The estimation of long-term diabetes complications risk is essential in the process of medical decision making. Guidelines for the management of Type 2 Diabetes Mellitus (T2DM) advocate calculating the Cardiovascular Disease (CVD) risk to initiate appropriate treatment. The objective of this study is to investigate the use of sophisticated machine learning techniques towards the development of personalized models able to predict the risk of fatal or non-fatal CVD incidence in T2DM patients. The important challenge of handling the unbalanced nature of the available dataset is addressed, by applying novel ensemble strategies. Hybrid Wavelet Neural Networks (HWNNs) and Self-Organizing Maps (SOMs) constitute the primary models for building ensembles following a sub-sampling approach. Different methods for combining the decisions of the primary models are applied and comparatively assessed. Data from the 5-year follow up of 560 patients with T2DM are used for development and evaluation purposes. The highest discrimination performance (Area Under the Curve (AUC): 71.48%) is achieved by taking into account both the HWNN- and SOM- based primary models' outputs. The proposed method is superior to the Binomial Linear Regression (BLR) model justifying the need to apply more sophisticated techniques in order to produce reliable CVD risk scores.

Rapid advances in antennas, propagation, electromagnetics, and materials are opening new and unexplored opportunities in body area sensing and stimulation. Next-generation wearables and implants are seamlessly providing round-the-clock monitoring. In turn, numerous applications are brought forward with the potential to ultimately transform healthcare, sports, consumer electronics, and beyond. This review paper provides a comprehensive overview, discusses challenges and opportunities, and indicates future directions for: (a) enabling technologies needed to make body area sensing and stimulation a reality, and (b) emerging bioelectromagnetics applications that may readily benefit from such technologies.

Background: The potential to harness the plurality of available data in real time along with advanced data analytics for the accurate prediction of influenza-like illness (ILI) outbreaks has gained significant scientific interest. Different methodologies based on the use of machine learning techniques and traditional and alternative data sources, such as ILI surveillance reports, weather reports, search engine queries, and social media, have been explored with the ultimate goal of being used in the development of electronic surveillance systems that could complement existing monitoring resources. Objective: The scope of this study was to investigate for the first time the combined use of ILI surveillance data, weather data, and Twitter data along with deep learning techniques toward the development of prediction models able to nowcast and forecast weekly ILI cases. By assessing the predictive power of both traditional and alternative data sources on the use case of ILI, this study aimed to provide a novel approach for corroborating evidence and enhancing accuracy and reliability in the surveillance of infectious diseases. Methods: The model’s input space consisted of information related to weekly ILI surveillance, web-based social (eg, Twitter) behavior, and weather conditions. For the design and development of the model, relevant data corresponding to the period of 2010 to 2019 and focusing on the Greek population and weather were collected. Long short-term memory (LSTM) neural networks were leveraged to efficiently handle the sequential and nonlinear nature of the multitude of collected data. The 3 data categories were first used separately for training 3 LSTM-based primary models. Subsequently, different transfer learning (TL) approaches were explored with the aim of creating various feature spaces combining the features extracted from the corresponding primary models’ LSTM layers for the latter to feed a dense layer. Results: The primary model that learned from weather data yielded better forecast accuracy (root mean square error [RMSE]=0.144; Pearson correlation coefficient [PCC]=0.801) than the model trained with ILI historical data (RMSE=0.159; PCC=0.794). The best performance was achieved by the TL-based model leveraging the combination of the 3 data categories (RMSE=0.128; PCC=0.822). Conclusions: The superiority of the TL-based model, which considers Twitter data, weather data, and ILI surveillance data, reflects the potential of alternative public sources to enhance accurate and reliable prediction of ILI spread. Despite its focus on the use case of Greece, the proposed approach can be generalized to other locations, populations, and social media platforms to support the surveillance of infectious diseases with the ultimate goal of reinforcing preparedness for future epidemics.

