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2.
Br J Anaesth ; 123(5): 688-695, 2019 11.
Artigo em Inglês | MEDLINE | ID: mdl-31558311

RESUMO

BACKGROUND: Postoperative mortality occurs in 1-2% of patients undergoing major inpatient surgery. The currently available prediction tools using summaries of intraoperative data are limited by their inability to reflect shifting risk associated with intraoperative physiological perturbations. We sought to compare similar benchmarks to a deep-learning algorithm predicting postoperative 30-day mortality. METHODS: We constructed a multipath convolutional neural network model using patient characteristics, co-morbid conditions, preoperative laboratory values, and intraoperative numerical data from patients undergoing surgery with tracheal intubation at a single medical centre. Data for 60 min prior to a randomly selected time point were utilised. Model performance was compared with a deep neural network, a random forest, a support vector machine, and a logistic regression using predetermined summary statistics of intraoperative data. RESULTS: Of 95 907 patients, 941 (1%) died within 30 days. The multipath convolutional neural network predicted postoperative 30-day mortality with an area under the receiver operating characteristic curve of 0.867 (95% confidence interval [CI]: 0.835-0.899). This was higher than that for the deep neural network (0.825; 95% CI: 0.790-0.860), random forest (0.848; 95% CI: 0.815-0.882), support vector machine (0.836; 95% CI: 0.802-870), and logistic regression (0.837; 95% CI: 0.803-0.871). CONCLUSIONS: A deep-learning time-series model improves prediction compared with models with simple summaries of intraoperative data. We have created a model that can be used in real time to detect dynamic changes in a patient's risk for postoperative mortality.


Assuntos
Aprendizado Profundo , Complicações Pós-Operatórias/mortalidade , Procedimentos Cirúrgicos Operatórios/mortalidade , Algoritmos , Comorbidade , Humanos , Missouri/epidemiologia , Redes Neurais (Computação) , Período Pós-Operatório , Valor Preditivo dos Testes , Estudos Retrospectivos , Medição de Risco/métodos , Máquina de Vetores de Suporte
3.
4.
Stud Health Technol Inform ; 267: 101-109, 2019 Sep 03.
Artigo em Inglês | MEDLINE | ID: mdl-31483261

RESUMO

One of the major obstacles for research on German medical reports is the lack of de-identified medical corpora. Previous de-identification tasks focused on non-German medical texts, which raised the demand for an in-depth evaluation of de-identification methods on German medical texts. Because of remarkable advancements in natural language processing using supervised machine learning methods on limited training data, we evaluated them for the first time on German medical reports using our annotated data set consisting of 113 medical reports from the cardiology domain. We applied state-of-the-art deep learning methods using pre-trained models as input to a bidirectional LSTM network and well-established conditional random fields for de-identification of German medical reports. We performed an extensive evaluation for de-identification and multiclass named entity recognition. Using rule based and out of domain machine learning methods as a baseline, the conditional random field improved F2-score from 70 to 93% for de-identification, the neural approach reached 96% in F2-score while keeping balanced precision and recall rates. These results show, that state-of-the-art machine learning methods can play a crucial role in de-identification of German medical reports.


Assuntos
Anonimização de Dados , Aprendizado Profundo , Registros Eletrônicos de Saúde , Aprendizado de Máquina , Processamento de Linguagem Natural
5.
Stud Health Technol Inform ; 267: 181-186, 2019 Sep 03.
Artigo em Inglês | MEDLINE | ID: mdl-31483271

RESUMO

Gene expression data is commonly available in cancer research and provides a snapshot of the molecular status of a specific tumor tissue. This high-dimensional data can be analyzed for diagnoses, prognoses, and to suggest treatment options. Machine learning based methods are widely used for such analysis. Recently, a set of deep learning techniques was successfully applied in different domains including bioinformatics. One of these prominent techniques are convolutional neural networks (CNN). Currently, CNNs are extending to non-Euclidean domains like graphs. Molecular networks are commonly represented as graphs detailing interactions between molecules. Gene expression data can be assigned to the vertices of these graphs, and the edges can depict interactions, regulations and signal flow. In other words, gene expression data can be structured by utilizing molecular network information as prior knowledge. Here, we applied graph CNN to gene expression data of breast cancer patients to predict the occurrence of metastatic events. To structure the data we utilized a protein-protein interaction network. We show that the graph CNN exploiting the prior knowledge is able to provide classification improvements for the prediction of metastatic events compared to existing methods.


