The Quality of Bankruptcy Data and its Impact on the Evaluation of Prediction Models: Creating and Testing a German Database

2018 ◽  
Author(s):  
Martin Huettemann ◽  
Tobias Lorsbach
Keyword(s):  
Prediction Models

How does the workload applied during the training week and the contextual factors affect the physical responses of professional soccer players in the match?

2021 ◽  
pp. 174795412199561
Author(s):  
Berni Guerrero-Calderón ◽  
Maximilian Klemp ◽  
José Alfonso Morcillo ◽  
Daniel Memmert
Keyword(s):  
Prediction Models ◽  
Contextual Factors ◽  
Soccer Players ◽  
Professional Soccer ◽  
Soccer Teams ◽  
Level Of Significance ◽  
Physical Output ◽  
Physical Responses ◽  
Positive Effect

The aim of this study was to examine whether match physical output can be predicted from the workload applied in training by professional soccer players. Training and match load records from two professional soccer teams belonging to the Spanish First and Second Division were collected through GPS technology over a season ( N = 1678 and N = 2441 records, respectively). The factors playing position, season period, quality of opposition, category and playing formation were considered into the analysis. The level of significance was set at p ≤ .05. The prediction models yielded a conditional R-squared in match of 0.51 in total distance (TD); 0.58 in high-intensity distance (HIRD, from 14 to 24 km · h−1); and 0.60 in sprint distance (SPD, >24 km·h−1). The main finding of this study was that the physical output of players in the match was predicted from the training-load performed during the previous training week. The training-TD negatively affected the match physical output while the training-HIRD showed a positive effect. Moreover, the contextual factors – playing position, season period, division and quality of opposition – affected the players’ physical output in the match. Therefore, these results suggest the appropriateness of programming lower training volume but increasing the intensity of the activity throughout the weekly microcycle, and considering contextual factors within the load programming.

Reviewing the use and quality of machine learning in developing clinical prediction models for cardiovascular disease

2021 ◽  
pp. postgradmedj-2020-139352
Author(s):  
Simon Allan ◽  
Raphael Olaiya ◽  
Rasan Burhan
Keyword(s):  
Machine Learning ◽  
Cardiovascular Disease ◽  
Prediction Models ◽  
Bone Fractures ◽  
Statin Treatment ◽  
Computer Algorithms ◽  
Clinical Prediction ◽  
Clinical Prediction Models ◽  
Heart Attacks

Cardiovascular disease (CVD) is one of the leading causes of death across the world. CVD can lead to angina, heart attacks, heart failure, strokes, and eventually, death; among many other serious conditions. The early intervention with those at a higher risk of developing CVD, typically with statin treatment, leads to better health outcomes. For this reason, clinical prediction models (CPMs) have been developed to identify those at a high risk of developing CVD so that treatment can begin at an earlier stage. Currently, CPMs are built around statistical analysis of factors linked to developing CVD, such as body mass index and family history. The emerging field of machine learning (ML) in healthcare, using computer algorithms that learn from a dataset without explicit programming, has the potential to outperform the CPMs available today. ML has already shown exciting progress in the detection of skin malignancies, bone fractures and many other medical conditions. In this review, we will analyse and explain the CPMs currently in use with comparisons to their developing ML counterparts. We have found that although the newest non-ML CPMs are effective, ML-based approaches consistently outperform them. However, improvements to the literature need to be made before ML should be implemented over current CPMs.

scholarly journals Risk prediction models for atherosclerotic cardiovascular disease: A systematic assessment with particular reference to Qatar

2021 ◽  
Vol 2021 (2) ◽  
Author(s):  
Aziz Sheikh ◽  
Ulugbek Nurmatov ◽  
Huda Amer Al-Katheeri ◽  
Rasmeh Ali Al Huneiti
Keyword(s):  
Risk Prediction ◽  
Prediction Models ◽  
Ethnically Diverse ◽  
Risk Of Bias ◽  
Low Risk ◽  
Atherosclerotic Cardiovascular Disease ◽  
Eligibility Criteria ◽  
Risk Prediction Models ◽  
Ascvd Risk

