24 results on '"Palczewska, Anna"'
Search Results
2. A Bayesian Mixture Model approach to expected possession values in rugby league.
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Sawczuk, Thomas, Palczewska, Anna, Jones, Ben, and Palczewski, Jan
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RUGBY League football , *ATHLETIC fields , *DATA structures , *PERSONAL property , *SPORTS - Abstract
This study aimed to introduce a novel Bayesian Mixture Model approach to the development of an EPV model in rugby league, which could produce a smooth pitch surface and estimate individual possession outcome probabilities. 99,966 observations from the 2021 Super League season were used. A set of 33 centres (30 in the field of play, 3 in the opposition try area) were located across the pitch. Each centre held the probability of five possession outcomes occurring (converted/unconverted try, penalty, drop goal and no points). Probabilities at each centre were interpolated to all locations on the pitch and estimated using a Bayesian approach. An EPV measure was derived from the possession outcome probabilities and their points value. The model produced a smooth pitch surface, which was able to provide different possession outcome probabilities and EPVs for every location on the pitch. Differences between team attacking and defensive plots were visualised and an actual vs expected player rating system was developed. The model provides significantly more flexibility than previous zonal approaches, allowing much more insightful results to be obtained. It could easily be adapted to other sports with similar data structures. [ABSTRACT FROM AUTHOR]
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- 2024
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3. Ranking strategies to support toxicity prediction: A case study on potential LXR binders
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Palczewska, Anna, Kovarich, Simona, Ciacci, Andrea, Fioravanzo, Elena, Bassan, Arianna, and Neagu, Daniel
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- 2019
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4. Identification of pattern mining algorithm for rugby league players positional groups separation based on movement patterns.
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Adeyemo, Victor Elijah, Palczewska, Anna, Jones, Ben, and Weaving, Dan
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RUGBY League football players , *MACHINE learning , *RUGBY League football , *IDENTIFICATION , *ALGORITHMS , *CLASSIFICATION algorithms - Abstract
The application of pattern mining algorithms to extract movement patterns from sports big data can improve training specificity by facilitating a more granular evaluation of movement. Since movement patterns can only occur as consecutive, non-consecutive, or non-sequential, this study aimed to identify the best set of movement patterns for player movement profiling in professional rugby league and quantify the similarity among distinct movement patterns. Three pattern mining algorithms (l-length Closed Contiguous [LCCspm], Longest Common Subsequence [LCS] and AprioriClose) were used to extract patterns to profile elite rugby football league hookers (n = 22 players) and wingers (n = 28 players) match-games movements across 319 matches. Jaccard similarity score was used to quantify the similarity between algorithms' movement patterns and machine learning classification modelling identified the best algorithm's movement patterns to separate playing positions. LCCspm and LCS movement patterns shared a 0.19 Jaccard similarity score. AprioriClose movement patterns shared no significant Jaccard similarity with LCCspm (0.008) and LCS (0.009) patterns. The closed contiguous movement patterns profiled by LCCspm best-separated players into playing positions. Multi-layered Perceptron classification algorithm achieved the highest accuracy of 91.02% and precision, recall and F1 scores of 0.91 respectively. Therefore, we recommend the extraction of closed contiguous (consecutive) over non-consecutive and non-sequential movement patterns for separating groups of players. [ABSTRACT FROM AUTHOR]
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- 2024
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5. Interpretation, identification and reuse of models : theory and algorithms with applications in predictive toxicology
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Palczewska, Anna Maria
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615.9 ,Model interpretation, Model identification, Model governance, Reuse of models, Predictive toxicology, Random forest model, Feature contributions, Pareto optimality - Abstract
This thesis is concerned with developing methodologies that enable existing models to be effectively reused. Results of this thesis are presented in the framework of Quantitative Structural-Activity Relationship (QSAR) models, but their application is much more general. QSAR models relate chemical structures with their biological, chemical or environmental activity. There are many applications that offer an environment to build and store predictive models. Unfortunately, they do not provide advanced functionalities that allow for efficient model selection and for interpretation of model predictions for new data. This thesis aims to address these issues and proposes methodologies for dealing with three research problems: model governance (management), model identification (selection), and interpretation of model predictions. The combination of these methodologies can be employed to build more efficient systems for model reuse in QSAR modelling and other areas. The first part of this study investigates toxicity data and model formats and reviews some of the existing toxicity systems in the context of model development and reuse. Based on the findings of this review and the principles of data governance, a novel concept of model governance is defined. Model governance comprises model representation and model governance processes. These processes are designed and presented in the context of model management. As an application, minimum information requirements and an XML representation for QSAR models are proposed. Once a collection of validated, accepted and well annotated models is available within a model governance