Search

This paper addresses the problem of data centers’ cost efficiency considering the potential of reusing the generated heat in district heating networks. We started by analyzing the requirements and heat reuse potential of a high performance computing data center and then we had defined a heat reuse model which simulates the thermodynamic processes from the server room. This allows estimating by means of Computational Fluid Dynamics simulations the temperature of the hot air recovered by the heat pumps from the server room allowing them to operate more efficiently. To address the time and space complexity at run-time we have defined a Multi-Layer Perceptron neural network infrastructure to predict the hot air temperature distribution in the server room from the training data generated by means of simulations. For testing purposes, we have modeled a virtual server room having a volume of 48 m3 and two typical 42U racks. The results show that using our model the heat distribution in the server room can be predicted with an error less than 1 °C allowing data centers to accurately estimate in advance the amount of waste heat to be reused and the efficiency of heat pump operation.

Strategies to acquire white space information is the single most significant functionality in cognitive radio networks (CRNs) and as such, it has gone some evolution to enhance information accuracy. The evolution trends are spectrum sensing, prediction algorithm and recently, geo-location database technique. Previously, spectrum sensing was the main technique for detecting the presence/absence of a primary user (PU) signal in a given radio frequency (RF) spectrum. However, this expectation could not materialized as a result of numerous technical challenges ranging from hardware imperfections to RF signal impairments. To convey the evolutionary trends in the development of white space information, we present a survey of the contemporary advancements in PU detection with emphasis on the practical deployment of CRNs i.e. Television white space (TVWS) networks. It is found that geo-location database is the most reliable technique to acquire TVWS information although, it is financially driven. Finally, using financially driven database model, this study compared the data-rate and spectral efficiency of FCC and Ofcom TV channelization. It was discovered that Ofcom TV channelization outperforms FCC TV channelization as a result of having higher spectrum bandwidth. We proposed the adoption of an all-inclusive TVWS information acquisition model as the future research direction for TVWS information acquisition techniques. [ABSTRACT FROM AUTHOR]

Allele-specific siRNAs (ASP-siRNAs) have emerged as promising therapeutic molecules owing to their selectivity to inhibit the mutant allele or associated single-nucleotide polymorphisms (SNPs) sparing the expression of the wild-type counterpart. Thus, a dedicated bioinformatics platform encompassing updated ASP-siRNAs and an algorithm for the prediction of their inhibitory efficacy will be helpful in tackling currently intractable genetic disorders. In the present study, we have developed the ASPsiRNA resource (http://crdd.osdd.net/servers/aspsirna/) covering three components viz (i) ASPsiDb, (ii) ASPsiPred, and (iii) analysis tools like ASP-siOffTar. ASPsiDb is a manually curated database harboring 4543 (including 422 chemically modified) ASP-siRNAs targeting 78 unique genes involved in 51 different diseases. It furnishes comprehensive information from experimental studies on ASP-siRNAs along with multidimensional genetic and clinical information for numerous mutations. ASPsiPred is a two-layered algorithm to predict efficacy of ASP-siRNAs for fully complementary mutant (Effmut) and wild-type allele (Effwild) with one mismatch by ASPsiPredSVM and ASPsiPredmatrix, respectively. In ASPsiPredSVM, 922 unique ASP-siRNAs with experimentally validated quantitative Effmut were used. During 10-fold cross-validation (10nCV) employing various sequence features on the training/testing dataset (T737), the best predictive model achieved a maximum Pearson's correlation coefficient (PCC) of 0.71. Further, the accuracy of the classifier to predict Effmut against novel genes was assessed by leave one target out cross-validation approach (LOTOCV). ASPsiPredmatrix was constructed from rule-based studies describing the effect of single siRNA:mRNA mismatches on the efficacy at 19 different locations of siRNA. Thus, ASPsiRNA encompasses the first database, prediction algorithm, and off-target analysis tool that is expected to accelerate research in the field of RNAibased therapeutics for human genetic diseases. [ABSTRACT FROM AUTHOR]

