Big Data Analytics (BDA) has garnered significant attention in both academia and industries, particularly in sectors such as healthcare, owing to the exponential growth of data and advancements in technology. The integration of data from diverse sources and the utilization of advanced analytical techniques has the potential to revolutionize healthcare by improving diagnostic accuracy, enabling personalized medicine, and enhancing patient outcomes. In this paper, we aim to provide a comprehensive literature review on the application of big data analytics in healthcare, focusing on its ecosystem, applications, and data sources. To achieve this, an extensive analysis of scientific studies published between 2013 and 2023 was conducted and overall 180 scientific studies were thoroughly evaluated, establishing a strong foundation for future research and identifying collaboration opportunities in the healthcare domain. The study delves into various application areas of BDA in healthcare, highlights successful implementations, and explores their potential to enhance healthcare outcomes while reducing costs. Additionally, it outlines the challenges and limitations associated with BDA in healthcare, discusses modelling tools and techniques, showcases deployed solutions, and presents the advantages of BDA through various real-world use cases. Furthermore, this study identifies and discusses key open research challenges in the field of big data analytics in healthcare, aiming to push the boundaries and contribute to enhanced healthcare outcomes and decision-making processes.

Prompt Engineering has emerged as a pivotal technique in Natural Language Processing, providing a flexible approach for leveraging pre-trained language models. Particularly, a prompt is used to instruct the model to adopt the nature of given prompts, which became a well-adoptable approach in wide areas of domains. Yet, existing prompt-guided frameworks are experiencing various challenges, such as crafting prompts for specific tasks to achieve clarity and conciseness and avoid ambiguity, which requires time and computational resources. Further existing methods heavily rely on the extensive labelled datasets, yet many domain-specific challenges exist, particularly in healthcare. This study presents Prompt-Eng, a novel framework emphasizing its wide-ranging applications in healthcare, where we design precise prompts with positive and negative aspects; we hypothesize that designing prompts in pairs helps models to generalize effectively. We delve into the significance of quick design and optimization, highlighting its influence in shaping model responses. In addition, we explore the increasing demand for prompts that are aware of the context in multimodal data analysis and the incorporation of prompt engineering in new machine-learning approaches. The essence of our approach is in creating tailored prompts, which serve as instructive guidelines for the models during the prediction procedure. The proposed methodology emphasizes utilizing context-aware prompt pairs to facilitate interpreting and extracting healthcare information from a health corpus by models. The study uses the medical MIMIC-III \footnotehttps://physionet.org/content/mimiciii/1.4/ corpus to predict medicine prescriptions. The paper also explores visual and textual prompts for X-ray image analysis for pneumonia prediction on the MIMIC-CXR \footnote\urlhttps://physionet.org/content/mimic-cxr/2.0.0/ dataset. This approach stands out from existing methods by addressing challenges such as clarity, conciseness, and context awareness, thereby enabling improved interpretation and extraction of healthcare information from diverse data sources.

Wearable telemonitoring of electrocardiogram (ECG) based on wireless body Area networks (WBAN) is a promising approach in next-generation patient-centric telecardiology solutions. In order to guarantee long-term effective operation of monitoring systems, the power consumption of the sensors must be strictly limited. Compressed sensing (CS) is an effective method to alleviate this problem. However, ECG signals in WBAN are usually non-sparse, and most traditional compressed sensing recovery algorithms have difficulty recovering non-sparse signals. In this paper, we proposed a fast and robust non-sparse signal recovery algorithm for wearable ECG telemonitoring. In the proposed algorithm, the alternating direction method of multipliers (ADMM) is used to accelerate the speed of block sparse Bayesian learning (BSBL) framework. We used the famous MIT-BIH Arrhythmia Database, MIT-BIH Long-Term ECG Database and ECG datasets collected in our practical wearable ECG telemonitoring system to verify the performance of the proposed algorithm. The experimental results show that the proposed algorithm can directly recover ECG signals with a satisfactory accuracy in a time domain without a dictionary matrix. Due to acceleration by ADMM, the proposed algorithm has a fast speed, and also it is robust for different ECG datasets. These results suggest that the proposed algorithm is very promising for wearable ECG telemonitoring.

Skin cancer is one of the most prevalent forms of cancer worldwide, and early and accurate diagnosis critically impacts patient outcomes. Given the sensitive nature of medical data and its fragmented distribution across institutions (data silos), privacy-preserving collaborative learning is essential to enable knowledge-sharing without compromising patient confidentiality. While federated learning (FL) offers a promising solution, existing methods struggle with heterogeneous and missing modalities across institutions, which reduce the diagnostic accuracy. To address these challenges, we propose an effective and flexible Personalized Multimodal Federated Learning framework (PMM-FL), which enables efficient cross-client knowledge transfer while maintaining personalized performance under heterogeneous and incomplete modality conditions. Our study contains three key contributions: (1) A hierarchical aggregation strategy that decouples multi-module aggregation from local deployment via global modular-separated aggregation and local client fine-tuning. Unlike conventional FL (which synchronizes all parameters in each round), our method adopts a frequency-adaptive synchronization mechanism, updating parameters based on their stability and functional roles. (2) A multimodal fusion approach based on multitask learning, integrating learnable modality imputation and attention-based feature fusion to handle missing modalities. (3) A custom dataset combining multi-year International Skin Imaging Collaboration(ISIC) challenge data (2018–2024) to ensure comprehensive coverage of diverse skin cancer types. We evaluate PMM-FL through diverse experiment settings, demonstrating its effectiveness in heterogeneous and incomplete modality federated learning settings, achieving 92.32% diagnostic accuracy with only a 2% drop in accuracy under 30% modality missingness, with a 32.9% communication overhead decline compared with baseline FL methods.

Link estimation is a fundamental component of forwarding protocols in wireless sensor networks. In low power forwarding, however, the asynchronous nature of widely adopted duty-cycled radio control brings new challenges to achieve accurate and real-time estimation. First, the repeatedly transmitted frames (called wake-up frame) increase the complexity of accurate statistic, especially with bursty channel contention and coexistent interference. Second, frequent update of every link status will soon exhaust the limited energy supply. In this paper, we propose meter, which is a distributed wake-up frame counter. Meter takes the opportunities of link overhearing to update link status in real time. Furthermore, meter does not only depend on counting the successfully decoded wake-up frames, but also counts the corrupted ones by exploiting the feasibility of ZigBee identification based on short-term sequence of the received signal strength. We implement meter in TinyOS and further evaluate the performance through extensive experiments on indoor and outdoor test beds. The results demonstrate that meter can significantly improve the performance of the state-of-the-art link estimation scheme.