7,950 research outputs found
Clinical Big Data and Deep Learning: Applications, Challenges, and Future Outlooks
The explosion of digital healthcare data has led to a surge of data-driven medical research based on machine learning. In recent years, as a powerful technique for big data, deep learning has gained a central position in machine learning circles for its great advantages in feature representation and pattern recognition. This article presents a comprehensive overview of studies that employ deep learning methods to deal with clinical data. Firstly, based on the analysis of the characteristics of clinical data, various types of clinical data (e.g., medical images, clinical notes, lab results, vital signs and demographic informatics) are discussed and details provided of some public clinical datasets. Secondly, a brief review of common deep learning models and their characteristics is conducted. Then, considering the wide range of clinical research and the diversity of data types, several deep learning applications for clinical data are illustrated: auxiliary diagnosis, prognosis, early warning, and other tasks. Although there are challenges involved in applying deep learning techniques to clinical data, it is still worthwhile to look forward to a promising future for deep learning applications in clinical big data in the direction of precision medicine
A systematic review of natural language processing applied to radiology reports
NLP has a significant role in advancing healthcare and has been found to be
key in extracting structured information from radiology reports. Understanding
recent developments in NLP application to radiology is of significance but
recent reviews on this are limited. This study systematically assesses recent
literature in NLP applied to radiology reports. Our automated literature search
yields 4,799 results using automated filtering, metadata enriching steps and
citation search combined with manual review. Our analysis is based on 21
variables including radiology characteristics, NLP methodology, performance,
study, and clinical application characteristics. We present a comprehensive
analysis of the 164 publications retrieved with each categorised into one of 6
clinical application categories. Deep learning use increases but conventional
machine learning approaches are still prevalent. Deep learning remains
challenged when data is scarce and there is little evidence of adoption into
clinical practice. Despite 17% of studies reporting greater than 0.85 F1
scores, it is hard to comparatively evaluate these approaches given that most
of them use different datasets. Only 14 studies made their data and 15 their
code available with 10 externally validating results. Automated understanding
of clinical narratives of the radiology reports has the potential to enhance
the healthcare process but reproducibility and explainability of models are
important if the domain is to move applications into clinical use. More could
be done to share code enabling validation of methods on different institutional
data and to reduce heterogeneity in reporting of study properties allowing
inter-study comparisons. Our results have significance for researchers
providing a systematic synthesis of existing work to build on, identify gaps,
opportunities for collaboration and avoid duplication
Learning A Multi-Task Transformer Via Unified And Customized Instruction Tuning For Chest Radiograph Interpretation
The emergence of multi-modal deep learning models has made significant
impacts on clinical applications in the last decade. However, the majority of
models are limited to single-tasking, without considering disease diagnosis is
indeed a multi-task procedure. Here, we demonstrate a unified transformer model
specifically designed for multi-modal clinical tasks by incorporating
customized instruction tuning. We first compose a multi-task training dataset
comprising 13.4 million instruction and ground-truth pairs (with approximately
one million radiographs) for the customized tuning, involving both image- and
pixel-level tasks. Thus, we can unify the various vision-intensive tasks in a
single training framework with homogeneous model inputs and outputs to increase
clinical interpretability in one reading. Finally, we demonstrate the overall
superior performance of our model compared to prior arts on various chest X-ray
benchmarks across multi-tasks in both direct inference and finetuning settings.
Three radiologists further evaluate the generated reports against the recorded
ones, which also exhibit the enhanced explainability of our multi-task model
Extracting information from radiology reports by Natural Language Processing and Deep Learning
This work was supported by the NLP4RARE-CM-UC3M, which was developed under the Interdisciplinary Projects Program for Young Researchers at University Carlos III of Madrid. The work was also supported by the Multiannual Agreement with UC3M in the line of Excellence of University Professors (EPUC3M17), and in the context of the V PRICIT (Regional Programme
of Research and Technological Innovation)
Application of Entity-BERT model based on neuroscience and brain-like cognition in electronic medical record entity recognition
IntroductionIn the medical field, electronic medical records contain a large amount of textual information, and the unstructured nature of this information makes data extraction and analysis challenging. Therefore, automatic extraction of entity information from electronic medical records has become a significant issue in the healthcare domain.MethodsTo address this problem, this paper proposes a deep learning-based entity information extraction model called Entity-BERT. The model aims to leverage the powerful feature extraction capabilities of deep learning and the pre-training language representation learning of BERT(Bidirectional Encoder Representations from Transformers), enabling it to automatically learn and recognize various entity types in medical electronic records, including medical terminologies, disease names, drug information, and more, providing more effective support for medical research and clinical practices. The Entity-BERT model utilizes a multi-layer neural network and cross-attention mechanism to process and fuse information at different levels and types, resembling the hierarchical and distributed processing of the human brain. Additionally, the model employs pre-trained language and sequence models to process and learn textual data, sharing similarities with the language processing and semantic understanding of the human brain. Furthermore, the Entity-BERT model can capture contextual information and long-term dependencies, combining the cross-attention mechanism to handle the complex and diverse language expressions in electronic medical records, resembling the information processing method of the human brain in many aspects. Additionally, exploring how to utilize competitive learning, adaptive regulation, and synaptic plasticity to optimize the model's prediction results, automatically adjust its parameters, and achieve adaptive learning and dynamic adjustments from the perspective of neuroscience and brain-like cognition is of interest.Results and discussionExperimental results demonstrate that the Entity-BERT model achieves outstanding performance in entity recognition tasks within electronic medical records, surpassing other existing entity recognition models. This research not only provides more efficient and accurate natural language processing technology for the medical and health field but also introduces new ideas and directions for the design and optimization of deep learning models
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