Methicillin-resistant Staphylococcus aureus (MRSA) is a critical public health threat within hospitals as well as long-term care facilities. Better understanding of MRSA risks, evaluation of interventions and forecasting MRSA rates are important public health problems. Existing forecasting models rely on statistical or neural network approaches, which lack epidemiological interpretability, and have limited performance. Mechanistic epidemic models are difficult to calibrate and limited in incorporating diverse datasets. We present CALYPSO, a hybrid framework that integrates neural networks with mechanistic metapopulation models to capture the spread dynamics of infectious diseases (i.e., MRSA) across healthcare and community settings. Our model leverages patient-level insurance claims, commuting data, and healthcare transfer patterns to learn region- and time-specific parameters governing MRSA spread. This enables accurate, interpretable forecasts at multiple spatial resolutions (county, healthcare facility, region, state) and supports counterfactual analyses of infection control policies and outbreak risks. We also show that CALYPSO improves statewide forecasting performance by over 4.5% compared to machine learning baselines, while also identifying high-risk regions and cost-effective strategies for allocating infection prevention resources.

- This paper addresses the critical need for efficient energy management in healthcare facilities, where fluctuating energy demands challenge both operational and sustainability goals. Traditional energy management methods often fall short in healthcare settings, lead ing to inefficiencies and increased costs. To address this, the paper explores AI - driven approaches for demand forecasting and load balancing, introducing a novel integration of LSTM (Long Short - Term Memory), g enetic a lgorithm, and SHAP (Shapley Additive E xplanations) specifically tailored for healthcare energy management. While LSTM has been widely used for time - series forecasting, its application in healthcare energy demand prediction is underexplored. Here, LSTM is demonstrated to significantly outperfor m ARIMA and Prophet models in handling complex, non - linear demand patterns. Results show that LSTM achieved a Mean Absolute Error (MAE) of 21.69 and Root Mean Square Error (RMSE) of 29.96, significantly improving upon Prophet (MAE: 59.78, RMSE: 81.22) and ARIMA (MAE: 87.73, RMSE: 125.22), highlighting its superior forecasting capability. Genetic algorithm is employed not only for optimising forecasting model parameters but also for dynamically improving load balancing strategies, ensuring adaptability to real - time energy fluctuations. Additionally, SHAP analysis is used to interpret the models and understan d the impact of various input features on predictions, enhancing model transparency and trustworthiness in energy decision - making. The combined LSTM - GA - SH AP approach offers a comprehensive framework that improves forecasting accuracy, enhances energy efficiency, and supports sustainability in healthcare environments. Future work could focus on real - time implementation and further hybridisation with reinforc ement learning for continuous optimisation. This study establishes a strong foundation for leveraging AI in healthcare energy management, showcasing its potential for scalability, efficiency, and resilience. Introduction Australia has a big capacity of using renewable energy in different regions ( Holloway, R, 2023; Rahimi et al., 2025) . Australian healthcare system plays a major role in using renewable energies. Optimising energy use in healthcare systems is essential due to the high and often unpredictable energy demands needed to run medical equipment, keep environmental conditions stable, and support constant patient care.

Dozens of patients have been killed in a drone attack on one of the last functioning hospitals in el-Fasher in Sudan's Darfur region. While it was not immediately clear who targeted the Saudi Hospital on Friday, medical sources quoted by AFP news agency said the same building was hit by a Rapid Support Forces (RSF) drone "a few weeks ago". Friday's attack killed at least 30 patients in the emergency department, the report added. Regional governor Mini Minawi posted graphic images of bloodied bodies on his X account on Saturday, saying that the attack "exterminated" more than 70 patients, including women and children. The Sudanese army has been at war with the paramilitary RSF, who have seized nearly the entire vast western region of Darfur, since April 2023.

Primary healthcare is a crucial strategy for achieving universal health coverage. South Asian countries are working to improve their primary healthcare system through their country specific policies designed in line with WHO health system framework using the six thematic pillars: Health Financing, Health Service delivery, Human Resource for Health, Health Information Systems, Governance, Essential Medicines and Technology, and an addition area of Cross-Sectoral Linkages [11]. Measuring the current accessibility of healthcare facilities and workforce availability is essential for improving healthcare standards and achieving universal health coverage in developing countries. Data-driven surveillance approaches are required that can provide rapid, reliable, and geographically scalable solutions to understand a) which communities and areas are most at risk of inequitable access and when, b) what barriers to health access exist, and c) how they can be overcome in ways tailored to the specific challenges faced by individual communities. We propose to harness current breakthroughs in Earth-observation (EO) technology, which provide the ability to generate accurate, up-to-date, publicly accessible, and reliable data, which is necessary for equitable access planning and resource allocation to ensure that vaccines, and other interventions reach everyone, particularly those in greatest need, during normal and crisis times. This requires collaboration among countries to identify evidence based solutions to shape health policy and interventions, and drive innovations and research in the region.

