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The Potential of Smartphone Apps in Informing Protobacco and Antitobacco Messaging Efforts Among Underserved Communities: Longitudinal Observational Study

The Potential of Smartphone Apps in Informing Protobacco and Antitobacco Messaging Efforts Among Underserved Communities: Longitudinal Observational Study

Next, we conducted optimized hot spot analysis, and the results showed that in Massachusetts, the heaviest smokers (based on the number of cigarettes smoked in the past 30 days) tended to report that they smoked in Dorchester Center, Roxbury Crossing, Lawrence

Edmund WJ Lee, Mesfin Awoke Bekalu, Rachel McCloud, Donna Vallone, Monisha Arya, Nathaniel Osgood, Xiaoyan Li, Sara Minsky, Kasisomayajula Viswanath

J Med Internet Res 2020;22(7):e17451


The Addition of Asynchronous Chat-Based Coaching to a Digital Behavioral Health Tool Promotes Support and Personalization

The Addition of Asynchronous Chat-Based Coaching to a Digital Behavioral Health Tool Promotes Support and Personalization

IPROCiprocIproceedings2369-6893JMIR PublicationsToronto, Canadav4i2e1179710.2196/11797AbstractAbstractThe Addition of Asynchronous Chat-Based Coaching to a Digital Behavioral Health Tool Promotes Support and PersonalizationHaleTimothyLinkAlissaMPH1myStrength, Inc1875 Lawrence

Alissa Link, Amy Lukowski, Abigail Hirsch

iproc 2018;4(2):e11797


Combining Contextualized Embeddings and Prior Knowledge for Clinical Named Entity Recognition: Evaluation Study

Combining Contextualized Embeddings and Prior Knowledge for Clinical Named Entity Recognition: Evaluation Study

models.Figure 2Log-likelihood of the forward and backward directions language models.Where θx represents the token representation layer, θs represents the Softmax layer, and LSTM and LSTM represent the forward and backward directions of the LSTM layer.In 2017, Peters

Min Jiang, Todd Sanger, Xiong Liu

JMIR Med Inform 2019;7(4):e14850


Multi-Level Representation Learning for Chinese Medical Entity Recognition: Model Development and Validation

Multi-Level Representation Learning for Chinese Medical Entity Recognition: Model Development and Validation

For example, Peters et al [12] explicitly showed that the lower layer fits into the local semantic relationships, the higher layer is suitable for longer-range relationships, and the final layer specializes in the language model.

Zhichang Zhang, Lin Zhu, Peilin Yu

JMIR Med Inform 2020;8(5):e17637