Machine learning applications in critical care

With the development of the omic technologies, the acquisition approaches of various biological data on different levels and types are becoming more mature. As a large amount of data will be produced in the process of diagnosis and treatment of diseases, it is necessary to utilize the artificial intelligence such as machine learning to analyze complex, multi-dimensional and multi-scale data and to construct clinical decision support tools. It will provide a method to figure out rapid and effective programs in diagnosis and treatment. In this process, the choice of artificial intelligence seems to be particularly important, such as machine learning. The article reviews the type and algorithm of machine learning used in clinical decision support, such as support vector machines, logistic regression, clustering algorithms, Bagging, random forests and deep learning. The application of machine learning and other methods in clinical decision support has been summarized and classified. The advantages and disadvantages of machine learning are elaborated. It will provide a reference for the selection between machine learning and other artificial intelligence methods in clinical decision support.

Offloading footwear is a critical intervention for preventing and treating diabetic foot ulcers (DFUs), as it reduces plantar pressure (PP) and promotes healing. However, decision-making around its prescription remains fragmented, with variability in feature selection, limited personalization, and diverse evaluation approaches. While guidelines, knowledge-based systems, and machine learning (ML) applications have explored elements of decision-making, no clinical decision support system (CDSS) currently exists to provide feature-level prescriptions. We conducted a narrative review of 45 studies, including 12 guidelines/protocols, 25 knowledge-based systems, and eight ML applications. Studies published from inception to August 2025 were included. Studies were thematically analyzed by type of knowledge, decision logic, evaluation methods, and associated technologies. Guidelines emphasize PP thresholds (≤200 kPa or ≥25–30% reduction) but lack detailed outputs. Knowledge-based systems apply rule-based and sensor-driven logic, integrating PP monitoring, adherence tracking, and usability testing. ML applications introduce predictive classification, optimization, and generative models, achieving high computational accuracy but with limited explainability and clinical validation. Evaluation practices remain fragmented: biomechanical testing dominates protocols, usability and adherence assessments are common in knowledge-based systems, while ML studies emphasize technical accuracy without linking to long-term clinical outcomes. From this synthesis, we propose a five-component framework for CDSS development that discusses: (1) minimum viable dataset, (2) hybrid decision architecture combining rules, optimization, and explainable ML, (3) structured feature-level outputs, (4) continuous validation and evaluation, and (5) integration into clinical and telehealth workflows. The proposed framework presents a pathway to transform fragmented approaches into scalable, patient-centered CDSSs. Prioritizing interoperable datasets, explainable models, and outcome-driven evaluation will be critical for clinical adoption and improved DFU care.

BackgroundDespite the interest in machine learning (ML) algorithms for analyzing real-world data (RWD) in healthcare, the use of ML in predicting time-to-event data, a common scenario in clinical practice, is less explored. ML models are capable of algorithmically learning from large, complex datasets and can offer advantages in predicting time-to-event data. We reviewed the recent applications of ML for survival analysis using RWD in healthcare.MethodsPUBMED and EMBASE were searched from database inception through March 2023 to identify peer-reviewed English-language studies of ML models for predicting time-to-event outcomes using the RWD. Two reviewers extracted information on the data source, patient population, survival outcome, ML algorithms, and the Area Under the Curve (AUC).ResultsOf 257 citations, 28 publications were included. Random survival forests (N = 16, 57%) and neural networks (N = 11, 39%) were the most popular ML algorithms. There was variability across AUC for these ML models (median 0.789, range 0.6–0.950). ML algorithms were predominately considered for predicting overall survival in oncology (N = 12, 43%). ML survival models were often used to predict disease prognosis or clinical events (N = 27, 96%) in the oncology, while less were used for treatment outcomes (N = 1, 4%).ConclusionsThe ML algorithms, random survival forests and neural networks, are mainly used for RWD to predict survival outcomes such as disease prognosis or clinical events in the oncology. This review shows that more opportunities remain to apply these ML algorithms to inform treatment decision-making in clinical practice. More methodological work is also needed to ensure the utility and applicability of ML models in survival outcomes.

