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Sleep Quality Prediction From Wearable Data Using Deep Learning

Sleep Quality Prediction From Wearable Data Using Deep Learning

The models used in our study were as follows:Logistic regression, a nondeep learning modelMulti-layer perceptrons (MLPs), a deep learning modelConvolutional neural network (CNN), a deep learning modelRecurrent neural networks (RNN), a deep learning modelLong

Aarti Sathyanarayana, Shafiq Joty, Luis Fernandez-Luque, Ferda Ofli, Jaideep Srivastava, Ahmed Elmagarmid, Teresa Arora, Shahrad Taheri

JMIR Mhealth Uhealth 2016;4(4):e125


A Deep Learning Method to Automatically Identify Reports of Scientifically Rigorous Clinical Research from the Biomedical Literature: Comparative Analytic Study

A Deep Learning Method to Automatically Identify Reports of Scientifically Rigorous Clinical Research from the Biomedical Literature: Comparative Analytic Study

study method consisted of the following steps, which are described in more detail in the sections below: (1) preparation of a dataset for training the deep learning models, (2) training and tuning deep learning models, (3) comparison of the deep learning approach

Guilherme Del Fiol, Matthew Michelson, Alfonso Iorio, Chris Cotoi, R Brian Haynes

J Med Internet Res 2018;20(6):e10281


Deep Learning Frameworks for Rapid Gram Stain Image Data Interpretation: Protocol for a Retrospective Data Analysis

Deep Learning Frameworks for Rapid Gram Stain Image Data Interpretation: Protocol for a Retrospective Data Analysis

IntroductionIn recent years, remarkable progress has been made in deep learning due to the emergence of big data processing technology. Deep learning is a family of machine learning that consists of multiple neurons in multiple layers.

Hee Kim, Thomas Ganslandt, Thomas Miethke, Michael Neumaier, Maximilian Kittel

JMIR Res Protoc 2020;9(7):e16843


Predicting Breast Cancer in Chinese Women Using Machine Learning Techniques: Algorithm Development

Predicting Breast Cancer in Chinese Women Using Machine Learning Techniques: Algorithm Development

We used three novel machine learning algorithms in this study: extreme gradient boosting (XGBoost), random forest (RF), and deep neural network (DNN), with traditional LR as a baseline comparison.MethodsDataset and Study PopulationIn this study, we used a balanced

Can Hou, Xiaorong Zhong, Ping He, Bin Xu, Sha Diao, Fang Yi, Hong Zheng, Jiayuan Li

JMIR Med Inform 2020;8(6):e17364


Deep Learning–Based Detection of Early Renal Function Impairment Using Retinal Fundus Images: Model Development and Validation

Deep Learning–Based Detection of Early Renal Function Impairment Using Retinal Fundus Images: Model Development and Validation

Among ophthalmology imaging techniques, retinal imaging has been used to establish deep learning models for detecting not only eye diseases (eg, diabetic retinopathy and glaucoma) [5,6] but also systemic cardiovascular risks [7].

Eugene Yu-Chuan Kang, Yi-Ting Hsieh, Chien-Hung Li, Yi-Jin Huang, Chang-Fu Kuo, Je-Ho Kang, Kuan-Jen Chen, Chi-Chun Lai, Wei-Chi Wu, Yih-Shiou Hwang

JMIR Med Inform 2020;8(11):e23472