Integrating Advanced Metabolomics and Machine Learning for Anti-Doping in Human Athletes

Artificial intelligence (AI) and machine learning (ML) algorithms have transformed various industries, including healthcare. Healthcare organizations are now using AI and ML algorithms to drive strategic leadership and decision-making, as they provide insights that help organizations manage resources, improve patient outcomes, and increase efficiency. This research paper examines how AI and ML algorithms are used in healthcare to drive strategic leadership. The paper also explores the benefits and challenges associated with using these technologies in healthcare. The study found that AI and ML algorithms can help healthcare organizations make data-driven decisions, optimize resource allocation, and improve patient outcomes. However, there are still challenges related to data quality and privacy that must be addressed to ensure that AI and ML algorithms are used effectively in healthcare.

Artificial intelligence: Friend or foe?

The aging and multimorbid population and health personnel shortages pose a substantial burden on primary health care. While predictive machine learning (ML) algorithms have the potential to address these challenges, concerns include transparency and insufficient reporting of model validation and effectiveness of the implementation in the clinical workflow. To systematically identify predictive ML algorithms implemented in primary care from peer-reviewed literature and US Food and Drug Administration (FDA) and Conformité Européene (CE) registration databases and to ascertain the public availability of evidence, including peer-reviewed literature, gray literature, and technical reports across the artificial intelligence (AI) life cycle. PubMed, Embase, Web of Science, Cochrane Library, Emcare, Academic Search Premier, IEEE Xplore, ACM Digital Library, MathSciNet, AAAI.org (Association for the Advancement of Artificial Intelligence), arXiv, Epistemonikos, PsycINFO, and Google Scholar were searched for studies published between January 2000 and July 2023, with search terms that were related to AI, primary care, and implementation. The search extended to CE-marked or FDA-approved predictive ML algorithms obtained from relevant registration databases. Three reviewers gathered subsequent evidence involving strategies such as product searches, exploration of references, manufacturer website visits, and direct inquiries to authors and product owners. The extent to which the evidence for each predictive ML algorithm aligned with the Dutch AI predictive algorithm (AIPA) guideline requirements was assessed per AI life cycle phase, producing evidence availability scores. The systematic search identified 43 predictive ML algorithms, of which 25 were commercially available and CE-marked or FDA-approved. The predictive ML algorithms spanned multiple clinical domains, but most (27 [63%]) focused on cardiovascular diseases and diabetes. Most (35 [81%]) were published within the past 5 years. The availability of evidence varied across different phases of the predictive ML algorithm life cycle, with evidence being reported the least for phase 1 (preparation) and phase 5 (impact assessment) (19% and 30%, respectively). Twelve (28%) predictive ML algorithms achieved approximately half of their maximum individual evidence availability score. Overall, predictive ML algorithms from peer-reviewed literature showed higher evidence availability compared with those from FDA-approved or CE-marked databases (45% vs 29%). The findings indicate an urgent need to improve the availability of evidence regarding the predictive ML algorithms' quality criteria. Adopting the Dutch AIPA guideline could facilitate transparent and consistent reporting of the quality criteria that could foster trust among end users and facilitating large-scale implementation.

A Primer on Machine Learning.

CORR Synthesis: When Should the Orthopaedic Surgeon Use Artificial Intelligence, Machine Learning, and Deep Learning?

A multi-model data fusion methodology for reservoir water quality based on machine learning algorithms and bayesian maximum entropy