The oil and gas industry faces constant pressure to enhance operational efficiency, reduce costs, and minimize equipment downtime. Machine learning (ML) applications in predictive maintenance have emerged as a transformative approach to achieving these goals. This review explores the role of machine learning in predictive maintenance, highlighting its potential to revolutionize maintenance strategies and improve asset management across the industry. Predictive maintenance leverages advanced algorithms to analyze historical and real-time data from equipment and sensors, enabling the identification of patterns and anomalies that precede equipment failures. By utilizing techniques such as supervised learning, unsupervised learning, and reinforcement learning, organizations can forecast equipment malfunctions and schedule maintenance activities proactively, thereby reducing unexpected downtimes and extending asset lifecycles. The implementation of ML in predictive maintenance provides several key benefits. Firstly, it enhances operational efficiency by optimizing maintenance schedules and minimizing unplanned outages, which are critical in the capital-intensive oil and gas sector. Secondly, it enables cost savings through more efficient resource allocation and reduced labor costs associated with reactive maintenance strategies. Thirdly, machine learning algorithms can continuously learn from new data, refining their predictive capabilities and improving accuracy over time. Several case studies illustrate the successful application of machine learning in predictive maintenance within the oil and gas industry. For example, ML models have been employed to predict pump failures, optimize drilling operations, and improve pipeline integrity monitoring. These applications not only lead to significant financial savings but also enhance safety by reducing the risk of catastrophic failures. In conclusion, machine learning applications in predictive maintenance represent a crucial advancement for the oil and gas industry. By harnessing the power of data-driven insights, organizations can enhance operational efficiency, reduce costs, and ultimately drive sustainable growth. This review emphasizes the transformative potential of machine learning in predictive maintenance, establishing it as a key strategy for success in the ever-evolving oil and gas landscape.

Chapter 15 - Clinical decision support systems to improve the diagnosis and management of respiratory diseases

The current review examines the state of knowledge and research on machine learning (ML) applications in horticultural production and the potential for predicting fresh produce losses and waste. Recently, ML has been increasingly applied in horticulture for efficient and accurate operations. Given the health benefits of fresh produce and the need for food and nutrition security, efficient horticultural production and postharvest management are important. This review aims to assess the application of ML in preharvest and postharvest horticulture and the potential of ML in reducing postharvest losses and waste by predicting their magnitude, which is crucial for management practices and policymaking in loss and waste reduction. The review starts by assessing the application of ML in preharvest horticulture. It then presents the application of ML in postharvest handling and processing, and lastly, the prospects for its application in postharvest loss and waste quantification. The findings revealed that several ML algorithms perform satisfactorily in classification and prediction tasks. Based on that, there is a need to further investigate the suitability of more models or a combination of models with a higher potential for classification and prediction. Overall, the review suggested possible future directions for research related to the application of ML in postharvest losses and waste quantification.

With the rapid development of computer science, machine learning has gradually penetrated into various research fields and led to the development of related fields to different degrees. At recent stage, machine learning (ML) algorithms have been able to extensively search for variable relationships from a large amount of observed data, thus helping people to discover more laws that were once unnoticed. As an emerging approach, ML can efficiently utilize and process large-volume data generated from high-throughput experiments in the field of materials and chemicals. In recent years, with the continuous improvement of various algorithmic tools, more and more researchers have applied ML to materials and chemical research, which has greatly broadened the research ideas and methods. This paper briefly introduces the development of machine learning in recent years; summarizes the classification methods of common molecular descriptors and their types; introduces the classification, common algorithms and application scenarios of different types of ML; reviews the relevant research results in the field of materials prediction and inverse synthesis analysis of machine learning; puts forward related suggestions for the application of ML for research; and finally summarizes the prospects for the future development of machine learning applied to the field of materials and chemical engineering.

Machine learning in natural and engineered water systems

Nowadays machine learning (ML) is used in all sorts of fields like health care, retail, travel, finance, social media, etc. ML system is used to learn from input data to construct a suitable model by continuously estimating, optimizing, and tuning parameters of the model. To attain the stated, Python programming language is one of the most flexible languages, and it does contain special libraries for ML applications, namely SciKit-Learn, TensorFlow, PyTorch, Keras, Theano, etc., which is great for linear algebra and getting to know kernel methods of machine learning. The Python programming language is great to use when working with ML algorithms and has easy syntax relatively. When taking the deep-dive into ML, choosing a framework can be daunting. The most common concern is to understand which of these libraries has the most momentum in ML system modeling and development. The major objective of this paper is to provide extensive knowledge on various Python libraries and different ML algorithms in comparison with meet multiple application requirements. This paper also reviewed various ML algorithms and application domains.KeywordsMachine LearningLibrariesPython

