Machine learning - Should healthcare "tay" away - Pittsburgh Thrival Conference 2016
•Download as PPTX, PDF•
2 likes•323 views
Machine learning and artificial intelligence already are and will continue to be beneficial in health care. However, serious pitfalls dot the health care landscape — while failed experiments like Twitter chatbots may be amusing, mistakes in a medical context can have serious consequences. This talk will demystify the basic concepts of machine learning and several of the freely available tools that are making this technology more and more accessible, as well as explore the pros and cons of machine learning’s expansion across the health care industry.
Recommended
More Related Content
Viewers also liked
Viewers also liked (10)
Similar to Machine learning - Should healthcare "tay" away - Pittsburgh Thrival Conference 2016
Similar to Machine learning - Should healthcare "tay" away - Pittsburgh Thrival Conference 2016 (20)
Recently uploaded
Recently uploaded (20)
Machine learning - Should healthcare "tay" away - Pittsburgh Thrival Conference 2016
- 1. Machine Learning – Should Healthcare “Tay” away Mohinder Dick Senior Software Architect dickm@upmc.edu @mobyware Joseph Wright Software Engineer wrightj13@upmc.edu @joegle September 22, 2016
- 2. | Goals Break down the perception that machine learning is too complex. Demonstrate the value of understanding machine learning in healthcare. Briefly discuss the unique challenges in healthcare.
- 3. | ©2015 UPMC Enterprises : PROPRIETARY3 Machine Learning can be perceived as Science Fiction
- 4. | ©2016 UPMC Enterprises : PROPRIETARY4 Machine Learning can be perceived as Complex and Vast in terms of research, terminology, and technology
- 5. | ©2016 UPMC Enterprises : PROPRIETARY5
- 6. | ©2016 UPMC Enterprises : PROPRIETARY ©2015 UPMC Enterprises : PROPRIETARY6
- 7. | 7 ©2016 UPMC Enterprises : PROPRIETARY
- 8. | What we’ll accomplish today • The basics of machine learning • Machine learning in Healthcare • The danger of “Taying” away • A cross-functional approach to ML in healthcare • Demo • Q & A • References ©2016 UPMC Enterprises : PROPRIETARY8
- 9. | ©2016 UPMC Enterprises : PROPRIETARY9 Machine Learning basics | Overview Machine Learning Supervised Unsupervised Linear Non Linear K-means Clustering Self Organizing Maps Linear Regression Logistic Regression NN Decision Trees Random Forces SIMPLE COMPLEX
- 10. | Machine Learning basics | Supervised Models ©2016 UPMC Enterprises : PROPRIETARY10 Unseen Data ML Model Predictions • ML algorithms process “unseen” data to give predictions • A model is a statistical representation of the algorithm’s experiences/training • Generally, the more data the more accurate the prediciton
- 11. | Machine Learning basics | Example of a Supervised Model ©2016 UPMC Enterprises : PROPRIETARY11 Facial Recognition Geometric features called “Haars” ML Model Is this a face? Non-linear model Predictions
- 12. | ©2016 UPMC Enterprises : PROPRIETARY12
- 13. | ©2016 UPMC Enterprises : PROPRIETARY13
- 14. | Machine Learning in Healthcare | General Case • Hospitals already have digital records, because of the ACA • Can use public algorithm and software to make predictions • Predictions save lives and/or lower costs ©2015 UPMC Enterprises : PROPRIETARY14 + SEPSIS + Adverse Drug Reaction + % Readmission Data (Pt EMR): Meds Labs Visit History Notes Model Predictions
- 15. | Machine Learning in Healthcare | Just ask “Tay” ©2015 UPMC Enterprises : PROPRIETARY15
- 16. | Machine Learning in Healthcare | Just ask “Tay” ©2016 UPMC Enterprises : PROPRIETARY16
- 17. | Machine Learning in Healthcare | Cross-functional ©2015 UPMC Enterprises : PROPRIETARY17 Multidisciplinary teams: Clinicians, Data Scientists, Pharmacists, Researchers, Physicians, Engineers…
- 18. | Machine Learning in Healthcare | Exemplar • System explicitly incorporates clinicians into the ML solution • Users provide feedback that improves the model over time • The ML solution gives the user insight into its reasoning and confidence ©2016 UPMC Enterprises : PROPRIETARY18 Clinical Documents Model Predictions ML Recommendations Clinician Feedback
- 19. | Machine Learning in Healthcare | Exemplar ©2016 UPMC Enterprises : PROPRIETARY19 Clinician can provide feedback to ML system in intuitive way. ML model provides clinician feedback about it’s confidence.
- 20. | Machine Learning in Healthcare | Grassroots • Combined multi-disciplines to get better result • Hard to classify heart-rates with ML or digital signal processing alone • Using both we can relatively accurately identify a person from their heart-rate. ©2015 UPMC Enterprises : PROPRIETARY20 Model
- 21. | Let’s teach machines • Demo ©2015 UPMC Enterprises : PROPRIETARY21
- 22. | Questions
- 23. | References Resource Location Presentation link EMR VisWeb for Search & Review (Dr. Hwa et al) http://vialab.science.uoit.ca/textvis2015/papers/T rivedi-textvis2015.pdf Joseph’s homepage http://joegle.com/hrv Clustering demo source-code https://github.com/MobyWare/ml-thrival- heartrate-demo-python Futuristic Heart image http://videorista.com/ ©2015 UPMC Enterprises : PROPRIETARY23
Editor's Notes
- Goal is to provide a soft landing for a topic that can be the stuff of science fiction Re demonstrate, illustration in healthcare If time permits we will do so with a quick demo.
- Machine learning is complex and vast: Research Terms Technologies
- Consider two visions of ML, one is way to complex. This is good to aspire to: ANN Threat modelling Advance image recognition Arrow anecdote
- Vision 2: Approachable for most of us here. Use simpler types of machine learning to serve clinical context: Facial recognition: OpenCV to find face. Glasses Estimate length of stay in hospital Determine which drugs are likely to be prescribed
- Multi-disciplinary approach breeds cooperation between man and machine.
- Prediction is the value of the target variable the machine comes up with. Label is the value you give it during training.
- Haars are rectangular regions Models for facial detection are typically non-linear. Trees or Net (neural nets) Haar cascades – Viola & Jones (real-time image detection)
- So all we have to do is add smart people & data. Soon we won’t even need doctors, right?
- Cannot be cavalier with algorithms in healthcare, stakes are high Use multi-disciplinary approach to ensure appropriate applicaton
- EMR VisWeb for Search & Review, Rebecca Haw e.t. at the university of pittsburgh