Practice Management

AutoDroid-V2: Boosting SLM-based GUI Agents via Code Generation

Large language models (LLMs) have brought exciting new advances to mobile UI agents, a long-standing research field that aims to complete arbitrary natural language tasks through mobile UI interactions. However, existing UI agents usually demand powerful large language models that are difficult to be deployed locally on end-users' devices, raising huge concerns about user privacy and centralized...

Aligning AI Research with the Needs of Clinical Coding Workflows: Eight Recommendations Based on US Data Analysis and Critical Review

Clinical coding is crucial for healthcare billing and data analysis. Manual clinical coding is labour-intensive and error-prone, which has motivated research towards full automation of the process. However, our analysis, based on US English electronic health records and automated coding research using these records, shows that widely used evaluation methods are not aligned with real clinical con...

CARL-GT: Evaluating Causal Reasoning Capabilities of Large Language Models

Causal reasoning capabilities are essential for large language models (LLMs) in a wide range of applications, such as education and healthcare. But ...

Examining Imbalance Effects on Performance and Demographic Fairness of Clinical Language Models

Data imbalance is a fundamental challenge in applying language models to biomedical applications, particularly in ICD code prediction tasks where la...

Deep Joint Source Channel Coding for Privacy-Aware End-to-End Image Transmission

Deep neural network (DNN)-based joint source and channel coding is proposed for privacy-aware end-to-end image transmission against multiple eavesdr...

Preventing Non-intrusive Load Monitoring Privacy Invasion: A Precise Adversarial Attack Scheme for Networked Smart Meters

Smart grid, through networked smart meters employing the non-intrusive load monitoring (NILM) technique, can considerably discern the usage patterns...

Process-Supervised Reward Models for Verifying Clinical Note Generation: A Scalable Approach Guided by Domain Expertise

Process-supervised reward models (PRMs), which verify large language model (LLM) outputs step-by-step, have achieved significant success in mathemat...

Energy-Constrained Information Storage on Memristive Devices in the Presence of Resistive Drift

In this paper, we examine the problem of information storage on memristors affected by resistive drift noise under energy constraints. We introduce ...

Using Instruction-Tuned Large Language Models to Identify Indicators of Vulnerability in Police Incident Narratives

Objectives: Compare qualitative coding of instruction tuned large language models (IT-LLMs) against human coders in classifying the presence or abse...

NoteContrast: Contrastive Language-Diagnostic Pretraining for Medical Text

Accurate diagnostic coding of medical notes is crucial for enhancing patient care, medical research, and error-free billing in healthcare organizati...

CSSDH: An Ontology for Social Determinants of Health to Operational Continuity of Care Data Interoperability

The rise of digital platforms has led to an increasing reliance on technology-driven, home-based healthcare solutions, enabling individuals to monit...

Assisted morbidity coding: the SISCO.web use case for identifying the main diagnosis in Hospital Discharge Records

Coding morbidity data using international standard diagnostic classifications is increasingly important and still challenging. Clinical coders and p...

Zero-Shot ATC Coding with Large Language Models for Clinical Assessments

Manual assignment of Anatomical Therapeutic Chemical (ATC) codes to prescription records is a significant bottleneck in healthcare research and oper...

Can large language models be privacy preserving and fair medical coders?

Protecting patient data privacy is a critical concern when deploying machine learning algorithms in healthcare. Differential privacy (DP) is a commo...

Feature Coding in the Era of Large Models: Dataset, Test Conditions, and Benchmark

Large models have achieved remarkable performance across various tasks, yet they incur significant computational costs and privacy concerns during b...

How chromatin interactions shed light on interpreting non-coding genomic variants: opportunities and future direc-tions

Genomic variants, including copy number variants (CNVs) and genome-wide associa-tion study (GWAS) single nucleotide polymorphisms (SNPs), represent ...

High-precision medical speech recognition through synthetic data and semantic correction: UNITED-MEDASR

Automatic Speech Recognition (ASR) systems in the clinical domain face significant challenges, notably the need to recognise specialised medical voc...

Robust Steganography with Boundary-Preserving Overflow Alleviation and Adaptive Error Correction

With the rapid evolution of the Internet, the vast amount of data has created opportunities for fostering the development of steganographic techniqu...

Sparse-Coding Variational Autoencoders.

The sparse coding model posits that the visual system has evolved to efficiently code natural stimuli using a sparse set of features from an overcompl...

Leveraging genomic deep learning models for non-coding variant effect prediction

The majority of genetic variants identified in genome-wide association studies of complex traits are non-coding, and characterizing their function r...