Childhood obesity constitutes a major risk factor for future adverse health conditions. Multicomponent parent–child interventions are considered effective in controlling weight. Τhe ENDORSE platform utilizes m-health technologies, Artificial Intelligence (AI), and serious games (SG) toward the creation of an innovative software ecosystem connecting healthcare professionals, children, and their parents in order to deliver coordinated services to combat childhood obesity. It consists of activity trackers, a mobile SG for children, and mobile apps for parents and healthcare professionals. The heterogeneous dataset gathered through the interaction of the end-users with the platform composes the unique user profile. Part of it feeds an AI-based model that enables personalized messages. A feasibility pilot trial was conducted involving 50 overweight and obese children (mean age 10.5 years, 52% girls, 58% pubertal, median baseline BMI z-score 2.85) in a 3-month intervention. Adherence was measured by means of frequency of usage based on the data records. Overall, a clinically and statistically significant BMI z-score reduction was achieved (mean BMI z-score reduction −0.21 ± 0.26, p-value < 0.001). A statistically significant correlation was revealed between the level of activity tracker usage and the improvement of BMI z-score (−0.355, p = 0.017), highlighting the potential of the ENDORSE platform.

Background: The COVID-19 pandemic has highlighted the need for robust and adaptable diagnostic tools capable of detecting the disease from diverse and continuously evolving data sources. Machine learning models, particularly convolutional neural networks, are promising in this regard. However, the dynamic nature of real-world data can lead to model drift, where the model’s performance degrades over time, as the underlying data distribution changes due to evolving disease characteristics, demographic shifts, and variations in recording conditions. Addressing this challenge is crucial to maintaining the accuracy and reliability of these models in ongoing diagnostic applications. Objective: This study aims to develop a comprehensive framework that not only monitors model drift over time but also uses adaptation mechanisms to mitigate performance fluctuations in COVID-19 detection models trained on dynamic cough audio data. Methods: Two crowdsourced COVID-19 audio datasets, namely COVID-19 Sounds and Coswara, were used for development and evaluation purposes. Each dataset was divided into 2 distinct periods, namely the development period and postdevelopment period. A baseline convolutional neural network model was initially trained and evaluated using data (ie, coughs from COVID-19 Sounds and shallow coughs from Coswara dataset) from the development period. To detect changes in data distributions and the model’s performance between these periods, the maximum mean discrepancy distance was used. Upon detecting significant drift, a retraining procedure was triggered to update the baseline model. The study explored 2 model adaptation approaches, unsupervised domain adaptation and active learning, both of which were comparatively assessed. Results: The baseline model achieved an area under the receiver operating characteristic curve of 69.13% and a balanced accuracy of 63.38% on the development test set of the COVID-19 Sounds dataset, while for the Coswara dataset, the corresponding values were 66.8% and 61.64%. A decline in performance was observed when the model was evaluated on data from the postdevelopment period, indicating the presence of model drift. The application of the unsupervised domain adaptation approach led to performance improvement in terms of balanced accuracy by up to 22% and 24% for the COVID-19 Sounds and Coswara datasets, respectively. The active learning approach yielded even greater improvement, corresponding to a balanced accuracy increase of up to 30% and 60% for the 2 datasets, respectively. Conclusions: The proposed framework successfully addresses the challenge of model drift in COVID-19 detection by enabling continuous adaptation to evolving data distributions. This approach ensures sustained model performance over time, contributing to the development of robust and adaptable diagnostic tools for COVID-19 and potentially other infectious diseases.

Cardiovascular Disease (CVD) is an important cause of disability and death among individuals with Diabetes Mellitus (DM). International clinical guidelines for the management of Type 2 DM (T2DM) are founded on primary and secondary prevention and favor the evaluation of CVD related risk factors towards appropriate treatment initiation. CVD risk prediction models can provide valuable tools for optimizing the frequency of medical visits and performing timely preventive and therapeutic interventions against CVD events. The integration of explainability modalities in these models can enhance human understanding on the reasoning process, maximize transparency and embellish trust towards the models’ adoption in clinical practice. The aim of the present study is to develop and evaluate an explainable personalized risk prediction model for the fatal or non-fatal CVD incidence in T2DM individuals. An explainable approach based on the eXtreme Gradient Boosting (XGBoost) and the Tree SHAP (SHapley Additive exPlanations) method is deployed for the calculation of the 5-year CVD risk and the generation of individual explanations on the model’s decisions. Data from the 5- year follow up of 560 patients with T2DM are used for development and evaluation purposes. The obtained results (AUC=71.13%) indicate the potential of the proposed approach to handle the unbalanced nature of the used dataset, while providing clinically meaningful insights about the ensemble model’s decision process.