Fundamentals of Clinical Data Science (Open-Access Book) - for healthcare & IT professionals: https://lnkd.in/eacNnjz
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Table of Contents

Part I. Data Collection

- Data Sources
- Data at Scale
- Standards in Healthcare Data
- Research Data Stewardship for Healthcare Professionals
- The EU’s General Data Protection Regulation (GDPR) in a Research Context

Part II. From Data to Model

- Preparing Data for Predictive Modelling
- Extracting Features from Time Series
- Prediction Modeling Methodology
- Diving Deeper into Models
- Reporting Standards & Critical Appraisal of Prediction Models

Part III. From Model to Application

- Clinical Decision Support Systems
- Mobile Apps
- Optimizing Care Processes with Operational Excellence & Process Mining
- Value-Based Health Care Supported by Data Science

#healthcare #datascience #digitalhealth #analytics #machinelearning #bigdata #populationhealth #ai #medicine #informatics #artificialintelligence #research #precisionmedicine #publichealth #science #health #innovation #technology #informationtechnology

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The Best and Most Current of Modern Natural Language Processing

Blog by Victor Sanh: https://lnkd.in/emch8gG

#NaturalLanguageProcessing #MachineLearning #NLP #DeepLearning #Research

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Have you heard of "R-Transformer?", a Recurrent Neural Network Enhanced Transformer

Recurrent Neural Networks have long been the dominating choice for sequence modeling. However, it severely suffers from two issues: impotent in capturing very long-term dependencies and unable to parallelize the sequential computation procedure.

Therefore, many non-recurrent sequence models that are built on convolution and attention operations have been proposed recently.

Here the authors propose the R-Transformer which enjoys the advantages of both RNNs and the multi-head attention mechanism while avoids their respective drawbacks.

The proposed model can effectively capture both local structures and global long-term dependencies in sequences without any use of position embeddings. They evaluated R-Transformer through extensive experiments with data from a wide range of domains and the empirical results show that R-Transformer outperforms the state-of-the-art methods by a large margin in most of the tasks.

Github code: https://lnkd.in/dpFckix

#research #algorithms #machinelearning #deeplearning #rnn

✴️ @AI_Python_EN