Forecasting Hospital Readmissions with Machine Learning

Hospital readmissions are regarded as a compounding economic factor for healthcare systems. In fact, the readmission rate is used in many countries as an indicator of the quality of services provided by a health institution. The ability to forecast patients’ readmissions allows for timely intervention and better post-discharge strategies, preventing future life-threatening events, and reducing medical costs to either the patient or the healthcare system. In this paper, four machine learning models are used to forecast readmissions: support vector machines with a linear kernel, support vector machines with an RBF kernel, balanced random forests, and weighted random forests. The dataset consists of 11,172 actual records of hospitalizations obtained from the General Hospital of Komotini “Sismanogleio” with a total of 24 independent variables. Each record is composed of administrative, medical-clinical, and operational variables. The experimental results indicate that the balanced random forest model outperforms the competition, reaching a sensitivity of 0.70 and an AUC value of 0.78

A diagnostic plot for estimating the tail index of a distribution

The problem of estimating the tail index in heavy-tailed distributions is very important in many applications. We propose a new graphical method that deals with this problem by selecting an appropriate number of upper order statistics. We also investigate the method’s theoretical properties are investigated. Several real datasets are analyzed using this new procedure and a simulation study is carried out to examine its performance in small, moderate and large samples. The results suggest that the new procedure overcomes many of the shortcomings present in some of the most common techniques—for example, the Hill and Zipf plots—used in the estimation of the tail index, and it performs very competitively when compared with other adaptive threshold procedures based on the asymptotic mean squared error of the Hill estimator.Fundação para a Ciência e a Tecnologia (FCT) - PRAXIS XXI

Green synthesis and characterization of silver nanoparticles produced using 'Arbutus Unedo' leaf extract

Metallic nanoparticles have received great attention from chemists, physicists, biologists and engineers who wish to use them for the development of a new generation of nanodevices. In the present study silver nanoparticles were synthesized from aqueous silver nitrate through a simple and eco-friendly route using leaf broth of Arbutus unedo, which acted as a reductant and stabilizer simultaneously. The aqueous silver ions when exposed to the leaf broth were reduced and stabilized over long periods of time resulting in the green synthesis of surface functionalized silver nanoparticles. The bio-reduced silver nanoparticles were appropriately characterized. The results revealed the formation of single crystalline Ag nanoparticles with a narrow size distribution for each sample. The particles, although discrete, were predominately coated with the organic leaf extract forming small aggregates, which makes them stable over long time periods and highly appropriate for coatings or biotechnology applications.Publicad

Low Temperature Combustion Optimization and Cycle-by-Cycle Variability Through Injection Optimization and Gas-to-Liquid Fuel-Blend Ratio

The advent of common rail technology alongside powerful control systems capable of delivering multiple accurate fuel charges during a single engine cycle has revolutionized the level of control possible in diesel combustion. This technology has opened a new path enabling low-temperature combustion (LTC) to become a viable combustion strategy. The aim of the research work presented within this paper is the understanding of how various engine parameters of LTC optimize the combustion both in terms of emissions and in terms of fuel efficiency. The work continues with an investigation of in-cylinder pressure and IMEP cycle-by-cycle variation. Attention will be given to how repeatability changes throughout the combustion cycle, identifying which parts within the cycle are least likely to follow the mean trend and why. Experiments were conducted on a single-cylinder 510cc boosted diesel engine. LTC was affected over varying rail pressure and combustion phasing. Single and split injection regimes of varying dwell-times were investigated. All injection conditions were phased across several crank-angles to demonstrate the interaction between emissions and efficiency. These tests were then repeated with blends of 30% and 50% gas-to-liquid (GTL)-diesel blends in order to determine whether there is any change in the trends of repeatability and variance with increasing GTL blend ratio. The experiments were evaluated in terms of emissions, fuel efficiency, and cyclic behavior. Specific attention was given to how the NO x -PM trade-off changes through increased injection complexity and increasing GTL blend ratio. The cyclic behavior was analyzed in terms of in-cylinder pressure standard deviation. This gives a behavior profile of the repeatability of in-cylinder pressure in comparison to the mean. Each condition was then compared to the behavior of equivalent injection conditions in conventional diesel combustion. Short-dwell split injection was shown to be beneficial for LTC, while NO x was shown to be reduced by the substitution of GTL in the fuel. In-cylinder pressure cyclic behavior was also shown to be comparable or superior to conventional combustion in every case examined. GTL improved this further, but not in proportion to its blend ratio