Objectives: Pulmonary nodules are commonly encountered at staging CTs in patients with extrathoracic malignancies, but their significance on a per-patient basis remains uncertain.Methods: We undertook a retrospective analysis of pulmonary nodules identified in patients with a diagnosis of breast cancer from 2010 - 2015, evaluating nodules present at a baseline CT (i.e. prevalent nodules). We reviewed 211 patients with 248 individual nodules.Results: The rate of malignancy in prevalent nodules is low, approximately 13 %. Variables associated with metastasis include pleural studding, hilar lymphadenopathy and the presence of extrapulmonary metastasis, as well as number of nodules, nodule size and nodule shape. Using a combination of these factors, we have developed an evidence-based multivariate decision tree to predict which nodules are malignant in these patients, which is 91 % accurate and 100 % sensitive for metastasis.Conclusions: We propose a simplified clinical prediction algorithm to guide radiologists and oncologists in managing patients with breast cancer and incidental pulmonary nodules.Key Points: • Incidental pulmonary nodules are common on computed tomography in breast cancer patients. • Nodules present at baseline have a lower malignancy risk than incident nodules. • We present an evidence-based decision algorithm predicting which nodules are likely malignant. • This algorithm can help direct patient management. [ABSTRACT FROM AUTHOR]

Background: Many plant pathogen secretory proteins are known to be elicitors or pathogenic factors, which play an important role in the host-pathogen interaction process. Bioinformatics approaches make possible the large scale prediction and analysis of secretory proteins from the Puccinia helianthi transcriptome. The internet-based software SignalP v4.1, TargetP v1.01, Big-PI predictor, TMHMM v2.0 and ProtComp v9.0 were utilized to predict the signal peptides and the signal peptide-dependent secreted proteins among the 35,286 ORFs of the P. helianthi transcriptome. Results: 908 ORFs (accounting for 2.6% of the total proteins) were identified as putative secretory proteins containing signal peptides. The length of the majority of proteins ranged from 51 to 300 amino acids (aa), while the signal peptides were from 18 to 20 aa long. Signal peptidase I (SpI) cleavage sites were found in 463 of these putative secretory signal peptides. 55 proteins contained the lipoprotein signal peptide recognition site of signal peptidase II (SpII). Out of 908 secretory proteins, 581 (63.8%) have functions related to signal recognition and transduction, metabolism, transport and catabolism. Additionally, 143 putative secretory proteins were categorized into 27 functional groups based on Gene Ontology terms, including 14 groups in biological process, seven in cellular component, and six in molecular function. Gene ontology analysis of the secretory proteins revealed an enrichment of hydrolase activity. Pathway associations were established for 82 (9.0%) secretory proteins. A number of cell wall degrading enzymes and three homologous proteins specific to Phytophthora sojae effectors were also identified, which may be involved in the pathogenicity of the sunflower rust pathogen. Conclusions: This investigation proposes a new approach for identifying elicitors and pathogenic factors. The eventual identification and characterization of 908 extracellularly secreted proteins will advance our understanding of the molecular mechanisms of interactions between sunflower and rust pathogen and will enhance our ability to intervene in disease states. [ABSTRACT FROM AUTHOR]

The popularity and development of wireless devices has led to a demand for widespread high-speed Internet access, including access for vehicles and other modes of high-speed transportation. The current widely deployed method for providing Internet Protocol (IP) services to mobile devices is the mobile IP. This includes a handover process for a mobile device to maintain its IP session while it switches between points of access. However, the mobile IP handover causes performance degradation due to its disruptive latency and high packet drop rate. This is largely problematic for vehicles, as they will be forced to transition between access points more frequently due to their higher speeds and frequent topological changes in vehicular networks. In this article, we discuss the different mobile IP handover solutions found within related literature and their potential for resolving issues pertinent to vehicular networks. First, we provide an overview of the mobile IP handover and its problematic components. This is followed by categorization and comparison between different mobile IP handover solutions, with an analysis of their benefits and drawbacks. [ABSTRACT FROM AUTHOR]

