相关论文: Deep learning-based NLP Data Pipeline for EHR Scan…

This paper combines Struts and Hibernate two architectures together, using DAO (Data Access Object) to store and access data. Then a set of dual-mode humidity medical image library suitable for deep network is established, and a dual-mode…

Dementia is under-recognized in the community, under-diagnosed by healthcare professionals, and under-coded in claims data. Information on cognitive dysfunction, however, is often found in unstructured clinician notes within medical records…

Although syntactic information is beneficial for many NLP tasks, combining it with contextual information between words to solve the coreference resolution problem needs to be further explored. In this paper, we propose an end-to-end parser…

Numerous deep learning-based solutions have been proposed for histopathological image analysis over the past years. While they usually demonstrate exceptionally high accuracy, one key question is whether their precision might be affected by…

In this research, we explored the improvement in terms of multi-class disease classification via pre-trained language models over Medical-Abstracts-TC-Corpus that spans five medical conditions. We excluded non-cancer conditions and examined…

Deep learning (DL) plays a more and more important role in our daily life due to its competitive performance in industrial application domains. As the core of DL-enabled systems, deep neural networks (DNNs) need to be carefully evaluated to…

Deep neural network models have recently achieved state-of-the-art performance gains in a variety of natural language processing (NLP) tasks (Young, Hazarika, Poria, & Cambria, 2017). However, these gains rely on the availability of large…

In this paper I have proposed a method to find the major pixel intensity inside the text and thresholding an image accordingly to make it easier to be used for optical character recognition (OCR) models. In our method, instead of editing…

Optical Coherence Tomography (OCT) provides valuable insights in ophthalmology, cardiology, and neurology due to high-resolution, cross-sectional images of the retina. One critical task for ophthalmologists using OCT is delineation of…

Automatic conversion of free-text radiology reports into structured data using Natural Language Processing (NLP) techniques is crucial for analyzing diseases on a large scale. While effective for tasks in widely spoken languages like…

Clinical Natural Language Processing (NLP) has become an emerging technology in healthcare that leverages a large amount of free-text data in electronic health records (EHRs) to improve patient care, support clinical decisions, and…

In recent years, the intersection of Natural Language Processing (NLP) and public health has opened innovative pathways for investigating various domains, including chronic pain in textual datasets. Despite the promise of NLP in chronic…

Optical neural networks are emerging as powerful machine learning and information processing tools because of their potential advantages in speed and energy efficiency. The training methods of these physical models, however, remain…

We investigate how to train a high quality optical character recognition (OCR) model for difficult historical typefaces on degraded paper. Through extensive grid searches, we obtain a neural network architecture and a set of optimal data…

Deep learning methods exhibit promising performance for predictive modeling in healthcare, but two important challenges remain: -Data insufficiency:Often in healthcare predictive modeling, the sample size is insufficient for deep learning…

Classifying chest radiographs is a time-consuming and challenging task, even for experienced radiologists. This provides an area for improvement due to the difficulty in precisely distinguishing between conditions such as pleural effusion,…

Professionals in modern healthcare systems are increasingly burdened by documentation workloads. Documentation of the initial patient anamnesis is particularly relevant, forming the basis of successful further diagnostic measures. However,…

The past few years has seen the application of machine learning utilised in the exploration of new materials. As in many fields of research - the vast majority of knowledge is published as text, which poses challenges in either a…

Stroke is a major public health problem, affecting millions worldwide. Deep learning has recently demonstrated promise for enhancing the diagnosis and risk prediction of stroke. However, existing methods rely on costly medical imaging…

Retrieval-augmented Generation (RAG) enhances Large Language Models (LLMs) by integrating external knowledge to reduce hallucinations and incorporate up-to-date information without retraining. As an essential part of RAG, external knowledge…