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Difficulty level: Beginner 

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Keywords: AI  or Machine learning  or OMOP 

Fluoroquinolone antibiotics and Aortic Aneurysm or Dissection 

The main objective of this project was to provide education on the use of data translated to the OMOP common data model. We aimed to showcase how the Atlas interface tool could be used to generate evidence for a highly relevant and significant research question. The clinical question that was...

Keywords: OMOP, Aortic Aneurysm, Fluoroquinolone antibiotics

Fluoroquinolone antibiotics and Aortic Aneurysm or Dissection  https://dresa.org.au/materials/fluoroquinolone-antibiotics-and-aortic-aneurysm-or-dissection The main objective of this project was to provide education on the use of data translated to the OMOP common data model. We aimed to showcase how the Atlas interface tool could be used to generate evidence for a highly relevant and significant research question. The clinical question that was used to demonstrate the process revolved around investigating the potential association between the use of fluoroquinolones to treat urinary tract infection and the risk of experiencing aortic aneurysm and dissection within 30 days, 3 months, or 12 months of treatment initiation compared to other commonly used antibiotics. The workshop aimed to describe how data are translated to the OMOP CDM, how cohorts can be derived in these data, how to execute a robust analysis, and lastly, how to interpret the results of the study. Specifically, we described the process of translating Australian medicines dispensing data to the OMOP CDM, including the translation of the Australia Pharmaceutical Benefits Schedule data to the international RxNorm standard vocabulary. The outcome of the project is an on-line training resource that highlights the process of study execution from start to finish. This training package will serve as an exemplar for researchers in Australia to unlock the value of their data that has been translated into the OMOP CDM. The audience for this project was database programmers, researchers, and decision-makers, and all those interested in using data to inform healthcare. Roger Ward, Nicole Pratt Christine Hallinan OMOP, Aortic Aneurysm, Fluoroquinolone antibiotics
Deep Learning for Natural Language Processing

This workshop is designed to be instructor led and consists of two parts.
Part 1 consists of a lecture-demo about text processing and a hands-on session for attendees to learn how to clean a dataset.
Part 2 consists of a lecture introducing Recurrent Neural Networks and a hands-on session for...

Keywords: Deep learning, NLP, Machine learning

Resource type: presentation, tutorial

Deep Learning for Natural Language Processing https://dresa.org.au/materials/deep-learning-for-natural-language-processing This workshop is designed to be instructor led and consists of two parts. Part 1 consists of a lecture-demo about text processing and a hands-on session for attendees to learn how to clean a dataset. Part 2 consists of a lecture introducing Recurrent Neural Networks and a hands-on session for attendees to train their own RNN. The Powerpoints contain the lecture slides, while the Jupyter notebooks (.ipynb) contain the hands-on coding exercises. This workshop introduces natural language as data for deep learning. We discuss various techniques and software packages (e.g. python strings, RegEx, NLTK, Word2Vec) that help us convert, clean, and formalise text data “in the wild” for use in a deep learning model. We then explore the training and testing of a Recurrent Neural Network on the data to complete a real world task. We will be using TensorFlow v2 for this purpose. datascienceplatform@monash.edu Deep learning, NLP, Machine learning
Introduction to Deep Learning and TensorFlow

This workshop is intended to run as an instructor guided live event and consists of two parts. Each part consists of a lecture and a hands-on coding exercise.
Part 1 - Introduction to Deep Learning and TensorFlow
Part 2 - Introduction to Convolutional Neural Networks
The Powerpoints contain...

Keywords: Deep learning, convolutional neural network, tensorflow, Machine learning

Resource type: presentation, tutorial

Introduction to Deep Learning and TensorFlow https://dresa.org.au/materials/introduction-to-deep-learning-and-tensorflow This workshop is intended to run as an instructor guided live event and consists of two parts. Each part consists of a lecture and a hands-on coding exercise. Part 1 - Introduction to Deep Learning and TensorFlow Part 2 - Introduction to Convolutional Neural Networks The Powerpoints contain the lecture slides, while the Jupyter notebooks (.ipynb) contain the hands-on coding exercises. This workshop is an introduction to how deep learning works and how you could create a neural network using TensorFlow v2. We start by learning the basics of deep learning including what a neural network is, how information passes through the network, and how the network learns from data through the automated process of gradient descent. Workshop attendees would build, train and evaluate a neural network using a cloud GPU (Google Colab). In part 2, we look at image data and how we could train a convolution neural network to classify images. Workshop attendees will extend their knowledge from the first part to design, train and evaluate this convolutional neural network. datascienceplatform@monash.edu Deep learning, convolutional neural network, tensorflow, Machine learning