Connected vehicle communication technology is rapidly developing in recent years, and host vehicle (HV) can send or receive the basic safety message (BSM) from the remote vehicles (RVs). However, there are few applications using this information to improve the driving safety. In this paper, we propose a collision warning predicted framework that provides connected automated vehicle and alert driver when time to collision (TTC) is within specified thresholds. After pre-processor RV BSM data, this paper transforms the RV position and calculates the relative position, distance and speed. Then, the RV trajectory is estimated by using Kalman filter algorithm, and the error statistics of the prediction of latitude and longitude are analysed. The prediction results show that it works on straight, corner and the curve road, but the latitude error is higher than the longitude error. At last this paper constructs the relative position radar map for the HV, in which it can show the relative position, speed and TTC information. Vehicle collision can be detected in real time and the vehicle can prevent the potential conflict accordingly by using these information. [ABSTRACT FROM PUBLISHER]

Background: Accurate methods capable of predicting the impact of single nucleotide variants (SNVs) are assuming ever increasing importance. There exists a plethora of in silico algorithms designed to help identify and prioritize SNVs across the human genome for further investigation. However, no tool exists to visualize the predicted tolerance of the genome to mutation, or the similarities between these methods. Results: We present the Genome Tolerance Browser (GTB, http://gtb.biocompute.org.uk): an online genome browser for visualizing the predicted tolerance of the genome to mutation. The server summarizes several in silico prediction algorithms and conservation scores: including 13 genome-wide prediction algorithms and conservation scores, 12 non-synonymous prediction algorithms and four cancer-specific algorithms. Conclusion: The GTB enables users to visualize the similarities and differences between several prediction algorithms and to upload their own data as additional tracks; thereby facilitating the rapid identification of potential regions of interest. [ABSTRACT FROM AUTHOR]

Objectives: Patients with single-ventricle physiology have a significant risk of cardiorespiratory deterioration between their first- and second-stage palliation surgeries. Detection of deterioration episodes may allow for early intervention and improved outcomes., Methods: A prospective study was executed at Nationwide Children's Hospital, Children's Hospital of Philadelphia, and Children's Hospital Colorado to collect physiologic data of subjects with single ventricle physiology during all hospitalizations between neonatal palliation and II surgeries using the Sickbay software platform (Medical Informatics Corp). Timing of cardiorespiratory deterioration events was captured via chart review. The predictive algorithm previously developed and validated at Texas Children's Hospital was applied to these data without retraining. Standard metrics such as receiver operating curve area, positive and negative likelihood ratio, and alert rates were calculated to establish clinical performance of the predictive algorithm., Results: Our cohort consisted of 58 subjects admitted to the cardiac intensive care unit and stepdown units of participating centers over 14 months. Approximately 28,991 hours of high-resolution physiologic waveform and vital sign data were collected using the Sickbay. A total of 30 cardiorespiratory deterioration events were observed. the risk index metric generated by our algorithm was found to be both sensitive and specific for detecting impending events one to two hours in advance of overt extremis (receiver operating curve = 0.927)., Conclusions: Our algorithm can provide a 1- to 2-hour advanced warning for 53.6% of all cardiorespiratory deterioration events in children with single ventricle physiology during their initial postop course as well as interstage hospitalizations after stage I palliation with only 2.5 alarms being generated per patient per day., (© 2023 The Authors.)

Nowadays, complex IoT devices are developed that support smart transportation service, medical alarm service, and smart surveillance camera which require communication to the computation server. Most of IoT devices communicate to their servers on fixed network through wireless channel. In this paper, we are focused on smart transportation service that is one of bus, train, airplane or ship that takes embedded IoT devices and passengers using IoT devices. Embedded IoT devices are communicated with embedded access router (AR) on transportation by Bluetooth/Z-wave/NFC, and passengers' IoT devices are communicated with their own egg/AR on fixed network, or public AR on transportation. Transportation environment has a major problem that mobile node moves fast through multiple cells. Each IoT device in transportation keeps updating its current point of attachment to its home agent and corresponding node. To overcome this problem we consider feature of transportation. It has two major features. The one is mobile device has fast mobility by transportation. The other one is mobile device moves fixed route. It causes mobile device has intra mobility between several subnets of one agency when it moves into a subway or highway. Based on two features, we design proxy based architecture and extend router advertisement message to alert join in proxy domain. Then, we are classified the mobility into intra proxy mobility and global mobility. And we proposed proxy based mobility management scheme using prediction algorithm. By numerical analysis, we shows proposed scheme reduces signaling overheads and increase packet transfer rate than NEMO and proxy MIP. [ABSTRACT FROM AUTHOR]