Assuntos
Neoplasias da Mama , Aprendizado Profundo , Humanos , Aprendizado de Máquina , Metástase Neoplásica , Redes Neurais (Computação)
6.
BMC Bioinformatics ; 20(1): 456, 2019 Sep 06.
Artigo em Inglês | MEDLINE | ID: mdl-31492094

RESUMO

*: Background In the search for therapeutic peptides for disease treatments, many efforts have been made to identify various functional peptides from large numbers of peptide sequence databases. In this paper, we propose an effective computational model that uses deep learning and word2vec to predict therapeutic peptides (PTPD). *: Results Representation vectors of all k-mers were obtained through word2vec based on k-mer co-existence information. The original peptide sequences were then divided into k-mers using the windowing method. The peptide sequences were mapped to the input layer by the embedding vector obtained by word2vec. Three types of filters in the convolutional layers, as well as dropout and max-pooling operations, were applied to construct feature maps. These feature maps were concatenated into a fully connected dense layer, and rectified linear units (ReLU) and dropout operations were included to avoid over-fitting of PTPD. The classification probabilities were generated by a sigmoid function. PTPD was then validated using two datasets: an independent anticancer peptide dataset and a virulent protein dataset, on which it achieved accuracies of 96% and 94%, respectively. *: Conclusions PTPD identified novel therapeutic peptides efficiently, and it is suitable for application as a useful tool in therapeutic peptide design.


Assuntos
Biologia Computacional/métodos , Aprendizado Profundo , Peptídeos/uso terapêutico , Bases de Dados de Ácidos Nucleicos , Descoberta de Drogas
7.
Comput Biol Chem ; 81: 1-8, 2019 Aug.
Artigo em Inglês | MEDLINE | ID: mdl-31442779

RESUMO

Literature contains over fifty years of accumulated methods proposed by researchers for predicting the secondary structures of proteins in silico. A large part of this collection is comprised of artificial neural network-based approaches, a field of artificial intelligence and machine learning that is gaining increasing popularity in various application areas. The primary objective of this paper is to put together the summary of works that are important but sparse in time, to help new researchers have a clear view of the domain in a single place. An informative introduction to protein secondary structure and artificial neural networks is also included for context. This review will be valuable in designing future methods to improve protein secondary structure prediction accuracy. The various neural network methods found in this problem domain employ varying architectures and feature spaces, and a handful stand out due to significant improvements in prediction. Neural networks with larger feature scope and higher architecture complexity have been found to produce better protein secondary structure prediction. The current prediction accuracy lies around the 84% marks, leaving much room for further improvement in the prediction of secondary structures in silico. It was found that the estimated limit of 88% prediction accuracy has not been reached yet, hence further research is a timely demand.


Assuntos
Aprendizado Profundo , Proteínas/química , Estrutura Secundária de Proteína
8.
Stud Health Technol Inform ; 264: 283-287, 2019 Aug 21.
Artigo em Inglês | MEDLINE | ID: mdl-31437930

RESUMO

Clinical text de-identification enables collaborative research while protecting patient privacy and confidentiality; however, concerns persist about the reduction in the utility of the de-identified text for information extraction and machine learning tasks. In the context of a deep learning experiment to detect altered mental status in emergency department provider notes, we tested several classifiers on clinical notes in their original form and on their automatically de-identified counterpart. We tested both traditional bag-of-words based machine learning models as well as word-embedding based deep learning models. We evaluated the models on 1,113 history of present illness notes. A total of 1,795 protected health information tokens were replaced in the de-identification process across all notes. The deep learning models had the best performance with accuracies of 95% on both original and de-identified notes. However, there was no significant difference in the performance of any of the models on the original vs. the de-identified notes.


Assuntos
Anonimização de Dados , Aprendizado Profundo , Confidencialidade , Registros Eletrônicos de Saúde , Humanos , Aprendizado de Máquina
9.
Genome Biol ; 20(1): 165, 2019 08 12.
Artigo em Inglês | MEDLINE | ID: mdl-31405383

RESUMO

To fully utilize the power of single-cell RNA sequencing (scRNA-seq) technologies for identifying cell lineages and bona fide transcriptional signals, it is necessary to combine data from multiple experiments. We present BERMUDA (Batch Effect ReMoval Using Deep Autoencoders), a novel transfer-learning-based method for batch effect correction in scRNA-seq data. BERMUDA effectively combines different batches of scRNA-seq data with vastly different cell population compositions and amplifies biological signals by transferring information among batches. We demonstrate that BERMUDA outperforms existing methods for removing batch effects and distinguishing cell types in multiple simulated and real scRNA-seq datasets.


