Clinical Assistant Diagnosis for Electronic Medical Record Based on Convolutional Neural Network

Yang, Zhongliang; Huang, Yudong; Jiang, Yiran; Sun, Yuxi; Zhang, Yujin; Luo, Ping

doi:10.1038/s41598-018-24389-w

Cited by 75 publications

(48 citation statements)

References 38 publications

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“…Deep learning has made significant improvement in research and started to make changes in our daily lives. In the medical field, many studies have applied deep learning and shown many great successes [78,[113][114][115][116][117][118][119][120][121]. One advantage of using deep learning to train a model is its capability to continue training when more data is available [27].…”

Section: Conclusion and Summarymentioning

confidence: 99%

The Application of Deep Learning in Cancer Prognosis Prediction

Zhu

Xie

Han

et al. 2020

Cancers

260

161

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Deep learning has been applied to many areas in health care, including imaging diagnosis, digital pathology, prediction of hospital admission, drug design, classification of cancer and stromal cells, doctor assistance, etc. Cancer prognosis is to estimate the fate of cancer, probabilities of cancer recurrence and progression, and to provide survival estimation to the patients. The accuracy of cancer prognosis prediction will greatly benefit clinical management of cancer patients. The improvement of biomedical translational research and the application of advanced statistical analysis and machine learning methods are the driving forces to improve cancer prognosis prediction. Recent years, there is a significant increase of computational power and rapid advancement in the technology of artificial intelligence, particularly in deep learning. In addition, the cost reduction in large scale next-generation sequencing, and the availability of such data through open source databases (e.g., TCGA and GEO databases) offer us opportunities to possibly build more powerful and accurate models to predict cancer prognosis more accurately. In this review, we reviewed the most recent published works that used deep learning to build models for cancer prognosis prediction. Deep learning has been suggested to be a more generic model, requires less data engineering, and achieves more accurate prediction when working with large amounts of data. The application of deep learning in cancer prognosis has been shown to be equivalent or better than current approaches, such as Cox-PH. With the burst of multi-omics data, including genomics data, transcriptomics data and clinical information in cancer studies, we believe that deep learning would potentially improve cancer prognosis.

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Section: Conclusion and Summarymentioning

confidence: 99%

The Application of Deep Learning in Cancer Prognosis Prediction

Zhu

Xie

Han

et al. 2020

Cancers

260

161

View full text Add to dashboard Cite

show abstract

“…Finally, the output y is sent to the Softmax classifier to obtain a predicted value for each health indicator [ 34 ].…”

Section: Methodsmentioning

confidence: 99%

Construction of medical equipment-based doctor health monitoring system

Wang

Cheng²,

Zhou³

et al. 2019

J Med Syst

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The health status of doctors has been overlooked by the society and even the doctors themselves, especially those doctors who work long hours. Their attention is always on patients, so they are more likely to ignore their own health problems. Therefore, in this paper, we propose a medical equipment-based doctor health monitoring system (hereinafter referred to as Doc-care). Doc-care can be used as a private health manager for doctors, and doctors can monitor their health indicators in real time while using medical equipment to aid diagnosis and treatment. When the doctor’s health status is neglected, Doc-care can protect the doctor’s health; combining with the convolutional neural network method to detect and grade the doctor’s health indicators, to assess the doctor’s real-time health status. After referring to the doctor’s past health data in the cloud server, giving appropriate advice and predictions about the doctor’s health status.

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“…In order to improve the efficiency of multiple convolutional networks, it is also possible to more directly simulate the dependence of adjacent tags in the segmentation, so that the final prediction energy can be Influenced by the model's value on nearby tags, this DCNN uses a cascade structure to implement the output of the first convolutional network as an additional input to the second convolutional network. At the same time, in order to enable DCNN to train more effectively, a residual connection can be used, that is, an independent connection bypassing the parameter layer is created in the network [14]. The convolutional layer involves three tensors: two feature maps and one convolution kernel, as in equation 1:…”

Section: Frame Of Image Segmentation Processing Based On Dcnn Algmentioning

confidence: 99%

Study on MRI Medical Image Segmentation Technology Based on CNN-CRF Model

2020

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Image segmentation is an important technique for segmenting images without overlapping each other and having their own features. It has been rapidly developed in the field of medical imaging, but there is currently a difference between classification accuracy and segmentation accuracy for medical image segmentation. In this paper, the deep convolutional neural network is combined with the cascading structure, and a uniform learning framework is established with the use conditional random field. This paper first adds a cascading structure under the deep convolutional neural networks (DCNN) framework to more effectively simulate the direct dependencies between spatial closure tags. Secondly, the conditional random field (CRF) is used for post-segmentation processing, which effectively solves the contradiction between the segmentation accuracy and the network depth and the number of pooling times in the traditional convolutional network. Secondly, the CRF is used for post-segmentation processing, which effectively solves the contradiction between the segmentation accuracy and the network depth and the number of pooling times in the traditional convolutional network. INDEX TERMS Image segmentation, deep convolutional neural network, cascade structure, conditional random field.

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Clinical Assistant Diagnosis for Electronic Medical Record Based on Convolutional Neural Network

Cited by 75 publications

References 38 publications

The Application of Deep Learning in Cancer Prognosis Prediction

The Application of Deep Learning in Cancer Prognosis Prediction

Construction of medical equipment-based doctor health monitoring system

Study on MRI Medical Image Segmentation Technology Based on CNN-CRF Model

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