Medical Event Detection Research Articles

Chinese medical event detection based on event frequency distribution ratio and document consistency.

Structured information especially medical events extracted from electronic medical records has extremely practical application value and play a basic role in various intelligent diagnosis and treatment systems. Fine-grained Chinese medical event detection is crucial in the process of structuring Chinese Electronic Medical Record (EMR). The current methods for detecting fine-grained Chinese medical events primarily rely on statistical machine learning and deep learning. However, they have two shortcomings: 1) they neglect to take into account the distribution characteristics of these fine-grained medical events. 2) they overlook the consistency in the distribution of medical events within each individual document. Therefore, this paper presents a fine-grained Chinese medical event detection method, which is based on event frequency distribution ratio and document consistency. To start with, a significant number of Chinese EMR texts are used to adapt the Chinese pre-training model BERT to the domain. Second, based on the fundamental features, the Event Frequency - Event Distribution Ratio (EF-DR) is devised to select distinct event information as supplementary features, taking into account the distribution of events within the EMR. Finally, using EMR document consistency within the model improves the outcome of event detection. Our experiments demonstrate that the proposed method significantly outperforms the baseline model.

An approach for medical event detection in Chinese clinical notes of electronic health records

BackgroundMedical event detection in narrative clinical notes of electronic health records (EHRs) is a task designed for reading text and extracting information. Most of the previous work of medical event detection treats the task as extracting concepts at word granularity, which omits the overall structural information of the clinical notes. In this work, we treat each clinical note as a sequence of short sentences and propose an end-to-end deep neural network framework.MethodsWe redefined the task as a sequence labelling task at short sentence granularity, and proposed a novel tag system correspondingly. The dataset were derived from a third-level grade-A hospital, consisting of 2000 annotated clinical notes according to our proposed tag system. The proposed end-to-end deep neural network framework consists of a feature extractor and a sequence labeller, and we explored different implementations respectively. We additionally proposed a smoothed Viterbi decoder as sequence labeller without additional parameter training, which can be a good alternative to conditional random field (CRF) when computing resources are limited.ResultsOur sequence labelling models were compared to four baselines which treat the task as text classification of short sentences. Experimental results showed that our approach significantly outperforms the baselines. The best result was obtained by using the convolutional neural networks (CNNs) feature extractor and the sequential CRF sequence labeller, achieving an accuracy of 92.6%. Our proposed smoothed Viterbi decoder achieved a comparable accuracy of 90.07% with reduced training parameters, and brought more balanced performance across all categories, which means better generalization ability.ConclusionsEvaluated on our annotated dataset, the comparison results demonstrated the effectiveness of our approach for medical event detection in Chinese clinical notes of EHRs. The best feature extractor is the CNNs feature extractor, and the best sequence labeller is the sequential CRF decoder. And it was empirically verified that our proposed smoothed Viterbi decoder could bring better generalization ability while achieving comparable performance to the sequential CRF decoder.

Bidirectional RNN for Medical Event Detection in Electronic Health Records.

Sequence labeling for extraction of medical events and their attributes from unstructured text in Electronic Health Record (EHR) notes is a key step towards semantic understanding of EHRs. It has important applications in health informatics including pharmacovigilance and drug surveillance. The state of the art supervised machine learning models in this domain are based on Conditional Random Fields (CRFs) with features calculated from fixed context windows. In this application, we explored recurrent neural network frameworks and show that they significantly out-performed the CRF models.

Watermarking Technique for Improved Management of Digital Medical Images

Although digital medical imaging can improve diagnostic accuracy, efficient data man- agement and protection of patient privacy are major concerns. For example, an inadvertently modified medical image or a disordered series of medical images such as successive images of a beating heart may result in a misdiagnosis. The optimal use of digital medical images should timely detection of medical events while minimizing misdiagnoses and pro- tecting patient privacy. Improved detection of abnormal medical events would also improve the safety of patient care delivery models. We use a watermarking technique to embed im- portable patient data such as identity (ID), serial number or region of interest in the back- ground of a medical image to ensure that the image is matched with the correct patient and to indicate its relationship to other images in a series. The proposed method can be used to embed information at JPEG compression ratios as high as 70%

MPEG-7 Visual Descriptors—Contributions for Automated Feature Extraction in Capsule Endoscopy

Recent advances in miniaturization led to the development of what is now called the endoscopic capsule. This small device is swallowed by a patient and films the whole gastrointestinal tract, allowing the detection of abnormalities. Currently, a doctor typically needs up to two hours to analyze a full exam, so automation is desirable. This paper presents a methodology for measuring the potential of selected visual MPEG-7 descriptors for the task of specific medical event detection such as blood, ulcers. Experiments show that the best results are obtained by the Scalable Color and Homogenous Texture descriptors, especially if only relevant coefficients are used.