Background: Atherosclerotic cardiovascular disease (ASCVD) is a common disease in the State of Qatar and results in considerable morbidity, impairment of quality of life and mortality. The American College of Cardiology/American Heart Association Pooled Cohort Equations (PCE) is currently used in Qatar to identify those at high risk of ASCVD. However, it is unclear if this is the optimal ASCVD risk prediction model for use in Qatar's ethnically diverse population. Aims: This systematic review aimed to identify, assess the methodological quality of and compare the properties of established ASCVD risk prediction models for the Qatari population. Methods: Two reviewers performed head-to-head comparisons of established ASCVD risk calculators systematically. Studies were independently screened according to predefined eligibility criteria and critically appraised using Prediction Model Risk Of Bias Assessment Tool. Data were descriptively summarized and narratively synthesized with reporting of key statistical properties of the models. Results: We identified 20,487 studies, of which 41 studies met our eligibility criteria. We identified 16 unique risk prediction models. Overall, 50% (n = 8) of the risk prediction models were judged to be at low risk of bias. Only 13% of the studies (n = 2) were judged at low risk of bias for applicability, namely, PREDICT and QRISK3.Only the PREDICT risk calculator scored low risk in both domains. Conclusions: There is no existing ASCVD risk calculator particularly well suited for use in Qatar's ethnically diverse population. Of the available models, PREDICT and QRISK3 appear most appropriate because of their inclusion of ethnicity. In the absence of a locally derived ASCVD for Qatar, there is merit in a formal head-to-head comparison between PCE, which is currently in use, and PREDICT and QRISK3.

Epidemiology of cancer-associated venous thrombosis

Blood ◽  
2013 ◽  
Vol 122 (10) ◽  
pp. 1712-1723 ◽  
Author(s):  
Jasmijn F. Timp ◽  
Sigrid K. Braekkan ◽  
Henri H. Versteeg ◽  
Suzanne C. Cannegieter
Keyword(s):  
Risk Factors ◽  
High Risk ◽  
Venous Thrombosis ◽  
Cancer Patients ◽  
Prediction Models ◽  
Related Factors ◽  
Treatment Measures ◽  
Risk Patients ◽  
Cancer Types

Abstract Cancer-associated venous thrombosis is a common condition, although the reported incidence varies widely between studies depending on patient population, start and duration of follow-up, and the method of detecting and reporting thrombotic events. Furthermore, as cancer is a heterogeneous disease, the risk of venous thrombosis depends on cancer types and stages, treatment measures, and patient-related factors. In general, cancer patients with venous thrombosis do not fare well and have an increased mortality compared with cancer patients without. This may be explained by the more aggressive type of malignancies associated with this condition. It is hypothesized that thromboprophylaxis in cancer patients might improve prognosis and quality of life by preventing thrombotic events. However, anticoagulant treatment leads to increased bleeding, particularly in this patient group, so in case of proven benefit of thromboprophylaxis, only patients with a high risk of venous thrombosis should be considered. This review describes the literature on incidence of and risk factors for cancer-associated venous thrombosis, with the aim to provide a basis for identification of high-risk patients and for further development and refinement of prediction models. Furthermore, knowledge on risk factors for cancer-related venous thrombosis may enhance the understanding of the pathophysiology of thrombosis in these patients.

Quality of Experience in Mobile Peer-to-Peer Streaming Systems

2012 ◽  
pp. 196-241
Author(s):  
Florence Agboma
Keyword(s):  
Quality Of Experience ◽  
Business Models ◽  
Prediction Models ◽  
Content Delivery ◽  
Peer To Peer ◽  
Network Resource ◽  
Relative Contribution ◽  
Qos Parameters ◽  
Streaming Systems

