Potential for Machine Learning in Burn Care

Author(s):  
Lydia Robb

Abstract Introduction Burn-related injuries are a leading cause of morbidity across the globe. Accurate assessment and treatment have been demonstrated to reduce the morbidity and mortality. This essay explores the forms of artificial intelligence to be implemented the field of burns management to optimise the care we deliver in the National Health Service (NHS) in the UK. Methods Machine Learning methods which predict or classify are explored. This includes linear and logistic regression, artificial neural networks, deep learning, and decision tree analysis. Discussion Utilizing Machine Learning in burns care holds potential from prevention, burns assessment, predicting mortality and critical care monitoring to healing time. Establishing a regional or national Machine Learning group would be the first step towards the development of these essential technologies. Conclusion The implementation of machine learning technologies will require buy-in from the NHS health boards, with significant implications with cost of investment, implementation, employment of machine learning teams and provision of training to medical professionals.

Kardiologiia ◽  
2020 ◽  
Vol 60 (10) ◽  
pp. 38-46
Author(s):  
B. I. Geltser ◽  
K. J. Shahgeldyan ◽  
V. Y. Rublev ◽  
V. N. Kotelnikov ◽  
A. B. Krieger ◽  
...  

Aim      To compare the accuracy of predicting an in-hospital fatal outcome for models based on current machine-learning technologies in patients with ischemic heart disease (IHD) after coronary bypass (CB) surgery.Material and methods  A retrospective analysis of 866 electronic medical records was performed for patients (685 men and 181 women) who have had a CB surgery for IHD in 2008–2018. Results of clinical, laboratory, and instrumental evaluations obtained prior to the CB surgery were analyzed. Patients were divided into two groups: group 1 included 35 (4 %) patients who died within the first 20 days of CB, and group 2 consisted of 831 (96 %) patients with a beneficial outcome of the surgery. Predictors of the in-hospital fatal outcome were identified by a multistep selection procedure with analysis of statistical hypotheses and calculation of weight coefficients. For construction of models and verification of predictors, machine-learning methods were used, including the multifactorial logistic regression (LR), random forest (RF), and artificial neural networks (ANN). Model accuracy was evaluated by three metrics: area under the ROC curve (AUC), sensitivity, and specificity. Cross validation of the models was performed on test samples, and the control validation was performed on a cohort of patients with IHD after CB, whose data were not used in development of the models.Results The following 7 risk factors for in-hospital fatal outcome with the greatest predictive potential were isolated from the EuroSCORE II scale: ejection fraction (EF) <30 %, EF 30-50 %, age of patients with recent MI, damage of peripheral arterial circulation, urgency of CB, functional class III-IV chronic heart failure, and 5 additional predictors, including heart rate, systolic blood pressure, presence of aortic stenosis, posterior left ventricular (LV) wall relative thickness index (RTI), and LV relative mass index (LVRMI). The models developed by the authors using LR, RF and ANN methods had higher AUC values and sensitivity compared to the classical EuroSCORE II scale. The ANN models including the RTI and LVRMI predictors demonstrated a maximum level of prognostic accuracy, which was illustrated by values of the quality metrics, AUC 93 %, sensitivity 90 %, and specificity 96 %. The predictive robustness of the models was confirmed by results of the control validation.Conclusion      The use of current machine-learning technologies allowed developing a novel algorithm for selection of predictors and highly accurate models for predicting an in-hospital fatal outcome after CB. 