Artificial intelligence and spine: rise of the machines

Owing to recent development in technology, major changes have been noticed in human being's life. Today's lives of human being are becoming more convenient (i.e., in terms of living standard). In current real-world applications, we have shifted our attention from wired devices to wireless devices. As a result, we moved into the era of smart technology, where a lot of Internet devices are connected together in a distributed and decentralized manner. Such Internet-connected devices (ICDs) or Internet of Things (IoTs) engender tremendous data (i.e., via communicating other smart devices). With the tremendous increase in the amount of data, there is a higher requirement to process this huge amount of data (generated through billions of ICDs) using efficient machine learning (ML) algorithms.In the past decade, we refer data mining algorithms to make some decision from collected data-sets. But, due to increasing data on a large scale, data mining fail to handle this data. So, as substitute of data mining algorithms and to refine this information in an efficient manner, we require tradition analytics algorithms, i.e., ML or data mining algorithms. In current scenario, some of the ML algorithms (available to analysis this data) are supervised (used with labeled data), unsupervised (used with unlabelled data) and semi-supervised (work as reward-based learning). Supervised learning algorithms are like linear regression, classification and k-nearest neighbor (KNN), etc. Whereas, unsupervised learning algorithms are clustering, k-means, etc. In general, ML focuses on building the systems that learn and hence improves with the knowledge and experience. Being the heart of artificial intelligence (AI) and data science, ML is gaining popularity day by day. Several algorithms have already been developed (in the past decade) for processing of data, although this field focuses on developing new learning algorithm for big data computability with minimum complexity (i.e., in terms of time and space). ML algorithms are not only applicable to computer science field but also extend to medical, psychological, marketing, manufacturing, automobile, etc.On another side, Big Data including deep learning are the two primary and highly demandable fields of data science. A subset of ML, computer vision or AI, deep learning is used here. The large (or massive) amount of data related to a specific domain which forms Big Data (in form of 5 V's like velocity, volume, value, variety, and veracity) contains valuable information related to various fields like marketing, automobile, finance, cyber security, medical, fraud detection, etc. Such real-world applications are creating a lot of information every day. The valuable (i.e., needful or meaningful) information are required to be processed (or retrieved) from analysis of this unstructured/ large amount of data for further processing of the data for future use (or for prediction). Big organizations have to accord with the tremendous volume of data for prediction, classification, decision making, etc. The use of ML algorithms for big data analytics, which extracts the high-level semantics from the valuable (meaningful) information form the data. It uses hierarchical process for efficient processing and retrieving the complex abstraction from the data.Hence, this chapter discusses several algorithms of ML, to analysis of Big Data. Also, the subset AI like ML algorithms, deep learning algorithms are being discussed here (i.e., to analysis this Big Data for efficient prediction). Later, this chapter focuses on benefits of ML, deep learning algorithms in analyzing tremendous volume of data (i.e., in unsupervised or unstructured form) for numerous complex problems like information retrieval, medical diagnosis, cognitive science, indexing using semantic analysis, data tagging, speech recognition, natural language processing, etc. Also, weakness, raised issues, and challenges (during analysis big data) using (in) ML or deep learning have been discussed in detail. In other words, research gaps in using ML, deep learning algorithms for big data will also be discussed (covering future research aspects/trends). Finally, this chapter discusses the significance of the smart era, computational intelligence, and AI in depth.

This research aims to explore the interdisciplinary connection between the field of neurology and artificial intelligence (AI) through machine learning (ML) algorithms. The central objective is to evaluate the current state of research in the Neuro-ML field and identify gaps in the literature that require additional approaches. To achieve this objective, 10 analyses were introduced that analyze the distribution of articles based on keywords, countries, years, publishers, and ML algorithms used in the context of neurological diseases. Surveys were also conducted to identify the diseases most frequently studied through ML algorithms. Thus, it was found that Alzheimer’s disease (37 articles for Support Vector Regression—SVR; 31 for Random Forest—RF), Parkinson’s disease (46 articles for SVM and 48 for RF), and multiple sclerosis (9 articles for SVM) are the most studied diseases in the field of Neuro-ML. The study analyzes Alzheimer’s, Parkinson’s, and multiple sclerosis in detail by focusing on diagnosis. The overall results highlight an increase in researchers’ interest in applying ML in neurology, with models such as SVM (597 articles), Artificial Neural Network (525 articles), and RF (457 articles) being the most used. The results highlighted three major gaps: the underrepresentation of rare diseases, the lack of standardization in evaluating the performance of ML models, and the lack of exploration of algorithms with greater implementation difficulty, such as Extreme Gradient Boosting and Multilayer Perceptron. The value analysis of the performance metrics of ML models demonstrates the ability to correctly classify neuro-degenerative diseases, with high accuracy in some cases (for example, 97.46% accuracy in Alzheimer’s diagnosis), but there may still be improvements. Future directions include exploring rare diseases, investigating underutilized algorithms, and developing standardized protocols for evaluating the performance of ML models, which will facilitate the comparison of results across different studies.

Machine Learning (ML) algorithms have resulted in considerable changes to health care, enabling early detection and diagnosis, including the classification and identification of Heart Disease (HD). The identification of HD through ML can aid practitioners in making accurate decisions regarding a patient’s health. This is a significant development due to HD now being the most prevalent disease worldwide, while its early diagnosis helps to save the patient’s life. ML algorithms reduce and understand HD symptoms. This study therefore proposes a novel approach differing from simple supervised ML algorithms. The research performed a comparative analysis using the dimensionality reduction algorithm known as Independent Component Analysis, as well as the ensemble technique and the Artificial Neural Network. The information employed for this analysis was obtained from the UCI ML Repository called Heart Disease. The proposed Artificial Neural Network and Adaboosting classifier demonstrated an accuracy in relation to the benchmark dataset of 0.880% and 0.821%, respectively. We thus concluded that that the dimensionality reduction of the Independent Component Analysis based classifier revealed a positive outcome, although with less accuracy than boosting and Multilayer Perceptron. To determine the performance of the algorithms, we used an accuracy score, precision, recall and F1-Score.

Machine Learning Reimagined: The Promise of Interpretability to Combat Bias.

Artificial intelligence in COPD: Possible applications and future prospects.

