Machine Learning for Understanding and Predicting Injuries in Football

Attempts to better understand the relationship between training and competition load and injury in football are essential for helping to understand adaptation to training programmes, assessing fatigue and recovery, and minimising the risk of injury and illness. To this end, technological advancements have enabled the collection of multiple points of data for use in analysis and injury prediction. The full breadth of available data has, however, only recently begun to be explored using suitable statistical methods. Advances in automatic and interactive data analysis with the help of machine learning are now being used to better establish the intricacies of the player load and injury relationship. In this article, we examine this recent research, describing the analyses and algorithms used, reporting the key findings, and comparing model fit. To date, the vast array of variables used in analysis as proxy indicators of player load, alongside differences in approach to key aspects of data treatment—such as response to data imbalance, model fitting, and a lack of multi-season data—limit a systematic evaluation of findings and the drawing of a unified conclusion. If, however, the limitations of current studies can be addressed, machine learning has much to offer the field and could in future provide solutions to the training load and injury paradox through enhanced and systematic analysis of athlete data

A multi-season machine learning approach to examine the training load and injury relationship in professional soccer

OBJECTIVES: The purpose of this study was to use machine learning to examine the relationship between training load and soccer injury with a multi-season dataset from one English Premier League club. METHODS: Participants were 35 male professional soccer players (aged 25.79±3.75 years, range 18–37 years; height 1.80±0.07 m, range 1.63–1.95 m; weight 80.70±6.78 kg, range 66.03–93.70 kg), with data collected from the 2014–2015 season until the 2018–2019 season. A total of 106 training loads variables (40 GPS data, 6 personal information, 14 physical data, 4 psychological data and 14 ACWR, 14 MSWR and 14 EWMA data) were examined in relation to 133 non-contact injuries, with a high imbalance ratio of 0.013. RESULTS: XGBoost and Artificial Neural Network were implemented to train the machine learning models using four and a half seasons’ data, with the developed models subsequently tested on the following half season’s data. During the first four and a half seasons, there were 341 injuries; during the next half season there were 37 injuries. To interpret and visualize the output of each model and the contribution of each feature (i.e., training load) towards the model, we used the Shapley Additive Explanations (SHAP) approach. Of 37 injuries, XGBoost correctly predicted 26 injuries, with recall and precision of 73% and 10% respectively. Artificial Neural Network correctly predicted 28 injuries, with recall and precision of 77% and 13% respectively. In the model using Artificial Neural Network (the relatively more accurate model), last injury area and weight appeared to be the most important features contributing to the prediction of injury. CONCLUSIONS: This was the first study of its kind to use Artificial Neural Network and a multi-season dataset for injury prediction. Our results demonstrate the potential to predict injuries with high recall, thereby identifying most of the injury cases, albeit, due to high class imbalance, precision suffered. This approach to using machine learning provides potentially valuable insights for soccer organizations and practitioners when monitoring load injuries

Nutrient Deficiency Correction in Ovarian Cancer Patients Following Surgical Treatment: a Clinical Case

Background. According to some studies, nutrient deficiencies reach an over-70% prevalence in ovarian cancer, among other gynaecological malignancies, thus constituting an important risk factor for postoperative mortality, surgical complications and longer hospital stays. Therefore, effective nutrient deficiency correction methods are warranted to improve the ovarian cancer outcomes, especially in patients following radical surgical interventions. New systematic evidence emerges in literature on the impact of such novel methods on the critical status of variant-category patients. Meanwhile, such evidence bears a recommendatory value only, with no current standard or protocol assumed for nutrient deficiency management. This issue presently remains open and requires careful research and analysis.Materials and methods. The clinical case demonstrates the efficacy of nutrient deficiency correction in an ovarian cancer patient following an individualised radical surgery.Results and discussion. The energy supplied on day 1 was >42%, >83% on day 3, and the target values had been achieved by day 7 of intensive therapy. The nutrient deficiency marker dynamics revealed the growth of transferrin, triglycerides and peripheral blood lymphocyte counts as early as by day 3 post-surgery. Albumin was the latest to respond, increasing only on day 7.Conclusion. The introduction of novel nutrition strategies and knowledge of their impact depend on further high-quality research, especially prospective studies, incorporating a  greater homogeneity of intervention types and clinical outcomes, as well as wider sampling of female ovarian cancer

ЛЕЧЕНИЕ ПАЦИЕНТОВ С РЕВМАТОИДНЫМ АРТРИТОМ ПРИ СОЧЕТАННОЙ ПАТОЛОГИИ

Combined pathology in patients with rheumatoid arthritis. Identification of the relationship with the effect of drug therapy. Сочетанная патология у пациентов с ревматоидным артритом. Выявление взаимосвязи с эффектом от медикаментозной терапии.

