The digitization of healthcare has led to an unprecedented growth in health-related data, offering new opportunities to transform clinical decision-making, disease prediction, and patient engagement. However, extracting actionable insights from diverse data sources such as electronic health records, wearable devices, and mobile health apps requires a fusion of domain knowledge in healthcare and technical expertise in data science. This paper presents a structured, interdisciplinary framework for digital health that emphasizes practical strategies for data acquisition, preprocessing, feature engineering, machine learning, and ethical data use. The model promotes a holistic understanding of digital health challenges and opportunities, preparing future professionals to apply computational tools in real-world healthcare environments responsibly.

This paper introduces a novel, non-invasive tool for building a holistic neurological-hematological diagnostic framework that can be used for early detection of infection and stroke. We proposed and developed a multimodal sensing approach, where an ear canal–based acoustic system can be used to identify brain activities and a smartphone-based facial video analysis platform to estimate white blood cell (WBC) and hemoglobin (Hb) levels. Our ear-based EEG methodology achieved a remarkable 96% classification accuracy from the low-frequency level (>30hz), with regression models with mean R2scores above 0.96 across all EEG bands. For hematological diagnostics, the system predicted WBC counts with a mean squared error (MSE) of 0.79 and Hb levels with a mean absolute percentage error (MAPE) of 8.24% using optimized support vector regression. These results demonstrate the feasibility of real-time, point-of-care physiological in-clinic, or remote monitoring without an invasive approach. Future work will focus on motion-artifact mitigation and large-scale validation. By bridging neurology and hematology through accessible AI-driven technologies, this work lays the foundation for next-generation mHealth-based non-invasive diagnostic tools.

In this study, we develop and compare quantum and classical machine learning-based chronic kidney disease prediction models. We used the "Chronic_Kidney_Disease Data Set" of the UCI Machine Learning Repository. We performed data preprocessing and applied feature engineering techniques to select the best features. We developed two quantum machine learning-based models and two classical machine learning-based models. We used a hybrid classical-quantum environment for building quantum machine learning models. Finally, we compared the performances of all four models. We found that the Quantum Support Vector Machine performs best among the quantum models. The model’s accuracy was 95% with a k-fold cross-validation score of 94.5% and an ROC-AUC score of 0.987. Among the classical models, the Support Vector Machine showed the highest performance with an accuracy of 92.5%, a k-fold cross-validation score of 93.9%, and a ROC-AUC score of 0.974. Overall, the Quantum Support Vector Machine outperformed all other developed models in terms of accuracy and validation scores. If the quantum models can be executed in a quantum computer instead of a hybrid environment, the models will exhibit higher accuracy and faster execution time. As the models are precisely predicting chronic kidney disease with high accuracy, we believe this study will act as an inspiring framework in the less-investigated field of quantum machine learning-based chronic kidney disease prediction.

This study suggests a way to utilize the existing medical ontology and natural language processing techniques to extract major medical concepts from lay vocabularies of health consumers on social media and group them based on the defined semantic types in the ontology. Diabetes-related discussions on Tumblr was used to test the efficiency of SpaCy and the Markov-Viterbi algorithm to map lay medical terms to the defined medical concepts in the UMLS. The system discussed in this paper can better analyze free texts, take care of word ambiguity and extract the lifestyle indicators from the daily life discussions of diabetic people on Tumblr. The findings of this study can contribute to developing health applications that track the health behavior of those living with chronic conditions such as diabetes. This approach can also assist researchers who are interested in processing lay languages used by health consumers to foster an understanding of their health behavior.

Chronic Fatigue Syndrome (CFS) is a disorder with complex symptoms among patients. In most cases, CFS sufferers describe severe body weakness, poor sleep and inability to perform their usual work as their primary complaints. Symptoms worsen when the patient attempts to do similar work as tolerated. To prevent the worsening of symptoms, the patients need to be aware of what intensity of work they can manage. In this paper, we propose CFSCare, a hardware and software-based system that uses ML models to measure CFS patients' daily activity and energy expenditure objectively. Through our developed App, CFSCare submits to the user a summary of the comprehensive reports of the user's activity and sends a recommendation to the user on how they can prevent acquiring symptoms of CFS brought about by over-exertion. We use an Android smartphone and wrist sensor (MetamotionC) to monitor their leg and hand activity. We develop ML models based on SVM and DT algorithms to predict particular leg and hand activities. Among the applied ML models, SVM exhibited a brilliant performance with 98% accuracy in predicting leg activities and an average to-fold cross-validation score of 94%. For the hand activities prediction, DT recorded the best accuracy of 96%, and the average score of to cross-validations is also 96%. Since CFS patients can tire with exertion after a small amount of daily activity, CFSCare can playa vital role in preventing the patient from over-exertion through the monitoring features.