How to analyze the features of stock price accurately and master the regularity of stock price changing with time quickly and effectively is of great theoretical and realistic significance and is an important research direction in financial field. For complicated non-linear and periodic variations of stock prices, a parallel computing model is proposed in this paper based on stock prediction algorithm of least squares support vector machine (LSSVM). The experiment has proved that the new method ensures the accuracy of prediction and also shortens the prediction time greatly, which can be adopted in large scale data processing and prediction in financial field extensively and is of great application value. [ABSTRACT FROM AUTHOR]

The RFID technology (Radio FrequencyIdentifi - cation) is used in the view of data transmission,by means of mobile transponders, known as tags and forreceiving that information, by means of reading devices,known as readers. Radio Frequency Identification (RFID)has become specific for tracking the items and carryingout solutions of data achieving within the supply chains ofenterprises, factories, trading companies or automationtechnologies. This present paper proposes creation of acomputerized system dedicated to selling companies,manufacturers and service units, with a view of usingproducts’ information and estimating by a predictionalgorithm the return time towards the customers ofchanged or repaired products being on warranty or postwarrantytime.

Objectives: To develop computer algorithms that can recognize physiologic patterns in traumatic brain injury patients that occur in advance of intracranial pressure and partial brain tissue oxygenation crises. The automated early detection of crisis precursors can provide clinicians with time to intervene in order to prevent or mitigate secondary brain injury.Design: A retrospective study was conducted from prospectively collected physiologic data. intracranial pressure, and partial brain tissue oxygenation crisis events were defined as intracranial pressure of greater than or equal to 20 mm Hg lasting at least 15 minutes and partial brain tissue oxygenation value of less than 10 mm Hg for at least 10 minutes, respectively. The physiologic data preceding each crisis event were used to identify precursors associated with crisis onset. Multivariate classification models were applied to recorded data in 30-minute epochs of time to predict crises between 15 and 360 minutes in the future.Setting: The neurosurgical unit of Ben Taub Hospital (Houston, TX).Subjects: Our cohort consisted of 817 subjects with severe traumatic brain injury.Measurements and Main Results: Our algorithm can predict the onset of intracranial pressure crises with 30-minute advance warning with an area under the receiver operating characteristic curve of 0.86 using only intracranial pressure measurements and time since last crisis. An analogous algorithm can predict the start of partial brain tissue oxygenation crises with 30-minute advanced warning with an area under the receiver operating characteristic curve of 0.91.Conclusions: Our algorithms provide accurate and timely predictions of intracranial hypertension and tissue hypoxia crises in patients with severe traumatic brain injury. Almost all of the information needed to predict the onset of these events is contained within the signal of interest and the time since last crisis. [ABSTRACT FROM AUTHOR]

Background: Multivariable risk prediction algorithms are useful for making clinical decisions and for health planning. While prediction algorithms for new onset of major depression in the primary care attendees in Europe and elsewhere have been developed, the performance of these algorithms in different populations is not known. The objective of this study was to validate the PredictD algorithm for new onset of major depressive episode (MDE) in the US general population. Methods: Longitudinal study design was conducted with approximate 3-year follow-up data from a nationally representative sample of the US general population. A total of 29,621 individuals who participated in Wave 1 and 2 of the US National Epidemiologic Survey on Alcohol and Related Conditions (NESARC) and who did not have an MDE in the past year at Wave 1 were included. The PredictD algorithm was directly applied to the selected participants. MDE was assessed by the Alcohol Use Disorder and Associated Disabilities Interview Schedule, based on the DSM-IV criteria. Results: Among the participants, 8% developed an MDE over three years. The PredictD algorithm had acceptable discriminative power (C-statistics = 0.708, 95% CI: 0.696, 0.720), but poor calibration (p < 0.001) with the NESARC data. In the European primary care attendees, the algorithm had a C-statistics of 0.790 (95% CI: 0.767, 0.813) with a perfect calibration. Conclusions: The PredictD algorithm has acceptable discrimination, but the calibration capacity was poor in the US general population despite of re-calibration. Therefore, based on the results, at current stage, the use of PredictD in the US general population for predicting individual risk of MDE is not encouraged. More independent validation research is needed. [ABSTRACT FROM AUTHOR]

