoembolization. MATERIALS AND METHODSForty-seven hepatocellular carcinoma patients (80 target tumors suitable for mRECIST measurements) were studied. They were treated with scheduled transarterial chemoembolization with doxorubicin-eluting microspheres every 5–7 weeks. Imaging follow-up (performed one month after each transarterial chemoembolization) included a standard, contrast-enhanced modality (computed tomography [CT] in 12 patients or magnetic resonance imaging [MRI] in 35 patients) and CEUS. The study focused on response evaluation after the third transarterial chemoembolization. CEUS required a bolus injection of an echo-enhancer and imaging with a dedicated, low mechanical index technique. The longest diameters of the enhancing target tumors were measured on the CEUS or CT/MRI, and mRECIST criteria were applied. Radiologic responses were correlated with overall survival and time to progression. RESULTSThe measurements of longest diameters of the enhancing target tumors were easily performed in all patients. According to mRECIST-CEUS and mRECIST-CT/MRI, complete response was recorded in five and six patients, partial response in 22 and 21 patients, stable disease in 16 and 14 patients, and progressive disease in four and six patients, respectively. There was a high degree of concordance between CEUS and CT/MRI (kappa coefficient=0.84, P < 0.001). Responders (complete+partial response) according to mRECIST-CEUS had a significantly longer mean overall survival and time to progression compared to nonresponders (37.1 vs. 11.0 months, P < 0.001 and 24.6 vs. 10.9 months, P = 0.007, respectively). CONCLUSIONThe mRECIST-CEUS combination is feasible and has prognostic value in the assessment of hepatocellular carcinoma following transarterial chemoembolization

Antonios Anagnostopoulos, DO 2020

Philadelphia DO Class of 2020 portrait.https://digitalcommons.pcom.edu/portraits_2020/1014/thumbnail.jp

Laparoscopic surgery in a child with a failing Fontan circulation

A child with early failure of a Fontan circulation was listed for cardiac transplantation and then developed a subhepatic abscess. Surgical drainage was deemed necessary after the failure of an attempted percutaneous procedure. Following a multidisciplinary discussion, a laparoscopic technique was chosen to optimise postoperative recovery. To our knowledge, the literature does not describe any case of laparoscopic surgery in a patient with a failing Fontan circulation. This case report highlights the physiological variations involved with this management strategy, discusses the implications and risks, and offers some recommendations

Blockchains for Supply Chain Management: Architectural Elements and Challenges Towards a Global Scale Deployment

Blockchains are attracting the attention of stakeholders in many industrial domains, including the logistics and supply chain industries. Blockchain technology can effectively contribute in recording every single asset throughout its flow on the supply chain, contribute in tracking orders, receipts, and payments, while track digital assets such as warranties and licenses in a unified and transparent way. The paper provides, through its methodology, a detailed analysis of the blockchain fit in the supply chain industry. It defines the specific elements of blockchain that affect supply chain such as scalability, performance, consensus mechanism, privacy considerations, location proof and cost, and details on the impact that blockchains will have in disrupting the supply chain industry. Discussing the tradeoff between consensus cost, throughput and validation time it proceeds with a suggested high-level architectural approach, and concludes as a result with a discussion on changes needed and challenges faced for an in-vivo deployment of blockchains in the supply chain industry. While the technological features of modern blockchains can effectively facilitate supply chain uses cases, the various challenges that still remain, bring in front of us a wide set of needed changes and further research efforts for achieving a global, production level blockchain for the supply chain industry

Improved batch fuzzy learning vector quantization for image compression

In this paper, we develop a batch fuzzy learning vector quantization algorithm that attempts to solve certain problems related to the implementation of fuzzy clustering in image compression. The algorithm’s structure encompasses two basic components. First, a modified objective function of the fuzzy c-means method is reformulated and then is minimized by means of an iterative gradient-descent procedure. Second, the overall training procedure is equipped with a systematic strategy for the transition from fuzzy mode, where each training vector is assigned to more than one codebook vectors, to crisp mode, where each training vector is assigned to only one codebook vector. The algorithm is fast and easy to implement. Finally, the simulation results show that the method is efficient and appears to be insensitive to the selection of the fuzziness parameter

Therapies in Cervical Cancer—Editorial

George Papanikolaou is famously quoted as saying “the first observation of cancer cells in the smear of the uterine cervix gave me one of the greatest thrills I ever experienced during my scientific career” [...

A Classification of NoSQL Data Stores Based on Key Design Characteristics

AbstractTraditional Relational Database Management Systems are continuously being replaced by NoSQL data stores as a result of the growing demand for big data applications. The emergence of a large number of implementations of such like systems is a contributing indicator. This paper deals with the analysis of some key design characteristics of NoSQL systems and uses these for their characterization based on their capabilities. Furthermore, it highlights the relationship between NoSQL systems and cloud infrastructures and explains the impact that the existence of one has to the other