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MDPI

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2078-2489

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2018 - 201922020 - 20253
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Publications 1 - 5 of 5
  • Artificial Intelligence and Machine Learning in the Diagnosis and Prognosis of Diseases Through Breath Analysis: A Scoping Review
    Item type: Review Article
    Kokkotis , Christos; Moustakidis , Serafeim; Swift, Stefan James; et al. (2025)
    Information
    Breath analysis is a non-invasive diagnostic method that offers insights into both physiological and pathological conditions. Exhaled breath contains volatile organic compounds, which act as biomarkers for disease detection, allowing for the monitoring of treatments and the tailoring of medicine to individuals. Recent advancements in chemical sensing, mass spectrometry, and spectroscopy have improved the ability to identify these biomarkers; however, traditional statistical approaches often struggle to handle the complexities of breath data. Artificial intelligence (AI) and machine learning (ML) have revolutionized breath analysis by uncovering intricate patterns among volatile breath markers, enhancing diagnostic precision, and facilitating real-time disease identification. Despite significant progress, challenges remain, including issues with data standardization, model interpretability, and the necessity for extensive and varied datasets. This study reviews the applications of ML in analyzing breath volatile organic compounds, highlighting methodological shortcomings and obstacles to clinical validation. A thorough literature review was performed using the PubMed and Scopus databases, which included studies that focused specifically on the role of machine learning in disease diagnosis and incidence prediction via breath analysis. Among the 524 articles reviewed, 97 satisfied the specified inclusion criteria. The selected studies applied ML techniques, fell within the scope of this review, and emphasize the potential of ML models for non-invasive diagnostics. The findings indicate that traditional ML methods dominate, while ensemble methods are on the rise, and deep learning (DL) techniques (especially CNNs and LSTMs) are increasingly used for classifying respiratory diseases. Techniques for feature selection (such as PCA and ML-based methods) were frequently implemented, though challenges related to explainability and data standardization persist. Future studies should focus on enhancing model transparency and developing methods to further integrate AI into the clinical setting to facilitate early disease detection and advance precision medicine.
  • Correlations and How to Interpret Them
    Item type: Journal Article
    Atmanspacher, Harald; Martin, Mike (2019)
    Information
    Correlations between observed data are at the heart of all empirical research that strives for establishing lawful regularities. However, there are numerous ways to assess these correlations, and there are numerous ways to make sense of them. This essay presents a bird’s eye perspective on different interpretive schemes to understand correlations. It is designed as a comparative survey of the basic concepts. Many important details to back it up can be found in the relevant technical literature. Correlations can (1) extend over time (diachronic correlations) or they can (2) relate data in an atemporal way (synchronic correlations). Within class (1), the standard interpretive accounts are based on causal models or on predictive models that are not necessarily causal. Examples within class (2) are (mainly unsupervised) data mining approaches, relations between domains (multiscale systems), nonlocal quantum correlations, and eventually correlations between the mental and the physical.
  • On Two-Stage Guessing
    Item type: Journal Article
    Graczyk, Robert; Sason, Igal (2021)
    Information
    Stationary memoryless sources produce two correlated random sequences Xn and Yn. A guesser seeks to recover Xn in two stages, by first guessing Yn and then Xn. The contributions of this work are twofold: (1) We characterize the least achievable exponential growth rate (in n) of any positive ρ-th moment of the total number of guesses when Yn is obtained by applying a deterministic function f component-wise to Xn. We prove that, depending on f, the least exponential growth rate in the two-stage setup is lower than when guessing Xn directly. We further propose a simple Huffman code-based construction of a function f that is a viable candidate for the minimization of the least exponential growth rate in the two-stage guessing setup. (2) We characterize the least achievable exponential growth rate of the ρ-th moment of the total number of guesses required to recover Xn when Stage 1 need not end with a correct guess of Yn and without assumptions on the stationary memoryless sources producing Xn and Yn.
  • Remotely monitoring cancer-related fatigue using the smart-phone:
    Item type: Journal Article
    Klaas, Vanessa C.; Tröster, Gerhard; Walt, Heinrich; et al. (2018)
    Information
    Cancer related fatigue is a chronic disease that may persist up to 10 years after successful cancer treatment and is one of the most prevalent problems in cancer survivors. Cancer related fatigue is a complex symptom that is not yet explained completely and there are only a few remedies with proven evidence. Patients do not necessarily follow a treatment plan with regular follow ups. As a consequence, physicians lack of knowledge how their patients are coping with their fatigue in daily life. To overcome this knowledge gap, we developed a smartphone-based monitoring system. A developed Android app provides activity data from smartphone sensors and applies experience based sampling to collect the patients’ subjective perceptions of their fatigue and interference of fatigue with the patients’ daily life. To evaluate the monitoring system in an observational study, we recruited seven patients suffering from cancer related fatigue and tracked them over two to three weeks. We collected around 2700 h of activity data and over 500 completed questionnaires. We analysed the average completion of answering the digital questionnaires and the wearing time of the smartphone. A within-subject analysis of the perceived fatigue, its interference and measured physical activity yielded in patient specific fatigue and activity patterns depending on the time of day. Physical activity level correlated stronger with the interference of fatigue than with the fatigue itself and the variance of the acceleration correlates stronger than absolute activity values. With this work, we provide a monitoring system used for cancer related fatigue. We show with an observational study that the monitoring system is accepted by our study cohort and that it provides additional details about the perceived fatigue and physical activity to a weekly paper-based questionnaire.
  • A novel approach for classification and forecasting of time series in particle accelerators
    Item type: Journal Article
    Li, Sichen; Zacharias, Mélissa; Snuverink, Jochem; et al. (2021)
    Information
    The beam interruptions (interlocks) of particle accelerators, despite being necessary safety measures, lead to abrupt operational changes and a substantial loss of beam time. A novel time series classification approach is applied to decrease beam time loss in the High-Intensity Proton Accelerator complex by forecasting interlock events. The forecasting is performed through binary classification of windows of multivariate time series. The time series are transformed into Recurrence Plots which are then classified by a Convolutional Neural Network, which not only captures the inner structure of the time series but also uses the advances of image classification techniques. Our best-performing interlock-to-stable classifier reaches an Area under the ROC Curve value of 0.71±0.01 compared to 0.65±0.01 of a Random Forest model, and it can potentially reduce the beam time loss by 0.5±0.2 s per interlock.
Publications 1 - 5 of 5