framework, they can be applied for new data. It may happen that there is more than one model available for the same endpoint. Which one to chose? The second part of this thesis proposes a theoretical framework and algorithms that enable automated identification of the most reliable model for new data from the collection of existing models. The main idea is based on partitioning of the search space into groups and assigning a single model to each group. The construction of this partitioning is difficult because it is a bi-criteria problem. The main contribution in this part is the application of Pareto points for the search space partition. The proposed methodology is applied to three endpoints in chemoinformatics and predictive toxicology. After having identified a model for the new data, we would like to know how the model obtained its prediction and how trustworthy it is. An interpretation of model predictions is straightforward for linear models thanks to the availability of model parameters and their statistical significance. For non linear models this information can be hidden inside the model structure. This thesis proposes an approach for interpretation of a random forest classification model. This approach allows for the determination of the influence (called feature contribution) of each variable on the model prediction for an individual data. In this part, there are three methods proposed that allow analysis of feature contributions. Such analysis might lead to the discovery of new patterns that represent a standard behaviour of the model and allow additional assessment of the model reliability for new data. The application of these methods to two standard benchmark datasets from the UCI machine learning repository shows a great potential of this methodology. The algorithm for calculating feature contributions has been implemented and is available as an R package called rfFC.
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- 2014
6. Optimising classification in sport: a replication study using physical and technical-tactical performance indicators to classify competitive levels in rugby league match-play.
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Adeyemo, Victor Elijah, Palczewska, Anna, Jones, Ben, Weaving, Dan, and Whitehead, Sarah
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RUGBY League football ,PHYSICAL mobility ,RANDOM forest algorithms ,FEATURE selection ,SPORTS sciences ,RUGBY football - Abstract
Determining key performance indicators and classifying players accurately between competitive levels is one of the classification challenges in sports analytics. A recent study applied Random Forest algorithm to identify important variables to classify rugby league players into academy and senior levels and achieved 82.0% and 67.5% accuracy for backs and forwards. However, the classification accuracy could be improved due to limitations in the existing method. Therefore, this study aimed to introduce and implement feature selection technique to identify key performance indicators in rugby league positional groups and assess the performances of six classification algorithms. Fifteen and fourteen of 157 performance indicators for backs and forwards were identified respectively as key performance indicators by the correlation-based feature selection method, with seven common indicators between the positional groups. Classification results show that models developed using the key performance indicators had improved performance for both positional groups than models developed using all performance indicators. 5-Nearest Neighbour produced the best classification accuracy for backs and forwards (accuracy = 85% and 77%) which is higher than the previous method's accuracies. When analysing classification questions in sport science, researchers are encouraged to evaluate multiple classification algorithms and a feature selection method should be considered for identifying key variables. [ABSTRACT FROM AUTHOR]
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- 2024
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7. Towards model governance in predictive toxicology
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Palczewska, Anna, Fu, Xin, Trundle, Paul, Yang, Longzhi, Neagu, Daniel, Ridley, Mick, and Travis, Kim
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- 2013
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8. Use of Kernel Density Estimation to understand the spatial trends of attacking possessions in rugby league
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Sawczuk, Thomas, Palczewska, Anna, Jones, Ben, and Palczewski, Jan
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FOS: Computer and information sciences ,Applications (stat.AP) ,Statistics - Applications - Abstract
Despite having the potential to provide significant insights into tactical preparations for future matches, very few studies have considered the spatial trends of team attacking possessions in rugby league. Those which have considered these trends have used grid based aggregation methods, which provide a discrete understanding of rugby league match play but may fail to provide a complete understanding of the spatial trends of attacking possessions due to the dynamic nature of the sport. In this study, we use Kernel Density Estimation (KDE) to provide a continuous understanding of the spatial trends of attacking possessions in rugby league on a team by team basis. We use the Wasserstein distance to understand the differences between teams (i.e. using all of each team's data) and within teams (i.e. using a single team's data against different opponents). Our results show that KDEs are able to provide interesting tactical insights at the between team level. Furthermore, at the within team level, the results are able to show patterns of spatial trends for attacking teams, which are present against some opponents but not others. The results could help sports practitioners to understand opposition teams' previous performances and prepare tactical strategies for matches against them., 12 pages, 4 figures, 3 tables
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- 2022
9. Moving beyond velocity derivatives; using global positioning system data to extract sequential movement patterns at different levels of rugby league match-play.