This chapter considers the various parameters that affect the user’s Quality-of-Experience (QoE) in mobile peer-to-peer streaming systems, which are a form of content delivery network. Network and content providers do not necessarily focus on users’ QoE when designing the content delivery strategies and business models. The outcome of this is quite often the over-provisioning of network resources and also a lack of knowledge in respect to the user’s satisfaction. The focus is the methodology for quantifying the user’s perception of service quality for mobile video services and user contexts. The statistical technique of discriminant analysis is employed in defining prediction models to map Quality-of-Service (QoS) parameters onto estimates of the user’s QoE ratings. The chapter considers the relative contribution of the QoS parameters to predicting user responses. The chapter also demonstrates the value of the prediction models in developing QoE management strategies in order to optimize network resource utilization. To investigate the versatility of the framework, a feasibility study was applied to a P2P TV system. P2P systems continue to develop and as such, not a lot is known about their QoE characteristics, which situation this chapter seeks to remedy.

scholarly journals Galling phenomena in metal forming

Friction ◽  
2020 ◽  
Author(s):  
Kuniaki Dohda ◽  
Masahito Yamamoto ◽  
Chengliang Hu ◽  
Laurent Dubar ◽  
Kornel F. Ehmann
Keyword(s):  
Metal Forming ◽  
Prediction Models ◽  
Growth Models ◽  
Future Research ◽  
Research Directions ◽  
Bulk Metal ◽  
Engineered Surfaces ◽  
Future Research Directions ◽  
Effects Of Temperature

AbstractGalling phenomena in metal forming not only affect the quality of the engineered surfaces but also the success or failure of the manufacturing operation itself. This paper reviews the different galling conditions in sheet and bulk metal forming processes along with their evolution and the effects of temperature on galling. A group of anti-galling methods employed to prevent galling defects are also presented in detail. The techniques for quantitatively measuring galling are introduced, and the related prediction models, including friction, wear, and galling growth models, are presented to better understand the underlying phenomena. Galling phenomena in other processes similar to those occurring in metal forming are also examined to suggest different ways of further studying galling in metal forming. Finally, future research directions for the study of galling in metal forming are suggested.

scholarly journals Combine Clustering and Machine Learning for Enhancing the Efficiency of Energy Baseline of Chiller System

Energies ◽  
2020 ◽  
Vol 13 (17) ◽  
pp. 4368 ◽  
Author(s):  
Chun-Wei Chen ◽  
Chun-Chang Li ◽  
Chen-Yu Lin
Keyword(s):  
Machine Learning ◽  
Prediction Accuracy ◽  
Prediction Models ◽  
Machine Learning Algorithms ◽  
Learning Models ◽  
Important Method ◽  
Gap Statistic ◽  
Machine Learning Model ◽  
Key Variables

Energy baseline is an important method for measuring the energy-saving benefits of chiller system, and the benefits can be calculated by comparing prediction models and actual results. Currently, machine learning is often adopted as a prediction model for energy baselines. Common models include regression, ensemble learning, and deep learning models. In this study, we first reviewed several machine learning algorithms, which were used to establish prediction models. Then, the concept of clustering to preprocess chiller data was adopted. Data mining, K-means clustering, and gap statistic were used to successfully identify the critical variables to cluster chiller modes. Applying these key variables effectively enhanced the quality of the chiller data, and combining the clustering results and the machine learning model effectively improved the prediction accuracy of the model and the reliability of the energy baselines.

A New Approach for Training-load Quantification in Elite-level Soccer: Contextual Factors

2020 ◽  
Author(s):  
Berni Guerrero-Calderón ◽  
Maximilian Klemp ◽  
Alfonso Castillo-Rodriguez ◽  
José Alfonso Morcillo ◽  
Daniel Memmert
Keyword(s):  
High Speed ◽  
Prediction Models ◽  
Contextual Factors ◽  
Soccer Players ◽  
Total Distance ◽  
Professional Soccer ◽  
Equivalent Distance ◽  
Physical Responses ◽  
Match Location

