scholarly journals Supporting the use of standardized nursing terminologies with automatic subject heading prediction: a comparison of sentence-level text classification methods

2019 ◽  
Vol 27 (1) ◽  
pp. 81-88 ◽  
Author(s):  
Hans Moen ◽  
Kai Hakala ◽  
Laura-Maria Peltonen ◽  
Henry Suhonen ◽  
Filip Ginter ◽  
...  
Keyword(s):  
Text Classification ◽  
Short Term Memory ◽  
University Hospital ◽  
Free Text ◽  
Classification Methods ◽  
Clinical Nursing ◽  
Sentence Level ◽  
Subject Headings ◽  
Memory Network ◽  
Better Than

Abstract Objective This study focuses on the task of automatically assigning standardized (topical) subject headings to free-text sentences in clinical nursing notes. The underlying motivation is to support nurses when they document patient care by developing a computer system that can assist in incorporating suitable subject headings that reflect the documented topics. Central in this study is performance evaluation of several text classification methods to assess the feasibility of developing such a system. Materials and Methods Seven text classification methods are evaluated using a corpus of approximately 0.5 million nursing notes (5.5 million sentences) with 676 unique headings extracted from a Finnish university hospital. Several of these methods are based on artificial neural networks. Evaluation is first done in an automatic manner for all methods, then a manual error analysis is done on a sample. Results We find that a method based on a bidirectional long short-term memory network performs best with an average recall of 0.5435 when allowed to suggest 1 subject heading per sentence and 0.8954 when allowed to suggest 10 subject headings per sentence. However, other methods achieve comparable results. The manual analysis indicates that the predictions are better than what the automatic evaluation suggests. Conclusions The results indicate that several of the tested methods perform well in suggesting the most appropriate subject headings on sentence level. Thus, we find it feasible to develop a text classification system that can support the use of standardized terminologies and save nurses time and effort on care documentation.

scholarly journals Leveraging Contextual Sentences for Text Classification by Using a Neural Attention Model

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
DanFeng Yan ◽  
Shiyao Guo
Keyword(s):  
Neural Network ◽  
Text Classification ◽  
Feature Representation ◽  
Context Information ◽  
Attention Model ◽  
Learning Framework ◽  
Word Level ◽  
Sentence Level ◽  
Memory Network ◽  
Short Time

We explored several approaches to incorporate context information in the deep learning framework for text classification, including designing different attention mechanisms based on different neural network and extracting some additional features from text by traditional methods as the part of representation. We propose two kinds of classification algorithms: one is based on convolutional neural network fusing context information and the other is based on bidirectional long and short time memory network. We integrate the context information into the final feature representation by designing attention structures at sentence level and word level, which increases the diversity of feature information. Our experimental results on two datasets validate the advantages of the two models in terms of time efficiency and accuracy compared to the different models with fundamental AM architectures.

scholarly journals An Approach Based on Multilevel Convolution for Sentence-Level Element Extraction of Legal Text

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Zhe Chen ◽  
Hongli Zhang ◽  
Lin Ye ◽  
Shang Li
Keyword(s):  
Text Classification ◽  
Short Term Memory ◽  
Text Data ◽  
Legal Text ◽  
Multilabel Classification ◽  
Sentence Level ◽  
Proposed Model ◽  
Level Model ◽  
Classification Tasks ◽  
Text Element

In the judicial field, with the increase of legal text data, the extraction of legal text elements plays a more and more important role. In this paper, we propose a sentence-level model of legal text element extraction based on the structure of multilabel text classification. Our proposed model contains an encoder and an improved decoder. The encoder applies multilevel convolutional neural networks (CNN) and Long Short-Term Memory (LSTM) as feature extraction networks to extract local neighborhood and context information from legal text, and a decoder applies LSTM with multiattention and full connection layer with an improved initialization method to decode and generate label sequences. To our best knowledge, it is one of the first attempts to apply a multilabel classification algorithm for element extraction of legal text. In order to verify the effectiveness of our model, we conduct experiments not only on three real legal text datasets but also on a general multilabel text classification dataset.The experimental results demonstrate that our proposed model outperforms baseline models on legal text datasets, and our model is competitive to baseline models on the general text multilabel classification dataset, which indicates that our proposed model is useful for multilabel classification tasks of ordinary texts and legal texts with an uncertain number of characters in words and short lengths.

scholarly journals P005 Rheumatology virtual clinics during COVID-19: are our patients satisfied?