The network time series is interfered by complex background information,and thus the forecasting is not precise, a network traffic prediction algorithm is thus proposed based on adaptive feature fourth-order cumulant, to make the model of network traffic data transmission structure,to use method of fourth-order cumulant adaptive feature extraction,and to realize accurate prediction and consistency estimates for flow. The simulation results show that traffic prediction, traffic prediction output beam directivity is better; sidelobe interference inhibition effect is desirable traffic prediction has strong anti-jamming capability; prediction precision is higher than that of traditional methods. [ABSTRACT FROM AUTHOR]

Protein-protein interaction (PPI) plays a major role in many biochemical processes. However, previous studies established many problems, such as the label-bias problem and lower prediction accuracy, etc. For solving these problems, we propose a novel method for PPI prediction. The method transforms PPI prediction into the sequential data labelling problem. Based on protein sequences properties, we design the model for constructing methods in conditional random fields (CRF) accordingly. Based on this model, we can determine the labeled sequence with maximum probability, and thereby, detect PPI. Experimental results on benchmark data sets show that our method is more accurate and efficient than other traditional methods. [ABSTRACT FROM AUTHOR]

The compression of vector data is very important for reducing the space needed for data storage and improving the efficiency of data transmission and processing in WebGIS. This paper focuses on the time efficiency of vector data compression with prediction functions and proposes a vector data compression method based on sector screening that significantly reduces the quantity of candidate vertices in prediction areas to improve time efficiency. Experimental results show that the time efficiency improved by 30%-40%. Our method was compared with the conventional Douglas-Peucker method. The tests confirmed that our method can achieve a larger compression ratio when using the same compression threshold value, while obtain greater time efficiency with relatively small threshold values. [ABSTRACT FROM AUTHOR]

Background: Suicidal ideation is one of the strongest predictors of recent and future suicide attempt. This study aimed to develop and validate a risk prediction algorithm for the recurrence of suicidal ideation among population with low moodMethods: 3035 participants from U.S National Epidemiologic Survey on Alcohol and Related Conditions with suicidal ideation at their lowest mood at baseline were included. The Alcohol Use Disorder and Associated Disabilities Interview Schedule, based on the DSM-IV criteria was used. Logistic regression modeling was conducted to derive the algorithm. Discrimination and calibration were assessed in the development and validation cohorts.Results: In the development data, the proportion of recurrent suicidal ideation over 3 years was 19.5 (95% CI: 17.7, 21.5). The developed algorithm consisted of 6 predictors: age, feelings of emptiness, sudden mood changes, self-harm history, depressed mood in the past 4 weeks, interference with social activities in the past 4 weeks because of physical health or emotional problems and emptiness was the most important risk factor. The model had good discriminative power (C statistic=0.8273, 95% CI: 0.8027, 0.8520). The C statistic was 0.8091 (95% CI: 0.7786, 0.8395) in the external validation dataset and was 0.8193 (95% CI: 0.8001, 0.8385) in the combined dataset.Limitations: This study does not apply to people with suicidal ideation who are not depressed.Conclusions: The developed risk algorithm for predicting the recurrence of suicidal ideation has good discrimination and excellent calibration. Clinicians can use this algorithm to stratify the risk of recurrence in patients and thus improve personalized treatment approaches, make advice and further intensive monitoring. [ABSTRACT FROM AUTHOR]