This paper introduces a novel concept of understanding cerebral dynamics by exploring the acoustic signals generated by blood flow in the brain (BFB) and mechanical resonant frequencies produced by the brain. The concept of this paper will be a groundbreaking approach to brain signal analysis through acoustic signals of BFB. Traditional methods for brain wave capture and analysis mostly depend on fMRI and EEG signals, which are now very popular and have some limitations in accessibility, cost, and real-time analysis capabilities. In this study, we seek the existing gaps in the current brain signal-capturing methods, and our theoretical underpinnings hypothesize that sound produced by blood flow in the brain (BFB) can be an innovative approach to capture the brain signal in a more user-friendly and accessible way. The feasibility of capturing and analyzing these BFB-sound is also discussed in this paper. We proposed a theoretical framework to capture the BFB sound through the human ear. The successful completion of this concept architecture will serve in different applications, from diagnosing neurological disorders to monitoring brain health, underscoring its potential to revolutionize non-invasive brain diagnostics. By synthesizing current knowledge and proposing innovative techniques, this paper aims to pave the way for new frontiers in understanding brain function through the brain sound generated by blood flow and captured from the human ears.

In this paper, we introduce a machine learning (ML)-based approach for Chronic Kidney Disease (CKD) and diabetes prediction and perform feature effect analysis through SHAP (SHapley Additive exPlanations). We utilize two publicly available clinical datasets and build five ML classifier models for the analysis. Among all models, CatBoost provides the best performance for both CKD and diabetes prediction. Our CatBoost model has an accuracy of 0.95, mean 10-fold cross-validation of 0.96, ROC-AUC score of 0.99, precision of 0.96, recall of 0.96, and F1-score of 0.96 for CKD. The CatBoost model shows an accuracy of 0.99, mean 10-fold cross-validation of 0.97, ROC-AUC score of 1.0, precision of 1.0, recall of 0.98, and F1-score of 0.99 for diabetes. We perform comprehensive feature effect analysis by computing SHAP values. This gives insights into the contribution of every feature to the model's predictions. The achieved results highlight that CatBoost is a robust choice for accurate and reliable predictions of CKD and diabetes. The findings of this study contribute to the corresponding field of ML approaches for medical diagnosis and illustrate the significance of feature effect analysis in understanding model predictions. With excellent results, our research has the potential to enhance the clinical decision-making process and improve patient outcomes.

Natural Language Processing (NLP) is one of the fundamental domains of Artificial Intelligence (AI). In this paper, we present a systematic review of NLP based research for recognizing Bangla speech with regular and regional dialects. We describe 23 research papers based on Bangla speech recognition in regular accents and regional dialects. Due to the cultural diversity, Bangla has many dialects with distinctive regional pronunciations, complex vocabulary, and syntax. These characteristics of Bangla create several challenges for implementing NLP successfully. In this paper, we focus on NLP's vital role in speech recognition, which is essential to virtual assistants, transcription services, and language-learning applications. We discuss several methods such as advanced language models, comprehensive datasets, and continuous adaptation to overcome the challenges of recognizing Bangla with NLP. Several algorithms, such as Deep Neural Networks, Gaussian Mixture Models (GMM), Linear Predictive Coding (LPC), Mel frequency cepstral coefficients (MFCC), etc. have been developed to detect the spoken words in Bangla. These existing research works have faced various challenges like scarcity of data, dialectal variability, computational resource requirements, etc. For mitigation of the challenges and further advancement in this research area, we discuss some research scopes including dialect identification through large datasets, low-resource dialect modeling, development of deep learning model for end-to-end ASR systems, continuous learning, etc. This research will help us understand the present status and challenges associated with NLP-based Bangla speech recognition.

The aim of this study is to enhance the accuracy and reduce the time complexity of predicting cardiac illnesses by utilizing patient vital signs exclusively. The importance of timely treatment in critical situations where quick decisions are necessary is emphasized here. Precise predictions have the potential to prevent health deterioration and even save lives. By analyzing vast datasets (images, texts etc.) and detecting subtle patterns, quantum machine learning algorithms (QML) can offer more accurate results in a shorter period compared to classical algorithms. The feature set used here is a novel one to be used for the quick detection and early prediction of cardiovascular diseases, and also a convenient one for this task. To predict heart diseases or abnormalities, both classical and quantum machine learning techniques have been employed in this paper. We have developed four models based on Support Vector Machine (SVM), Neural Network (NN), Quantum Support Vector Machine (QSVM), and Quantum Neural Network (QNN) and compared their performance on a balanced sample of a vast dataset for heart diseases prediction. After comparing the models' performance, we found that Quantum Support Vector Machine (QSVM) performed best with an accuracy of 75% and to-fold Cross-validation score of 80%.

