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Clinical prediction from structured electronic health records (EHRs) is challenging due to high dimensionality, heterogeneity, class imbalance, and distribution shift. While tabular in-context learning (TICL) and retrieval-augmented methods…
Using causal relations to guide decision making has become an essential analytical task across various domains, from marketing and medicine to education and social science. While powerful statistical models have been developed for inferring…
The advent of the Internet era has led to an explosive growth in the Electronic Health Records (EHR) in the past decades. The EHR data can be regarded as a collection of clinical events, including laboratory results, medication records,…
Clinical Pathways (CP) are medical management plans developed to standardize patient treatment activities, optimize resource usage, reduce expenses, and improve the quality of healthcare services. Most CPs currently in use are paper-based…
This study investigates the accessibility of open-source electronic health record (EHR) systems for individuals who are visually impaired or blind. Ensuring the accessibility of EHRs to visually impaired users is critical for the diversity,…
Due to the rapid growth of information available about individual patients, most physicians suffer from information overload when they review patient information in health information technology systems. In this manuscript, we present a…
Data-driven optimization models have the potential to significantly improve hospital capacity management, particularly during demand surges, when effective allocation of capacity is most critical and challenging. However, integrating models…
The promise of AI in medicine depends on learning from data that reflect what matters to patients and clinicians. Most existing models are trained on electronic health records (EHRs), which capture biological measures but rarely…
A substantial administrative burden is placed on healthcare professionals as they manage and progress through their careers. Identity verification, pre-employment screening and appraisals: the bureaucracy associated with each of these…
The use of machine learning and AI on electronic health records (EHRs) holds substantial potential for clinical insight. However, this approach faces challenges due to data heterogeneity, sparsity, temporal misalignment, and limited labeled…
Clinical research on smart health has an increasing demand for intelligent and clinic-oriented medical image computing algorithms and platforms that support various applications. To this end, we have developed SenseCare research platform,…
The concept of an intelligent augmented reality (AR) assistant has significant, wide-ranging applications, with potential uses in medicine, military, and mechanics domains. Such an assistant must be able to perceive the environment and…
Predicting the patient's clinical outcome from the historical electronic medical records (EMR) is a fundamental research problem in medical informatics. Most deep learning-based solutions for EMR analysis concentrate on learning the…
With the rapid advancement of Large Language Models (LLMs) and their outstanding performance in semantic and contextual comprehension, the potential of LLMs in specialized domains warrants exploration. This paper introduces the NoteAid EHR…
Background: Electronic Health Records (EHRs) contain rich information of patients' health history, which usually include both structured and unstructured data. There have been many studies focusing on distilling valuable information from…
Recommender systems are quintessential applications of human-computer interaction. Widely utilized in daily life, they offer significant convenience but also present numerous challenges, such as the information cocoon effect, privacy…
Electronic phenotyping is the task of ascertaining whether an individual has a medical condition of interest by analyzing their medical record and is foundational in clinical informatics. Increasingly, electronic phenotyping is performed…
Electronic Health Record (EHR) has become an essential tool in the healthcare ecosystem, providing authorized clinicians with patients' health-related information for better treatment. While most developed countries are taking advantage of…
Designing suitable tasks for visualization evaluation remains challenging. Traditional evaluation techniques commonly rely on 'low-level' or 'open-ended' tasks to assess the efficacy of a proposed visualization, however, nontrivial…
Despite the general assumption that completely locked-in state (CLIS) patients remain conscious and aware of their environment, the effectiveness of brain-computer interfaces (BCIs) in facilitating communication has been limited, as…