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International Journal of Molecular... Sep 2022Recent technological innovations in the field of mass spectrometry have supported the use of metabolomics analysis for precision medicine. This growth has been allowed... (Review)
Review
Recent technological innovations in the field of mass spectrometry have supported the use of metabolomics analysis for precision medicine. This growth has been allowed also by the application of algorithms to data analysis, including multivariate and machine learning methods, which are fundamental to managing large number of variables and samples. In the present review, we reported and discussed the application of artificial intelligence (AI) strategies for metabolomics data analysis. Particularly, we focused on widely used non-linear machine learning classifiers, such as ANN, random forest, and support vector machine (SVM) algorithms. A discussion of recent studies and research focused on disease classification, biomarker identification and early diagnosis is presented. Challenges in the implementation of metabolomics-AI systems, limitations thereof and recent tools were also discussed.
Topics: Algorithms; Artificial Intelligence; Machine Learning; Precision Medicine; Support Vector Machine
PubMed: 36232571
DOI: 10.3390/ijms231911269 -
Scientific Reports Aug 2022Some machine learning applications do not allow for data augmentation or are applied to modalities where the augmentation is difficult to define. Our study aimed to...
Some machine learning applications do not allow for data augmentation or are applied to modalities where the augmentation is difficult to define. Our study aimed to develop a new method in semi-supervised learning (SSL) applicable to various modalities of data (images, sound, text), especially when augmentation is hard or impossible to define, i.e., medical images. Assuming that all samples, labeled and unlabeled, come from the same data distribution, we can say that labeled and unlabeled data sets used in the semi-supervised learning tasks are similar. Based on this observation, the data embeddings created by the classifier should also be similar for both sets. In our method, finding these embeddings is achieved based on two models-classifier and an auxiliary discriminator model, inspired by the Generative Adversarial Network (GAN) learning process. The classifier is trained to build embeddings for labeled and unlabeled datasets to cheat discriminator, which recognizes whether the embedding comes from a labeled or unlabeled dataset. The method was named the DGSSC from Discriminator Guided Semi-Supervised Classifier. The experimental research aimed evaluation of the proposed method on the classification task in combination with the teacher-student approach and comparison with other SSL methods. In most experiments, training the networks with the DGSSC method improves accuracy with the teacher-student approach. It does not deteriorate the accuracy of any experiment.
Topics: Algorithms; Humans; Machine Learning; Supervised Machine Learning
PubMed: 36038620
DOI: 10.1038/s41598-022-18947-6 -
Environmental Research Aug 2023Accurately determining the second-order rate constant with e (k) for organic compounds (OCs) is crucial in the e induced advanced reduction processes (ARPs). In this...
Accurately determining the second-order rate constant with e (k) for organic compounds (OCs) is crucial in the e induced advanced reduction processes (ARPs). In this study, we collected 867 k values at different pHs from peer-reviewed publications and applied machine learning (ML) algorithm-XGBoost and deep learning (DL) algorithm-convolutional neural network (CNN) to predict k. Our results demonstrated that the CNN model with transfer learning and data augmentation (CNN-TL&DA) greatly improved the prediction results and overcame over-fitting. Furthermore, we compared the ML/DL modeling methods and found that the CNN-TL&DA, which combined molecular images (MI), achieved the best overall performance (R = 0.896, RMSE = 0.362, MAE = 0.261) when compared to the XGBoost algorithm combined with Mordred descriptors (MD) (0.692, RMSE = 0.622, MAE = 0.399) and Morgan fingerprint (MF) (R = 0.512, RMSE = 0.783, MAE = 0.520). Moreover, the interpretation of the MD-XGBoost and MF-XGBoost models using the SHAP method revealed the significance of MDs (e.g., molecular size, branching, electron distribution, polarizability, and bond types), MFs (e.g, aromatic carbon, carbonyl oxygen, nitrogen, and halogen) and environmental conditions (e.g., pH) that effectively influence the k prediction. The interpretation of the 2D molecular image-CNN (MI-CNN) models using the Grad-CAM method showed that they correctly identified key functional groups such as -CN, -NO, and -X functional groups that can increase the k values. Additionally, almost all electron-withdrawing groups and a small part of electron-donating groups for the MI-CNN model can be highlighted for estimating k. Overall, our results suggest that the CNN approach has smaller errors when compared to ML algorithms, making it a promising candidate for predicting other rate constants.
