Introduction to supervised machine learning in clinical epidemiology.

Global navigation satellite system (GNSS) site coordinate time series provides essential data for geodynamic and geophysical studies, realisation of a regional or global geodetic reference frames, and crustal deformation research. The coordinate time series has been conventionally modelled by least squares (LS) fitting with harmonic functions, alongside many other analysis methods. As a key limitation, the traditional modelling approaches simply use the functions of time variable, despite good knowledge of various underlying physical mechanisms responsible for the site displacements. This paper examines the use of machine learning (ML) models to reflect the effects or residential effects of physical variables related to Sun and the Moon ephemerides, polar motion, temperature, atmospheric pressure, and hydrology on the site displacements. To form the ML problem, these variables are constructed as the input vector of each ML training sample, while the vertical displacement of a GNSS site is regarded as the output value. In the evaluation experiments, three ML approaches, namely the gradient boosting decision tree (GBDT) approach, long short-term memory (LSTM) approach, and support vector machine (SVM) approach, are introduced and evaluated with the time series datasets collected from 9 GNSS sites over the period of 13 years. The results indicate that all three approaches achieve similar fitting precision in the range of 3–5 mm in the vertical displacement component, which is an improvement in over 30% with respect to the traditional LS fitting precision in the range of 4–7 mm. The prediction of the vertical time series with the three ML approaches shows the precision in the range of 4–7 mm over the future 24- month period. The results also indicate the relative importance of different physical features causing the displacements of each site. Overall, ML approaches demonstrate better performance and effectiveness in modelling and prediction of GNSS time series, thus impacting maintenance of geodetic reference frames, geodynamics, geophysics, and crustal deformation analysis.

The incidence of colorectal cancer (CRC) is increasing in adults younger than 50, and early screening remains challenging due to cost and under-utilization. To identify individuals aged 35-50 years who may benefit from early screening, we developed a prediction model using machine learning and electronic health record (EHR)-derived factors. We enrolled 3,116 adults aged 35-50 at average-risk for CRC and underwent colonoscopy between 2017-2020 at a single center. Prediction outcomes were (1) CRC and (2) CRC or high-risk polyps. We derived our predictors from EHRs (e.g., demographics, obesity, laboratory values, medications, and zip code-derived factors). We constructed four machine learning-based models using a training set (random sample of 70% of participants): regularized discriminant analysis, random forest, neural network, and gradient boosting decision tree. In the testing set (remaining 30% of participants), we measured predictive performance by comparing C-statistics to a reference model (logistic regression). The study sample was 55.1% female, 32.8% non-white, and included 16 (0.05%) CRC cases and 478 (15.3%) cases of CRC or high-risk polyps. All machine learning models predicted CRC with higher discriminative ability compared to the reference model [e.g., C-statistics (95%CI); neural network: 0.75 (0.48-1.00) vs. reference: 0.43 (0.18-0.67); P = 0.07] Furthermore, all machine learning approaches, except for gradient boosting, predicted CRC or high-risk polyps significantly better than the reference model [e.g., C-statistics (95%CI); regularized discriminant analysis: 0.64 (0.59-0.69) vs. reference: 0.55 (0.50-0.59); P<0.0015]. The most important predictive variables in the regularized discriminant analysis model for CRC or high-risk polyps were income per zip code, the colonoscopy indication, and body mass index quartiles. Machine learning can predict CRC risk in adults aged 35-50 using EHR with improved discrimination. Further development of our model is needed, followed by validation in a primary-care setting, before clinical application.

Introduction: Machine learning has emerged as a powerful tool for data analysis, enabling systems to identify patterns and make predictions without explicit programming. Among its various approaches, unsupervised learning plays a crucial role in discovering hidden structures within data, especially in scenarios where labeled examples are scarce or costly to obtain. This study provides a comprehensive analysis of unsupervised learning techniques, with a particular focus on clustering and reinforcement learning. Research significance: This study provides an in-depth exploration of unsupervised learning techniques, emphasizing their ability to identify patterns in data without the need for labeled training examples. This is particularly significant in domains where acquiring labeled data is costly or impractical. By highlighting the role of reinforcement learning in unsupervised systems, the research advances the understanding of how agents improve behavior through rewards and penalties, which has implications for robotics and strategic game-playing applications. Methodology: Other options include K-Nearest Neighbors (KNN), Neural Networks, Support Vector Machines (SVM), and Decision Trees. Assessment Criteria: Memory Usage, Accuracy, Training Speed, and Error Rate. Result: According to the results, K-Nearest Neighbors (KNN) had the lowest quality, while neural networks had the highest quality. Conclusion: According to the GRA approach, neural networks are the most valuable datasets for machine learning algorithms. Key words: Unsupervised Learning, Reinforcement Learning, Clustering, Generalization &amp; Over fitting, Decision Trees &amp; Random Forests, Neural Networks &amp; Deep Learning, Ensemble Methods, and Medical Imaging &amp; Cyber security.

