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Found 21 papers, 4 papers with code
Rephrasing Electronic Health Records for Pretraining Clinical Language Models
Clinical language models are important for many applications in healthcare, but their development depends on access to extensive clinical text for pretraining.
Improved GUI Grounding via Iterative Narrowing
Graphical User Interface (GUI) grounding plays a crucial role in enhancing the capabilities of Vision-Language Model (VLM) agents.
e-Health CSIRO at RRG24: Entropy-Augmented Self-Critical Sequence Training for Radiology Report Generation
The Shared Task on Large-Scale Radiology Report Generation (RRG24) aims to expedite the development of assistive systems for interpreting and reporting on chest X-ray (CXR) images.
e-Health CSIRO at "Discharge Me!" 2024: Generating Discharge Summary Sections with Fine-tuned Language Models
Our results show that conditioning on the content of discharge summary prior to the target sections is effective for the generation task.
Improving Text-based Early Prediction by Distillation from Privileged Time-Series Text
In this study, we propose to treat this neglected text as privileged information available during training to enhance early prediction modeling through knowledge distillation, presented as Learning using Privileged tIme-sEries Text (LuPIET).
Automated ICD Coding using Extreme Multi-label Long Text Transformer-based Models
XR-Transformer, the new SOTA model in the general extreme multi-label text classification domain, and XR-LAT, a novel adaptation of the XR-Transformer model, were also trained on the MIMIC-III dataset.
Multi Label Text Classification
Multi-Label Text Classification
+1
Applying data technologies to combat AMR: current status, challenges, and opportunities on the way forward
Antimicrobial resistance (AMR) is a growing public health threat, estimated to cause over 10 million deaths per year and cost the global economy 100 trillion USD by 2050 under status quo projections.
Hierarchical Label-wise Attention Transformer Model for Explainable ICD Coding
In this study, we propose a hierarchical label-wise attention Transformer model (HiLAT) for the explainable prediction of ICD codes from clinical documents.
De-identifying Australian Hospital Discharge Summaries: An End-to-End Framework using Ensemble of Deep Learning Models
Our end-to-end de-identification framework consists of three components: 1) Annotation: labelling of PII in the 600 hospital discharge summaries using five pre-defined categories: person, address, date of birth, individual identification number, phone/fax number; 2) Modelling: training six named entity recognition (NER) deep learning base-models on balanced and imbalanced datasets; and evaluating ensembles that combine all six base-models, the three base-models with the best F1 scores and the three base-models with the best recall scores respectively, using token-level majority voting and stacking methods; and 3) De-identification: removing PII from the hospital discharge summaries.
Stochastic Multi-level Composition Optimization Algorithms with Level-Independent Convergence Rates
We show that the first algorithm, which is a generalization of \cite{GhaRuswan20} to the $T$ level case, can achieve a sample complexity of $\mathcal{O}(1/\epsilon^6)$ by using mini-batches of samples in each iteration.
A Label Attention Model for ICD Coding from Clinical Text
In this paper, we propose a new label attention model for automatic ICD coding, which can handle both the various lengths and the interdependence of the ICD code related text fragments.
Ranked #7 on
Medical Code Prediction
on MIMIC-III
Identifying Patients with Pain in Emergency Departments using Conventional Machine Learning and Deep Learning
Pain is the main symptom that patients present with to the emergency department (ED).
Clinical Document Classification Using Labeled and Unlabeled Data Across Hospitals
Reviewing radiology reports in emergency departments is an essential but laborious task.
Identifying Risk Factors For Heart Disease in Electronic Medical Records: A Deep Learning Approach
Various deep learning architectures were devised and evaluated for extracting heart disease risk factors from clinical documents.
Automatic Diagnosis Coding of Radiology Reports: A Comparison of Deep Learning and Conventional Classification Methods
Diagnosis autocoding services and research intend to both improve the productivity of clinical coders and the accuracy of the coding.
The Benefits of Word Embeddings Features for Active Learning in Clinical Information Extraction
This study investigates the use of unsupervised word embeddings and sequence features for sample representation in an active learning framework built to extract clinical concepts from clinical free text.