Topics: Deep Learning; Electrons; Neural Networks, Computer; Machine Learning; Algorithms
PubMed: 37105290
DOI: 10.1016/j.envres.2023.115996 -
The Journal of Allergy and Clinical... Aug 2023
Topics: Humans; Deep Learning; Machine Learning; Decision Support Systems, Clinical; Drug Hypersensitivity
PubMed: 36931329
DOI: 10.1016/j.jaci.2023.03.004 -
American Journal of Epidemiology Nov 2023Deep learning methods are increasingly being applied to problems in medicine and health care. However, few epidemiologists have received formal training in these... (Review)
Review
Deep learning methods are increasingly being applied to problems in medicine and health care. However, few epidemiologists have received formal training in these methods. To bridge this gap, this article introduces the fundamentals of deep learning from an epidemiologic perspective. Specifically, this article reviews core concepts in machine learning (e.g., overfitting, regularization, and hyperparameters); explains several fundamental deep learning architectures (convolutional neural networks, recurrent neural networks); and summarizes training, evaluation, and deployment of models. Conceptual understanding of supervised learning algorithms is the focus of the article; instructions on the training of deep learning models and applications of deep learning to causal learning are out of this article's scope. We aim to provide an accessible first step towards enabling the reader to read and assess research on the medical applications of deep learning and to familiarize readers with deep learning terminology and concepts to facilitate communication with computer scientists and machine learning engineers.
Topics: Humans; Deep Learning; Epidemiologists; Neural Networks, Computer; Algorithms; Machine Learning
PubMed: 37139570
DOI: 10.1093/aje/kwad107 -
Sensors (Basel, Switzerland) Apr 2023Recently, various sophisticated methods, including machine learning and artificial intelligence, have been employed to examine health-related data. Medical professionals... (Review)
Review
Recently, various sophisticated methods, including machine learning and artificial intelligence, have been employed to examine health-related data. Medical professionals are acquiring enhanced diagnostic and treatment abilities by utilizing machine learning applications in the healthcare domain. Medical data have been used by many researchers to detect diseases and identify patterns. In the current literature, there are very few studies that address machine learning algorithms to improve healthcare data accuracy and efficiency. We examined the effectiveness of machine learning algorithms in improving time series healthcare metrics for heart rate data transmission (accuracy and efficiency). In this paper, we reviewed several machine learning algorithms in healthcare applications. After a comprehensive overview and investigation of supervised and unsupervised machine learning algorithms, we also demonstrated time series tasks based on past values (along with reviewing their feasibility for both small and large datasets).
Topics: Artificial Intelligence; Health Care Sector; Machine Learning; Algorithms; Unsupervised Machine Learning
PubMed: 37177382
DOI: 10.3390/s23094178 -
Sensors (Basel, Switzerland) Feb 2022The rapid growth and adaptation of medical information to identify significant health trends and help with timely preventive care have been recent hallmarks of the...
The rapid growth and adaptation of medical information to identify significant health trends and help with timely preventive care have been recent hallmarks of the modern healthcare data system. Heart disease is the deadliest condition in the developed world. Cardiovascular disease and its complications, including dementia, can be averted with early detection. Further research in this area is needed to prevent strokes and heart attacks. An optimal machine learning model can help achieve this goal with a wealth of healthcare data on heart disease. Heart disease can be predicted and diagnosed using machine-learning-based systems. Active learning (AL) methods improve classification quality by incorporating user-expert feedback with sparsely labelled data. In this paper, five (MMC, Random, Adaptive, QUIRE, and AUDI) selection strategies for multi-label active learning were applied and used for reducing labelling costs by iteratively selecting the most relevant data to query their labels. The selection methods with a label ranking classifier have hyperparameters optimized by a grid search to implement predictive modelling in each scenario for the heart disease dataset. Experimental evaluation includes accuracy and F-score with/without hyperparameter optimization. Results show that the generalization of the learning model beyond the existing data for the optimized label ranking model uses the selection method versus others due to accuracy. However, the selection method was highlighted in regards to the F-score using optimized settings.
