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Anesthesiology Clinics Sep 2021With the tremendous volume of data captured during surgeries and procedures, critical care, and pain management, the field of anesthesiology is uniquely suited for the... (Review)
Review
With the tremendous volume of data captured during surgeries and procedures, critical care, and pain management, the field of anesthesiology is uniquely suited for the application of machine learning, neural networks, and closed loop technologies. In the past several years, this area has expanded immensely in both interest and clinical applications. This article provides an overview of the basic tenets of machine learning, neural networks, and closed loop devices, with emphasis on the clinical applications of these technologies.
Topics: Anesthesia; Anesthesiology; Artificial Intelligence; Deep Learning; Humans; Machine Learning
PubMed: 34392886
DOI: 10.1016/j.anclin.2021.03.012 -
Journal of Nuclear Medicine : Official... Sep 2019The aim of this review is to provide readers with an update on the state of the art, pitfalls, solutions for those pitfalls, future perspectives, and challenges in the... (Review)
Review
The aim of this review is to provide readers with an update on the state of the art, pitfalls, solutions for those pitfalls, future perspectives, and challenges in the quickly evolving field of radiomics in nuclear medicine imaging and associated oncology applications. The main pitfalls were identified in study design, data acquisition, segmentation, feature calculation, and modeling; however, in most cases, potential solutions are available and existing recommendations should be followed to improve the overall quality and reproducibility of published radiomics studies. The techniques from the field of deep learning have some potential to provide solutions, especially in terms of automation. Some important challenges remain to be addressed but, overall, striking advances have been made in the field in the last 5 y.
Topics: Deep Learning; Diagnostic Imaging; Humans; Image Interpretation, Computer-Assisted; Image Processing, Computer-Assisted; Machine Learning; Nuclear Medicine; Positron-Emission Tomography
PubMed: 31481588
DOI: 10.2967/jnumed.118.220582 -
Academic Radiology Jan 2020Artificial intelligence and deep learning are areas of high interest for radiology investigators at present. However, the field of machine learning encompasses multiple... (Review)
Review
Artificial intelligence and deep learning are areas of high interest for radiology investigators at present. However, the field of machine learning encompasses multiple statistics-based techniques useful for investigators, which may be complementary to deep learning approaches. After a refresher in basic statistical concepts, relevant considerations for machine learning practitioners are reviewed: regression, classification, decision boundaries, and bias-variance tradeoff. Regularization, ground truth, and populations are discussed along with compute and data management principles. Advanced statistical machine learning techniques including bootstrapping, bagging, boosting, decision trees, random forest, XGboost, and support vector machines are reviewed along with relevant examples from the radiology literature.
Topics: Algorithms; Artificial Intelligence; Machine Learning; Radiology; Support Vector Machine
PubMed: 31818379
DOI: 10.1016/j.acra.2019.07.030 -
Experimental Biology and Medicine... Nov 2023The ever-increasing number of chemicals has raised public concerns due to their adverse effects on human health and the environment. To protect public health and the... (Review)
Review
The ever-increasing number of chemicals has raised public concerns due to their adverse effects on human health and the environment. To protect public health and the environment, it is critical to assess the toxicity of these chemicals. Traditional and toxicity assays are complicated, costly, and time-consuming and may face ethical issues. These constraints raise the need for alternative methods for assessing the toxicity of chemicals. Recently, due to the advancement of machine learning algorithms and the increase in computational power, many toxicity prediction models have been developed using various machine learning and deep learning algorithms such as support vector machine, random forest, -nearest neighbors, ensemble learning, and deep neural network. This review summarizes the machine learning- and deep learning-based toxicity prediction models developed in recent years. Support vector machine and random forest are the most popular machine learning algorithms, and hepatotoxicity, cardiotoxicity, and carcinogenicity are the frequently modeled toxicity endpoints in predictive toxicology. It is known that datasets impact model performance. The quality of datasets used in the development of toxicity prediction models using machine learning and deep learning is vital to the performance of the developed models. The different toxicity assignments for the same chemicals among different datasets of the same type of toxicity have been observed, indicating benchmarking datasets is needed for developing reliable toxicity prediction models using machine learning and deep learning algorithms. This review provides insights into current machine learning models in predictive toxicology, which are expected to promote the development and application of toxicity prediction models in the future.
Topics: Humans; Deep Learning; Machine Learning; Neural Networks, Computer; Algorithms; Drug-Related Side Effects and Adverse Reactions
PubMed: 38057999
DOI: 10.1177/15353702231209421 -
Sensors (Basel, Switzerland) Jun 2021Embedded systems technology is undergoing a phase of transformation owing to the novel advancements in computer architecture and the breakthroughs in machine learning... (Review)
Review
Embedded systems technology is undergoing a phase of transformation owing to the novel advancements in computer architecture and the breakthroughs in machine learning applications. The areas of applications of embedded machine learning (EML) include accurate computer vision schemes, reliable speech recognition, innovative healthcare, robotics, and more. However, there exists a critical drawback in the efficient implementation of ML algorithms targeting embedded applications. Machine learning algorithms are generally computationally and memory intensive, making them unsuitable for resource-constrained environments such as embedded and mobile devices. In order to efficiently implement these compute and memory-intensive algorithms within the embedded and mobile computing space, innovative optimization techniques are required at the algorithm and hardware levels. To this end, this survey aims at exploring current research trends within this circumference. First, we present a brief overview of compute intensive machine learning algorithms such as hidden Markov models (HMM), -nearest neighbors (-NNs), support vector machines (SVMs), Gaussian mixture models (GMMs), and deep neural networks (DNNs). Furthermore, we consider different optimization techniques currently adopted to squeeze these computational and memory-intensive algorithms within resource-limited embedded and mobile environments. Additionally, we discuss the implementation of these algorithms in microcontroller units, mobile devices, and hardware accelerators. Conclusively, we give a comprehensive overview of key application areas of EML technology, point out key research directions and highlight key take-away lessons for future research exploration in the embedded machine learning domain.
