This 3 hour instructional workshop provides an introduction to techniques in multi-task modeling for deep learning techniques to perform automated clinical judgement studies (Naranjo Question Answering). We have organized successful workshops in MedInfo 2015, AMIA 2017, 2018 and have developed deep learning models for clinical judgement studies and for state-of-art natural language processing (including clinical event detection in electronic health record notes document, sentence classification, semantic entailment and question answering). In this workshop, we would cover the fundamentals of multi-task learning and deep learning. We will demonstrate the methodologies on the application of the widely accepted Naranjo questionnaire for clinical judgement studies. For deep learning, we will describe the state-of-the-art Recurrent Neural Networks and attention modeling. The focus would rest on using widely used Python programming language and its deep learning packages, such as PyTorch, to quickly implement a prototype and test different multi-task deep learning models. These deep learning techniques can be extended to other clinical natural language classification tasks other than predicting answers to Naranjo questionnaire.

Learning Objective: After participating in this session, the learner should be better able to:
1. Utilize python for processing their data from the raw format.
2. Understand the basic architecture of a deep learning model espeically sequence models and implement them on tasks of natural language processing. Also, the learner would be able to use attention mechanism over these deep learning models to boost their performance.
3. Learn the importance of Naranjo questionnaire for clinical judgement studies.
4. Formulate a problem as a multi-task learning problem where relavent to improve the prediction accuracy of the models.
5. Understand the difference in relation extraction between an adverse drug reaction and medication when compared to causal relation between them.


Bhanu Pratap Singh Rawat (Presenter)
UMass Amherst

Abhyuday Jagannatha (Presenter)
UMass Amherst

Fei Li (Presenter)
UMass Lowell

Hong Yu (Presenter)
UMass Lowell

Presentation Materials: