Machine learning in epilepsy.

Due to brain immaturity, pediatric epilepsy has some unique challenges and opportunities in seizure management. Multivariate analysis and machine learning methods have been increasingly used in childhood epilepsy, mainly in seizure detection and prediction, epileptogenic lesion identification, and clinical outcome prediction. In order to provide an overview of this field, this paper reviewed such studies and found that these methods have made it possible to detect seizures on electroencephalogram (EEG) and detect lesions on imaging automatically. In addition, although seizure occurrence has been regarded as random or unpredictable for long, it has been found that seizures may occur non-randomly in complex patient-specific patterns. Preictal changes on EEG can be detected and distinguished from interictal activities by machine learning approaches, which makes it possible to predict seizure occurrence, but there are significant obstacles in seizure prediction, for example, sufficient clinical data and good machine learning algorithms are needed to identify complex seizure occurrence patterns. Further, outcome studies using multivariate analysis and machine learning methods have identified outcome predictors for seizure outcome prediction. To make these relatively new methods accurate and reliable, confirmatory studies are warranted and further research is needed to improve these methods. It is anticipated that multivariate analysis and machine learning will contribute more to identifying complex seizure patterns, epileptogenic lesions, and outcome predictors to improve seizure detection/prediction, lesion detection, and seizure outcome prediction, which will lead to better seizure control to prevent seizure-related accidents/injury in children with epilepsy, reduce mortality rate, improve quality of life and eventually set them free from seizures.

Status epilepticus prevention, ambulatory monitoring, early seizure detection and prediction in at-risk patients

The advancement in technologies such as Artificial Intelligence (AI) and the Internet of Things (IoT) has enabled the development of a variety of healthcare applications and services such as Remote Health Monitoring (RHM). Developing an RHM system requires integrating various medical sensors that generate a vast amount of data. The integration of AI and Machine Learning (ML) models in the RHM system for analysis of the medical data can enable the development of a decision support system that can reduce costs and save valuable time for clinicians and caregivers. This thesis is focused on leveraging ML and AI technique sto address the challenges in RHM with a specific emphasis on improving the performance of AI-based decision support systems. This is achieved by addressing limitations in current AI-based models and developing novel Random Neural Network (RNN)-based models for fall detection and epilepsy detection and prediction. Initially the thesis presents a literature review on state-of-the-art machine learning techniques used in remote healthcare solutions, automatic fall activity recognition for elderly populations, and epileptic seizure detection and prediction. Furthermore, the study presents a novel fall detection method based on RNN. The results obtained from the proposed model are compared with traditional classification algorithms such as Artificial Neural Networks, Convolutional Neural Networks, and state-of-the-art fall detection schemes. The proposed RNN-based method demonstrates significant improvements in accuracy compared to other methods. The proposed scheme achieved an overall accuracy of 98% in detecting falls. Additionally, several other parameters such as precision, recall, specificity, and F-measure show that the proposed algorithm has better generalization capabilities when compared with other traditional machine learning schemes. Furthermore, the proposed RNN is also compared with a recent scheme and the obtained results demonstrate the superiority of the proposed scheme. Additionally, the thesis presents two novel RNN-based classification schemes for detecting epileptic seizures. In the first scheme statistical features are extracted from raw EEG data, while the second method incorporates frequency analysis techniques such as Discrete Wavelet Transform (DWT) and Fast Fourier Transform. The performance of these models is evaluated and compared with other classification algorithms, demonstrating the effectiveness of RNN-based models for epilepsy detection. Finally, a novel RNN-based epilepsy prediction model is proposed for classification of EEG data into three classes i.e. ictal, pre-ictal, and inter-ictal. The model pre-processes the EEG data by decomposing the signals into five frequency sub-bands using DWT. After pre-processing, six statistical features are extracted from each frequency sub-band which a reused as an input vector to train the RNN model. The proposed scheme achieves an overall accuracy of 95.66% in seizure prediction, outperforming other state-of-the-art models.

