The digitization of the healthcare industry has led to a growing number of applications that use machine learning and image processing techniques to improve the diagnostic process. These applications utilize a variety of medical data, including laboratory results, clinical findings, MRI scans, tomographic images, and radiological images. In addition, free-text healthcare documentation, such as well-structured discharge summaries, contains valuable information. Natural Language Processing encompasses the development of automated systems for generating health reports. This process involves using domain-specific knowledge and prior knowledge to extract relevant information from medical records. This article investigates the use of natural language processing techniques for chest X-ray classification. A total of 14 distinct impressions derived from chest radiography findings from the MIMIC-CXR dataset were used in a multi-label classification procedure. Six distinct language models derived from the BERT language model, along with three distinct classification algorithms, were employed to evaluate the effectiveness of the models and the dataset for multi-label categorization. The experimental results showed a successful prediction rate of 80.47% for 14 distinct impressions within the dataset.

Today’s technological improvements have made ubiquitous healthcare systems that converge into smart healthcare applications in order to solve patients’ problems, to communicate effectively with patients, and to improve healthcare service quality. The first step of building a smart healthcare information system is representing the healthcare data as connected, reachable, and sharable. In order to achieve this representation, ontologies are used to describe the healthcare data. Combining ontological healthcare data with the used and obtained data can be maintained by storing the entire health domain data inside big data stores that support both relational and graph-based ontological data. There are several big data stores and different types of big data sets in the healthcare domain. The goal of this paper is to determine the most applicable ontology data store for storing the big healthcare data. For this purpose, AllegroGraph and Oracle 12c data stores are compared based on their infrastructural capacity, loading time, and query response times. Hence, healthcare ontologies (GENE Ontology, Gene Expression Ontology (GEXO), Regulation of Transcription Ontology (RETO), Regulation of Gene Expression Ontology (REXO)) are used to measure the ontology loading time. Thereafter, various queries are constructed and executed for GENE ontology in order to measure the capacity and query response times for the performance comparison between AllegroGraph and Oracle 12c triple stores.

This study presents the development of a novel molecularly imprinted electrochemical sensor for the sensitive and selective detection of vitamin E acetate (VEA) in e-cigarette liquids, a critical step in addressing the rising public health concern of e-cigarette, or vaping, product use-associated lung injury. VEA-imprinted polymeric nanoparticles, intended to serve as the recognition element on the sensor surface, were synthesized using surfactant-free emulsion polymerization. The synthesized polymer was characterized using Fourier Transformed Infrared Spectroscopy, scanning electron microscope, and zeta potential analyses. The sensor, fabricated using VEA-imprinted poly­(HMA-co-PA)/Nafion on screen-printed carbon electrodes, demonstrated a limit of quantification (LoQ) of 112.3 μg/mL (3.3× S/N) with a wide linear range extending to 3.0 mg/mL (10× S/N). While the sensor exhibited limitations in detecting VEA at concentrations below the LoQ, the integration of machine learning algorithms effectively mitigated these challenges. Machine learning models successfully classified the presence of VEA, even at subdetection limit concentrations, significantly enhancing the sensor’s analytical capabilities. Rigorous testing on real-world e-cigarette liquid samples yielded high recovery rates (96.83% ± 2.79–102.56% ± 3.84), validating the sensor’s accuracy and selectivity in complex matrices. This research not only establishes a promising platform for the rapid and sensitive detection of VEA in e-cigarette liquids but also underscores the transformative potential of integrating artificial intelligence with sensor technologies for addressing critical public health challenges.