Sepsis is a leading cause of mortality in the intensive care unit. Early prediction of sepsis can reduce the overall mortality rate and cost of sepsis treatment. Some studies have predicted mortality and development of sepsis using machine learning models. However, there is a gap between the creation of different machine learning algorithms and their implementation in clinical practice.This study utilized data from the Medical Information Mart for Intensive Care III. We established and compared the gradient boosting decision tree (GBDT), logistic regression (LR), k-nearest neighbor (KNN), random forest (RF), and support vector machine (SVM).A total of 3937 sepsis patients were included, with 34.3% mortality in the Medical Information Mart for Intensive Care III group. In our comparison of 5 machine learning models (GBDT, LR, KNN, RF, and SVM), the GBDT model showed the best performance with the highest area under the receiver operating characteristic curve (0.992), recall (94.8%), accuracy (95.4%), and F1 score (0.933). The RF, SVM, and KNN models showed better performance (area under the receiver operating characteristic curve: 0.980, 0.898, and 0.877, respectively) than the LR (0.876).The GBDT model showed better performance than other machine learning models (LR, KNN, RF, and SVM) in predicting the mortality of patients with sepsis in the intensive care unit. This could be used to develop a clinical decision support system in the future.

Classification and prediction tasks are common in health research. With the increasing availability of vast health data repositories (e.g. electronic medical record databases) and advances in computing power, traditional statistical approaches are being augmented or replaced with machine learning (ML) approaches to classify and predict health outcomes. ML describes the automated process of identifying ("learning") patterns in data to perform tasks. Developing an ML model includes selecting between many ML models (e.g. decision trees, support vector machines, neural networks); model specifications such as hyperparameter tuning; and evaluation of model performance. This process is conducted repeatedly to find the model and corresponding specifications that optimize some measure of model performance. ML models can make more accurate classifications and predictions than their statistical counterparts and confer greater flexibility when modelling unstructured data or interactions between covariates; however, many ML models require larger sample sizes to achieve good classification or predictive performance and have been criticized as "black box" for their poor transparency and interpretability. ML holds potential in family medicine for risk profiling of patients' disease risk and clinical decision support to present additional information at times of uncertainty or high demand. In the future, ML approaches are positioned to become commonplace in family medicine. As such, it is important to understand the objectives that can be addressed using ML approaches and the associated techniques and limitations. This article provides a brief introduction into the use of ML approaches for classification and prediction tasks in family medicine.

It is undoubtful that artificial intelligence (AI) is being the trend of computer science and this trend is still ongoing in the far future even though technologies are being developed suddenly fast because computer science does not reach the limitation of approaching biological world yet. Machine learning (ML), which is a branch of AI, is a spearhead but not a key of AI because it sets first bricks to build up an infinitely long bridge from computer to human intelligence, but it is also vulnerable to environmental changes or input errors. There are three typical types of ML such as supervised learning, unsupervised learning, and reinforcement learning (RL) where RL, which is adapt progressively to environmental changes, can alleviate vulnerability of machine learning but only RL is not enough because the resilience of RL is based on iterative adjustment technique, not based on naturally inherent aspects like data mining approaches and moreover, mathematical fundamentals of RL lean forwards swing of stochastic process. Fortunately, artificial neural network, or neural network (NN) in short, can support all three types of ML including supervised learning, unsupervised learning, and RL where the implicitly regressive mechanism with high order through many layers under NN can improve the resilience of ML. Moreover, applications of NN are plentiful and multiform because three ML types are supported by NN; besides, NN training by backpropagation algorithm is simple and effective, especially for sample of data stream. Therefore, this study research is an introduction to NN with easily understandable explanations about mathematical aspects under NN as a beginning of stepping into deep learning which is based on multilayer NN. Deep learning, which is producing amazing results in the world of AI, is undoubtfully being both spearhead and key of ML with expectation that ML improved itself by deep learning will become both spearhead and key of AI, but this expectation is only for ML researchers because there are many AI subdomains are being invented and developed in such a way that we cannot understand exhaustedly. It is more important to recall that NN, which essentially simulates human neuron system, is appropriate to the philosophy of ML that constructs an infinitely long bridge from computer to human intelligence.

