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Asthma Exacerbation Prediction and Risk Factor Analysis Based on a Time-Sensitive, Attentive Neural Network: Retrospective Cohort Study

Asthma Exacerbation Prediction and Risk Factor Analysis Based on a Time-Sensitive, Attentive Neural Network: Retrospective Cohort Study

matrix (Vc: concept vocabulary size and Dc: concept embedding dimension), we mapped each clinical concept into a concept-embedding vector:where Cij is the generated concept-embedding vector and is the one hot vector denoting the existence of clinical concept j

Yang Xiang, Hangyu Ji, Yujia Zhou, Fang Li, Jingcheng Du, Laila Rasmy, Stephen Wu, W Jim Zheng, Hua Xu, Degui Zhi, Yaoyun Zhang, Cui Tao

J Med Internet Res 2020;22(7):e16981


The Development of an Automated Device for Asthma Monitoring for Adolescents: Methodologic Approach and User Acceptability

The Development of an Automated Device for Asthma Monitoring for Adolescents: Methodologic Approach and User Acceptability

Figure 5 illustrates the schematic overview of the audio data processing by ADAM. Although our initial intention was to detect both coughing and wheezing, we were only able to successfully apply our automated techniques to coughs.

Hyekyun Rhee, Sarah Miner, Mark Sterling, Jill S. Halterman, Eileen Fairbanks

JMIR Mhealth Uhealth 2014;2(2):e27


The Deep Learning–Based Recommender System “Pubmender” for Choosing a Biomedical Publication Venue: Development and Validation Study

The Deep Learning–Based Recommender System “Pubmender” for Choosing a Biomedical Publication Venue: Development and Validation Study

⨁vm (1), where ⨁ is the concatenation operator, m is the maximum length of abstracts (a scalar), and vi:i+j refers to the vector of concatenation of the words wi,wi+1,…,wi+j.

Xiaoyue Feng, Hao Zhang, Yijie Ren, Penghui Shang, Yi Zhu, Yanchun Liang, Renchu Guan, Dong Xu

J Med Internet Res 2019;21(5):e12957


Evaluating the Validity of an Automated Device for Asthma Monitoring for Adolescents: Correlational Design

Evaluating the Validity of an Automated Device for Asthma Monitoring for Adolescents: Correlational Design

These findings are useful for an initial understanding of the validity of ADAM and for providing direction for further studies.

Hyekyun Rhee, Michael J Belyea, Mark Sterling, Mark F Bocko

J Med Internet Res 2015;17(10):e234


Machine Learning Classifiers for Twitter Surveillance of Vaping: Comparative Machine Learning Study

Machine Learning Classifiers for Twitter Surveillance of Vaping: Comparative Machine Learning Study

, epochs: 10, batch_size: 16LSTM-CNNRelevancemax_features: 166,395, embed_size: 300, max_len: 75, optimizer: adam, filters: 50, kernel_size: 2, epochs: 10, batch_size: 16BiLSTMcRelevancemax_features: 166,395, embed_size: 300, max_len: 75, optimizer: adam, epochs

Shyam Visweswaran, Jason B Colditz, Patrick O’Halloran, Na-Rae Han, Sanya B Taneja, Joel Welling, Kar-Hai Chu, Jaime E Sidani, Brian A Primack

J Med Internet Res 2020;22(8):e17478


Mobile Behavioral Therapy for Headache: Pilot Study

Mobile Behavioral Therapy for Headache: Pilot Study

Behavioral Therapy for Headache: Pilot StudyHaleTimothyKirellAdam1BioTrak Therapeutics320 E 65th StreetNew York, NY,United States524 1934adam@biotraktherapeutics.comShingletonRebeccaDr PhD11BioTrak TherapeuticsNew York, NYUnited StatesCorresponding Author: Adam

Adam Kirell, Rebecca Shingleton

iproc 2018;4(2):e11814