Information Technology

Automating the Addiction Behaviors Checklist for Problematic Opioid Use Identification.

IMPORTANCE: Individuals whose chronic pain is managed with opioids are at high risk of developing an opioid use disorder. Electronic health records (EHR) allow large-scale studies to identify a continuum of problematic opioid use, including opioid use disorder. Traditionally, this is done through diagnostic codes, which are often unreliable and underused.

The application of natural language processing technology in hospital network information management systems: Potential for improving diagnostic accuracy and efficiency.

BACKGROUND: Processing scanned documents in electronic health records (EHR) was one of the problem in hospital network information management systems (HNIMS). To overcome this difficulty, the complex interactions among natural language processing (NLP), optical character recognition (OCR) and image preprocessing was used.

Unsupervised discovery of clinical disease signatures using probabilistic independence.

OBJECTIVE: This study uses probabilistic independence to disentangle patient-specific sources of disease and their signatures in Electronic Health Rec...

Review learning: Real world validation of privacy preserving continual learning across medical institutions.

When a deep learning model is trained sequentially on different datasets, it often forgets the knowledge learned from previous data, a problem known a...

Artificial Intelligence in the Clinic: Creating Harmony or Just Adding Noise?

Although still limited, the integration of artificial intelligence (AI) in health care has rapidly expanded in the past few years, especially in oncol...

Integrating large language models with human expertise for disease detection in electronic health records.

OBJECTIVE: Electronic health records (EHR) are widely available to complement administrative data-based disease surveillance and healthcare performanc...

Development and validation of the provider documentation summarization quality instrument for large language models.

OBJECTIVES: As large language models (LLMs) are integrated into electronic health record (EHR) workflows, validated instruments are essential to evalu...

Sharing patient technology preferences with care networks: Stakeholders' views of the "Let's Talk Tech" decision aid for dementia care.

BackgroundLet's Talk Tech (LTT) is a self-administered web intervention for people with memory loss and their care partners that supports decision-mak...

Intercept Cancer: Cancer Pre-Screening with Large Scale Healthcare Foundation Models

Cancer screening, leading to early detection, saves lives. Unfortunately, existing screening techniques require expensive and intrusive medical proc...

LPASS: Linear Probes as Stepping Stones for vulnerability detection using compressed LLMs

Large Language Models (LLMs) are being extensively used for cybersecurity purposes. One of them is the detection of vulnerable codes. For the sake o...

Dynamic Malware Classification of Windows PE Files using CNNs and Greyscale Images Derived from Runtime API Call Argument Conversion

Malware detection and classification remains a topic of concern for cybersecurity, since it is becoming common for attackers to use advanced obfusca...

Training LLMs for EHR-Based Reasoning Tasks via Reinforcement Learning

We present EHRMIND, a practical recipe for adapting large language models (LLMs) to complex clinical reasoning tasks using reinforcement learning wi...

Position Paper: Metadata Enrichment Model: Integrating Neural Networks and Semantic Knowledge Graphs for Cultural Heritage Applications

The digitization of cultural heritage collections has opened new directions for research, yet the lack of enriched metadata poses a substantial chal...

Exploring Scaling Laws for EHR Foundation Models

The emergence of scaling laws has profoundly shaped the development of large language models (LLMs), enabling predictable performance gains through ...

The Role of AI in Early Detection of Life-Threatening Diseases: A Retinal Imaging Perspective

Retinal imaging has emerged as a powerful, non-invasive modality for detecting and quantifying biomarkers of systemic diseases-ranging from diabetes...

Semi-supervised Clustering Through Representation Learning of Large-scale EHR Data

Electronic Health Records (EHR) offer rich real-world data for personalized medicine, providing insights into disease progression, treatment respons...

Ontology- and LLM-based Data Harmonization for Federated Learning in Healthcare

The rise of electronic health records (EHRs) has unlocked new opportunities for medical research, but privacy regulations and data heterogeneity rem...

MedDreamer: Model-Based Reinforcement Learning with Latent Imagination on Complex EHRs for Clinical Decision Support

Timely and personalized treatment decisions are essential across a wide range of healthcare settings where patient responses vary significantly and ...

MLRan: A Behavioural Dataset for Ransomware Analysis and Detection

Ransomware remains a critical threat to cybersecurity, yet publicly available datasets for training machine learning-based ransomware detection mode...

Bridging Electronic Health Records and Clinical Texts: Contrastive Learning for Enhanced Clinical Tasks

Conventional machine learning models, particularly tree-based approaches, have demonstrated promising performance across various clinical prediction...