Machine learning is artificial intelligence (AI) which can predict the output variable with the fed input features. This allows computers to learn from experience without being programmed. The outcome variable in machine learning algorithm may be continuous variable or categorical variable. Supervised machine learning is commonly applied artificial intelligence (AI) in medical field. Decision tree, gradient boost machine (GBM) learning, extreme GBM (XGBM), Support vector machine, K nearest neighbour and multi-layer perceptron are few machine learning algorithms which are being utilised to address the classification and regression problems. Though the incidence of difficult intubation (DI) is rare, occurrence of such event in an unanticipated situation can result in development of arrhythmias due to desaturation and cardiac arrest if not intervened on time. It is preferred to choose the physical parameters that can predict the difficult airway more accurately in clinical scenario and train the algorithm rather than including all the non-specific parameters. Body mass index (BMI) [>30 kg.m-2: anticipated difficult mask ventilation (DMV), direct laryngoscopy (DL) and DI], inter-insicor distance (IID) (<2 cm: anticipated DL), modified Mallampati (MMP) (Grade 1 and 2: Ease of intubation; Grade 3 and 4: anticipated DI), temporomandibular distance (TMD) (<6.5 cm - anticipated DI), restriction of neck extension (if present: anticipated DL and DI), receded mandible (if present: anticipated DL and DI), and poor submandibular space compliance (if present: anticipated DL and DI) parameters which are used to predict DA by clinical assessment, can be used to feed to train the machine learning algorithm. Despite using these sophisticated tools, extubation may fail and patients require reintubation in ICU. It is very challenging to assess the lung compliance in spontaneously breathing patients as compliance will be overestimated due to generation of negative pressure. Cause for which patient has been placed on mechanical ventilation is resolved/resolving, BMI (>30 kg.m-2), intact sensorium (absence of delirium), absence of consolidation, absence of copious secretions, oxygenation status (PaO2/FiO2: >250), ventilation status (paCO2: 30-45 mmHg), measure of work of breathing (respiratory rate, rapid shallow breathing index), heart rate and blood pressure during spontaneous breathing trial (SBT) and diaphragmatic thickness fraction can be used as input features to predict the success of extubation in critically ill patients. With widespread utility of applications in medical fraternity, applications for prediction of difficult airway (or for weaning success) can be programmed which can be accessed by the clinicians to predict DA, thereby all the preparations for managing DA may be done to prevent adverse consequences of unanticipated difficult airway.

The cognitive impairment known as dementia affects millions of individuals throughout the globe. The use of machine learning (ML) and deep learning (DL) algorithms has shown great promise as a means of early identification and treatment of dementia. Dementias such as Alzheimer’s Dementia, frontotemporal dementia, Lewy body dementia, and vascular dementia are all discussed in this article, along with a literature review on using ML algorithms in their diagnosis. Different ML algorithms, such as support vector machines, artificial neural networks, decision trees, and random forests, are compared and contrasted, along with their benefits and drawbacks. As discussed in this article, accurate ML models may be achieved by carefully considering feature selection and data preparation. We also discuss how ML algorithms can predict disease progression and patient responses to therapy. However, overreliance on ML and DL technologies should be avoided without further proof. It’s important to note that these technologies are meant to assist in diagnosis but should not be used as the sole criteria for a final diagnosis. The research implies that ML algorithms may help increase the precision with which dementia is diagnosed, especially in its early stages. The efficacy of ML and DL algorithms in clinical contexts must be verified, and ethical issues around the use of personal data must be addressed, but this requires more study.

To identify which Machine Learning (ML) algorithms are the most successful in predicting and diagnosing breast cancer according to accuracy rates. The "College of Wisconsin Breast Cancer Dataset", which consists of 569 data and 30 features, was classified using Support Vector Machine (SVM), Naive Bayes (NB), Random Forest (RF), Decision Tree (DT), K-Nearest Neighbor (KNN), Logistic Regression (LR), Multilayer Perceptron (MLP), Linear Discriminant Analysis (LDA), XgBoost (XGB), Ada-Boost (ABC) and Gradient Boosting (GBC) ML algorithms. Before the classification process, the dataset was preprocessed. Sensitivity, accuracy, and definiteness metrics were used to measure the success of the methods. Compared to other ML algorithms used in the study, the GBC ML algorithm was found to be the most successful method in the classification of tumors with an accuracy of 99.12%. The XGB ML algorithm was found to be the lowest method with an accuracy rate of 88.10%. In addition, it was determined that the general accuracy rates of the 11 ML algorithms used in the study varied between 88-95%. When the results obtained from the ML classifiers used in the study are evaluated, the efficiency of the GBC algorithm in the classification of tumors is obvious. It can be said that the success rates obtained from 11 different ML algorithms used in the study are valuable in terms of being used to predict different cancer types.