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Collins, Neil, White, Ryan, Palczewska, Anna, Weaving, Dan, Dalton-Barron, Nicholas, and Jones, Ben
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GLOBAL Positioning System ,KRUSKAL-Wallis Test ,STATISTICS ,DISCRIMINANT analysis ,RUGBY football ,BODY movement ,DESCRIPTIVE statistics ,ATHLETIC ability ,SPORTS events ,DATA analysis ,DATA analytics ,ALGORITHMS - Abstract
This study aims to (a) quantify the movement patterns during rugby league match-play and (b) identify if differences exist by levels of competition within the movement patterns and units through the sequential movement pattern (SMP) algorithm. Global Positioning System data were analysed from three competition levels; four Super League regular (regular-SL), three Super League (semi-)Finals (final-SL) and four international rugby league (international) matches. The SMP framework extracted movement pattern data for each athlete within the dataset. Between competition levels, differences were analysed using linear discriminant analysis (LDA). Movement patterns were decomposed into their composite movement units; then Kruskal–Wallis rank-sum and Dunn post-hoc were used to show differences. The SMP algorithm found 121 movement patterns comprised mainly of "walk" and "jog" based movement units. The LDA had an accuracy score of 0.81, showing good separation between competition levels. Linear discriminant 1 and 2 explained 86% and 14% of the variance. The Kruskal–Wallis found differences between competition levels for 9 of 17 movement units. Differences were primarily present between regular-SL and international with other combinations showing less differences. Movement units which showed significant differences between competition levels were mainly composed of low velocities with mixed acceleration and turning angles. The SMP algorithm found 121 movement patterns across all levels of rugby league match-play, of which, 9 were found to show significant differences between competition levels. Of these nine, all showed significant differences present between international and domestic, whereas only four found differences present within the domestic levels. This study shows the SMP algorithm can be used to differentiate between levels of rugby league and that higher levels of competition may have greater velocity demands. Highlights This study shows that movement patterns and movement units can be used to investigate team sports through the application of the SMP framework One hundred and twenty-one movement patterns were found to be present within rugby league match-play, with the walk- and jog-based movement units most prevalent. No movement pattern was unique to a single competition level. Further analysis revealed that the majority of movement units analysed had significant differences between international and domestic rugby league, whereas only four movement units (i.e. f,m,n,q) had significant differences within the two domestic rugby league levels. International rugby league had higher occurrences of the movement patterns consisting of higher velocity movement units (ie. T,S,y). This suggests that international rugby league players may need greater high velocity exposure in training. [ABSTRACT FROM AUTHOR]
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- 2023
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10. Using Pareto points for model identification in predictive toxicology
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Palczewska, Anna, Neagu, Daniel, and Ridley, Mick
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- 2013
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11. Clustering of match running and performance indicators to assess between- and within-playing position similarity in professional rugby league.