Experimental variables that affect human hepatocyte MV transduction in liver chimeric mice

Adeno-associated virus (AAV) vector serotypes vary in their ability to transduce hepatocytes from different species. Chimeric mouse models harboring human hepatocytes have shown translational promise for liver-directed gene therapies. However, many variables that influence human hepatocyte transduction and transgene expression in such models remain poorly defined. Here, we aimed to test whether three experimental conditions influence AAV transgene expression in immunodeficient, fumaryl-acetoactetate-hydrolase-deficient (Fah(-/-)) chimeric mice repopulated with primary human hepatocytes. We examined the effects of the murine liver injury cycle, human donor variability, and vector doses on hepatocyte transduction with various AAV serotypes expressing a green fluorescent protein (GFP). We determined that the timing of AAV vector challenge in the liver injury cycle resulted in up to 7-fold differences in the percentage of GFP expressing human hepatocytes. The GFP+ hepatocyte frequency varied 7-fold between human donors without, however, changing the relative transduction efficiency between serotypes for an individual donor. There was also a clear relationship between AAV vector doses and human hepatocyte transduction and transgene expression. We conclude that several experimental variables substantially affect human hepatocyte transduction in the Fah(-/-) chimera model, attention to which may improve reproducibility between findings from different laboratories

THINK Back: KNowledge-based Interpretation of High Throughput data

Results of high throughput experiments can be challenging to interpret. Current approaches have relied on bulk processing the set of expression levels, in conjunction with easily obtained external evidence, such as co-occurrence. While such techniques can be used to reason probabilistically, they are not designed to shed light on what any individual gene, or a network of genes acting together, may be doing. Our belief is that today we have the information extraction ability and the computational power to perform more sophisticated analyses that consider the individual situation of each gene. The use of such techniques should lead to qualitatively superior results

Add-on topiramate in the treatment of refractory partial-onset epilepsy: Clinical experience of outpatient epilepsy clinics from 11 general hospitals

SummaryAn open, prospective, observational study was performed to assess efficacy and adverse-event profile of topiramate as add-on therapy in epilepsy. Outpatient neurology clinics from 11 general hospitals in Greece participated in the study. In total, 211 patients with treatment resistant partial-onset seizures who met the inclusion criteria, were studied. After baseline evaluation, topiramate was given at a target dose of 200mg/day over a 1-month titration period. In the subsequent maintenance period, the topiramate dose could be varied according to the clinical results. Patients were followed for in total 6 months, with monthly visits and regular physical, neurological and laboratory examinations. Seizure frequencies decreased to 35–40% of baseline values following 3 months of treatment and remained relatively constant thereafter. The average monthly seizure frequency over the 6-month study period was 4.61, compared to 9.21 at baseline. The number of responders (patients with at least 50% reduction in seizure frequency) followed a similar pattern, i.e., increase during the first 3 months levelling off at a final 80–85% response rate. Of those completing the study, 30% had been seizure-free for at least 3 months and 12% for 5 months. Topiramate was well tolerated, no deviations in laboratory values were found. Adverse events appeared to occur less frequently, and antiepileptic effects were more pronounced in this prospective open-label study than in earlier reports from randomised controlled trials. The nature of the patient population and the application of individualised dose optimisation are proposed as contributing factors to explain the favourable results of this study

EXMOTIF: efficient structured motif extraction

BACKGROUND: Extracting motifs from sequences is a mainstay of bioinformatics. We look at the problem of mining structured motifs, which allow variable length gaps between simple motif components. We propose an efficient algorithm, called EXMOTIF, that given some sequence(s), and a structured motif template, extracts all frequent structured motifs that have quorum q. Potential applications of our method include the extraction of single/composite regulatory binding sites in DNA sequences. RESULTS: EXMOTIF is efficient in terms of both time and space and is shown empirically to outperform RISO, a state-of-the-art algorithm. It is also successful in finding potential single/composite transcription factor binding sites. CONCLUSION: EXMOTIF is a useful and efficient tool in discovering structured motifs, especially in DNA sequences. The algorithm is available as open-source at: