Get our free extension to see links to code for papers anywhere online!

Chrome logo  Add to Chrome

Firefox logo Add to Firefox
Picture for Jennifer A. Pacheco

Jennifer A. Pacheco
Alert button

Natural language processing to identify lupus nephritis phenotype in electronic health records


Dec 20, 2021
Yu Deng, Jennifer A. Pacheco, Anh Chung, Chengsheng Mao, Joshua C. Smith, Juan Zhao, Wei-Qi Wei, April Barnado, Chunhua Weng, Cong Liu, Adam Cordon, Jingzhi Yu, Yacob Tedla, Abel Kho, Rosalind Ramsey-Goldman, Theresa Walunas, Yuan Luo

Add code
Alert button

Figure 1 for Natural language processing to identify lupus nephritis phenotype in electronic health records
Figure 2 for Natural language processing to identify lupus nephritis phenotype in electronic health records
Figure 3 for Natural language processing to identify lupus nephritis phenotype in electronic health records

   Access Paper or Ask Questions
  • Share via Twitter
  • Share via Facebook
  • Share via LinkedIn
  • Share via Whatsapp
  • Share via Messenger
  • Share via Email

Identification of Predictive Sub-Phenotypes of Acute Kidney Injury using Structured and Unstructured Electronic Health Record Data with Memory Networks


Apr 10, 2019
Zhenxing Xu, Jingyuan Chou, Xi Sheryl Zhang, Yuan Luo, Tamara Isakova, Prakash Adekkanattu, Jessica S. Ancker, Guoqian Jiang, Richard C. Kiefer, Jennifer A. Pacheco, Luke V. Rasmussen, Jyotishman Pathak, Fei Wang

Add code
Alert button

Figure 1 for Identification of Predictive Sub-Phenotypes of Acute Kidney Injury using Structured and Unstructured Electronic Health Record Data with Memory Networks
Figure 2 for Identification of Predictive Sub-Phenotypes of Acute Kidney Injury using Structured and Unstructured Electronic Health Record Data with Memory Networks
Figure 3 for Identification of Predictive Sub-Phenotypes of Acute Kidney Injury using Structured and Unstructured Electronic Health Record Data with Memory Networks
Figure 4 for Identification of Predictive Sub-Phenotypes of Acute Kidney Injury using Structured and Unstructured Electronic Health Record Data with Memory Networks

   Access Paper or Ask Questions
  • Share via Twitter
  • Share via Facebook
  • Share via LinkedIn
  • Share via Whatsapp
  • Share via Messenger
  • Share via Email

Evaluating the Portability of an NLP System for Processing Echocardiograms: A Retrospective, Multi-site Observational Study


Apr 02, 2019
Prakash Adekkanattu, Guoqian Jiang, Yuan Luo, Paul R. Kingsbury, Zhenxing Xu, Luke V. Rasmussen, Jennifer A. Pacheco, Richard C. Kiefer, Daniel J. Stone, Pascal S. Brandt, Liang Yao, Yizhen Zhong, Yu Deng, Fei Wang, Jessica S. Ancker, Thomas R. Campion, Jyotishman Pathak

Add code
Alert button

Figure 1 for Evaluating the Portability of an NLP System for Processing Echocardiograms: A Retrospective, Multi-site Observational Study
Figure 2 for Evaluating the Portability of an NLP System for Processing Echocardiograms: A Retrospective, Multi-site Observational Study
* Under review with AMIA 2019 
   Access Paper or Ask Questions
  • Share via Twitter
  • Share via Facebook
  • Share via LinkedIn
  • Share via Whatsapp
  • Share via Messenger
  • Share via Email