scholarly journals Deep learning facilitates rapid classification of human and veterinary clinical narratives

2018 ◽  
Author(s):  
Arturo Lopez Pineda ◽  
Oliver J. Bear Don’t Walk ◽  
Guhan R. Venkataraman ◽  
Ashley M. Zehnder ◽  
Sandeep Ayyar ◽  
...  
Keyword(s):  
Public Health ◽  
Medical Records ◽  
Short Term Memory ◽  
Classification Performance ◽  
Clinical Settings ◽  
Strong Positive Correlation ◽  
Training Samples ◽  
Cohort Selection ◽  
Data Source ◽  
Evaluation Metric

ABSTRACTObjectiveCurrently, dedicated tagging staff spend considerable effort assigning clinical codes to patient summaries for public health purposes, and machine-learning automated tagging is bottlenecked by availability of electronic medical records. Veterinary medical records, a largely untapped data source that could benefit both human and non-human patients, could fill the gap.Materials and MethodsIn this retrospective study, we trained long short-term memory (LSTM) recurrent neural networks (RNNs) on 52,722 human and 89,591 veterinary records. We established relevant baselines by training Decision Trees (DT) and Random Forests (RF) on the same data. We finally investigated the effect of merging data across clinical settings and probed model portability.ResultsWe show that the LSTM-RNNs accurately classify veterinary/human text narratives into top-level categories with an average weighted macro F1, score of 0.735/0.675 respectively. The evaluation metric for the LSTM was 7 and 8% higher than that of the DT and RF models respectively. We generally did not find evidence of model portability albeit moderate performance increases in select categories.DiscussionWe see a strong positive correlation between number of training samples and classification performance, which is promising for future efforts. The use of LSTM-RNN models represents a scalable structure that could prove useful in cohort selection, which could in turn better address emerging public health concerns.ConclusionDigitization of human and veterinary health information will continue to be a reality. Our approach is a step forward for these two domains to learn from, and inform, one another.

scholarly journals An Adversarial Generative Network for Crop Classification from Remote Sensing Timeseries Images

2020 ◽  
Vol 13 (1) ◽  
pp. 65
Author(s):  
Jingtao Li ◽  
Yonglin Shen ◽  
Chao Yang
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Short Term Memory ◽  
Complete Classification ◽  
Support Vector ◽  
Classification Models ◽  
Training Samples ◽  
Agricultural Applications ◽  
Crop Classification ◽  
Increasing Demand

Due to the increasing demand for the monitoring of crop conditions and food production, it is a challenging and meaningful task to identify crops from remote sensing images. The state-of the-art crop classification models are mostly built on supervised classification models such as support vector machines (SVM), convolutional neural networks (CNN), and long- and short-term memory neural networks (LSTM). Meanwhile, as an unsupervised generative model, the adversarial generative network (GAN) is rarely used to complete classification tasks for agricultural applications. In this work, we propose a new method that combines GAN, CNN, and LSTM models to classify crops of corn and soybeans from remote sensing time-series images, in which GAN’s discriminator was used as the final classifier. The method is feasible on the condition that the training samples are small, and it fully takes advantage of spectral, spatial, and phenology features of crops from satellite data. The classification experiments were conducted on crops of corn, soybeans, and others. To verify the effectiveness of the proposed method, comparisons with models of SVM, SegNet, CNN, LSTM, and different combinations were also conducted. The results show that our method achieved the best classification results, with the Kappa coefficient of 0.7933 and overall accuracy of 0.86. Experiments in other study areas also demonstrate the extensibility of the proposed method.

scholarly journals Data Augmentation and Spectral Structure Features for Limited Samples Hyperspectral Classification

2021 ◽  
Vol 13 (4) ◽  
pp. 547
Author(s):  
Wenning Wang ◽  
Xuebin Liu ◽  
Xuanqin Mou
Keyword(s):  
Classification Accuracy ◽  
Data Augmentation ◽  
Classification Problem ◽  
Classification Performance ◽  
Spectral Structure ◽  
Limited Sample ◽  
Sample Classification ◽  
Training Samples ◽  
Traditional Classification ◽  
Hyperspectral Classification

For both traditional classification and current popular deep learning methods, the limited sample classification problem is very challenging, and the lack of samples is an important factor affecting the classification performance. Our work includes two aspects. First, the unsupervised data augmentation for all hyperspectral samples not only improves the classification accuracy greatly with the newly added training samples, but also further improves the classification accuracy of the classifier by optimizing the augmented test samples. Second, an effective spectral structure extraction method is designed, and the effective spectral structure features have a better classification accuracy than the true spectral features.

scholarly journals Taken to Court: Defending Public Health Authority to Access Medical Records during an Outbreak Investigation

2015 ◽  
Vol 130 (3) ◽  
pp. 278-283 ◽  
Author(s):  
Elizabeth R. Daly ◽  
Jeanne P. Herrick ◽  
Elizabeth X. Maynard ◽  
José T. Montero ◽  
Christine Adamski ◽  
...  
Keyword(s):  
Public Health ◽  
Health Authority ◽  
Medical Records ◽  
Outbreak Investigation ◽  
Public Health Authority