Smartphone mechanocardiography (sMCG) is a new technique that allows patients to record a cardiac rhythm strip using a smartphone built-in tri-axial accelerometer and gyroscope. In this study, we investigated how a self-applied sMCG can reliably contribute to the differentiation of atrial fibrillation (AFib) from the sinus rhythm (SR). A study sample of 300 elderly adults including 150 AFib cases with persistent and paroxysmal AFib was recruited. Among the 300 subjects, 182 subjects (82 AFib) completed two recordings, one physician-applied and one self-applied. The remaining patients (n = 118) were nervous, in a quite poor condition, and not interested in or capable of concentrating on using a smartphone in the acute setting of a hospital in order to perform the self-applied recording. Two data processing frameworks were used, knowledge-based learning (KL) which is a rule-based algorithm and machine learning (ML) which is an automated classification technique. For the ML approach, we considered four classifiers, namely random forest (RF), extreme gradient boosting (XGB), support vector machines (SVM), and artificial neural network (NN). For evaluation, a leave-one-subject-out cross-validation was adopted for the ML approach. Compared to physician-interpreted ECG-derived labels, the KL approach predicted AFib with sensitivity values of 0.963 and 0.976, specificity values of 0.980 and 0.929, and F-measure values of 0.972 and 0.952 for the physician- and self-applied measurements, respectively. Similarly, NN which was the best ML classifier according to the F-measure values, demonstrated, on average, sensitivity values of 0.976 and 0.938, specificity values of 0.962 and 0.936, and F-measure values of 0.969 and 0.937, respectively. All other classifiers delivered quite similar results. The sMCG technology for AFib detection, supported by the KL and ML approaches, can accurately differentiate AFib from SR in both physician- and self-applied recording scenarios. This new technology can help to screen patients with episodic or undiagnosed AFib and also be used as a home-based self-applied monitoring technique.

BackgroundNearly 20% of pregnancies in patients with Systemic lupus erythematosus (SLE) result in an adverse pregnancy outcome (APO); early identification of women with SLE who are at high risk of...

Prediction of retained placenta in holstein dairy cows based on machine learning approaches: Comparative analysis of risk factors and predictive algorithms.

BackgroundFollowing visible successes on a wide range of predictive tasks, machine learning techniques are attracting substantial interest from medical researchers and clinicians. We address the need for capacity development in this area by providing a conceptual introduction to machine learning alongside a practical guide to developing and evaluating predictive algorithms using freely-available open source software and public domain data.MethodsWe demonstrate the use of machine learning techniques by developing three predictive models for cancer diagnosis using descriptions of nuclei sampled from breast masses. These algorithms include regularized General Linear Model regression (GLMs), Support Vector Machines (SVMs) with a radial basis function kernel, and single-layer Artificial Neural Networks. The publicly-available dataset describing the breast mass samples (N=683) was randomly split into evaluation (n=456) and validation (n=227) samples.We trained algorithms on data from the evaluation sample before they were used to predict the diagnostic outcome in the validation dataset. We compared the predictions made on the validation datasets with the real-world diagnostic decisions to calculate the accuracy, sensitivity, and specificity of the three models. We explored the use of averaging and voting ensembles to improve predictive performance. We provide a step-by-step guide to developing algorithms using the open-source R statistical programming environment.ResultsThe trained algorithms were able to classify cell nuclei with high accuracy (.94 -.96), sensitivity (.97 -.99), and specificity (.85 -.94). Maximum accuracy (.96) and area under the curve (.97) was achieved using the SVM algorithm. Prediction performance increased marginally (accuracy =.97, sensitivity =.99, specificity =.95) when algorithms were arranged into a voting ensemble.ConclusionsWe use a straightforward example to demonstrate the theory and practice of machine learning for clinicians and medical researchers. The principals which we demonstrate here can be readily applied to other complex tasks including natural language processing and image recognition.

