Latest AI and machine learning research in information technology for healthcare professionals.
Large-scale pretraining has transformed modeling of language and other data types, but its potential remains underexplored in healthcare with structured electronic health records (EHRs). We present a novel generative pretraining strategy for sequential EHR data using next-visit event prediction. Our model learns to autoregressively generate various tokenized clinical events for the next visit ba...
OBJECTIVE: A large proportion of electronic health record (EHR) data consists of unstructured medical language text. The formatting of this text is often flexible and inconsistent, making it challenging to use for predictive modeling, clinical decision support, and data mining. Large language models' (LLMs) ability to understand context and semantic variations makes them promising tools for standa...
Starting in the 1970s with robots that were physically isolated from contact with their human co-workers, robots now collaborate with human workers to...
We present Federated Timeline Synthesis (FTS), a novel framework for training generative foundation models across distributed timeseries data applie...
Identification of key variables such as medications, diseases, relations from health records and clinical notes has a wide range of applications in ...
With current advancement in hybermedia knowledges, the privacy of digital information has developed a critical problem. To overawed the susceptibili...
Quantum Neural Networks (QNNs), a prominent approach in Quantum Machine Learning (QML), are emerging as a powerful alternative to classical machine ...
Magnetic Resonance Fingerprinting (MRF) is a fast quantitative MR Imaging technique that provides multi-parametric maps with a single acquisition. N...
Patient cohort retrieval is a pivotal task in medical research and clinical practice, enabling the identification of specific patient groups from ex...
Multivariate time series anomaly detection (MTS-AD) is critical in domains like healthcare, cybersecurity, and industrial monitoring, yet remains ch...
Retrieval-Augmented Generation (RAG) systems are emerging as a key approach for grounding Large Language Models (LLMs) in external knowledge, addres...
The emergence of new-generation artificial intelligence technology has brought numerous innovations to the healthcare field, including telemedicine an...
Electronic Health Records (EHR)-based disease prediction models have demonstrated significant clinical value in promoting precision medicine and ena...
The integration of AI/ML into medical devices is rapidly transforming healthcare by enhancing diagnostic and treatment facilities. However, this adv...
Chest X ray (CXR) imaging remains a critical diagnostic tool for thoracic conditions, but current automated systems face limitations in pathology co...
Electronic Health Record (EHR) data encompass diverse modalities -- text, images, and medical codes -- that are vital for clinical decision-making. ...
The Medical Information Mart for Intensive Care (MIMIC) datasets have become the Kernel of Digital Health Research by providing freely accessible, d...
The concept bottleneck model (CBM), as a technique improving interpretability via linking predictions to human-understandable concepts, makes high-r...
We explore the role of ontologies in enhancing hybrid modeling and simulation through improved semantic rigor, model reusability, and interoperabili...
Accurate classification of software bugs is essential for improving software quality. This paper presents a rule-based automated framework for class...