Assuntos
Aprendizado Profundo , Perfilação da Expressão Gênica/métodos , Análise de Sequência de RNA/métodos , Algoritmos , Humanos , Leucócitos Mononucleares/metabolismo , Pâncreas/citologia , Pâncreas/metabolismo , Análise de Célula Única/métodos , Linfócitos T/metabolismo
10.
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi ; 36(4): 670-676, 2019 Aug 25.
Artigo em Chinês | MEDLINE | ID: mdl-31441270

RESUMO

Computer-aided diagnosis based on computed tomography (CT) image can realize the detection and classification of pulmonary nodules, and improve the survival rate of early lung cancer, which has important clinical significance. In recent years, with the rapid development of medical big data and artificial intelligence technology, the auxiliary diagnosis of lung cancer based on deep learning has gradually become one of the most active research directions in this field. In order to promote the deep learning in the detection and classification of pulmonary nodules, we reviewed the research progress in this field based on the relevant literatures published at domestic and overseas in recent years. This paper begins with a brief introduction of two widely used lung CT image databases: lung image database consortium and image database resource initiative (LIDC-IDRI) and Data Science Bowl 2017. Then, the detection and classification of pulmonary nodules based on different network structures are introduced in detail. Finally, some problems of deep learning in lung CT image nodule detection and classification are discussed and conclusions are given. The development prospect is also forecasted, which provides reference for future application research in this field.


Assuntos
Aprendizado Profundo , Neoplasias Pulmonares/diagnóstico por imagem , Nódulo Pulmonar Solitário/diagnóstico por imagem , Tomografia Computadorizada por Raios X , Humanos , Interpretação de Imagem Radiográfica Assistida por Computador , Reprodutibilidade dos Testes
11.
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi ; 36(4): 677-683, 2019 Aug 25.
Artigo em Chinês | MEDLINE | ID: mdl-31441271

RESUMO

With the development of image-guided surgery and radiotherapy, the demand for medical image registration is stronger and the challenge is greater. In recent years, deep learning, especially deep convolution neural networks, has made excellent achievements in medical image processing, and its research in registration has developed rapidly. In this paper, the research progress of medical image registration based on deep learning at home and abroad is reviewed according to the category of technical methods, which include similarity measurement with an iterative optimization strategy, direct estimation of transform parameters, etc. Then, the challenge of deep learning in medical image registration is analyzed, and the possible solutions and open research are proposed.


Assuntos
Aprendizado Profundo , Diagnóstico por Imagem , Redes Neurais (Computação) , Processamento de Imagem Assistida por Computador , Pesquisa
13.
Zhongguo Yi Liao Qi Xie Za Zhi ; 43(4): 255-258, 2019 Jul 30.
Artigo em Chinês | MEDLINE | ID: mdl-31460715

RESUMO

In this paper, the classification and location of neuroblastoma in NMR images are realized by using Deep Neural Network(CNN) algorithm as the core technology. The module is integrated to realize the development of computer-aided diagnostic software. It is used to make up for the gap in the field of intelligent identification and accurate positioning of neuroblastoma in the current nuclear magnetic resonance detection technology, effectively reduce the work intensity of doctors reading films, and further promote the clinical application and technical development of nuclear magnetic resonance detection technology in the diagnosis of neuroblastoma.


Assuntos
Algoritmos , Aprendizado Profundo , Neuroblastoma , Humanos , Imagem por Ressonância Magnética , Redes Neurais (Computação) , Neuroblastoma/diagnóstico por imagem
14.
Stud Health Technol Inform ; 264: 1556-1557, 2019 Aug 21.
Artigo em Inglês | MEDLINE | ID: mdl-31438229

RESUMO

The demand for AI to improve patients outcome has been increased; we, therefore, aim to establish the diagnostic values of AI in diabetic retinopathy by pooling the published studies of deep learning on this subject. A total of eight studies included which evaluated deep learning in a total of 706,922 retinal images. The overall pooled area under receiver operating curve (AUROC) was 98.93% (95%CI:98.37%-99.49%). However, the overall pooled sensitivity and specificity for detecting referable diabetic retinopathy (RDR) was 74% (95% CI: 73%-74%), and 95% (95% CI: 95%-95%). The findings of this study show that deep learning had high sensitivity and specificity for identifying diabetic retinopathy.


Assuntos
Retinopatia Diabética , Algoritmos , Inteligência Artificial , Aprendizado Profundo , Humanos
15.
Stud Health Technol Inform ; 264: 1596-1597, 2019 Aug 21.
Artigo em Inglês | MEDLINE | ID: mdl-31438249

RESUMO

Nephrosis is disease characterized by abnormal protein loss from impaired kidney. We constructed early prediction model using machine learning from clinical time series data, that can predict onset of nephrosis for more than one month. Long short-term memory capable of recognizing temporal sequential data patterns, was adopted as early prediction model for nephrosis. We verified our proposed prediction model has higher accuracy compared with those of baseline classifiers by 5-fold cross validation.


