Difference between revisions of "User:ZFarahany"
From REU@MU
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==Week 2== | ==Week 2== | ||
| + | '''''Monday''''' | ||
| + | *Attended data ethics talk from Dr. Brylow | ||
| + | *Read "Predicting Opioid Use Disorder using Random Forest" by Wadekar et. al. | ||
| Line 52: | Line 55: | ||
*What is "gain"? | *What is "gain"? | ||
*How to fix "data noise"? | *How to fix "data noise"? | ||
| + | |||
| + | ===Predicting Opioid Use Disorder using Random Forest=== | ||
| + | Objectives | ||
| + | *To use Random Forest on a public dataset to make a predictive model for determining OUD diagnosis | ||
| + | Useful info | ||
| + | *First age of marijuana consumption, mental illness status, and age in that order are the biggest predictors of OUD from this study | ||
| + | *Useful references for the existence of the Opioid crisis | ||
| + | Questions | ||
| + | *What is downsampling? | ||
Revision as of 23:46, 7 June 2021
Zach Farahany's profile
About me: Hi, I'm Zach. I'm a Data Science and Computational Mathematics major at Marquette University. My research project is Predicting Risk of Opioid Use Disorder with a focus on the impact from Covid-19.
Contents
Work Log
Week 1
Tuesday
- Attended REU Orientation and learned beginner python
Wednesday
- Learned data visualization and basic machine learning
Thursday
- Good research practices talk from Dr. Brylow
- Meeting with Dr. Praveen for project expectations
Friday
- Made personal webpage and completed research plan
Sunday
- Read "Predicting Opioid Overdose Readmission and Opioid Use Disorder with Machine Learning"
Week 2
Monday
- Attended data ethics talk from Dr. Brylow
- Read "Predicting Opioid Use Disorder using Random Forest" by Wadekar et. al.
Literature Summaries
Predicting Opioid Overdose Readmission and Opioid Use Disorder with Machine Learning
Objectives
- Use multiple machine learning models and multiple data types to predict the likelihood of hospital readmission following an opioid overdose and diagnosis of opioid use disorder after being prescribed an opioid
Useful Info
- AUC value is a rate of correct prediction
- Hospital info is meant to be anonymized, patient identifiers must be removed
- T40 codes are used as identifiers of various conditions including Covid and OUD
- Various methods of cleaning and compiling hospital records into more useful data frames
- Various viable machine learning models that could be used on my data
- 10 fold cross-validation methods of machine learning
- SMOTE used for class balancing
- Various limitations of hospital data, the data does not include non-registered opioid use or addiction
- "Black box" structure of machine learning models
- Deep learning models such as RNN GRU and LSTM
- Doctor AI used for EHR(Electronic Health Record) data
Questions
- Why is SMOTE necessary?
- What is "gain"?
- How to fix "data noise"?
Predicting Opioid Use Disorder using Random Forest
Objectives
- To use Random Forest on a public dataset to make a predictive model for determining OUD diagnosis
Useful info
- First age of marijuana consumption, mental illness status, and age in that order are the biggest predictors of OUD from this study
- Useful references for the existence of the Opioid crisis
Questions
- What is downsampling?
