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Nature Reviews. Molecular Cell Biology Jan 2022The expanding scale and inherent complexity of biological data have encouraged a growing use of machine learning in biology to build informative and predictive models of... (Review)
Review
The expanding scale and inherent complexity of biological data have encouraged a growing use of machine learning in biology to build informative and predictive models of the underlying biological processes. All machine learning techniques fit models to data; however, the specific methods are quite varied and can at first glance seem bewildering. In this Review, we aim to provide readers with a gentle introduction to a few key machine learning techniques, including the most recently developed and widely used techniques involving deep neural networks. We describe how different techniques may be suited to specific types of biological data, and also discuss some best practices and points to consider when one is embarking on experiments involving machine learning. Some emerging directions in machine learning methodology are also discussed.
Topics: Animals; Biology; Deep Learning; Humans; Machine Learning; Neural Networks, Computer
PubMed: 34518686
DOI: 10.1038/s41580-021-00407-0 -
Circulation Nov 2015Spurred by advances in processing power, memory, storage, and an unprecedented wealth of data, computers are being asked to tackle increasingly complex learning tasks,... (Review)
Review
Spurred by advances in processing power, memory, storage, and an unprecedented wealth of data, computers are being asked to tackle increasingly complex learning tasks, often with astonishing success. Computers have now mastered a popular variant of poker, learned the laws of physics from experimental data, and become experts in video games - tasks that would have been deemed impossible not too long ago. In parallel, the number of companies centered on applying complex data analysis to varying industries has exploded, and it is thus unsurprising that some analytic companies are turning attention to problems in health care. The purpose of this review is to explore what problems in medicine might benefit from such learning approaches and use examples from the literature to introduce basic concepts in machine learning. It is important to note that seemingly large enough medical data sets and adequate learning algorithms have been available for many decades, and yet, although there are thousands of papers applying machine learning algorithms to medical data, very few have contributed meaningfully to clinical care. This lack of impact stands in stark contrast to the enormous relevance of machine learning to many other industries. Thus, part of my effort will be to identify what obstacles there may be to changing the practice of medicine through statistical learning approaches, and discuss how these might be overcome.
Topics: Algorithms; Humans; Machine Learning; Medicine
PubMed: 26572668
DOI: 10.1161/CIRCULATIONAHA.115.001593 -
Translational Vision Science &... Feb 2020To present an overview of current machine learning methods and their use in medical research, focusing on select machine learning techniques, best practices, and deep...
PURPOSE
To present an overview of current machine learning methods and their use in medical research, focusing on select machine learning techniques, best practices, and deep learning.
METHODS
A systematic literature search in PubMed was performed for articles pertinent to the topic of artificial intelligence methods used in medicine with an emphasis on ophthalmology.
RESULTS
A review of machine learning and deep learning methodology for the audience without an extensive technical computer programming background.
CONCLUSIONS
Artificial intelligence has a promising future in medicine; however, many challenges remain.
TRANSLATIONAL RELEVANCE
The aim of this review article is to provide the nontechnical readers a layman's explanation of the machine learning methods being used in medicine today. The goal is to provide the reader a better understanding of the potential and challenges of artificial intelligence within the field of medicine.
Topics: Artificial Intelligence; Deep Learning; Machine Learning; Neural Networks, Computer; Ophthalmology
PubMed: 32704420
DOI: 10.1167/tvst.9.2.14 -
Journal of Internal Medicine Dec 2018Machine learning (ML) is a burgeoning field of medicine with huge resources being applied to fuse computer science and statistics to medical problems. Proponents of ML... (Review)
Review
Machine learning (ML) is a burgeoning field of medicine with huge resources being applied to fuse computer science and statistics to medical problems. Proponents of ML extol its ability to deal with large, complex and disparate data, often found within medicine and feel that ML is the future for biomedical research, personalized medicine, computer-aided diagnosis to significantly advance global health care. However, the concepts of ML are unfamiliar to many medical professionals and there is untapped potential in the use of ML as a research tool. In this article, we provide an overview of the theory behind ML, explore the common ML algorithms used in medicine including their pitfalls and discuss the potential future of ML in medicine.
