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recurrent neural network

Samuele Fiorini, Farshid Hajati, Annalisa Barla, Federico Girosi
INTRODUCTION: The first line of treatment for people with Diabetes mellitus is metformin. However, over the course of the disease metformin may fail to achieve appropriate glycemic control, and a second-line therapy may become necessary. In this paper we introduce Tangle, a time span-guided neural attention model that can accurately and timely predict the upcoming need for a second-line diabetes therapy from administrative data in the Australian adult population. The method is suitable for designing automatic therapy review recommendations for patients and their providers without the need to collect clinical measures...
2019: PloS One
Rui Antunes, Sérgio Matos
The scientific literature contains large amounts of information on genes, proteins, chemicals and their interactions. Extraction and integration of this information in curated knowledge bases help researchers support their experimental results, leading to new hypotheses and discoveries. This is especially relevant for precision medicine, which aims to understand the individual variability across patient groups in order to select the most appropriate treatments. Methods for improved retrieval and automatic relation extraction from biomedical literature are therefore required for collecting structured information from the growing number of published works...
January 1, 2019: Database: the Journal of Biological Databases and Curation
Jian Ren, Eric A Singer, Evita Sadimin, David J Foran, Xin Qi
Background: Grading of prostatic adenocarcinoma is based on the Gleason scoring system and the more recently established prognostic grade groups. Typically, prostate cancer grading is performed by pathologists based on the morphology of the tumor on hematoxylin and eosin (H and E) slides. In this study, we investigated the histopathological image features with various survival models and attempted to study their correlations. Methods: Three texture methods (speeded-up robust features, histogram of oriented gradient, and local binary pattern) and two convolutional neural network (CNN)-based methods were applied to quantify histopathological image features...
2019: Journal of Pathology Informatics
Jonathan C Kao
Recurrent neural networks (RNNs) are increasingly being used to model complex cognitive and motor tasks performed by behaving animals. RNNs are trained to reproduce animal behavior while also capturing key statistics of empirically recorded neural activity. In this manner, the RNN can be viewed as an in silico circuit whose computational elements share similar motifs with the cortical area it is modeling. Further, as the RNN's governing equations and parameters are fully known, they can be analyzed to propose hypotheses for how neural populations compute...
October 16, 2019: Journal of Neurophysiology
Shervin Malmasi, Wendong Ge, Naoshi Hosomura, Alexander Turchin
OBJECTIVE: To comparatively evaluate a range of Natural Language Processing (NLP) approaches for Information Extraction (IE) of low-prevalence concepts in clinical notes on the example of decline of insulin therapy recommendation by patients. MATERIALS AND METHODS: We evaluated the accuracy of detection of documentation of decline of insulin therapy by patients using sentence-level naïve Bayes, logistic regression and support vector machine (SVM)-based classification (with and without SMOTE oversampling), token-level sequence labelling using conditional random fields (CRFs), uni- and bi-directional recurrent neural network (RNN) models with GRU and LSTM cells, and rule-based detection using Canary platform...
October 13, 2019: Journal of Biomedical Informatics
Chen Zhang, Jie He, Ziyang Liu, Lu Xing, Yinhai Wang
In order to address the time pattern problems in free-floating car sharing, in this paper, the authors offer a comprehensive time-series method based on deep learning theory. According to car2go booking record data in Seattle area. Firstly, influence of time and location on the free-floating car-sharing usage pattern is analyzed, which reveals an apparent doublet pattern for time and dependence usage amount on population. Then, on the basis of the long-short-term memory recurrent neural network (LSTM-RNN), hourly variation in short-term traffic characteristics including travel demand and travel distance are modeled...
2019: PloS One
Jérémie Cabessa
In neural computation, the essential information is generally encoded into the neurons via their spiking configurations, activation values or (attractor) dynamics. The synapses and their associated plasticity mechanisms are, by contrast, mainly used to process this information and implement the crucial learning features. Here, we propose a novel Turing complete paradigm of neural computation where the essential information is encoded into discrete synaptic states, and the updating of this information achieved via synaptic plasticity mechanisms...