Class imbalance should not throw you off balance: Choosing the right classifiers and performance metrics for brain decoding with imbalanced data

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 in natural and engineered water systems

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

Edge computing enables smart IoT-based systems via concurrent and continuous execution of latency-sensitive machine learning (ML) applications. These edge-based machine learning systems are often battery-powered (i.e., energy-limited). They use heterogeneous resources with diverse computing performance (e.g., CPU, GPU, and/or FPGA) to fulfill the latency constraints of ML applications. The challenge is to allocate user requests for different ML applications on the Heterogeneous Edge Computing Systems (HEC) with respect to both the energy and latency constraints of these systems. To this end, we study and analyze resource allocation solutions that can increase the on-time task completion rate while considering the energy constraint. Importantly, we investigate edge-friendly (lightweight) multi-objective mapping heuristics that do not become biased toward a particular application type to achieve the objectives; instead, the heuristics consider "fairness" across the concurrent ML applications in their mapping decisions. Performance evaluations demonstrate that the proposed heuristic outperforms widely-used heuristics in heterogeneous systems in terms of the latency and energy objectives, particularly, at low to moderate request arrival rates. We observed 8.9% improvement in on-time task completion rate and 12.6% in energy-saving without imposing any significant overhead on the edge system.

Electroencephalography (EEG) data is derived by sampling continuous neurological time series signals. In order to prepare EEG signals for machine learning, the signal must be divided into manageable segments. The current naive approach uses arbitrary fixed time slices, which may have limited biological relevance because brain states are not confined to fixed intervals. We investigate whether adaptive segmentation methods are beneficial for machine learning EEG analysis. We introduce a novel adaptive segmentation method, CTXSEG, that creates variable-length segments based on statistical differences in the EEG data and propose ways to use them with modern machine learning approaches that typically require fixed-length input. We assess CTXSEG using controllable synthetic data generated by our novel signal generator CTXGEN. We validate on a real-world use case by replacing fixed-length segmentation in the preprocessing step of a typical EEG machine learning pipeline for seizure detection, and offer guidance on how application may be extended to other problem domains. We found that using CTXSEG to prepare EEG data improves seizure detection performance compared to fixed-length approaches when evaluated using a standardized framework, without modifying the machine learning method, and requires fewer segments. This work demonstrates that adaptive segmentation with CTXSEG can be readily applied to modern machine learning approaches, with potential to improve performance. It is a promising alternative to fixed-length segmentation for signal preprocessing and should be considered as part of the standard preprocessing repertoire in EEG machine learning applications.

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.

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.

Machine learning (ML) is a sub-field of artificial intelligence that uses computer algorithms to extract patterns from raw data, acquire knowledge without human input, and apply this knowledge for various tasks. Traditional statistical methods that classify or regress data have limited capacity to handle large datasets that have a low signal-to-noise ratio. In contrast to traditional models, ML relies on fewer assumptions, can handle larger and more complex datasets, and does not require predictors or interactions to be pre-specified, allowing for novel relationships to be detected. In this review, we discuss the rationale for the use and applications of ML in heart failure, including disease classification, early diagnosis, early detection of decompensation, risk stratification, optimal titration of medical therapy, effective patient selection for devices, and clinical trial recruitment. We discuss how ML can be used to expedite implementation and close healthcare gaps in learning healthcare systems. We review the limitations of ML, including opaque logic and unreliable model performance in the setting of data errors or data shift. Whilst ML has great potential to improve clinical care and research in HF, the applications must be externally validated in prospective studies for broad uptake to occur.

