scholarly journals Applications and limitations of machine learning in radiation oncology

2019 ◽  
Vol 92 (1100) ◽  
pp. 20190001 ◽  
Author(s):  
Daniel Jarrett ◽  
Eleanor Stride ◽  
Katherine Vallis ◽  
Mark J. Gooding
Keyword(s):  
Machine Learning ◽  
Radiation Oncology ◽  
Learning Algorithms ◽  
Significant Risk ◽  
Ground Truth ◽  
Multimodal Fusion ◽  
External Beam Radiation ◽  
Learning Approaches ◽  
Beam Radiation

Machine learning approaches to problem-solving are growing rapidly within healthcare, and radiation oncology is no exception. With the burgeoning interest in machine learning comes the significant risk of misaligned expectations as to what it can and cannot accomplish. This paper evaluates the role of machine learning and the problems it solves within the context of current clinical challenges in radiation oncology. The role of learning algorithms within the workflow for external beam radiation therapy are surveyed, considering simulation imaging, multimodal fusion, image segmentation, treatment planning, quality assurance, and treatment delivery and adaptation. For each aspect, the clinical challenges faced, the learning algorithms proposed, and the successes and limitations of various approaches are analyzed. It is observed that machine learning has largely thrived on reproducibly mimicking conventional human-driven solutions with more efficiency and consistency. On the other hand, since algorithms are generally trained using expert opinion as ground truth, machine learning is of limited utility where problems or ground truths are not well-defined, or if suitable measures of correctness are not available. As a result, machines may excel at replicating, automating and standardizing human behaviour on manual chores, meanwhile the conceptual clinical challenges relating to definition, evaluation, and judgement remain in the realm of human intelligence and insight.

Role of machine learning algorithms over heart diseases prediction

2020 ◽  
Author(s):  
Siva Kumar Jonnavithula ◽  
Abhilash Kumar Jha ◽  
Modepalli Kavitha ◽  
Singaraju Srinivasulu
Keyword(s):  
Machine Learning ◽  
Heart Diseases ◽  
Learning Algorithms ◽  
Machine Learning Algorithms

scholarly journals Searching for improvements in predicting human eye colour from DNA

2021 ◽  
Author(s):  
Magdalena Kukla-Bartoszek ◽  
Paweł Teisseyre ◽  
Ewelina Pośpiech ◽  
Joanna Karłowska-Pik ◽  
Piotr Zieliński ◽  
...  
Keyword(s):  
Machine Learning ◽  
Regression Models ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Sequencing Analysis ◽  
Learning Approaches ◽  
Human Eye ◽  
Software Analysis ◽  
Whole Exome ◽  
Eye Colour

AbstractIncreasing understanding of human genome variability allows for better use of the predictive potential of DNA. An obvious direct application is the prediction of the physical phenotypes. Significant success has been achieved, especially in predicting pigmentation characteristics, but the inference of some phenotypes is still challenging. In search of further improvements in predicting human eye colour, we conducted whole-exome (enriched in regulome) sequencing of 150 Polish samples to discover new markers. For this, we adopted quantitative characterization of eye colour phenotypes using high-resolution photographic images of the iris in combination with DIAT software analysis. An independent set of 849 samples was used for subsequent predictive modelling. Newly identified candidates and 114 additional literature-based selected SNPs, previously associated with pigmentation, and advanced machine learning algorithms were used. Whole-exome sequencing analysis found 27 previously unreported candidate SNP markers for eye colour. The highest overall prediction accuracies were achieved with LASSO-regularized and BIC-based selected regression models. A new candidate variant, rs2253104, located in the ARFIP2 gene and identified with the HyperLasso method, revealed predictive potential and was included in the best-performing regression models. Advanced machine learning approaches showed a significant increase in sensitivity of intermediate eye colour prediction (up to 39%) compared to 0% obtained for the original IrisPlex model. We identified a new potential predictor of eye colour and evaluated several widely used advanced machine learning algorithms in predictive analysis of this trait. Our results provide useful hints for developing future predictive models for eye colour in forensic and anthropological studies.

scholarly journals Assessing biases, relaxing moralism: On ground-truthing practices in machine learning design and application

