222 citations · 236 across the 12 of their papers we have counts for
11 papers · 1 filter
Multilingual Clinical NER for Diseases and Medications Recognition in Cardiology Texts using BERT Embeddings
Manuela Daniela Danu, George Marica, Constantin Suciu +2
The rapidly increasing volume of electronic health record (EHR) data underscores a pressing need to unlock biomedical knowledge from unstructured clinical texts to support advancem…
CVE-LLM : Ontology-Assisted Automatic Vulnerability Evaluation Using Large Language Models
Rikhiya Ghosh, Hans-Martin von Stockhausen, Martin Schmitt +3
The National Vulnerability Database (NVD) publishes over a thousand new vulnerabilities monthly, with a projected 25 percent increase in 2024, highlighting the crucial need for rap…
CVE-LLM : Automatic vulnerability evaluation in medical device industry using large language models
Rikhiya Ghosh, Oladimeji Farri, Hans-Martin von Stockhausen +2
The healthcare industry is currently experiencing an unprecedented wave of cybersecurity attacks, impacting millions of individuals. With the discovery of thousands of vulnerabilit…
Fusion of Domain-Adapted Vision and Language Models for Medical Visual Question Answering
Cuong Nhat Ha, Shima Asaadi, Sanjeev Kumar Karn +3
Vision-language models, while effective in general domains and showing strong performance in diverse multi-modal applications like visual question-answering (VQA), struggle to main…
General-Purpose vs. Domain-Adapted Large Language Models for Extraction of Structured Data from Chest Radiology Reports
Ali H. Dhanaliwala, Rikhiya Ghosh, Sanjeev Kumar Karn +4
Radiologists produce unstructured data that can be valuable for clinical care when consumed by information systems. However, variability in style limits usage. Study compares syste…
shs-nlp at RadSum23: Domain-Adaptive Pre-training of Instruction-tuned LLMs for Radiology Report Impression Generation
Sanjeev Kumar Karn, Rikhiya Ghosh, Kusuma P +1
Instruction-tuned generative Large language models (LLMs) like ChatGPT and Bloomz possess excellent generalization abilities, but they face limitations in understanding radiology r…
RadLing: Towards Efficient Radiology Report Understanding
Rikhiya Ghosh, Sanjeev Kumar Karn, Manuela Daniela Danu +3
Most natural language tasks in the radiology domain use language models pre-trained on biomedical corpus. There are few pretrained language models trained specifically for radiolog…
Differentiable Multi-Agent Actor-Critic for Multi-Step Radiology Report Summarization
Sanjeev Kumar Karn, Ning Liu, Hinrich Schuetze +1
The IMPRESSIONS section of a radiology report about an imaging study is a summary of the radiologist's reasoning and conclusions, and it also aids the referring physician in confir…
DR-BiLSTM: Dependent Reading Bidirectional LSTM for Natural Language Inference
Reza Ghaeini, Sadid A. Hasan, Vivek Datla +7
We present a novel deep learning architecture to address the natural language inference (NLI) task. Existing approaches mostly rely on simple reading mechanisms for independent enc…
Condensed Memory Networks for Clinical Diagnostic Inferencing
Aaditya Prakash, Siyuan Zhao, Sadid A. Hasan +5
Diagnosis of a clinical condition is a challenging task, which often requires significant medical investigation. Previous work related to diagnostic inferencing problems mostly con…
Neural Paraphrase Generation with Stacked Residual LSTM Networks
Aaditya Prakash, Sadid A. Hasan, Kathy Lee +4
In this paper, we propose a novel neural approach for paraphrase generation. Conventional para- phrase generation methods either leverage hand-written rules and thesauri-based alig…