AbstractThe aims of this study were to analyse the physical responses of professional soccer players during training considering the contextual factors of match location, season period, and quality of the opposition; and to establish prediction models of physical responses during training sessions. Training data was obtained from 30 professional soccer players from Spanish La Liga using global positioning technology (N=1365 performances). A decreased workload was showed during training weeks prior to home matches, showing large effects in power events, equivalent distance, total distance, walk distance and low-speed running distance. Also, the quality of the opposition also affected the training workload (p<0.05). All regression-models showed moderate effects, with an adjusted R2 of 0.37 for metabolic-work, 0.34 for total distance covered, 0.25 for high-speed running distance (18–21 km·h−1), 0.29 for very high-speed running distance (21–24 km·h−1), 0.22 for sprint running distance (>24 km·h−1) and 0.34 for equivalent distance. The main finding of this study was the great association of match location, season period and quality of opposition on the workload performed by players in the training week before the match; and the development of workload prediction-models considering these contextual factors, thus proposing a new and innovative approach to quantify the workload in soccer.

scholarly journals Perturbation Based Learning for Structured NLP Tasks with Application to Dependency Parsing

2019 ◽  
Vol 7 ◽  
pp. 643-659
Author(s):  
Amichay Doitch ◽  
Ram Yazdi ◽  
Tamir Hazan ◽  
Roi Reichart
Keyword(s):  
Prediction Models ◽  
Learning Algorithm ◽  
Solution Space ◽  
Optimal Strategies ◽  
Expressive Power ◽  
Entire Solution ◽  
Dependency Parsing ◽  
Computationally Efficient ◽  
Cross Lingual

The best solution of structured prediction models in NLP is often inaccurate because of limited expressive power of the model or to non-exact parameter estimation. One way to mitigate this problem is sampling candidate solutions from the model’s solution space, reasoning that effective exploration of this space should yield high-quality solutions. Unfortunately, sampling is often computationally hard and many works hence back-off to sub-optimal strategies, such as extraction of the best scoring solutions of the model, which are not as diverse as sampled solutions. In this paper we propose a perturbation-based approach where sampling from a probabilistic model is computationally efficient. We present a learning algorithm for the variance of the perturbations, and empirically demonstrate its importance. Moreover, while finding the argmax in our model is intractable, we propose an efficient and effective approximation. We apply our framework to cross-lingual dependency parsing across 72 corpora from 42 languages and to lightly supervised dependency parsing across 13 corpora from 12 languages, and demonstrate strong results in terms of both the quality of the entire solution list and of the final solution. 1

scholarly journals Pulsed Light treatment below a Critical Fluence (3.82 J/cm2) minimizes photo-degradation and browning of a model Phenolic (Gallic Acid) Solution

Foods ◽  
2019 ◽  
Vol 8 (9) ◽  
pp. 380 ◽  
Author(s):  
Wiktor ◽  
Mandal ◽  
Pratap Singh
Keyword(s):  
Acid Solution ◽  
Gallic Acid ◽  
Prediction Models ◽  
Pulse Frequency ◽  
Food Preservation ◽  
Nutrient Loss ◽  
Pulsed Light ◽  
Cumulative Energy ◽  
The Impact

Pulsed light (PL) is one of the most promising non-thermal technologies used in food preservation and processing. Its application results in reduction of microbial load as well as influences the quality of food. The data about the impact of PL on bioactive compounds is ambiguous, therefore the aim of this study was to analyze the effect of PL treatment of a gallic acid aqueous solution—as a model system of phenolic abundant liquid food matrices. The effect of PL treatment was evaluated based on colour, phenolic content concentration and antioxidant activity measured by DPPH assay using a design of experiments approach. The PL fluence (which is the cumulative energy input) was varied by varying the pulse frequency and time. Using Response Surface Methodology, prediction models were developed for the effect of fluence on gallic acid properties. It was demonstrated that PL can modify the optical properties of gallic acid and cause reactions and degradation of gallic acid. However, application of PL did not significantly alter the overall quality of the model gallic acid solution at low fluence levels. Cluster analysis revealed that below 3.82 J/cm2, changes in gallic acid were minimal, and this fluence level could be used as the critical level for food process design aiming to minimize nutrient loss.

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