Learning-Based Rule-Extraction From Support Vector Machines: Performance On Benchmark Data Sets

Over the last decade, rule-extraction from neural networks (ANN) techniques have been developed to explain how classification and regression are realised by the ANN. Yet, this is not the case for support vector machines (SVMs) which also demonstrate an inability to explain the process by which a learning result was reached and why a decision is being made. Rule-extraction from SVMs is important, especially for applications such as medical diagnosis. In this paper, an approach for learning-based rule-extraction from support vector machines is outlined, including an evaluation of the quality of the extracted rules in terms of fidelity, accuracy, consistency and comprehensibility. In addition, the rules are verified by use of knowledge from the problem domains as well as other classification techniques to assure correctness and validity

Knowledge Initialisation for Support Vector Machines

Since their introduction more than a decade ago, support vector machines (SVMs) have shown good performance in a number of application areas, including text classification, pattern recognition and bioinformatics. However, the success of SVMs comes at a cost - there is no way to utilise prior knowledge. SVMs are purely inductive learning machines. In this paper, a novel approach for rule initialisation for support vector machines is presented. The application domain is medical diagnosis. The approach presented here uses domain knowledge in the form of propositional rules to create a virtual data set to bias an SVM. The virtual data set is combined with real data for SVM learning. Knowledge initialisation results in better classification accuracy and enhanced rule quality compared with purely inductive learning

Learning-based Rule-Extraction from Support Vector Machines

In recent years, support vector machines (SVMs) have shown good performance in a number of application areas, including text classification. However, the success of SVMs comes at a cost - an inability to explain the process by which a learning result was reached and why a decision is being made. Rule-extraction from SVMs is important for the acceptance of this machine learning technology, especially for applications such as medical diagnosis. It is crucial for the users to understand how the system makes a decision. In this paper, a novel approach for rule-extraction from support vector machines is presented. This approach handles rule-extraction as a learning task, which proceeds in two steps. The first is to use the labeled patterns from a data set to train an SVM. The second step is to use the generated model to predict the label (class) for an extended data set or different, unlabeled data set. The resulting patterns are then used to train a decision tree learning system and to extract the corresponding rule sets. The output rule sets are verified against available knowledge for the domain problem (e.g. a medical expert), and other classification techniques, to assure correctness and validity of rules

Hybrid rule-extraction from support vector machines

Rule-extraction from artificial neural networks(ANNs) as well as support vector machines (SVMs) provide explanations for the decisions made by these systems. This explanation capability is very important in applications such as medical diagnosis. Over the last decade, a multitude of algorithms for rule-extraction from ANNs have been developed. However, rule-extraction from SVMs is not widely available yet.In this paper, a hybrid approach for rule-extraction from SVMs is outlined. This approach has two basic components: (1) data reduction using a logistic regression model and (2) learning based rule-extraction. The quality of the extracted rules is then evaluated in terms of fidelity, accuracy, consistency and comprehensibility. The rules are also verified against the available knowledge from the domain problem (diabetes) to assure correctness and validity

Eclectic rule-extraction from support vector machines

Support vector machines (SVMs) have shown superior performance compared to other machine learning techniques, especially in classification problems. Yet one limitation of SVMs is the lack of an explanation capability which is crucial in some applications, e.g. in the medical and security domains. In this paper, a novel approach for eclectic rule- extraction from support vector machines is presented. This approach utilizes the knowledge acquired by the SVM and represented in its support vectors as well as the parameters associated with them. The approach includes three stages; training, propositional rule- extraction and rule quality evaluation. Results from four different experiments have demonstrated the value of the approach for extracting comprehensible rules of high accuracy and fidelity

Controlled-Release Carbamazepine Matrix Granules and Tablets Comprising Lipophilic and Hydrophilic Components

The objective of this study was to investigate the effect of lipophilic (Compritol® 888 ATO) and hydrophilic components (combination of HPMC and Avicel) on the release of carbamazepine from granules and corresponding tablet. Wet granulation followed by compression was employed for preparation of granules and tablets. The matrix swelling behavior was investigated. The dissolution profiles of each formulation were compared to those of Tegretol® CR tablets and the mean dissolution time (MDT), dissolution efficiency (DE%), and similarity factor (f2 factor) were calculated. It was found that increase in the concentration of HPMC results in reduction in the release rate from granules and achievement of zero-order is difficult from the granules. The amount of HPMC plays a dominant role for the drug release. The release mechanism of CBZ from matrix tablet formulations follows non-Fickian diffusion shifting to Case II by the increase of HPMC content, indicating significant contribution of erosion. Increasing in drug loading resulted in acceleration of the drug release and in anomalous controlled-release mechanism due to delayed hydration of the tablets. These results suggest that wet granulation followed by compression could be a suitable method to formulate sustained release CBZ tablets

High-throughput retrotransposon-based genetic diversity of maize germplasm assessment and analysis