The increasing integration of telecommunications with financial services has brought about significant advancements in the accessibility and efficiency of financial transactions. However, this convergence has also led to a rise in fraudulent activities, posing substantial risks to both service providers and users. The application of machine learning (ML) in detecting fraud within telecommunication-based financial transactions offers a promising solution to these challenges. This abstract explores the potential of ML techniques to enhance the detection and prevention of fraud in this domain. Machine learning algorithms, particularly those specializing in anomaly detection, pattern recognition, and predictive modeling, are well-suited to identifying fraudulent activities in real-time. These algorithms can analyze vast amounts of transaction data to detect irregularities that may indicate fraud, such as unusual transaction patterns, deviations from normal behavior, and other red flags that traditional rule-based systems might overlook. By continuously learning from new data, ML models can adapt to emerging fraud tactics, making them highly effective in a rapidly evolving threat landscape. Furthermore, the integration of ML with big data analytics allows for the processing and analysis of large-scale transactional data, enhancing the accuracy and speed of fraud detection. Techniques such as supervised learning, unsupervised learning, and reinforcement learning are particularly effective in categorizing transaction types and identifying potential frauds with minimal human intervention. The use of ML also enables the automation of fraud detection processes, reducing operational costs and increasing the efficiency of fraud management systems. This abstract highlights the critical role of machine learning in enhancing the security of telecommunication-based financial transactions. The ability of ML to detect and prevent fraud in real-time not only mitigates risks but also improves trust and reliability in telecommunication financial services. As fraudsters continue to develop sophisticated methods, the ongoing refinement of ML algorithms will be essential in maintaining robust defenses against financial fraud in the telecommunication sector. Keywords: ML, Detecting, Fraud, Telecommunication-Based, Financial Transactions.

In this chapter, a brief overview of the role and applications of machine learning (ML) algorithms in future wireless cellular networks is presented, more specifically, in the context of self-organizing networks (SONs). SON is a promising and innovative concept, in which future networks are expected to analyze and use historical data in order to improve and adapt themselves to the network conditions. For this to be possible, however, algorithms that are capable of extracting patterns from data and learn from previous actions are necessary. This chapter highlights the utilization and possible applications of ML algorithms in future cellular networks. A brief introduction of ML and SON is presented, followed by an analysis of current state of the art solutions involving ML in SON. Lastly, guidelines on the utilization of intelligent algorithms in SON and future research trends in the area are highlighted and conclusions are drawn.

Machine learning applications for anomaly detection in Smart Water Metering Networks: A systematic review

Hardware Trojans (HTs), maliciously inserted in an integrated circuit during untrusted design or fabrication process pose critical threat to the system security. With the ever increasing capabilities of an adversary to subvert the system during run-time, it is imperative to detect the manifested Trojans in order to reinforce the trust in hardware. In this regard, Machine Learning (ML) algorithms, with their intrinsic capability to execute feature engineering at high learning rates, are emerging as promising candidates to be utilized by system defenders. In this paper, we explore Trojan detection mechanisms that are based on ML, and thereby investigate the prowess of the ML algorithms in bolstering system security. Furthermore, we analyze the efficiency of each proposed Trojan detection strategy based on the underlying ML algorithm. Finally, we underline some problems with existing Trojan detection approaches and discuss future research in the interest of improved performance of the employed ML algorithms, thus aiding in enhancing the intended hardware security.

Airport scheduling has an important optimization value in air transportation, and its complexity and multiple constraints make it difficult for existing methods to deal with it comprehensively. This paper is a review paper, that systematically summarizes the application of genetic algorithms and machine learning in the scheduling of flights, the scheduling of airport resource allocation, and the scheduling of airport external resource allocation. Through the analysis of many studies, this paper summarizes the advantages and disadvantages of different algorithms in various scheduling scenarios and points out the advantages of genetic algorithms in dealing with complex multi-objective optimization problems. In addition, this paper explores the potential of combining machine learning with genetic algorithms to further enhance the effect of scheduling optimization. This paper provides an important reference for future research and helps the innovation and practical application of methods in the field of airport scheduling.

This comprehensive review explores the design and applications of machine learning (ML) techniques to acoustic metamaterials (AMs) and phononic crystals (PnCs), with a particular focus on deep learning (DL). AMs and PnCs, characterized by artificially designed microstructures and geometries, offer unique acoustic properties for precise control and manipulation of sound waves. ML, including DL, in combination with traditional artificial design have promoted the design process, enabling data-driven approaches for feature identification, design optimization, and intelligent parameter search. ML algorithms process extensive AM data to discover novel structures and properties, enhancing overall acoustic performance. This review presents an in-depth exploration of applications associated with ML techniques in AMs and PnCs, highlighting specific advantages, challenges and potential solutions of applying of using ML algorithms associated with ML techniques. By bridging acoustic engineering and ML, this review paves the way for future breakthroughs in acoustic research and engineering.