Latest AI and machine learning research in information technology for healthcare professionals.
Medical research, particularly in predicting patient outcomes, heavily relies on medical time series data extracted from Electronic Health Records (EHR), which provide extensive information on patient histories. Despite rigorous examination, labeling errors are inevitable and can significantly impede accurate predictions of patient outcome. To address this challenge, we propose an \textbf{A}tten...
The increasing volume of healthcare textual data requires computationally efficient, yet highly accurate classification approaches able to handle the nuanced and complex nature of medical terminology. This research presents Knowledge Distillation for Healthcare Multi-Label Text Classification (KDH-MLTC), a framework leveraging model compression and Large Language Models (LLMs). The proposed appr...
Predicting treatment response is an important problem in real-world applications, where the heterogeneity of the treatment response remains a signific...
The adoption of standardized imaging protocols in retinal imaging is critical to overcoming challenges posed by fragmented data formats across devices...
Irregular temporal data, characterized by varying recording frequencies, differing observation durations, and missing values, presents significant c...
We present a comprehensive analysis of deep learning approaches for Electronic Health Record (EHR) time-series imputation, examining how the interplay...
Anomaly detection is a fundamental task in machine learning and data mining, with significant applications in cybersecurity, industrial fault diagno...
The integration of machine learning (ML) in cyber physical systems (CPS) is a complex task due to the challenges that arise in terms of real-time de...
The integration of machine-learning technologies into radiology practice has the potential to significantly enhance diagnostic workflows and patient c...
The standardization of clinical data elements (CDEs) aims to ensure consistent and comprehensive patient information across various healthcare syste...
Online anomaly detection is essential in fields such as cybersecurity, healthcare, and industrial monitoring, where promptly identifying deviations ...
The rapid digitalization of communication systems has elevated Interactive Voice Response (IVR) technologies to become critical interfaces for custo...
Falls pose a substantial risk to elderly individuals, especially those over 65, often leading to severe consequences. This project investigates the po...
Objective: To develop and evaluate a scalable methodology for harmonizing inconsistent units in large-scale clinical datasets, addressing a key barr...
Digital diabetes management technologies (DDMTs) have emerged as promising tools for improving glycemic control in patients with type 2 diabetes melli...
BACKGROUND: Progression free survival (PFS) is a critical clinical outcome endpoint during cancer management and treatment evaluation. Yet, PFS is oft...
PURPOSE: This study reports the implementation of a proof-of-concept, artificial intelligence (AI)-driven clinical decision support system for detecti...
INTRODUCTION: Identifying individuals with dementia is crucial for prevalence estimation and service planning, but reliable, scalable methods are lack...
IMPORTANCE: The increase of electronic health record (EHR) work negatively impacts clinician well-being. One potential solution is incorporating an am...
The Hawkes process (HP) is commonly used to model event sequences with self-reinforcing dynamics, including electronic health records (EHRs). Tradit...