Maize is one of the world’s most important crops and a model for grass genome research. Long terminal repeat (LTR) retrotransposons comprise most of the maize genome; their ability to produce new copies makes them efficient high-throughput genetic markers. Inter-Retrotransposon-Amplified Polymorphisms (IRAPs) were used to study the genetic diversity of maize germplasm. Five LTR retrotransposons (Huck, Tekay, Opie, Ji, and Grande) were chosen, based on their large number of copies in the maize genome, whereas polymerase chain reaction primers were designed based on consensus LTR sequences. The LTR primers showed high quality and reproducible DNA fingerprints, with a total of 677 bands including 392 polymorphic bands showing 58% polymorphism between maize hybrid lines. These markers were used to identify genetic similarities among all lines of maize. Analysis of genetic similarity was carried out based on polymorphic amplicon profiles and genetic similarity phylogeny analysis. This diversity was expected to display ecogeographical patterns of variation and local adaptation. The clustering method showed that the varieties were grouped into three clusters differing in ecogeographical origin. Each of these clusters comprised divergent hybrids with convergent characters. The clusters reflected the differences among maize hybrids and were in accordance with their pedigree. The IRAP technique is an efficient high-throughput genetic marker-generating method.Peer reviewe

Bifidobacterium lactis in Treatment of Children with Acute Diarrhea. A Randomized Double Blind Controlled Trial

BACKGROUND: Probiotics are becoming increasingly popular treatment for children diarrhea. Although there are several probiotic strains potentially useful, researches were often limited to certain strains.AIM: To test Bifidobacterium lactis on morbidity of acute diarrhea in children less than 2 years.SUBJECTS AND METHODS: A randomized double-blind controlled clinical trial was conducted in 50 children (1 - 23 months) admitted with acute diarrhea to the Pediatric Hospital, Cairo University and were randomly assigned to receive in addition to usual treatment of diarrhea according to WHO guidelines; one of two treatments either milk formula non-supplemented (n = 25) or supplemented (n = 25) with Bifidobacterium lactis 14.5 x 106 CFU/100 ml daily for one week. Primary outcomes were frequency and duration of diarrhea and hospital stay. Secondary outcomes were duration of fever and vomiting episodes. Safety and tolerance were also recorded.RESULTS: On admission, patients’ characteristics of both groups (50 cases) were similar. For children who received the probiotics for one week; mean duration of diarrhoea was shorter than in controls (3.12 ± 0.92 vs. 4.10 ± 0.94 days) (P = 0.02), number of motions per day was less than in controls (3.96 ± 0.62 vs. 4.46 ± 0.85) (P = 0.04) and discharge from hospital <2 days was more frequent than in controls (72% vs. 44%) (P = 0.048). There was no effect on fever (P = 0.63) or vomiting (P = 0.54).CONCLUSION: Bifidobacterium lactis probiotics in supplemented milk formula decreased significantly frequency, duration of diarrhea, and hospital stay than usual treatment alone in children with acute diarrhea. Additional researches on other uncommon local probiotic species should be encouraged

Prevalence, associated factors and outcomes of pressure injuries in adult intensive care unit patients: the DecubICUs study

Funder: European Society of Intensive Care Medicine; doi: http://dx.doi.org/10.13039/501100013347Funder: Flemish Society for Critical Care NursesAbstract: Purpose: Intensive care unit (ICU) patients are particularly susceptible to developing pressure injuries. Epidemiologic data is however unavailable. We aimed to provide an international picture of the extent of pressure injuries and factors associated with ICU-acquired pressure injuries in adult ICU patients. Methods: International 1-day point-prevalence study; follow-up for outcome assessment until hospital discharge (maximum 12 weeks). Factors associated with ICU-acquired pressure injury and hospital mortality were assessed by generalised linear mixed-effects regression analysis. Results: Data from 13,254 patients in 1117 ICUs (90 countries) revealed 6747 pressure injuries; 3997 (59.2%) were ICU-acquired. Overall prevalence was 26.6% (95% confidence interval [CI] 25.9–27.3). ICU-acquired prevalence was 16.2% (95% CI 15.6–16.8). Sacrum (37%) and heels (19.5%) were most affected. Factors independently associated with ICU-acquired pressure injuries were older age, male sex, being underweight, emergency surgery, higher Simplified Acute Physiology Score II, Braden score 3 days, comorbidities (chronic obstructive pulmonary disease, immunodeficiency), organ support (renal replacement, mechanical ventilation on ICU admission), and being in a low or lower-middle income-economy. Gradually increasing associations with mortality were identified for increasing severity of pressure injury: stage I (odds ratio [OR] 1.5; 95% CI 1.2–1.8), stage II (OR 1.6; 95% CI 1.4–1.9), and stage III or worse (OR 2.8; 95% CI 2.3–3.3). Conclusion: Pressure injuries are common in adult ICU patients. ICU-acquired pressure injuries are associated with mainly intrinsic factors and mortality. Optimal care standards, increased awareness, appropriate resource allocation, and further research into optimal prevention are pivotal to tackle this important patient safety threat