Breast cancer is one of the leading causes of cancer mortality. Breast cancer patients in developing countries, especially sub-Saharan Africa, South Asia, and South America, suffer from the highest mortality rate in the world. One crucial factor contributing to the global disparity in mortality rate is long delay of diagnosis due to a severe shortage of trained pathologists, which consequently has led to a large proportion of late-stage presentation at diagnosis. The delay between the initial development of symptoms and the receipt of a diagnosis could stretch upwards 15 months. To tackle this critical healthcare disparity, this research has developed a deep learning-based diagnosis system for metastatic breast cancer that can achieve high diagnostic accuracy as well as computational efficiency. Based on our evaluation, the MobileNetV2-based diagnostic model outperformed the more complex VGG16, ResNet50 and ResNet101 models in diagnostic accuracy, model generalization, and model training efficiency. The visual comparisons between the model prediction and ground truth have demonstrated that the MobileNetV2 diagnostic models can identify very small cancerous nodes embedded in a large area of normal cells which is challenging for manual image analysis. Equally Important, the light weighted MobleNetV2 models were computationally efficient and ready for mobile devices or devices of low computational power. These advances empower the development of a resource-efficient and high performing AI-based metastatic breast cancer diagnostic system that can adapt to under-resourced healthcare facilities in developing countries. This research provides an innovative technological solution to address the long delays in metastatic breast cancer diagnosis and the consequent disparity in patient survival outcome in developing countries.

Over the course of the past two decades, a substantial body of research has substantiated the viability of utilising cardiac signals as a biometric modality. This paper presents a novel approach for patient identification in healthcare systems using electrocardiogram signals. A convolutional neural network is used to classify users based on images extracted from ECG signals. The proposed identification system is evaluated in multiple databases, providing a comprehensive understanding of its potential in real-world scenarios. The impact of Cardiovascular Diseases on generic user identification has been largely overlooked in previous studies. The presented method takes into account the cardiovascular condition of the patients, ensuring that the results obtained are not biased or limited. Furthermore, the results obtained are consistent and reliable, with lower error rates and higher accuracy metrics, as demonstrated through extensive experimentation. All these features make the proposed method a valuable contribution to the field of patient identification in healthcare systems, and make it a strong contender for practical applications.

The challenges of using inadequate online recruitment systems can be addressed with machine learning and software engineering techniques. Bi-directional personalization reinforcement learning-based architecture with active learning can get recruiters to recommend qualified applicants and also enable applicants to receive personalized job recommendations. This paper focuses on how machine learning techniques can enhance the recruitment process in the travel nursing industry by helping speed up data acquisition using a multi-model data service and then providing personalized recommendations using bi-directional reinforcement learning with active learning. This need was especially evident when trying to respond to the overwhelming needs of healthcare facilities during the COVID-19 pandemic. The need for traveling nurses and other healthcare professionals was more evident during the lockdown period. A data service was architected for job feed processing using an orchestration of natural language processing (NLP) models that synthesize job-related data into a database efficiently and accurately. The multi-model data service provided the data necessary to develop a bi-directional personalization system using reinforcement learning with active learning that could recommend travel nurses and healthcare professionals to recruiters and provide job recommendations to applicants using an internally developed smart match score as a basis. The bi-directional personalization reinforcement learning-based architecture with active learning combines two personalization systems - one that runs forward to recommend qualified candidates for jobs and another that runs backward and recommends jobs for applicants.

Chatbot Development Cost: A few years ago, a chatbot would have resembled a miracle. Whenever you visit a website, you will get a message "Hello! How can I help you?" on the webpage's side corner and you would have felt thrilled after all a robot asking you queries. It was the biggest revolutionary aspect of technology. AI-integrated Chatbots are undoubtedly revolutionizing all industries (Such as IT, Ecommerce, Real Estate, Travel, and many more) around the globe.

The necessity of data driven decisions in healthcare strategy formulation is rapidly increasing. A reliable framework which helps identify factors impacting a Healthcare Provider Facility or a Hospital (from here on termed as Facility) Market Share is of key importance. This pilot study aims at developing a data driven Machine Learning - Regression framework which aids strategists in formulating key decisions to improve the Facilitys Market Share which in turn impacts in improving the quality of healthcare services. The US (United States) healthcare business is chosen for the study; and the data spanning across 60 key Facilities in Washington State and about 3 years of historical data is considered. In the current analysis Market Share is termed as the ratio of facility encounters to the total encounters among the group of potential competitor facilities. The current study proposes a novel two-pronged approach of competitor identification and regression approach to evaluate and predict market share, respectively. Leveraged model agnostic technique, SHAP, to quantify the relative importance of features impacting the market share. The proposed method to identify pool of competitors in current analysis, develops Directed Acyclic Graphs (DAGs), feature level word vectors and evaluates the key connected components at facility level. This technique is robust since its data driven which minimizes the bias from empirical techniques. Post identifying the set of competitors among facilities, developed Regression model to predict the Market share. For relative quantification of features at a facility level, incorporated SHAP a model agnostic explainer. This helped to identify and rank the attributes at each facility which impacts the market share.

Medical and health care facilities have improved since the emergence and incorporation of machine learning technologies. The application of machine learning in healthcare facilities has always increased the possibilities of patient satisfaction and the best healthcare treatment. Let us discuss the five best use cases that machine learning-based healthcare software development can offer the patient with the best outcomes in terms of treatment and facilities rendered at healthcare facilities. It is one of the best contributions that machine learning has made to the healthcare sector and changing the way patients get treated. The clinical decision support tool helps in analyzing huge data volume to recognize the kind of disease and to decide that treatment stage.