Коррекция нутритивной недостаточности пациентов с раком яичников на фоне хирургического лечения. Клинический случай

Background. According to some studies, nutrient deficiencies reach an over-70% prevalence in ovarian cancer, among other gynaecological malignancies, thus constituting an important risk factor for postoperative mortality, surgical complications and longer hospital stays. Therefore, effective nutrient deficiency correction methods are warranted to improve the ovarian cancer outcomes, especially in patients following radical surgical interventions. New systematic evidence emerges in literature on the impact of such novel methods on the critical status of variant-category patients. Meanwhile, such evidence bears a recommendatory value only, with no current standard or protocol assumed for nutrient deficiency management. This issue presently remains open and requires careful research and analysis.Materials and methods. The clinical case demonstrates the efficacy of nutrient deficiency correction in an ovarian cancer patient following an individualised radical surgery.Results and discussion. The energy supplied on day 1 was >42%, >83% on day 3, and the target values had been achieved by day 7 of intensive therapy. The nutrient deficiency marker dynamics revealed the growth of transferrin, triglycerides and peripheral blood lymphocyte counts as early as by day 3 post-surgery. Albumin was the latest to respond, increasing only on day 7.Conclusion. The introduction of novel nutrition strategies and knowledge of their impact depend on further high-quality research, especially prospective studies, incorporating a  greater homogeneity of intervention types and clinical outcomes, as well as wider sampling of female ovarian cancer. Введение. Среди гинекологических злокачественных новообразований распространенность недостаточности питания при раке яичников достигает в некоторых анализах более 70 %, представляя собой важный фактор риска послеоперационной смертности, хирургических осложнений и продолжительности пребывания в стационаре. Следовательно, поиск эффективных методов коррекции нутритивной недостаточности, в особенности у пациентов, подвергшихся радикальным оперативным вмешательствам, имеет решающее значение для улучшения исходов пациентов с раком яичников. В литературе появляются структурированные данные о влиянии новых методик коррекции нутритивной недостаточности на течение критического состояния разнообразных групп пациентов различного профиля. Однако эти данные несут исключительно рекомендательный характер и отсутствует какой-либо стандарт или протокол коррекции нутритивной недостаточности. В настоящее время этот вопрос остается открытым для обсуждения и требует тщательного изучения и анализа.Материалы и методы. В данной статье на примере клинического случая продемонстрирована эффективность коррекции нутритивной недостаточности пациентки с опухолью яичников после радикальной операции по индивидуальному протоколу.Результаты и обсуждение. Установлено, что доставленная энергия на 1-е сутки составила более 42 %, на 3-и сутки обеспечивалось более 83 % целевых показателей, которые были достигнуты уже к 7-м суткам интенсивной терапии. Динамика маркеров нутритивной недостаточности демонстрирует, что прирост трансферрина, триглицеридов и лимфоцитов периферической крови у пациентки регистрировался уже на 3-и сутки оперативного лечения. Позже всех отреагировал уровень альбумина, содержание которого начало увеличиваться только на 7-е сутки.Заключение. Необходимы дальнейшие высококачественные исследования, особенно проспективные исследования, с большей однородностью между типами вмешательств и клиническими исходами, включая большое количество женщин с раком яичников, чтобы предложить новые стратегии питания и изучить влияние таких стратегий.

Automated Adaptation Strategies for Stream Learning

Automation of machine learning model development is increasingly becoming an established research area. While automated model selection and automated data pre-processing have been studied in depth, there is, however, a gap concerning automated model adaptation strategies when multiple strategies are available. Manually developing an adaptation strategy can be time consuming and costly. In this paper we address this issue by proposing the use of flexible adaptive mechanism deployment for automated development of adaptation strategies. Experimental results after using the proposed strategies with five adaptive algorithms on 36 datasets confirm their viability. These strategies achieve better or comparable performance to the custom adaptation strategies and the repeated deployment of any single adaptive mechanism

Multiple adaptive mechanisms for data-driven soft sensors

Recent data-driven soft sensors often use multiple adaptive mechanisms to cope with non-stationary environments. These mechanisms are usually deployed in a prescribed order which does not change. In this work we use real world data from the process industry to compare deploying adaptive mechanisms in a fixed manner to deploying them in a flexible way, which results in varying adaptation sequences. We demonstrate that flexible deployment of available adaptive methods coupled with techniques such as cross-validatory selection and retrospective model correction can benefit the predictive accuracy over time. As a vehicle for this study, we use a soft-sensor for batch processes based on an adaptive ensemble method which employs several adaptive mechanisms to react to the changes in data

Phase composition and its spatial distribution in antique copper coins: Neutron tomography and diffraction studies

The chemical and elementary composition, internal arrangement, and spatial distribution of the components of ancient Greek copper coins were studied using XRF analysis, neutron diffraction and neutron tomography methods. The studied coins are interesting from a historical and cultural point of view, as they are “Charon’s obol’s”. These coins were discovered at the location of an ancient Greek settlement during archaeological excavations on the “Volna-1” necropolis in Krasnodar Region, Russian Federation. It was determined that the coins are mainly made of a bronze alloy, a tin content that falls in the range of 1.1(2)–7.9(3) wt.%. All coins are highly degraded; corrosion and patina areas occupy volumes from ~27 % to ~62 % of the original coin volumes. The neutron tomography method not only provided 3D data of the spatial distribution of the bronze alloy and the patina with corrosion contamination inside coin volumes, but also restored the minting pattern of several studied coins. Taking into account the obtained results, the origin and use of these coins in the light of historical and economic processes of the Bosporan Kingdom are discussed