2020 ◽  
Vol 37 (2) ◽  
pp. 60-68
Author(s):  
Denise Carter

Artificial intelligence (AI) and machine learning (ML) technologies are rapidly maturing and proliferating through all public and private sectors. The potential for these technologies to do good and to help us in our everyday lives is immense. But there is a risk that unless managed and controlled AI can also cause us harm. Questions about regulation, what form it takes and who is responsible for governance are only just beginning to be answered. In May 2019, 42 countries came together to support a global governance framework for AI. The Organisation for Economic Co-operation and Development (OECD) Principles on Artificial Intelligence (OECD (2019) OECD principles on AI. Available at: https://www.oecd.org/going-digital/ai/principles/ (accessed 2 March 2020)) saw like-minded democracies of the world commit to common AI values of trust and respect. In Europe, the European Commission’s (EC) new president, Ursula von der Leyen has made calls for a General Data Protection Regulation style. As a first step the EC has published a white paper: ‘On Artificial Intelligence – A European Approach to Excellence and Trust’ (European Commission (2020) Report, Europa, February). In February 2020, the UK government has published a report on ‘Artificial Intelligence in the Public Sector’ (The Committee on Standards in Public Life (2020) Artificial intelligence and public standards. Report, UK Government, February). This article discusses some of the potential threats AI may hold if left unregulated. It provides a brief overview of the regulatory activities for AI worldwide, and in more detail the current UK AI regulatory landscape. Finally, the article looks at the role that the information professional might play in AI and ML.


Author(s):  
I.V. Dorovskih ◽  
O.V. Senko ◽  
V.Ya. Chuchupal ◽  
A.A. Dokukin ◽  
A.V. Kuznetsova

The purpose of this study was to investigate the possibility to use electroencephalography for early diagnostics of dementia and for objective assessment of disease severity and neurometabolic treatment results. The study was based on application of machine learning methods for computer diagnosis of dementia by the energy spectra of EEG signals. Effectiveness of various machine learning technologies was investigated in order to separate different groups of patients with varying severity of dementia from healthy ones and patients with pre-dementia disorders according to the vectors of spectral indicators. Applying of cross-validation procedure showed that separation of the group with dementia from the group of people with normal physiological aging and groups of young people reaches 0.783 and 0.786, respectively by parameter ROC AUC. The results of the study allow to make an assumption, that the algorithmic assessment of dementia severity by EEG corresponds to the actual course of the disease. So, the number of cases with algorithmically identified positive dynamics significantly exceeds the number of cases with algorithmically detected negative dynamics after neurometabolic therapy in the group with mild dementia. In a combined group with both average and heavy severity of the disease such an increase was not observed.


2019 ◽  
Vol 25 (5) ◽  
pp. 716-742 ◽  
Author(s):  
Gang Kou ◽  
Xiangrui Chao ◽  
Yi Peng ◽  
Fawaz E. Alsaadi ◽  
Enrique Herrera-Viedma

Financial systemic risk is an important issue in economics and financial systems. Trying to detect and respond to systemic risk with growing amounts of data produced in financial markets and systems, a lot of researchers have increasingly employed machine learning methods. Machine learning methods study the mechanisms of outbreak and contagion of systemic risk in the financial network and improve the current regulation of the financial market and industry. In this paper, we survey existing researches and methodologies on assessment and measurement of financial systemic risk combined with machine learning technologies, including big data analysis, network analysis and sentiment analysis, etc. In addition, we identify future challenges, and suggest further research topics. The main purpose of this paper is to introduce current researches on financial systemic risk with machine learning methods and to propose directions for future work.


Cells ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 3169
Author(s):  
Ning Zhang ◽  
Yameng Wu ◽  
Yu Guo ◽  
Yu Sa ◽  
Qifeng Li ◽  
...  

In the field of gliomas research, the broad availability of genetic and image information originated by computer technologies and the booming of biomedical publications has led to the advent of the big-data era. Machine learning methods were applied as possible approaches to speed up the data mining processes. In this article, we reviewed the present situation and future orientations of machine learning application in gliomas within the context of workflows to integrate analysis for precision cancer care. Publicly available tools or algorithms for key machine learning technologies in the literature mining for glioma clinical research were reviewed and compared. Further, the existing solutions of machine learning methods and their limitations in glioma prediction and diagnostics, such as overfitting and class imbalanced, were critically analyzed.


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