Assuntos
Aprendizado Profundo , Nefrose , Diagnóstico Precoce , Humanos , Aprendizado de Máquina , Nefrose/diagnóstico
16.
Stud Health Technol Inform ; 264: 438-441, 2019 Aug 21.
Artigo em Inglês | MEDLINE | ID: mdl-31437961

RESUMO

We aimed to develop a deep learning model for the prediction of the risk of advanced colorectal cancer in Taiwanese adults. We collected data of 58152 patients from the Taiwan National Health Insurance database from 1999 to 2013. All patients' comorbidities and medications history were included in the development of the convolution neural network (CNN) model. We also used 3-year medical data of all patients before the diagnosed colorectal cancer (CRC) as the dimensional time in the model. The area under the receiver operating characteristic curve (AUC), sensitivity, and specificity were computed to measure the performance of the model. The results showed the mean (SD) of AUC of the model was 0.922 (0.004). Moreover, the performance of the model observed the sensitivity of 0.837, specificity of 0.867, and 0.532 for PPV value. Our study utilized CNN to develop a prediction model for CRC, based on non-image and multi-dimensional medical records.


Assuntos
Neoplasias Colorretais , Aprendizado Profundo , Algoritmos , Humanos , Aprendizado de Máquina , Taiwan
17.
Stud Health Technol Inform ; 264: 477-481, 2019 Aug 21.
Artigo em Inglês | MEDLINE | ID: mdl-31437969

RESUMO

Huntington Disease (HD) is a genetic neurodegenerative disease which leads to involuntary movements and impaired balance. These changes have been quantified using footstep pressure sensor mats such as Protokinetics' Zeno Walkway. Drawing from distances between recorded footsteps, patients' disease severity have been measured in terms of high level gait characteristics such as gait width and stride length. However, little attention has been paid to the pressure data collected during formation of individual footsteps. This work investigates the potential of classifying patient disease severity based on individual footstep pressure data using deep learning techniques. Using the Motor Subscale of the Unified HD Rating Scale (UHDRS) as the gold standard, our experiments showed that using VGG16 and similar modules can achieve classification accuracy of 89%. Image pre-processing are key steps for better model performance. This classification accuracy is compared to results based on 3D CNN (82%) and SVM (86.9%).


Assuntos
Doença de Huntington , Doenças Neurodegenerativas , Aprendizado Profundo , Marcha , Análise da Marcha , Humanos
18.
Stud Health Technol Inform ; 264: 482-486, 2019 Aug 21.
Artigo em Inglês | MEDLINE | ID: mdl-31437970

RESUMO

Recently, the National Institutes of Health (NIH) published a chest X-ray image database named "ChestX-ray8", which contains 108,948 X-ray images that are labeled with eight types of diseases. Identifying the pathologies from the clinical images is a challenging task even for human experts, and to develop computer-aided diagnosis systems to help humans identify the pathologies from images is an urgent need. In this study, we applied the deep learning methods to identify the cardiomegaly from the X-ray images. We tested our algorithms on a dataset containing 600 images, and obtained the best performance with an area under the curve (AUC) of 0.87 using the transfer learning method. This result indicates the feasibility of developing computer-aided diagnosis systems for different pathologies from X-rays using deep learning techniques.


Assuntos
Algoritmos , Cardiomegalia , Diagnóstico por Computador , Área Sob a Curva , Aprendizado Profundo , Humanos
19.
BMC Bioinformatics ; 20(1): 415, 2019 Aug 06.
Artigo em Inglês | MEDLINE | ID: mdl-31387547

RESUMO

BACKGROUND: Predicting the effect of drug-drug interactions (DDIs) precisely is important for safer and more effective drug co-prescription. Many computational approaches to predict the effect of DDIs have been proposed, with the aim of reducing the effort of identifying these interactions in vivo or in vitro, but room remains for improvement in prediction performance. RESULTS: In this study, we propose a novel deep learning model to predict the effect of DDIs more accurately.. The proposed model uses autoencoders and a deep feed-forward network that are trained using the structural similarity profiles (SSP), Gene Ontology (GO) term similarity profiles (GSP), and target gene similarity profiles (TSP) of known drug pairs to predict the pharmacological effects of DDIs. The results show that GSP and TSP increase the prediction accuracy when using SSP alone, and the autoencoder is more effective than PCA for reducing the dimensions of each profile. Our model showed better performance than the existing methods, and identified a number of novel DDIs that are supported by medical databases or existing research. CONCLUSIONS: We present a novel deep learning model for more accurate prediction of DDIs and their effects, which may assist in future research to discover novel DDIs and their pharmacological effects.


Assuntos
Aprendizado Profundo , Interações de Medicamentos , Modelos Teóricos , Área Sob a Curva , Bases de Dados Factuais , Humanos , Redes Neurais (Computação) , Máquina de Vetores de Suporte
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