For the disadvantages that the existing prediction models without considering the neighbors' influence and the difference between long term profiles and short term profiles, this paper proposed a prediction model of microblog users' profiles, which was based on RBF neural network to improve the prediction precision and enhance the recommendation efficiency of products and service. The model divided the profiles into long term and short term profiles, and users' influence into themselves and neighbors'. Based on this, this model adopted the RBF regularization network which had predominant learning ability and closeness to predict users' profiles.In the experiment on Tencent Weibo, about 4.31%, 14.53% average prediction offset and 0.31, 48.12 average variance of prediction offset for long term profiles and short term profiles respectively are achieved, and the result shows that this method is superior to others in prediction precision and stability. [ABSTRACT FROM AUTHOR]

Traffic issue has become one of the most concerns for citizens in metropolis. It is extremely hard to find an available vehicle in hot region during the peak hour. Due to the supply–demand contradiction, the authority is still seeking effective and economical carpooling solutions for “Taxi taking dilemma” without adding more vehicles. This paper mainly focuses on building a network-based carpooling scheme by analyzing the feasibility and related issues. Firstly, we provide some fundamental analysis for taxi carpooling based on the real taxis’ GPS traces. By setting up a correspondence relationship between physical locations and the coordinates of map, a macro impression about the space and time distribution of get on points for Beijing taxis are illustrated. According to the prediction algorithm, both the original and estimated benefits of carpooling are shown quantitatively. Secondly, four different network-based sharing schemes are presented and compared. The conditions of successful route matching are qualitatively studied. By identifying the merits and weaknesses comprehensively, we propose a new hybrid carpooling scheme. The concerns relate with the interaction process and packet structuring are considered carefully. A novel perspective for distinguishing vehicle and pedestrian in software design is also pointed out. [ABSTRACT FROM AUTHOR]

Background: Prevention of binge eating through just-in-time mobile interventions requires the prediction of respective high-risk times, for example, through preceding affective states or associated contexts. However, these factors and states are highly idiographic; thus, prediction models based on averages across individuals often fail., Objective: We developed an idiographic, within-individual binge-eating prediction approach based on ecological momentary assessment (EMA) data., Methods: We first derived a novel EMA-item set that covers a broad set of potential idiographic binge-eating antecedents from literature and an eating disorder focus group (n=11). The final EMA-item set (6 prompts per day for 14 days) was assessed in female patients with bulimia nervosa or binge-eating disorder. We used a correlation-based machine learning approach (Best Items Scale that is Cross-validated, Unit-weighted, Informative, and Transparent) to select parsimonious, idiographic item subsets and predict binge-eating occurrence from EMA data (32 items assessing antecedent contextual and affective states and 12 time-derived predictors)., Results: On average 67.3 (SD 13.4; range 43-84) EMA observations were analyzed within participants (n=13). The derived item subsets predicted binge-eating episodes with high accuracy on average (mean area under the curve 0.80, SD 0.15; mean 95% CI 0.63-0.95; mean specificity 0.87, SD 0.08; mean sensitivity 0.79, SD 0.19; mean maximum reliability of r D 0.40, SD 0.13; and mean r CV 0.13, SD 0.31). Across patients, highly heterogeneous predictor sets of varying sizes (mean 7.31, SD 1.49; range 5-9 predictors) were chosen for the respective best prediction models., Conclusions: Predicting binge-eating episodes from psychological and contextual states seems feasible and accurate, but the predictor sets are highly idiographic. This has practical implications for mobile health and just-in-time adaptive interventions. Furthermore, current theories around binge eating need to account for this high between-person variability and broaden the scope of potential antecedent factors. Ultimately, a radical shift from purely nomothetic models to idiographic prediction models and theories is required., (©Ann-Kathrin Arend, Tim Kaiser, Björn Pannicke, Julia Reichenberger, Silke Naab, Ulrich Voderholzer, Jens Blechert. Originally published in JMIR Medical Informatics (https://medinform.jmir.org), 23.02.2023.)