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Dalton-Barron, Nicholas, Palczewska, Anna, Weaving, Dan, Rennie, Gordon, Beggs, Clive, Roe, Gregory, and Jones, Ben
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RUNNING , *TEAM sports , *RUGBY football , *ATHLETIC ability , *CLUSTER analysis (Statistics) - Abstract
This study aimed to determine the similarity between and within positions in professional rugby league in terms of technical performance and match displacement. Here, the analyses were repeated on 3 different datasets which consisted of technical features only, displacement features only, and a combined dataset including both. Each dataset contained 7617 observations from the 2018 and 2019 Super League seasons, including 366 players from 11 teams. For each dataset, feature selection was initially used to rank features regarding their importance for predicting a player's position for each match. Subsets of 12, 11, and 27 features were retained for technical, displacement, and combined datasets for subsequent analyses. Hierarchical cluster analyses were then carried out on the positional means to find logical groupings. For the technical dataset, 3 clusters were found: (1) props, loose forwards, second-row, hooker; (2) halves; (3) wings, centres, fullback. For displacement, 4 clusters were found: (1) second-rows, halves; (2) wings, centres; (3) fullback; (4) props, loose forward, hooker. For the combined dataset, 3 clusters were found: (1) halves, fullback; (2) wings and centres; (3) props, loose forward, hooker, second-rows. These positional clusters can be used to standardise positional groups in research investigating either technical, displacement, or both constructs within rugby league. [ABSTRACT FROM AUTHOR]
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- 2022
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12. Sequential movement pattern-mining (SMP) in field-based team-sport: A framework for quantifying spatiotemporal data and improve training specificity?
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White, Ryan, Palczewska, Anna, Weaving, Dan, Collins, Neil, and Jones, Ben
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GLOBAL Positioning System , *TEAM sports , *ATHLETIC ability - Abstract
Athlete external load is typically quantified as volumes or discretised threshold values using distance, speed and time. A framework accounting for the movement sequences of athletes has previously been proposed using radio frequency data. This study developed a framework to identify sequential movement sequences using GPS-derived spatiotemporal data in team-sports and establish its stability. Thirteen rugby league players during one match were analysed to demonstrate the application of the framework. The framework (Sequential Movement Pattern-mining [SMP]) applies techniques to analyse i) geospatial data (i.e., decimal degree latitude and longitude), ii) determine players turning angles, iii) improve movement descriptor assignment, thus improving movement unit formation and iv) improve the classification and identification of players' frequent SMP. The SMP framework allows for sub-sequences of movement units to be condensed, removing repeated elements, which offers a novel technique for the quantification of similarities or dis-similarities between players and playing positions. The SMP framework provides a robust and stable method that allows, for the first time the analysis of GPS-derived data and identifies the frequent SMP of field-based team-sport athletes. The application of the SMP framework in practice could optimise the outcomes of training of field-based team-sport athletes by improving training specificity. [ABSTRACT FROM AUTHOR]
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- 2022
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13. Development of an expected possession value model to analyse team attacking performances in rugby league.
- Author
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Sawczuk, Thomas, Palczewska, Anna, and Jones, Ben
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RUGBY League football , *MARKOV processes , *TEAMS , *SPORTS forecasting - Abstract
This study aimed to evaluate team attacking performances in rugby league via expected possession value (EPV) models. Location data from 59,233 plays in 180 Super League matches across the 2019 Super League season were used. Six EPV models were generated using arbitrary zone sizes (EPV-308 and EPV-77) or aggregated according to the total zone value generated during a match (EPV-37, EPV-19, EPV-13 and EPV-9). Attacking sets were considered as Markov Chains, allowing the value of each zone visited to be estimated based on the outcome of the possession. The Kullback-Leibler Divergence was used to evaluate the reproducibility of the value generated from each zone (the reward distribution) by teams between matches. Decreasing the number of zones improved the reproducibility of reward distributions between matches but reduced the variation in zone values. After six previous matches, the subsequent match's zones had been visited on 95% or more occasions for EPV-19 (95±4%), EPV-13 (100±0%) and EPV-9 (100±0%). The KL Divergence values were infinity (EPV-308), 0.52±0.05 (EPV-77), 0.37±0.03 (EPV-37), 0.20±0.02 (EPV-19), 0.13±0.02 (EPV-13) and 0.10±0.02 (EPV-9). This study supports the use of EPV-19 and EPV-13, but not EPV-9 (too little variation in zone values), to evaluate team attacking performance in rugby league. [ABSTRACT FROM AUTHOR]
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- 2021
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14. A league-wide investigation into variability of rugby league match running from 322 Super League games.