Ophthalmologists and WhatsApp: Teleophthalmology is already here

2021 ◽  
pp. 112067212110026
Author(s):  
Edward Barayev ◽  
Ofri Vorobichik Berar ◽  
Gad Dotan ◽  
Alon Skaat ◽  
Orly Gal-Or ◽  
...  
Keyword(s):  
Medical Records ◽  
Cross Sectional Study ◽  
Work Hours ◽  
Clinical Settings ◽  
Sectional Study ◽  
Cross Sectional ◽  
Policy Makers ◽  
Media Messages ◽  
Rapid Responses ◽  
Wide Availability

Purpose: To estimate the extent of WhatsApp utilization using text and media messages for inter-physician consultations among ophthalmologists (residents and specialists) at various clinical settings and its perceived benefits for ophthalmologists and their patients. We also aimed to detect obstacles that concern ophthalmologists when using WhatsApp as a consultation platform. Methods: This was a cross-sectional study using a self-administered survey through Google Forms, which was sent to 660 practicing ophthalmologists during April to May 2020. Results: One hundred and ninety-two ophthalmologists completed the questionnaire, 151 of which (78.6%) were specialists and 41 (21.4%) were residents. Most ophthalmologists reported using WhatsApp at least once a day for both personal and professional use. Residents reported lower rates of contacting patients using WhatsApp than specialists (1.51 ± 0.98 vs 2.72 ± 1.32, p < 0.001). Respondents reported WhatsApp consultations frequently replaced referrals of patients to other physicians, with a median of once a week. 97.8% of residents and 91.4% of specialists reported the ability to share media is a major advantage of WhatsApp over other medias, followed by rapid responses for consultations. Conclusion: Many ophthalmologists already use WhatsApp as a tool for professional consultations with other providers, mainly thanks to its simplicity and wide availability. Residents use it more frequently than specialists, and ranked it higher when asked how much WhatsApp has improved the clinical setting. Policy makers should address concerns brought up by physicians, such as documentation in medical records and proper compensation for consulting ophthalmologists during and after work hours.

Fake News Detection Using Recurrent Neural Networks and Distributed Representations for the Portuguese Language

2021 ◽  
Author(s):  
Guilherme Zanini Moreira ◽  
Marcelo Romero ◽  
Manassés Ribeiro
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Short Term Memory ◽  
Classification Performance ◽  
Fake News ◽  
Distributed Representations ◽  
Memory Network ◽  
Long Short Term Memory ◽  
Media Channels ◽  
Damage Rule

After the advent of Web, the number of people who abandoned traditional media channels and started receiving news only through social media has increased. However, this caused an increase of the spread of fake news due to the ease of sharing information. The consequences are various, with one of the main ones being the possible attempts to manipulate public opinion for elections or promotion of movements that can damage rule of law or the institutions that represent it. The objective of this work is to perform fake news detection using Distributed Representations and Recurrent Neural Networks (RNNs). Although fake news detection using RNNs has been already explored in the literature, there is little research on the processing of texts in Portuguese language, which is the focus of this work. For this purpose, distributed representations from texts are generated with three different algorithms (fastText, GloVe and word2vec) and used as input features for a Long Short-term Memory Network (LSTM). The approach is evaluated using a publicly available labelled news dataset. The proposed approach shows promising results for all the three distributed representation methods for feature extraction, with the combination word2vec+LSTM providing the best results. The results of the proposed approach shows a better classification performance when compared to simple architectures, while similar results are obtained when the approach is compared to deeper architectures or more complex methods.

scholarly journals Evaluation of staging, cytoreduction and second-look operation of 119 ovarian cancer patients

1997 ◽  
Vol 115 (5) ◽  
pp. 1542-1547
Author(s):  
Eddie Fernando Candido Murta ◽  
Jurandyr Moreira de Andrade ◽  
Maurício Mesquita Sabino de Freitas ◽  
Sérgio Bighetti
Keyword(s):  
Ovarian Cancer ◽  
Retrospective Study ◽  
Medical Records ◽  
Residual Disease ◽  
School Of Medicine ◽  
Second Look ◽  
Gynecology And Obstetrics ◽  
Data Source ◽  
Level Of Significance ◽  
Second Look Operation

OBJECTIVE:This study was conducted on patients with ovarian cancer in order to evaluate survival. DESIGN: A retrospective study of 119 cases of ovarian cancer from January 1977 to December 1992 with observation until 1993. LOCATION: Department of Gynecology and Obstetrics, Ribeirão Preto School of Medicine, São Paulo University. PARTICIPANTS: Of the 119 cases, 70 (58.8%) presented epithelial carcinomas and 21 (17.6%) tumors of the sexual girdle/stroma. DATA SOURCE: The data were obtained from the medical records of the patients. MEASUREMENT: Statistical analysis of survival time was based on the nonparametric Mann-Whitney test with the level of significance set at P < 0.05. RESULTS: The patients with a negative second look had a mean survival of 79.4 ± 48.5 months versus 24.2 ± 15.1 months for patients with a positive second look (P < 0.02). CONCLUSIONS: It is concluded that patients with a negative second look present a better prognosis compared to those with residual disease.