2019: PloS One
Jisun Park, Mingyun Wen, Yunsick Sung, Kyungeun Cho
Nowadays, deep learning methods based on a virtual environment are widely applied to research and technology development for autonomous vehicle's smart sensors and devices. Learning various driving environments in advance is important to handle unexpected situations that can exist in the real world and to continue driving without accident. For training smart sensors and devices of an autonomous vehicle well, a virtual simulator should create scenarios of various possible real-world situations. To create reality-based scenarios, data on the real environment must be collected from a real driving vehicle or a scenario analysis process conducted by experts...
October 14, 2019: Sensors
Jehyeok Rew, Sungwoo Park, Yongjang Cho, Seungwon Jung, Eenjun Hwang
Observing animal movements enables us to understand animal behavior changes, such as migration, interaction, foraging, and nesting. Based on spatiotemporal changes in weather and season, animals instinctively change their position for foraging, nesting, or breeding. It is known that moving patterns are closely related to their traits. Analyzing and predicting animals' movement patterns according to spatiotemporal change offers an opportunity to understand their unique traits and acquire ecological insights into animals...
October 11, 2019: Sensors
Hong Zeng, Chen Yang, Hua Zhang, Zhenhua Wu, Jiaming Zhang, Guojun Dai, Fabio Babiloni, Wanzeng Kong
Fatigue driving can easily lead to road traffic accidents and bring great harm to individuals and families. Recently, electroencephalography- (EEG-) based physiological and brain activities for fatigue detection have been increasingly investigated. However, how to find an effective method or model to timely and efficiently detect the mental states of drivers still remains a challenge. In this paper, we combine common spatial pattern (CSP) and propose a light-weighted classifier, LightFD, which is based on gradient boosting framework for EEG mental states identification...
2019: Computational Intelligence and Neuroscience
Binbin Chen, Michael S Khodadoust, Niclas Olsson, Lisa E Wagar, Ethan Fast, Chih Long Liu, Yagmur Muftuoglu, Brian J Sworder, Maximilian Diehn, Ronald Levy, Mark M Davis, Joshua E Elias, Russ B Altman, Ash A Alizadeh
Accurate prediction of antigen presentation by human leukocyte antigen (HLA) class II molecules would be valuable for vaccine development and cancer immunotherapies. Current computational methods trained on in vitro binding data are limited by insufficient training data and algorithmic constraints. Here we describe MARIA (major histocompatibility complex analysis with recurrent integrated architecture; ), a multimodal recurrent neural network for predicting the likelihood of antigen presentation from a gene of interest in the context of specific HLA class II alleles...
October 14, 2019: Nature Biotechnology
Robert Chen, Walter F Stewart, Jimeng Sun, Kenney Ng, Xiaowei Yan
BACKGROUND: We determined the impact of data volume and diversity and training conditions on recurrent neural network methods compared with traditional machine learning methods. METHODS AND RESULTS: Using longitudinal electronic health record data, we assessed the relative performance of machine learning models trained to detect a future diagnosis of heart failure in primary care patients. Model performance was assessed in relation to data parameters defined by the combination of different data domains (data diversity), the number of patient records in the training data set (data quantity), the number of encounters per patient (data density), the prediction window length, and the observation window length (ie, the time period before the prediction window that is the source of features for prediction)...
October 2019: Circulation. Cardiovascular Quality and Outcomes
Luiz C F Ribeiro, Luis C S Afonso, João P Papa
Parkinson's Disease (PD) is a clinical syndrome that affects millions of people worldwide. Although considered as a non-lethal disease, PD shortens the life expectancy of the patients. Many studies have been dedicated to evaluating methods for early-stage PD detection, which includes machine learning techniques that employ, in most cases, motor dysfunctions, such as tremor. This work explores the time dependency in tremor signals collected from handwriting exams. To learn such temporal information, we propose a model based on Bidirectional Gated Recurrent Units along with an attention mechanism...
October 4, 2019: Computers in Biology and Medicine
Haoming Li, Jinghui Fang, Shengfeng Liu, Xiaowen Liang, Xin Yang, Zixin Mai, Manh The Van, Tianfu Wang, Zhiyi Chen, Dong Ni
Transvaginal ultrasound (TVUS) is widely used in infertility treatment. The size and shape of the ovary and follicles must be measured manually for assessing their physiological status by sonographers. However, this process is extremely time-consuming and operator-dependent. In this study, we propose a novel composite network, namely CR-Unet, to simultaneously segment the ovary and follicles in TVUS. The CR-Unet incorporates the spatial recurrent neural network (RNN) into a plain U-Net. It can effectively learn multi-scale and long-range spatial contexts to combat the challenges of this task, such as the poor image quality, low contrast, boundary ambiguity, and complex anatomy shapes...
