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Determinants of Training Corpus Size for Clinical Text Classification

Introduction: Clinical text classification using natural language processing (NLP) models requires adequate training data to achieve optimal performance. For that, 200-500 documents are typically annotated. The number is constrained by time and costs and lacks justification of the sample size requirements and their relationship to text vocabulary properties. Methods: Using the publicly available...

Adversarial Drift-Aware Predictive Transfer: Toward Durable Clinical AI

Clinical AI systems frequently suffer performance decay post-deployment due to temporal data shifts, such as evolving populations, diagnostic coding updates (e.g., ICD-9 to ICD-10), and systemic shocks like the COVID-19 pandemic. Addressing this ``aging'' effect via frequent retraining is often impractical due to computational costs and privacy constraints. To overcome these hurdles, we introduce ...

Regularizing Log-Linear Cost Models for Inpatient Stays by Merging ICD-10 Codes

Cost models in healthcare research must balance interpretability, accuracy, and parameter consistency. However, interpretable models often struggle ...

Federated Learning for ICD Classification with Lightweight Models and Pretrained Embeddings

This study investigates the feasibility and performance of federated learning (FL) for multi-label ICD code classification using clinical notes from...

UdonCare: Hierarchy Pruning for Unseen Domain Discovery in Predictive Healthcare

Domain generalization has become a critical challenge in clinical prediction, where patient cohorts often exhibit shifting data distributions that d...

Revolutionising osseous biopsy: the impact of artificial intelligence in the era of personalized medicine.

In a rapidly evolving healthcare environment, artificial intelligence (AI) is transforming diagnostic techniques and personalized medicine. This is al...

ClinBench-HPB: A Clinical Benchmark for Evaluating LLMs in Hepato-Pancreato-Biliary Diseases

Hepato-pancreato-biliary (HPB) disorders represent a global public health challenge due to their high morbidity and mortality. Although large langua...

A General Knowledge Injection Framework for ICD Coding

ICD Coding aims to assign a wide range of medical codes to a medical text document, which is a popular and challenging task in the healthcare domain...

Lexical associations can characterize clinical documentation trends related to palliative care and metastatic cancer.

Palliative care is known to improve quality of life in advanced cancer. Natural language processing offers insights to how documentation around pallia...

A Modular Approach for Clinical SLMs Driven by Synthetic Data with Pre-Instruction Tuning, Model Merging, and Clinical-Tasks Alignment

High computation costs and latency of large language models such as GPT-4 have limited their deployment in clinical settings. Small language models ...

Few-Shot Learning of Medical Coding Systems: A Case Study on Death Certificates with BERT and Mistral.

Identifying the Underlying Cause of Death accurately is crucial for effective healthcare policy and planning. The World Health Organization recommends...

Exploring the Potential of GPT-4 in Creating Billing Codes from Clinic Notes.

Creating standardized billing codes from clinic notes is challenging due to the complexity of over 22,000 codes and the unstructured nature of medical...

Assessing the validity of ICD-10 administrative data in coding comorbidities.

OBJECTIVES: Administrative data are commonly used to inform chronic disease prevalence and support health informatic research. This study assessed the...

Can Reasoning LLMs Enhance Clinical Document Classification?

Clinical document classification is essential for converting unstructured medical texts into standardised ICD-10 diagnoses, yet it faces challenges ...

AnesBench: Multi-Dimensional Evaluation of LLM Reasoning in Anesthesiology

The application of large language models (LLMs) in the medical field has gained significant attention, yet their reasoning capabilities in more spec...

Explainable ICD Coding via Entity Linking

Clinical coding is a critical task in healthcare, although traditional methods for automating clinical coding may not provide sufficient explicit ev...

Efficient Multimodal 3D Object Detector via Instance-Level Contrastive Distillation

Multimodal 3D object detectors leverage the strengths of both geometry-aware LiDAR point clouds and semantically rich RGB images to enhance detectio...

Leveraging Taxonomy Similarity for Next Activity Prediction in Patient Treatment

The rapid progress in modern medicine presents physicians with complex challenges when planning patient treatment. Techniques from the field of Pred...

Advancing Multimodal In-Context Learning in Large Vision-Language Models with Task-aware Demonstrations

Multimodal in-context learning (ICL) has emerged as a key capability of Large Vision-Language Models (LVLMs), driven by their increasing scale and a...

RuCCoD: Towards Automated ICD Coding in Russian

This study investigates the feasibility of automating clinical coding in Russian, a language with limited biomedical resources. We present a new dat...