scholarly journals Change Detection in Hyperspectral Images Using Recurrent 3D Fully Convolutional Networks

2018 ◽  
Vol 10 (11) ◽  
pp. 1827 ◽  
Author(s):  
Ahram Song ◽  
Jaewan Choi ◽  
Youkyung Han ◽  
Yongil Kim
Keyword(s):  
Deep Learning ◽  
Change Detection ◽  
Spatial Information ◽  
Short Term Memory ◽  
Hyperspectral Images ◽  
Convolutional Network ◽  
Ground Truth Data ◽  
Fully Convolutional Network ◽  
Training Samples ◽  
Multi Temporal

Hyperspectral change detection (CD) can be effectively performed using deep-learning networks. Although these approaches require qualified training samples, it is difficult to obtain ground-truth data in the real world. Preserving spatial information during training is difficult due to structural limitations. To solve such problems, our study proposed a novel CD method for hyperspectral images (HSIs), including sample generation and a deep-learning network, called the recurrent three-dimensional (3D) fully convolutional network (Re3FCN), which merged the advantages of a 3D fully convolutional network (FCN) and a convolutional long short-term memory (ConvLSTM). Principal component analysis (PCA) and the spectral correlation angle (SCA) were used to generate training samples with high probabilities of being changed or unchanged. The strategy assisted in training fewer samples of representative feature expression. The Re3FCN was mainly comprised of spectral–spatial and temporal modules. Particularly, a spectral–spatial module with a 3D convolutional layer extracts the spectral–spatial features from the HSIs simultaneously, whilst a temporal module with ConvLSTM records and analyzes the multi-temporal HSI change information. The study first proposed a simple and effective method to generate samples for network training. This method can be applied effectively to cases with no training samples. Re3FCN can perform end-to-end detection for binary and multiple changes. Moreover, Re3FCN can receive multi-temporal HSIs directly as input without learning the characteristics of multiple changes. Finally, the network could extract joint spectral–spatial–temporal features and it preserved the spatial structure during the learning process through the fully convolutional structure. This study was the first to use a 3D FCN and a ConvLSTM for the remote-sensing CD. To demonstrate the effectiveness of the proposed CD method, we performed binary and multi-class CD experiments. Results revealed that the Re3FCN outperformed the other conventional methods, such as change vector analysis, iteratively reweighted multivariate alteration detection, PCA-SCA, FCN, and the combination of 2D convolutional layers-fully connected LSTM.

An outbreak of syphilis in Oslo

2002 ◽  
Vol 13 (6) ◽  
pp. 370-372 ◽  
Author(s):  
A M Halsos ◽  
K Edgardh
Keyword(s):  
Public Health ◽  
Hiv Infection ◽  
Medical Records ◽  
University Hospital ◽  
Oral Sex ◽  
Sexual Contacts ◽  
Male Patients ◽  
Sex With Men ◽  
Latent Syphilis ◽  
Retrospective Investigation

During 1999 and 2000, an outbreak of syphilis occurred in Norway: 93 cases were reported to the National Institute of Public Health. This report summarizes a retrospective investigation of the medical records of 60 patients with primary, secondary and early latent syphilis treated during 1999–2000 at the Department of STD at the Ullevål University Hospital in Oslo. Five women and 55 men were treated, mean age 38.6 and 44.9 years, respectively. Of the 60 cases, 14 (23.3%) had primary, 39 (65.0) secondary and seven (11.7%) early latent syphilis. Men who have sex with men (MSM) constituted 78.2% (43/55) of the male patients. Transmission among MSM was related to casual sexual contacts in bathhouses in Oslo. Two cases occurred among men with previously diagnosed HIV infection. Two new cases of HIV were reported. Condom use was inconsistent, and seldom used for oral sex.

scholarly journals Nonnegative Signal Decomposition with Supervision

2013 ◽  
Vol 2013 ◽  
pp. 1-8
Author(s):  
Teng Li ◽  
Huan Chang ◽  
Jun Wu
Keyword(s):  
Objective Function ◽  
Image Classification ◽  
Pattern Classification ◽  
Optimization Problem ◽  
Real Data ◽  
Classification Performance ◽  
Signal Decomposition ◽  
Weak Supervision ◽  
Training Samples ◽  
Novel Algorithm

This paper presents a novel algorithm to numerically decompose mixed signals in a collaborative way, given supervision of the labels that each signal contains. The decomposition is formulated as an optimization problem incorporating nonnegative constraint. A nonnegative data factorization solution is presented to yield the decomposed results. It is shown that the optimization is efficient and decreases the objective function monotonically. Such a decomposition algorithm can be applied on multilabel training samples for pattern classification. The real-data experimental results show that the proposed algorithm can significantly facilitate the multilabel image classification performance with weak supervision.

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