Topics: Algorithms; Decision Support Systems, Clinical; Forecasting; Humans; Machine Learning; Medicine; Precision Medicine; Supervised Machine Learning; Unsupervised Machine Learning
PubMed: 30102808
DOI: 10.1111/joim.12822 -
Behavior Therapy Sep 2020Machine learning is increasingly used in mental health research and has the potential to advance our understanding of how to characterize, predict, and treat mental... (Review)
Review
Machine learning is increasingly used in mental health research and has the potential to advance our understanding of how to characterize, predict, and treat mental disorders and associated adverse health outcomes (e.g., suicidal behavior). Machine learning offers new tools to overcome challenges for which traditional statistical methods are not well-suited. This paper provides an overview of machine learning with a specific focus on supervised learning (i.e., methods that are designed to predict or classify an outcome of interest). Several common supervised learning methods are described, along with applied examples from the published literature. We also provide an overview of supervised learning model building, validation, and performance evaluation. Finally, challenges in creating robust and generalizable machine learning algorithms are discussed.
Topics: Algorithms; Humans; Machine Learning; Supervised Machine Learning
PubMed: 32800297
DOI: 10.1016/j.beth.2020.05.002 -
Minerva Cardiology and Angiology Feb 2022This paper reviews recent cardiology literature and reports how artificial intelligence tools (specifically, machine learning techniques) are being used by physicians in... (Review)
Review
This paper reviews recent cardiology literature and reports how artificial intelligence tools (specifically, machine learning techniques) are being used by physicians in the field. Each technique is introduced with enough details to allow the understanding of how it works and its intent, but without delving into details that do not add immediate benefits and require expertise in the field. We specifically focus on the principal Machine learning based risk scores used in cardiovascular research. After introducing them and summarizing their assumptions and biases, we discuss their merits and shortcomings. We report on how frequently they are adopted in the field and suggest why this is the case based on our expertise in machine learning. We complete the analysis by reviewing how corresponding statistical approaches compare with them. Finally, we discuss the main open issues in applying machine learning tools to cardiology tasks, also drafting possible future directions. Despite the growing interest in these tools, we argue that there are many still underutilized techniques: while neural networks are slowly being incorporated in cardiovascular research, other important techniques such as semi-supervised learning and federated learning are still underutilized. The former would allow practitioners to harness the information contained in large datasets that are only partially labeled, while the latter would foster collaboration between institutions allowing building larger and better models.
Topics: Artificial Intelligence; Cardiology; Machine Learning; Neural Networks, Computer; Supervised Machine Learning
PubMed: 34338485
DOI: 10.23736/S2724-5683.21.05709-4 -
Current Hypertension Reports Nov 2022To provide an overview of the literature regarding the use of machine learning algorithms to predict hypertension. A systematic review was performed to select recent... (Review)
Review
PURPOSE OF REVIEW
To provide an overview of the literature regarding the use of machine learning algorithms to predict hypertension. A systematic review was performed to select recent articles on the subject.
RECENT FINDINGS
The screening of the articles was conducted using a machine learning algorithm (ASReview). A total of 21 articles published between January 2018 and May 2021 were identified and compared according to variable selection, train-test split, data balancing, outcome definition, final algorithm, and performance metrics. Overall, the articles achieved an area under the ROC curve (AUROC) between 0.766 and 1.00. The algorithms most frequently identified as having the best performance were support vector machines (SVM), extreme gradient boosting (XGBoost), and random forest. Machine learning algorithms are a promising tool to improve preventive clinical decisions and targeted public health policies for hypertension. However, technical factors such as outcome definition, availability of the final code, predictive performance, explainability, and data leakage need to be consistently and critically evaluated.