Background and objective Cardiac arrhythmias are disorders in terms of speed or rhythm in the heart's electrical system. Atrial fibrillation (AFib) is the most common sustained arrhythmia that affects a large number of persons. Electrophysiologic study (EPS) procedures are used to study fibrillation in patients; they consist of inducing a controlled fibrillation in surgical room to analyze electrical heart reactions or to decide for implanting medical devices (i.e., pacemaker). Nevertheless, the spontaneous induction may generate an undesired AFib, which may induce risk for patient and thus a critical issue for physicians. We study the unexpected AFib onset, aiming to identify signal patterns occurring in time interval preceding an event of spontaneous (i.e., not inducted) fibrillation. Profiling such signal patterns allowed to design and implement an AFib prediction algorithm able to early identify a spontaneous fibrillation. The objective is to increase the reliability of EPS procedures. Methods We gathered data signals collected by a General Electric Healthcare's CardioLab electrophysiology recording system (i.e., a polygraph). We extracted superficial and intracavitary cardiac signals regarding 50 different patients studied at the University Magna Graecia Cardiology Department. By studying waveform (i.e., amplitude and energy) of intracavitary signals before the onset of the arrhythmia, we were able to define patterns related to AFib onsets that are side effects of an inducted fibrillation. Results A framework for atrial fibrillation prediction during electrophysiological studies has been developed. It includes a prediction algorithm to alert an upcoming AFib onset. Tests have been performed on an intracavitary cardiac signals data set, related to patients studied in electrophysiological room. Also, results have been validated by the clinicians, proving that the framework can be useful in case of integration with the polygraph, helping physicians in managing and controlling of patient status during EPS. [ABSTRACT FROM AUTHOR]

For nonbinary response variable depending on a finite collection of factors with values in a finite subset of R the problem of the optimal forecast is considered. The quality of prediction is described by the error function involving a penalty function. The criterion of almost sure convergence to unknown error function for proposed estimates constructed by means of a prediction algorithm and K -fold cross-validation procedure is established. It is demonstrated that imposed conditions admit the efficient verification. The developed approach permits to realize the dimensionality reduction of factors under consideration. One can see that the results obtained provide the base to identify the set of significant factors. Such problem arises, e.g., in medicine and biology. The central limit theorem for proposed statistics is proven as well. In this way one can indicate the approximate confidence intervals for employed error function. [ABSTRACT FROM AUTHOR]

Background & Aims Liver stiffness ( LS), spleen stiffness ( SS) and serum markers have been proposed to non-invasively assess portal hypertension or oesophageal varices ( EV) in cirrhotic patients. We aimed to evaluate the performance of a stepwise algorithm that combines Lok score with LS and SS for diagnosing high-risk EV ( HREV) and to compare it with other already-validated non-invasive methods. Methods We performed a cross-sectional study including 136 consecutive compensated cirrhotic patients with various aetiologies, divided into training (90) and validation (46) set. Endoscopy was performed within 6 months from inclusion for EV screening. Spleen diameter was assessed by ultrasonography. LS and SS were measured using Fibroscan. Lok score, platelet count/spleen diameter ratio, LSM-spleen diameter to platelet ratio score and oesophageal varices risk score ( EVRS) were calculated and their diagnostic accuracy for HREV was assessed. The algorithm classified patients as having/not-having HREV. Its performance was tested and compared in both groups. Results In the training set, all variables could select patients with HREV with moderate accuracy, the best being LSPS ( AUROC = 0.818; 0.93 sensitivity; 0.63 specificity). EVRS, however, was the only independent predictor of HREV ( OR = 1.521; P = 0.032). The algorithm correctly classified 69 (76.66%) patients in the training set ( P < 0.0001) and 36 (78.26%) in the validation one. In the validation group, the algorithm performed slightly better than LSPS and EVRS, showing 100% sensitivity and negative predicted value. Conclusion The stepwise algorithm combining Lok score, LS and SS could be used to select patients at low risk of having HREV and who may benefit from more distanced endoscopic evaluation. [ABSTRACT FROM AUTHOR]