CMR-EXTR extracts structured data from CMR reports at 99.65% variable-level accuracy using teacher-student LLM distillation and three-principle uncertainty estimation for quality control.
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ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission
27 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
abstract
Clinical notes contain information about patients that goes beyond structured data like lab values and medications. However, clinical notes have been underused relative to structured data, because notes are high-dimensional and sparse. This work develops and evaluates representations of clinical notes using bidirectional transformers (ClinicalBERT). ClinicalBERT uncovers high-quality relationships between medical concepts as judged by humans. ClinicalBert outperforms baselines on 30-day hospital readmission prediction using both discharge summaries and the first few days of notes in the intensive care unit. Code and model parameters are available.
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representative citing papers
HullFT performs test-time finetuning by sparse convex reconstruction of query embeddings followed by gradient caching on repeated examples, yielding better quality-efficiency tradeoffs than prior TTFT methods.
LLM-rephrased synthetic clinical notes preserve core information and utility for coarse prediction tasks but lose fine-grained details such as ICD codes, with chunk-wise rephrasing as a partial mitigation that trades off factual accuracy.
NanoResearch introduces a tri-level co-evolving framework of skills, memory, and policy to personalize LLM-powered research automation across projects and users.
RG-inspired lattice models for piecewise GLMs provide explicit interpretable partitions and a replica-analysis-derived scaling law for regularization that allows increasing complexity without expected rise in generalization loss.
A deep kernel learning architecture with transformer feature extraction on clinical-BERT embeddings and Gaussian process backend identifies three glaucoma subgroups by decoupling progression trajectories from current visual acuity in multimodal EHR data.
REBench is a new benchmark that consolidates existing datasets into a large collection of binaries with knowledge-base-driven ground truth to enable fair LLM evaluation on stripped-binary type and name recovery.
CURA improves calibration of clinical LM risk predictions by combining individual error alignment with neighborhood-based soft labels without harming discrimination on MIMIC-IV tasks.
EncFormer reduces online MPC communication by 1.4x-30.4x and end-to-end latency by 1.3x-9.8x versus prior hybrid FHE-MPC systems for private GPT- and BERT-style inference while preserving accuracy.
TRACE removes 47.3% of text from clinical notes by targeting bloat and preserves performance on information extraction and outcome prediction tasks.
The paper introduces a new taxonomy that groups AI-driven psychological computing tasks by their underlying computational patterns into four categories and reviews over 300 works from the pre-trained model to LLM eras.
LLaVA-Med is created via curriculum fine-tuning on PubMed figure-caption pairs and GPT-4 self-instructed data, achieving competitive or better results than prior supervised models on three biomedical VQA benchmarks.
BloombergGPT is a 50B parameter LLM trained on a 708B token mixed financial and general dataset that outperforms prior models on financial benchmarks while preserving general LLM performance.
Sequence embeddings from diagnosis histories improve prediction of 93 of 131 incident disease blocks and event-free survival beyond age, sex, and comorbidity burden in large-scale hospital data.
Proposes a multi-modal multi-span medical QA framework and new dataset that outputs answers containing both text and relevant images.
Training a LoRA adapter on 6,900 examples derived from MIMIC-III notes reduces expected calibration error from 0.1269 to 0.0398 and Brier score from 0.199 to 0.145 for clinical event prediction.
Single-agent LLM frameworks outperform naive multi-agent systems in multimodal clinical risk prediction tasks and are better calibrated.
LLMs match or beat supervised BERT models on detecting whether a discharge note contains an actionable clinical task but trail on classifying the exact type of action, pointing to the need for datasets that explain why each span was labeled actionable.
Autoregressive transformer modeling with missingness-aware contrastive pre-training outperforms baselines on MIMIC-IV and eICU benchmarks and mitigates divergent behavior from removed modalities in clinical trajectories.
CGCL progressively trains LLMs to generate Toulmin-structured clinical diagnostic arguments across three curriculum stages, achieving accuracy and reasoning quality comparable to RL methods with improved stability and efficiency.
Retina-RAG combines a retinal classifier, LoRA-tuned Qwen2.5-VL, and RAG to jointly grade DR, detect ME, and generate reports, reaching F1 scores of 0.731 and 0.948 while exceeding baselines on ROUGE-L and SBERT metrics.
Dense retrieval plus query reformulation and reranking reaches 60.49% accuracy on MedQA USMLE, outperforming other setups while domain-specialized models make better use of the retrieved evidence.
Llama 3.1 annotates Polish medical texts to train DistilBERT classifiers achieving F1 scores above 0.80 that are 500 times smaller than the teacher model.
Authors introduce MLM and CLM specialization methods that avoid memorizing identifiers in sensitive training data while aiming for a privacy-utility tradeoff on medical datasets.
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Retina-RAG: Retrieval-Augmented Vision-Language Modeling for Joint Retinal Diagnosis and Clinical Report Generation
Retina-RAG combines a retinal classifier, LoRA-tuned Qwen2.5-VL, and RAG to jointly grade DR, detect ME, and generate reports, reaching F1 scores of 0.731 and 0.948 while exceeding baselines on ROUGE-L and SBERT metrics.