Topics: Algorithms; Computers, Handheld; Machine Learning; Neural Networks, Computer; Support Vector Machine
PubMed: 34203119
DOI: 10.3390/s21134412 -
International Journal of Molecular... Jun 2022In recent years, deep learning has emerged as a highly active research field, achieving great success in various machine learning areas, including image processing,...
In recent years, deep learning has emerged as a highly active research field, achieving great success in various machine learning areas, including image processing, speech recognition, and natural language processing, and now rapidly becoming a dominant tool in biomedicine [...].
Topics: Computational Biology; Deep Learning; Image Processing, Computer-Assisted; Machine Learning; Natural Language Processing
PubMed: 35743052
DOI: 10.3390/ijms23126610 -
Knee Surgery, Sports Traumatology,... Apr 2023Supervised learning is the most common form of machine learning utilized in medical research. It is used to predict outcomes of interest or classify positive and/or... (Review)
Review
Supervised learning is the most common form of machine learning utilized in medical research. It is used to predict outcomes of interest or classify positive and/or negative cases with a known ground truth. Supervised learning describes a spectrum of techniques, ranging from traditional regression modeling to more complex tree boosting, which are becoming increasingly prevalent as the focus on "big data" develops. While these tools are becoming increasingly popular and powerful, there is a paucity of literature available that describe the strengths and limitations of these different modeling techniques. Typically, there is no formal training for health care professionals in the use of machine learning models. As machine learning applications throughout medicine increase, it is important that physicians and other health care professionals better understand the processes underlying application of these techniques. The purpose of this study is to provide an overview of commonly used supervised learning techniques with recent case examples within the orthopedic literature. An additional goal is to address disparities in the understanding of these methods to improve communication within and between research teams.
Topics: Humans; Supervised Machine Learning; Algorithms; Machine Learning; Orthopedic Procedures
PubMed: 36222893
DOI: 10.1007/s00167-022-07181-2 -
Methods in Molecular Biology (Clifton,... 2022Machine learning (ML) already accelerates discoveries in many scientific fields and is the driver behind several new products. Recently, growing sample sizes enabled the...
Machine learning (ML) already accelerates discoveries in many scientific fields and is the driver behind several new products. Recently, growing sample sizes enabled the use of ML approaches in larger omics studies. This work provides a guide through a typical analysis of an omics dataset using ML. As an example, this chapter demonstrates how to build a model predicting Drug-Induced Liver Injury based on transcriptomics data contained in the LINCS L1000 dataset. Each section covers best practices and pitfalls starting from data exploration and model training including hyperparameter search to validation and analysis of the final model. The code to reproduce the results is available at https://github.com/Evotec-Bioinformatics/ml-from-omics .
Topics: Chemical and Drug Induced Liver Injury; Humans; Machine Learning; Support Vector Machine
PubMed: 34731480
DOI: 10.1007/978-1-0716-1787-8_18 -
Behavioral Sciences & the Law May 2019For decades, our ability to predict suicide has remained at near-chance levels. Machine learning has recently emerged as a promising tool for advancing suicide science,... (Review)
Review
For decades, our ability to predict suicide has remained at near-chance levels. Machine learning has recently emerged as a promising tool for advancing suicide science, particularly in the domain of suicide prediction. The present review provides an introduction to machine learning and its potential application to open questions in suicide research. Although only a few studies have implemented machine learning for suicide prediction, results to date indicate considerable improvement in accuracy and positive predictive value. Potential barriers to algorithm integration into clinical practice are discussed, as well as attendant ethical issues. Overall, machine learning approaches hold promise for accurate, scalable, and effective suicide risk detection; however, many critical questions and issues remain unexplored.
Topics: Algorithms; Cluster Analysis; Decision Support Techniques; Ethics, Medical; Humans; Longitudinal Studies; Machine Learning; Probability; Research; Risk Assessment; Suicide; Unsupervised Machine Learning; Suicide Prevention
PubMed: 30609102
DOI: 10.1002/bsl.2392 -
Neuroradiology Dec 2021Artificial intelligence (AI) is playing an ever-increasing role in Neuroradiology. (Review)
Review
PURPOSE
Artificial intelligence (AI) is playing an ever-increasing role in Neuroradiology.
METHODS
When designing AI-based research in neuroradiology and appreciating the literature, it is important to understand the fundamental principles of AI. Training, validation, and test datasets must be defined and set apart as priorities. External validation and testing datasets are preferable, when feasible. The specific type of learning process (supervised vs. unsupervised) and the machine learning model also require definition. Deep learning (DL) is an AI-based approach that is modelled on the structure of neurons of the brain; convolutional neural networks (CNN) are a commonly used example in neuroradiology.
RESULTS
Radiomics is a frequently used approach in which a multitude of imaging features are extracted from a region of interest and subsequently reduced and selected to convey diagnostic or prognostic information. Deep radiomics uses CNNs to directly extract features and obviate the need for predefined features.
CONCLUSION
Common limitations and pitfalls in AI-based research in neuroradiology are limited sample sizes ("small-n-large-p problem"), selection bias, as well as overfitting and underfitting.
Topics: Artificial Intelligence; Deep Learning; Humans; Machine Learning; Neural Networks, Computer; Prognosis
PubMed: 34537858
DOI: 10.1007/s00234-021-02813-9