Commentary: Machine learning and cardiac surgery risk prediction

The amount of data medical databases doubles every 20 months, and physicians are at a loss to analyze them. Also, traditional data analysis has difficulty to identify outliers and patterns in big data and data with multiple exposure / outcome variables and analysis-rules for surveys and questionnaires, currently common methods of data collection, are, essentially, missing. Consequently, proper data-based health decisions will soon be impossible. Obviously, it is time that medical and health professionals mastered their reluctance to use machine learning methods and this was the incentive for the authors to complete a series of three textbooks entitled Learning in Medicine Part and Three, Springer Heidelberg Germany, 2012-2013, describing in a nonmathematical way over sixty machine learning methodologies, as available in SPSS statistical software and other major software programs. Although well received, it came to our attention that physicians and students often lacked time to read the entire books, and requested a small book, without background information and theoretical discussions and highlighting technical details. For this reason we produced a 100 page cookbook, entitled Machine Learning in Medicine - Cookbook One, with data examples available at extras.springer.com for self-assessment and with reference to the above textbooks for background information. Already at the completion of this cookbook we came to realize, that many essential methods were not covered. The current volume, entitled Machine Learning in Medicine - Cookbook Two is complementary to the first and also intended for providing a more balanced view of the field and thus, as a must-read not only for physicians and students, but also for any one involved in the process and progress of health and health care. Similarly to Learning in Medicine - Cookbook the current work will describe stepwise analyses of over twenty machine learning methods, that are, likewise, based on the three major machine learning methodologies:Cluster methodologies (Chaps. 1-3)Linear methodologies (Chaps. 4-11)Rules methodologies (Chaps. 12-20)In extras.springer.com the data files of the examples are given, as well as XML (Extended Mark up Language), SPS (Syntax) and ZIP (compressed) files for outcome predictions in future patients. In addition to condensed versions of the methods, fully described in the above three textbooks, an introduction is given to SPSS Modeler (SPSS' data mining workbench) in the Chaps. 15, 18, 19, while improved statistical methods like various automated analyses and Monte Carlo simulation models are in the Chaps. 1, 5, 7 and 8. We should emphasize that all of the methods described have been successfully applied in practice by the authors, both of them professors in applied statistics and machine learning at the European Community College of Pharmaceutical Medicine in Lyon France. We recommend the current work not only as a training companion to investigators and students, because of plenty of step by step analyses given, but also as a brief introductory text to jaded clinicians new to the methods. For the latter purpose, background and theoretical information have been replaced with the appropriate references to the above textbooks, while single sections addressing general purposes, main scientific questions and conclusions are given in place. Finally, we will demonstrate that modern machine learning performs sometimes better than traditional statistics does. learning may have little options for adjusting confounding and interaction, but you can add propensity scores and interaction variables to almost any machine learning method.

The increasing complexity of urban transportation systems and the growing volume of vehicles have made traffic congestion a persistent challenge in modern cities. Efficient traffic flow prediction is essential for mitigating congestion, improving road safety, optimizing traffic signal control, and enhancing overall transportation efficiency. In recent years, artificial intelligence (AI) has emerged as a transformative tool in the field of traffic management, offering sophisticated algorithms capable of modeling, analyzing, and predicting complex traffic patterns with high accuracy. The application of AI in traffic flow prediction leverages vast amounts of real-time and historical data to generate precise forecasts, supporting data-driven decision-making by urban planners and traffic control authorities. The prediction of traffic flow involves analyzing time-series data that exhibit nonlinear, dynamic, and often stochastic behavior. Traditional statistical models, such as autoregressive integrated moving average (ARIMA), have proven to be limited in handling the high dimensionality and variability inherent in traffic systems. In contrast, AI algorithms possess the capacity to learn and adapt from complex data inputs without the need for explicit programming, making them particularly suitable for traffic-related applications. AI algorithms used in traffic flow prediction can be broadly categorized into machine learning (ML) and deep learning (DL) approaches. Machine learning algorithms such as k-nearest neighbors (KNN), support vector machines (SVM), decision trees, and random forests have demonstrated effectiveness in short-term traffic prediction tasks. These algorithms are capable of identifying hidden patterns in traffic data and adjusting to changes in traffic behavior over time. Ensemble methods, which combine the strengths of multiple learning models, further enhance prediction accuracy and robustness. Deep learning algorithms, a subfield of AI inspired by the human brain’s neural architecture, have shown exceptional performance in capturing spatial-temporal dependencies in traffic data. Recurrent neural networks (RNNs), particularly long short-term memory (LSTM) networks and gated recurrent units (GRUs), are widely used for their ability to process sequential data and retain information over extended time intervals. Convolutional neural networks (CNNs) are employed to extract spatial features from traffic sensor data or road network imagery. Hybrid models that integrate CNNs with RNNs have achieved high levels of predictive precision by simultaneously learning spatial and temporal correlations. In addition to supervised learning methods, unsupervised and reinforcement learning techniques are also applied in traffic flow prediction. Clustering algorithms, such as k-means and DBSCAN, assist in identifying traffic patterns, while reinforcement learning models optimize adaptive traffic signal control systems by learning optimal actions through environmental interaction. This study explores the different types of AI algorithms used in traffic flow prediction, examining their theoretical foundations, structural differences, and practical applications. It aims to evaluate the comparative advantages of various algorithms in addressing the challenges of real-time traffic prediction in increasingly complex transportation networks.

Chemical heuristics have been fundamental to the advancement of chemistry and materials science. These heuristics are typically established by scientists using knowledge and creativity to extract patterns from limited datasets. Machine learning offers opportunities to perfect this approach using computers and larger datasets. Here, we discuss the relationships between traditional heuristics and machine learning approaches. We show how traditional rules can be challenged by large-scale statistical assessment and how traditional concepts commonly used as features are feeding the machine learning techniques. We stress the waste involved in relearning chemical rules and the challenges in terms of data size requirements for purely data-driven approaches. Our view is that heuristic and machine learning approaches are at their best when they work together.

Machine learning-based landslide susceptibility assessment with optimized ratio of landslide to non-landslide samples