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Dalton-Barron, Nicholas, Palczewska, Anna, McLaren, Shaun J., Rennie, Gordon, Beggs, Clive, Roe, Gregory, and Jones, Ben
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RUGBY League football ,MICROTECHNOLOGY ,GLOBAL Positioning System ,COEFFICIENTS (Statistics) ,DATA analysis - Abstract
Purpose:This study investigated sources of variability in the overall and phase-specific running match characteristics in elite rugby league. Methods:Microtechnology data were collected from 11 Super League (SL) teams, across 322 competitive matches within the 2018 and 2019 seasons. Total distance, high-speed running (HSR) distance (>5.5 m·s
−1 ), average speed, and average acceleration were assessed. Variability was determined using linear mixed models, with random intercepts specified for player, position, match, and club. Results:Large within-player coefficients of variation (CV) were found across whole match, ball-in-play, attack and defence for total distance (CV range = 24% to 35%) and HSR distance (37% to 96%), whereas small to moderate CVs (≤10%) were found for average speed and average acceleration. Similarly, there was higher between-player, -position, and -match variability in total distance and HSR distance when compared with average speed and average acceleration across all periods. All metrics were stable between-teams (≤5%), except HSR distance (16% to 18%). The transition period displayed the largest variability of all phases, especially for distance (up to 42%) and HSR distance (up to 165%). Conclusion:Absolute measures of displacement display large within-player and between-player, -position, and -match variability, yet average acceleration and average speed remain relatively stable across all match-periods. [ABSTRACT FROM AUTHOR]- Published
- 2021
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15. Force-velocity characteristics of lower extremity muscles in male high-altitude climbers.
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Stefańska, Małgorzata, Dębiec-Bąk, Agnieszka, Widelak, Justyna, Palczewska, Anna, Skrzek, Anna, Dominiak, Piotr, Kucharski, Wojciech, and Kubasiak, Katarzyna
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LEG muscles ,MOUNTAINEERS ,CONTROL groups ,KNEE muscles ,POSTURAL balance ,PROPRIOCEPTION - Abstract
Introduction. The study aimed to assess the force-velocity parameters of knee muscles in male high-altitude climbers and to compare the obtained results with the control group. Methods. Overall, 31 male subjects participated in the tests. The study group comprised 12 world-renowned Polish high-altitude climbers. The control group consisted of 19 professional soldiers on active duty. The groups did not differ significantly in the average age, body weight, or height. The force-velocity parameters of knee muscles were assessed under isokinetic conditions. The velocities of 60°/s and 180°/s were used. Results. The values of peak torque, total work, average power, and the agonist/antagonist ratio were higher and the acceleration and deceleration times were shorter in the study group in comparison with the control group. In particular, the differences in the parameters describing the knee flexors of both limbs proved to be statistically significant. The p-value of the t-test for the dominant limb knee flexors at the velocity of 60°/s was 0.0134 for peak torque, 0.0198 for total work, and 0.0019 for mean power. At the velocity of 180°/s, the p values equalled 0.0001, < 0.0001, and 0.0002, respectively. The effect size of each test was greater than 0.92. Conclusions. Significant differences in force-velocity parameters of knee muscles were observed between the group of highmountain climbers and the control group. The increase in the agonist/antagonist ratio and the decrease in the acceleration and deceleration times recorded in the group of high-altitude climbers are indicative of a change in the postural and dynamic mechanisms. [ABSTRACT FROM AUTHOR]
- Published
- 2021
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16. Machine learning, materiality and governance: A health and social care case study.
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Keen, Justin, Ruddle, Roy, Palczewski, Jan, Aivaliotis, Georgios, Palczewska, Anna, Megone, Christopher, and Macnish, Kevin
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MACHINE learning ,MEDICAL care ,INFORMATION superhighway ,MACHINE tools ,SCHEDULING - Abstract
There is a widespread belief that machine learning tools can be used to improve decision-making in health and social care. At the same time, there are concerns that they pose threats to privacy and confidentiality. Policy makers therefore need to develop governance arrangements that balance benefits and risks associated with the new tools. This article traces the history of developments of information infrastructures for secondary uses of personal datasets, including routine reporting of activity and service planning, in health and social care. The developments provide broad context for a study of the governance implications of new tools for the analysis of health and social care datasets. We find that machine learning tools can increase the capacity to make inferences about the people represented in datasets, although the potential is limited by the poor quality of routine data, and the methods and results are difficult to explain to other stakeholders. We argue that current local governance arrangements are piecemeal, but at the same time reinforce centralisation of the capacity to make inferences about individuals and populations. They do not provide adequate oversight, or accountability to the patients and clients represented in datasets. [ABSTRACT FROM AUTHOR]
- Published
- 2021
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17. Public Services, Personal Data and Machine Learning: Prospects for Infrastructures and Ecosystems.