This paper presents a systematic literature review with respect to application of data science and machine learning (ML) to heart failure (HF) datasets with the intention of generating both a synthesis of relevant findings and a critical evaluation of approaches, applicability and accuracy in order to inform future work within this field. This paper has a particular intention to consider ways in which the low uptake of ML techniques within clinical practice could be resolved. Literature searches were performed on Scopus (2014-2021), ProQuest and Ovid MEDLINE databases (2014-2021). Search terms included ‘heart failure’ or ‘cardiomyopathy’ and ‘machine learning’, ‘data analytics’, ‘data mining’ or ‘data science’. 81 out of 1688 articles were included in the review. The majority of studies were retrospective cohort studies. The median size of the patient cohort across all studies was 1944 (min 46, max 93260). The largest patient samples were used in readmission prediction models with the median sample size of 5676 (min. 380, max. 93260). Machine learning methods focused on common HF problems: detection of HF from available dataset, prediction of hospital readmission following index hospitalization, mortality prediction, classification and clustering of HF cohorts into subgroups with distinctive features and response to HF treatment. The most common ML methods used were logistic regression, decision trees, random forest and support vector machines. Information on validation of models was scarce. Based on the authors’ affiliations, there was a median 3:1 ratio between IT specialists and clinicians. Over half of studies were co-authored by a collaboration of medical and IT specialists. Approximately 25% of papers were authored solely by IT specialists who did not seek clinical input in data interpretation. The application of ML to datasets, in particular clustering methods, enabled the development of classification models assisting in testing the outcomes of patients with HF. There is, however, a tendency to over-claim the potential usefulness of ML models for clinical practice. The next body of work that is required for this research discipline is the design of randomised controlled trials (RCTs) with the use of ML in an intervention arm in order to prospectively validate these algorithms for real-world clinical utility.

e17570 Background: Ovarian cancer(OC) is the leading cause of death from gynecologic malignancy. Current challenges include lack of diagnostic tools, predictive biomarkers, and identifying appropriate surgical candidates. Machine learning(ML) is an emerging field that can make accurate projections by making inferences on data and may play a crucial role in OC.The objective of the current study was to review the literature on application of ML in OC and report the most commonly used algorithms and their performance in comparison to existing prediction tools and traditional regression models. Methods: This is a systematic review of published literature from January 1985 to March 2021 on the use of ML in OC. An extensive search of electronic library databases was conducted. Four independent reviewers screened the articles initially by title then full text. Quality was assessed using the MINORS criteria. P-values were generated using the Pearson’s Chi-squared(x2) test to compare performance of ML models with traditional statistics. No p-values were reported if only one study was available. Results: Among 4,295 articles screened, 88 studies on ML in OC were included. The mean age of OC patients was 54.7 years(11-90) and the most common stages at diagnosis were:Stage III (39.9%) and IV (34%). Applications of ML were in clinical datasets(33%, n = 29), preoperative diagnostics(30.7%, n = 27), serum biomarkers (21.6%, n = 19), genomics (12.5%, n = 11), and prediction of cytoreductive outcomes (2.3%, n = 2). The most commonly applied algorithms were Support Vector Machine [SVM](28%, n = 33)and Neural Networks[NN] (25.28%). Over the past decades, the number of publications on ML in OC increased three-fold from 20(1994-2010) to 67 (2011–2021). Only 9 (10%) studies compared ML techniques with existing prediction tools, or traditional regression models. Among 29 clinical dataset studies, 4 compared ML with traditional logistic regression(LR). Two studies reported better performance with ML compared to LR but not significant(accuracy: 0.88 vs 0.84, p = 0.15), one study performed comparably(accuracy: 0.1 vs 0.1) while one study performed worse(accuracy: 0.1 vs 0.97). Only one preoperative diagnostic study compared ML techniques with LR. SVM classifiers outperformed LR in classifying ovarian masses as benign or malignant(sensitivity: 0.88 vs. 0.70). One serum biomarker study compared LR with ML algorithms; LR performed better using two biomarkers for predicting OC(accuracy: 0.97 vs. 0.94). Among five studies reporting overall survival outcomes, only one study compared survival ML techniques using NN with LR and showed that NN classifiers outperformed LR in predicting overall survival(AUC: 0.72 vs. 0.62). Conclusions: This is the first systematic review exploring the literature on ML algorithms in OC. Most ML models outperformed traditional models. However, larger datasets would be required to validate findings.

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.