Topics: Cardiovascular Diseases; Delivery of Health Care; Heart Diseases; Humans; Machine Learning; Supervised Machine Learning
PubMed: 35161928
DOI: 10.3390/s22031184 -
Mathematical Biosciences and... Sep 2021The study expects to solve the problems of insufficient labeling, high input dimension, and inconsistent task input distribution in traditional lifelong machine...
The study expects to solve the problems of insufficient labeling, high input dimension, and inconsistent task input distribution in traditional lifelong machine learning. A new deep learning model is proposed by combining feature representation with a deep learning algorithm. First, based on the theoretical basis of the deep learning model and feature extraction. The study analyzes several representative machine learning algorithms, and compares the performance of the optimized deep learning model with other algorithms in a practical application. By explaining the machine learning system, the study introduces two typical algorithms in machine learning, namely ELLA (Efficient lifelong learning algorithm) and HLLA (Hierarchical lifelong learning algorithm). Second, the flow of the genetic algorithm is described, and combined with mutual information feature extraction in a machine algorithm, to form a composite algorithm HLLA (Hierarchical lifelong learning algorithm). Finally, the deep learning model is optimized and a deep learning model based on the HLLA algorithm is constructed. When K = 1200, the classification error rate reaches 0.63%, which reflects the excellent performance of the unsupervised database algorithm based on this model. Adding the feature model to the updating iteration process of lifelong learning deepens the knowledge base ability of lifelong machine learning, which is of great value to reduce the number of labels required for subsequent model learning and improve the efficiency of lifelong learning.
Topics: Algorithms; Databases, Factual; Deep Learning; Information Storage and Retrieval; Machine Learning
PubMed: 34814265
DOI: 10.3934/mbe.2021376 -
Journal of Neural Engineering Nov 2020In an increasingly data-driven world, artificial intelligence is expected to be a key tool for converting big data into tangible benefits and the healthcare domain is no... (Review)
Review
In an increasingly data-driven world, artificial intelligence is expected to be a key tool for converting big data into tangible benefits and the healthcare domain is no exception to this. Machine learning aims to identify complex patterns in multi-dimensional data and use these uncovered patterns to classify new unseen cases or make data-driven predictions. In recent years, deep neural networks have shown to be capable of producing results that considerably exceed those of conventional machine learning methods for various classification and regression tasks. In this paper, we provide an accessible tutorial of the most important supervised machine learning concepts and methods, including deep learning, which are potentially the most relevant for the medical domain. We aim to take some of the mystery out of machine learning and depict how machine learning models can be useful for medical applications. Finally, this tutorial provides a few practical suggestions for how to properly design a machine learning model for a generic medical problem.
Topics: Artificial Intelligence; Machine Learning; Neural Networks, Computer; Supervised Machine Learning
PubMed: 33036008
DOI: 10.1088/1741-2552/abbff2 -
Magnetic Resonance in Medical Sciences... Oct 2022This article presents an overview of deep learning (DL) and its applications to function approximation for MR in medicine. The aim of this article is to help readers... (Review)
Review
This article presents an overview of deep learning (DL) and its applications to function approximation for MR in medicine. The aim of this article is to help readers develop various applications of DL. DL has made a large impact on the literature of many medical sciences, including MR. However, its technical details are not easily understandable for non-experts of machine learning (ML).The first part of this article presents an overview of DL and its related technologies, such as artificial intelligence (AI) and ML. AI is explained as a function that can receive many inputs and produce many outputs. ML is a process of fitting the function to training data. DL is a kind of ML, which uses a composite of many functions to approximate the function of interest. This composite function is called a deep neural network (DNN), and the functions composited into a DNN are called layers. This first part also covers the underlying technologies required for DL, such as loss functions, optimization, initialization, linear layers, non-linearities, normalization, recurrent neural networks, regularization, data augmentation, residual connections, autoencoders, generative adversarial networks, model and data sizes, and complex-valued neural networks.The second part of this article presents an overview of the applications of DL in MR and explains how functions represented as DNNs are applied to various applications, such as RF pulse, pulse sequence, reconstruction, motion correction, spectroscopy, parameter mapping, image synthesis, and segmentation.
Topics: Artificial Intelligence; Deep Learning; Machine Learning; Neural Networks, Computer
PubMed: 34544924
DOI: 10.2463/mrms.rev.2021-0040