For measuring human anxiety Competitive State Anxiety Inventory (CSAI) is a very popular method. Though this method is widely used in determining anxiety in the sports area, recently, this method is also deployed in other activities. Our objective was to determine the CSAI-based assessment non-invasively using HR-HRV for three different tasks. A baseline video and a target video of the same subject are used and the outputs are the competitive state anxiety inventor are proposed in this study. The framework uses a support vector regression, based on the abstract features derived from the deep learning, heart rate and heart rate variability features from the remote photoplethysmography signal extracted from the facial videos. Deep learning could provide good estimate results. The results proved the rPPG features could improve the accuracy of the cognitive anxiety and somatic anxiety prediction. The competitive State Anxiety Inventory (CSAI) can be assessed by HR-HRV analysis from the facial video. The machine learning algorithm can predict human anxiety in CSAI units despite sports activities. The result from our framework (StressFusion) shows a strong correlation between the ground truth (from UBFC-Phys dataset) and predicted anxiety scores.

The paper presents chaos-theory-based human attention level detection from the electroencephalogram (EEG) signals. In the medical field, “human attention level” can be referred to as the “attentional state,”, which helps to understand an individual's attention capacity in various crucial moments. In this study, we have deployed secondary analysis on existing methods by implementing chaos theory on the PhysioNet dataset. We investigate different vital parameters and values to predict human attention level from the EEG dataset. We calculated time delay, embedding dimension, and correlation dimension from the participants' EEG data to determine the parameters' values: specifically, for detecting of human attention level from EEG signals. By calculating the 95% confidence interval (CI), the time delay has an average of 2.50 seconds, and the embedding dimension and correlation dimension have an average value of 4.41 and 2.23, respectively. We also observed a similar embedded signal in the reconstructed phase space (RPS) of participant's EEG signals. The statistical and chaos-based plot can potentially investigate human attention parameters and develop a robust EEG signal prediction system. Overall, this proposed framework serves as a resource on the latest nonlinearity detection techniques to detect human attention levels utilizing EEG signal analysis. Clinical Relevance - The effectiveness of the chaos-based non-linear analysis method for detecting human attention levels in EEG signals depends on its potential impact on diagnosis and treatment, integration into clinical practice, benefits, and risks.

Conversational agents have gained their ground in our daily life and various domains including healthcare. Chronic condition self-management is one of the promising healthcare areas in which conversational agents demonstrate significant potential to contribute to alleviating healthcare burdens from chronic conditions. This survey paper introduces and outlines types of conversational agents, their generic architecture and workflow, the implemented technologies, and their application to chronic condition self-management.

Heart Rate (HR) and Heart Rate Variability (HRV) is an essential measurement to know the heart’s cardiovascular condition. Many works have been done for measuring HR-HRV based on the facial video non-invasively. In this paper, based on our previous work experience of measuring HR-HRV by Remote photoplethysmography signals (rPPG) analysis, we have built a prediction model from the 10-second time series data extracted from a facial video. In this work, we have used the instantaneous public dataset with several data models to predict the HR-HRV, and stress levels exclusively from the dataset. We have used here some of the popular algorithms appropriate for this task. We have also analyzed the stress level classification on the gender of a subject using the same facial videos with 16 different classifiers resulting in almost perfect accuracy for several classifiers.

Accurate and valid health information is crucial for effective medical management. Failure to collect adequate information from physical and mental health examinations can be a barrier to Virtual medical platforms and telemedicine. In this paper, we propose the non-invasive “Dream” project prototype to monitor and record heart rate (HR), heart rate-variation (HRV) (for physical health), and stress (for mental health) using only a smart-phone. This non-invasive mobile application, “Dream” uses the front camera to capture video to calculate HR-HRV and stress. The full “Dream” project encompasses our previous facial video HR-HRV and stress work. We have also compared our proposed “Dream” project with 39 works in this area. We found a significant positive difference between our proposed “Dream” project compared to other projects in respect to user accessibility, application, cost-effectiveness, hospitalization monitoring, and human health status. Furthermore, we can apply “Dream” in remote human health monitoring, driver monitoring, and creating vital sign records non-invasively without a health care assistant. Especially during a pandemic, this virtual health monitoring system can be useful for scaled-up telemedicine to serve the remote population.