Rheumatology ◽  
2021 ◽  
Vol 60 (Supplement_1) ◽  
Author(s):  
Fajer A Altamimi ◽  
Una Martin
Keyword(s):  
Medical Information ◽  
Worker Safety ◽  
University Hospital ◽  
Free Text ◽  
Risk Of Infection ◽  
Further Training ◽  
Return Envelope ◽  
The Right ◽  
Reduced Risk

Abstract Background/Aims  Telemedicine can be broadly defined as the use of telecommunication technologies to provide medical information and services. It can be audio, visual, or text. Its use has increased dramatically during the COVID-19 pandemic to ensure patient and healthcare worker safety. Any healthcare professional can engage with it. It carries benefits like reduced stress and expense of traveling, maintenance of social distancing, and reduced risk of infection. There are some potential drawbacks such as lack of physical examination, liability and technological issues. Methods  A questionnaire was sent to 200 patients, selected from different virtual clinics (new and review, doctor and ANP led) run between March and May 2020 in the rheumatology department of University Hospital Waterford. We formulated 14 questions to cover the following aspects: demography, the purpose of the consult, punctuality, feedback, medico-legal concerns, and free text for comments. A self-addressed return envelope was included. Results  83 responses were received. 2 were excluded. The ratio of females to male respondents was 59: 41, with the majority over 60 years old. The main appointment type was review 67 (83%). 80% of patients were called either before or at the time of their scheduled appointment. The vast majority (98.8%) of our patients had confidence in our data protection and trusted our system to maintain their confidentiality. 95% stated that they felt comfortable, were given enough time to explain their health problem and felt free from stress. The respondents who preferred attending the clinic in person (17 in total) compared to the virtual were mostly follow up patients- 12 vs. 5 new. Conclusion  Patient satisfaction among those surveyed was high, despite having to introduce the service abruptly during the COVID-19 pandemic. There are many improvements we can adopt to improve our service and even maintain after the pandemic as a way of communicating with our stable patients. As we are covering a large geographical catchment, we can continue to implement the virtual clinic for some appointments. We should prioritize our efforts on identifying the right patient and the type of service we can offer, further training of staff, and increasing awareness of the patients as to how to get the most out of a virtual appointment. Disclosure  F.A. Altamimi: None. U. Martin: None. C. Sheehy: None.

scholarly journals Small Floating Target Detection Method Based on Chaotic Long Short-Term Memory Network

2021 ◽  
Vol 9 (6) ◽  
pp. 651
Author(s):  
Yan Yan ◽  
Hongyan Xing
Keyword(s):  
High Frequency ◽  
Short Term Memory ◽  
Detection Method ◽  
Low Frequency ◽  
Sea Clutter ◽  
Short Term ◽  
Term Memory ◽  
Memory Network ◽  
Long Short Term Memory ◽  
Small Targets

In order for the detection ability of floating small targets in sea clutter to be improved, on the basis of the complete ensemble empirical mode decomposition (CEEMD) algorithm, the high-frequency parts and low-frequency parts are determined by the energy proportion of the intrinsic mode function (IMF); the high-frequency part is denoised by wavelet packet transform (WPT), whereas the denoised high-frequency IMFs and low-frequency IMFs reconstruct the pure sea clutter signal together. According to the chaotic characteristics of sea clutter, we proposed an adaptive training timesteps strategy. The training timesteps of network were determined by the width of embedded window, and the chaotic long short-term memory network detection was designed. The sea clutter signals after denoising were predicted by chaotic long short-term memory (LSTM) network, and small target signals were detected from the prediction errors. The experimental results showed that the CEEMD-WPT algorithm was consistent with the target distribution characteristics of sea clutter, and the denoising performance was improved by 33.6% on average. The proposed chaotic long- and short-term memory network, which determines the training step length according to the width of embedded window, is a new detection method that can accurately detect small targets submerged in the background of sea clutter.

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