- Author
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Keen, Justin, Ruddle, Roy, Palczewski, Jan, Aivaliotis, Georgios, Adnan, Muhammad, Palczewska, Anna, and Megone, Christopher
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- 2019
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18. Comparison of the Predictive Performance and Interpretability of Random Forest and Linear Models on Benchmark Data Sets.
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Marchese Robinson, Richard L., Palczewska, Anna, Palczewski, Jan, and Kidley, Nathan
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- 2017
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19. The Scope and Capabilities of ITS – the Case of Lodz.
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Kozlowski, Remigiusz, Palczewska, Anna, and Jablonski, Jakub
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- 2016
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20. Comparing the CORAL and Random Forest Approaches for Modelling the In Vitro Cytotoxicity of Silica Nanomaterials.
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Cassano, Antonio, Robinson, Richard L. Marchese, Palczewska, Anna, Puzyn, Tomasz, Gajewicz, Agnieszka, Lang Tran, Manganelli, Serena, and Cronin, Mark T. D.
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- 2016
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21. Using computational methods for the prediction of drug vehicles.
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Mistry, Pritesh, Palczewska, Anna, Neagu, Daniel, and Trundle, Paul
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- 2014
- Full Text
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22. Interpreting Random Forest Classification Models Using a Feature Contribution Method.
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Palczewska, Anna, Palczewski, Jan, Marchese Robinson, Richard, and Neagu, Daniel
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- 2014
- Full Text
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23. Interpreting random forest models using a feature contribution method.
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Palczewska, Anna, Palczewski, Jan, Robinson, Richard Marchese, and Neagu, Daniel
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- 2013
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24. Characterising and analysing performance sequences in rugby league using match events data
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Sawczuk, Thomas, Palczewska, Anna, and Jones, Ben
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rugby league ,performance analysis ,markov ,kernel density estimation ,wasserstein distance ,bayesian - Abstract
The ability to accurately evaluate player and team performances in professional sport is particularly valuable. Doing so provides competitive advantages include extracting important information regarding the tactical strategies of future oppositions and producing player rating systems. A common method of evaluating player and team performances is via expected possession value (EPV) models. EPV models assign a value to every location and/or action on the pitch, which reflects the probability of points being scored within a given time period. EPV models have been produced in several sports, including football, basketball and ice hockey. However, there is limited research surrounding these models in rugby league. Rugby league has a unique set of rules, including a six tackle attacking set and five possible scoring options at the end of a possession. These two factors, alongside the poor data availability in the sport ensure that the majority of previous methods cannot be adapted for use in rugby league. Therefore the aim of this thesis was to develop new methodologies evaluating player and team performances in rugby league. In the first section of this thesis (studies 1 and 2), previous Markov models using zonal approaches were applied, adapted and extended in rugby league to provide insights into player and team performances. Six EPV models were produced with varying zone sizes using Markov Reward Processes. The Kullback-Leibler Divergence was used to evaluate the zone sizes which could reproduce future team attacking performances. The model was then extended to incorporate actions and context nodes using Markov Decision Processes. Novel methods of evaluating player and team performances were also produced. In the second section (studies 3 and 4), novel models producing smooth pitch surfaces were developed. The spatial trends of team attacking performances were evaluated using Kernel Density Estimation. Two novel Wasserstein distance metrics were used to provide valuable insights into team performances. A novel approach to the estimation of individual possession outcomes was also proposed using a Bayesian mixture model approach. The model used linear and bilinear interpolation techniques for its weights to produce a smooth pitch surface. Novel performance metrics evaluating player and team performances were also created. The research provides new methodologies for use within rugby league, providing zonal and smooth EPV models through which player and team performances can be evaluated. Professional experts were impressed with the results they provided and validated their use within the sport.
- Published
- 2023
- Full Text
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