The integration of motivational strategies and self-management theory with mHealth tools is a promising approach to changing the behavior of patients with chronic disease. In this manuscript, we describe the development and current architecture of a prototype voice-activated self-monitoring application (VoiS) which is based on these theories. Unlike prior mHealth applications which require textual input, VoiS app relies on the more convenient and adaptable approach of asking users to verbally input markers of diabetes and hypertension control through a smart speaker. The VoiS app can provide real-time feedback based on these markers; thus, it has the potential to serve as a remote, regular, source of feedback to support behavior change. To enhance the usability and acceptability of the VoiS application, we will ask a diverse group of patients to use it in real-world settings and provide feedback on their experience. We will use this feedback to optimize tool performance, so that it can provide patients with an improved understanding of their chronic conditions. The VoiS app can also facilitate remote sharing of chronic disease control with healthcare providers, which can improve clinical efficacy and reduce the urgency and frequency of clinical care encounters. Because the VoiS app will be configured for use with multiple platforms, it will be more robust than existing systems with respect to user accessibility and acceptability.

Next Step Clinic (NSC) is a neighborhood-based mental clinic in Milwaukee in the USA for early identification and intervention of Autism spectrum disorder (ASD) children. NSC's primary goal is to serve the underserved families in that area with children aged 15 months to 10 years who have ASD symptoms free of cost. Our proposed and implemented Electronic Medical Records (NSC: EMR) has been developed for NSC. This paper describes the NSC: EMR's design specification and whole development process with the workflow control of this system in NSC. This NSC: EMR has been used to record the patient’s medical data and make appointments both physically or virtually. The integration of standardized psychological evaluation form has reduced the paperwork and physical storage burden for the family navigator. By deploying the system, the family navigator can increase their productivity from the screening to all intervention processes to deal with ASD children. Even in the lockdown time, due to the pandemic of COVID-19, about 84 ASD patients from the deprived family at that area got registered and took intervention through this NSC: EMR. The usability and cost-effective feature has already shown the potential of NSC: EMR, and it will be scaled to serve a large population in the USA and beyond.

This paper describes a mobile-based care system (mCARE), which is a novel tool to routinely and systematically collect behavior and developmental parameter children with Autism Spectrum Disorder (ASD) from the low-and middle-income countries (LMICs) like Bangladesh. It is the first clinical decision support system for the ASD children in LMICs that has been proved efficient to assist them in data-driven decision making by using a remote Experience Sampling Method (ESM). Unlike other ESM, instead of taking a report from patients, mCARE introduce caregivers as the reporter. mCARE has deployed Value Sensitive Design (VSD) and Human-Computer Interaction (HCI) in its design process as well as Fogg's Behavior Model (FBM) to assist caregivers in developing and sustain the habit of logging data regularly over a long period. mCARE has accumulated its goal by implementing three tools: mCARE-APP, mCARE-SMS, and mCARE-DMP. The successful completion of the project using this mCARE can ensure the overall quality of care (QoC) of ASD in LMICs, including Bangladesh. The usability and cost-benefit analysis have also shown the potential of mCARE to be scaled to serve a large population in Bangladesh and beyond.

From the statistics, almost 5 billion people in 2020 will be connected to Social Media (SM). Studies have drawn attention to the harms of SM to the health of students; it affects their attention span, memory, sleep, vision, and overall physical, mental, and social health. In this paper, we investigate the effects of SM use on the health and academic performance of students at the University of Sharjah. This study shows that students with more self-regulation have better control over social media use. A cross-sectional mixed approach (CSMA) was used to conduct the research using both quantitative and qualitative data. Out of 300 student participants in our study, the majority of them used Instagram, followed by WhatsApp and Twitter. Students reported an average time of 3-4 hours per day on social media; however, qualitative data showed that many students spent all day on social media. A majority of the students used social media to chat with friends and make new connections. They agreed that their use of social media has reduced reading of paper-based resources and has affected their grammar and writing skills. The use of SM delayed their bedtime and left fewer hours for sleep and caused eyestrain, neck/shoulder pain, fatigue, and poor posture, with declining physical activity. This study concludes that social media use does affect academic performance and health among the students of the University of Sharjah. Considering the negative consequences of extensive social media use, universities need to create awareness programs and can incorporate this as a topic in health education and awareness courses. Our study also generated new information and insights about the effects of high levels of SM usage on the health and academic performance among university students, thereby creating opportunities for further research.