2021 ◽  
Vol 8 (1) ◽  
pp. 205395172110135
Author(s):  
Florian Jaton
Keyword(s):  
Machine Learning ◽  
William James ◽  
A Priori ◽  
Learning Algorithms ◽  
Three Dimensional ◽  
Ground Truth ◽  
Machine Learning Algorithms ◽  
Ground Truthing ◽  
Set Up ◽  
The Moment

This theoretical paper considers the morality of machine learning algorithms and systems in the light of the biases that ground their correctness. It begins by presenting biases not as a priori negative entities but as contingent external referents—often gathered in benchmarked repositories called ground-truth datasets—that define what needs to be learned and allow for performance measures. I then argue that ground-truth datasets and their concomitant practices—that fundamentally involve establishing biases to enable learning procedures—can be described by their respective morality, here defined as the more or less accounted experience of hesitation when faced with what pragmatist philosopher William James called “genuine options”—that is, choices to be made in the heat of the moment that engage different possible futures. I then stress three constitutive dimensions of this pragmatist morality, as far as ground-truthing practices are concerned: (I) the definition of the problem to be solved (problematization), (II) the identification of the data to be collected and set up (databasing), and (III) the qualification of the targets to be learned (labeling). I finally suggest that this three-dimensional conceptual space can be used to map machine learning algorithmic projects in terms of the morality of their respective and constitutive ground-truthing practices. Such techno-moral graphs may, in turn, serve as equipment for greater governance of machine learning algorithms and systems.

scholarly journals An analytical survey on the role of machine learning algorithms in case of intrusion detection

2020 ◽  
Vol 5 (19) ◽  
pp. 32-35
Author(s):  
Anand Vijay ◽  
Kailash Patidar ◽  
Manoj Yadav ◽  
Rishi Kushwah
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Handling Mechanism ◽  
The Impact

In this paper an analytical survey on the role of machine learning algorithms in case of intrusion detection has been presented and discussed. This paper shows the analytical aspects in the development of efficient intrusion detection system (IDS). The related study for the development of this system has been presented in terms of computational methods. The discussed methods are data mining, artificial intelligence and machine learning. It has been discussed along with the attack parameters and attack types. This paper also elaborates the impact of different attack and handling mechanism based on the previous papers.

Role of Machine Learning Approaches in Remaining Useful Prediction: A Review

2021 ◽  
pp. 361-370
Author(s):  
Kalaiarasan Sekar ◽  
Shahani Aman Shah ◽  
A. Antony Athithan ◽  
A. Mukil
Keyword(s):  
Machine Learning ◽  
Learning Approaches

Radiation Therapy

2019 ◽  
pp. 734-736
Author(s):  
Sameer R. Keole
Keyword(s):  
Radiation Therapy ◽  
Ionizing Radiation ◽  
Radiation Oncology ◽  
External Beam Radiation Therapy ◽  
External Beam Radiation ◽  
External Beam ◽  
Beam Radiation ◽  
Radiation Fields ◽  
Diagnostic Radiation ◽  
Oncology Treatment

Radiation oncology is the specialty of medicine in which ionizing radiation is used to treat both malignant and benign conditions. The term radiation therapy (RT) is used, in part, as a differentiator from diagnostic radiation. In radiation oncology, treatment is provided with a team-based approach by physicians, nurses, physicists, dosimetrists, and radiation therapists. Dosimetrists perform the initial planning and mapping of the radiation fields. Radiation therapists deliver the treatment with external beam radiation therapy machines.

The Model Selection Curse

2019 ◽  
Vol 1 (2) ◽  
pp. 127-140 ◽  
Author(s):  
Kfir Eliaz ◽  
Ran Spiegler
Keyword(s):  
Machine Learning ◽  
Model Selection ◽  
Learning Algorithms ◽  
Personal Data ◽  
Estimation Procedure ◽  
Machine Learning Algorithms ◽  
Human Interaction ◽  
Truth Telling ◽  
Agent Based

A statistician takes an action on behalf of an agent, based on the agent’s self-reported personal data and a sample involving other people. The action that he takes is an estimated function of the agent’s report. The estimation procedure involves model selection. We ask the following question: Is truth-telling optimal for the agent given the statistician’s procedure? We analyze this question in the context of a simple example that highlights the role of model selection. We suggest that our simple exercise may have implications for the broader issue of human interaction with machine learning algorithms. (JEL C52)

Sign in / Sign up

Export Citation Format

Share Document