October 7, 2019: IEEE Journal of Biomedical and Health Informatics
Gesa Lange, Mario Senden, Alexandra Radermacher, Peter De Weerd
Previous research has shown that performance of a novice skill can be easily interfered with by subsequent training of another skill. We address the open questions whether extensively trained skills show the same vulnerability to interference as novice skills and which memory mechanism regulates interference between expert skills. We developed a recurrent neural network model of V1 able to learn from feedback experienced over the course of a long-term orientation discrimination experiment. After first exposing the model to one discrimination task for 3480 consecutive trials, we assessed how its performance was affected by subsequent training in a second, similar task...
September 30, 2019: Neural Networks: the Official Journal of the International Neural Network Society
Zheng Chen, Meng Pang, Zixin Zhao, Shuainan Li, Rui Miao, Yifan Zhang, Xiaoyue Feng, Xin Feng, Yexian Zhang, Meiyu Duan, Lan Huang, Fengfeng Zhou
MOTIVATION: Deep neural network algorithms were utilized in predicting various biomedical phenotypes recently, and demonstrated very good prediction performances without selecting features. This study proposed a hypothesis that the deep neural network models may be further improved by feature selection algorithms. RESULTS: A comprehensive comparative study was carried out by evaluating 11 feature selection algorithms on three conventional deep neural network (DNN) algorithms, i...
October 8, 2019: Bioinformatics
Tim C Kietzmann, Courtney J Spoerer, Lynn K A Sörensen, Radoslaw M Cichy, Olaf Hauk, Nikolaus Kriegeskorte
The human visual system is an intricate network of brain regions that enables us to recognize the world around us. Despite its abundant lateral and feedback connections, object processing is commonly viewed and studied as a feedforward process. Here, we measure and model the rapid representational dynamics across multiple stages of the human ventral stream using time-resolved brain imaging and deep learning. We observe substantial representational transformations during the first 300 ms of processing within and across ventral-stream regions...
October 7, 2019: Proceedings of the National Academy of Sciences of the United States of America
Verónica Barroso-García, Gonzalo C Gutiérrez-Tobal, Leila Kheirandish-Gozal, Daniel Álvarez, Fernando Vaquerizo-Villar, Pablo Núñez, Félix Del Campo, David Gozal, Roberto Hornero
BACKGROUND AND OBJECTIVE: In-laboratory overnight polysomnography (PSG) is the gold standard method to diagnose the Sleep Apnoea-Hypopnoea Syndrome (SAHS). PSG is a complex, expensive, labour-intensive and time-consuming test. Consequently, simplified diagnostic methods are desirable. We propose the analysis of the airflow (AF) signal by means of recurrence plots (RP) features. The main goal of our study was to evaluate the utility of the information from RPs of the AF signals to detect paediatric SAHS at different levels of severity...
September 18, 2019: Computer Methods and Programs in Biomedicine
Zengyan Hong, Xiangxiang Zeng, Leyi Wei, Xiangrong Liu
MOTIVATION: Identification of enhancer-promoter interactions (EPIs) is of great significance to human development. However, experimental methods to identify EPIs cost too much in terms of time, manpower and money. Therefore, more and more research efforts are focused on developing computational methods to solve this problem. Unfortunately, most existing computational methods require a variety of genomic data, which are not always available, especially for a new cell line. Therefore, it limits the large-scale practical application of methods...
September 6, 2019: Bioinformatics
Imon Banerjee, Selen Bozkurt, Jennifer Lee Caswell-Jin, Allison W Kurian, Daniel L Rubin
PURPOSE: Electronic medical records (EMRs) and population-based cancer registries contain information on cancer outcomes and treatment, yet rarely capture information on the timing of metastatic cancer recurrence, which is essential to understand cancer survival outcomes. We developed a natural language processing (NLP) system to identify patient-specific timelines of metastatic breast cancer recurrence. PATIENTS AND METHODS: We used the OncoSHARE database, which includes merged data from the California Cancer Registry and EMRs of 8,956 women diagnosed with breast cancer in 2000 to 2018...
October 2019: JCO Clinical Cancer Informatics
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