Topics: Algorithms; Area Under Curve; Humans; Hypertension; Machine Learning; Support Vector Machine
PubMed: 35731335
DOI: 10.1007/s11906-022-01212-6 -
Nature Biomedical Engineering Dec 2022The development of medical applications of machine learning has required manual annotation of data, often by medical experts. Yet, the availability of large-scale... (Review)
Review
The development of medical applications of machine learning has required manual annotation of data, often by medical experts. Yet, the availability of large-scale unannotated data provides opportunities for the development of better machine-learning models. In this Review, we highlight self-supervised methods and models for use in medicine and healthcare, and discuss the advantages and limitations of their application to tasks involving electronic health records and datasets of medical images, bioelectrical signals, and sequences and structures of genes and proteins. We also discuss promising applications of self-supervised learning for the development of models leveraging multimodal datasets, and the challenges in collecting unbiased data for their training. Self-supervised learning may accelerate the development of medical artificial intelligence.
Topics: Artificial Intelligence; Machine Learning; Medicine; Supervised Machine Learning; Delivery of Health Care
PubMed: 35953649
DOI: 10.1038/s41551-022-00914-1 -
Computers in Biology and Medicine Feb 2021Rapid diagnosing is crucial for controlling malaria. Various studies have aimed at developing machine learning models to diagnose malaria using blood smear images;...
BACKGROUND
Rapid diagnosing is crucial for controlling malaria. Various studies have aimed at developing machine learning models to diagnose malaria using blood smear images; however, this approach has many limitations. This study developed a machine learning model for malaria diagnosis using patient information.
METHODS
To construct datasets, we extracted patient information from the PubMed abstracts from 1956 to 2019. We used two datasets: a solely parasitic disease dataset and total dataset by adding information about other diseases. We compared six machine learning models: support vector machine, random forest (RF), multilayered perceptron, AdaBoost, gradient boosting (GB), and CatBoost. In addition, a synthetic minority oversampling technique (SMOTE) was employed to address the data imbalance problem.
RESULTS
Concerning the solely parasitic disease dataset, RF was found to be the best model regardless of using SMOTE. Concerning the total dataset, GB was found to be the best. However, after applying SMOTE, RF performed the best. Considering the imbalanced data, nationality was found to be the most important feature in malaria prediction. In case of the balanced data with SMOTE, the most important feature was symptom.
CONCLUSIONS
The results demonstrated that machine learning techniques can be successfully applied to predict malaria using patient information.
Topics: Humans; Machine Learning; Malaria; Neural Networks, Computer; Support Vector Machine
PubMed: 33290932
DOI: 10.1016/j.compbiomed.2020.104151 -
IEEE Reviews in Biomedical Engineering 2021Electroencephalography (EEG) has been a staple method for identifying certain health conditions in patients since its discovery. Due to the many different types of... (Review)
Review
Electroencephalography (EEG) has been a staple method for identifying certain health conditions in patients since its discovery. Due to the many different types of classifiers available to use, the analysis methods are also equally numerous. In this review, we will be examining specifically machine learning methods that have been developed for EEG analysis with bioengineering applications. We reviewed literature from 1988 to 2018 to capture previous and current classification methods for EEG in multiple applications. From this information, we are able to determine the overall effectiveness of each machine learning method as well as the key characteristics. We have found that all the primary methods used in machine learning have been applied in some form in EEG classification. This ranges from Naive-Bayes to Decision Tree/Random Forest, to Support Vector Machine (SVM). Supervised learning methods are on average of higher accuracy than their unsupervised counterparts. This includes SVM and KNN. While each of the methods individually is limited in their accuracy in their respective applications, there is hope that the combination of methods when implemented properly has a higher overall classification accuracy. This paper provides a comprehensive overview of Machine Learning applications used in EEG analysis. It also gives an overview of each of the methods and general applications that each is best suited to.
Topics: Algorithms; Electroencephalography; Humans; Machine Learning; Signal Processing, Computer-Assisted; Support Vector Machine
PubMed: 32011262
DOI: 10.1109/RBME.2020.2969915