Doctor Digital will see you now
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Machine learning -- the use of computational algorithms to find patterns in data -- is increasingly being deployed in clinical contexts to support diagnosis and treatment decisions. In the context of growing volumes of clinical data available in electronic form, there is an opportunity to realise dramatic changes in the practice of medicine through the application of large-scale health data analytics and predictive modeling. This talk will introduce a vision for the use of data-driven methods in health, while also raising important questions about the implementation of this vision: is it conceivable that one day your doctor might be replaced by a digital system? What are the risks?
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Doctor Digital will see you now
- 1. James Bailey and KarinVerspoor School of Computing and Information Systems Health and Biomedical Informatics Centre The University of Melbourne @karinv @ConverSci
- 4. •Learning Health Systems •What is machine learning? •Example current applications of machine learning in health •Challenges and Open Issues
- 5. LearningHealthSystems Health systems – at any level of scale – become learning systems when they can, continuously and routinely, study and improve themselves Arnold Milstein (2013) NEJM
- 10. •Learning Health Systems •What is machine learning? •Example current applications of machine learning in health •Challenges and Open Issues
- 11. Big Data Machine Learning Artificial Intelligence “Intelligent machines and software”
- 12. Question Answer Historical data Machine Learning Algorithm Machine Learning Prediction Model
- 15. 13/03/2016 10:03 26amNetflix Page 1 of 4https://www.netflix.com/Kids Fuller House The Wiggles My Little Pony Mako Mermaids H2O: Just Add Water Good Luck Charlie Pokémon Recently watched Top Picks for Kids Popular Action Kids Categories Exit KidsExit KidsSearch Kids... + + Ad feedback This item: The Martian by Andy Weir Paperback $8.92 The Revenant: A Novel of Revenge by Michael Punke Paperback $9.52 The Life We Bury by Allen Eskens Paperback $8.75 Customers Who Bought This Item Also Bought Page 1 of 15 The Revenant: A Novel of Revenge 1,250 Paperback $9.52 Ready Player One: A Novel 9,210 Paperback $8.37 The Life We Bury 1,896 Paperback $8.75 The 5th Wave: The First Book of the 5th Wave Series 2,006 Paperback $6.70 The Boys in the Boat: Nine Americans and Their Epic Quest for Gold at the… 17,056 in Boating Paperback $9.15 Michael Punke› Ernest Cline› Allen Eskens› Rick Yancey› Daniel James Brown› #1 Best Seller
- 16. plasma <= 0.2137 entropy = 0.9311 samples = 698 value = [456, 242] age <= -0.3787 entropy = 0.7128 samples = 445 value = [358, 87] True BMI <= -0.2551 entropy = 0.9631 samples = 253 value = [98, 155] False BMI <= -0.1279 entropy = 0.4173 samples = 249 value = [228, 21] BMI <= -0.7128 entropy = 0.9217 samples = 196 value = [130, 66] entropy = 0.1093 samples = 138 value = [136, 2] entropy = 0.6604 samples = 111 value = [92, 19] entropy = 0.2918 samples = 39 value = [37, 2] entropy = 0.9752 samples = 157 value = [93, 64] plasma <= 0.7758 entropy = 0.9031 samples = 69 value = [47, 22] plasma <= 1.4004 entropy = 0.8516 samples = 184 value = [51, 133] entropy = 0.5813 samples = 36 value = [31, 5] entropy = 0.9993 samples = 33 value = [16, 17] entropy = 0.9514 samples = 124 value = [46, 78] entropy = 0.4138 samples = 60 value = [5, 55] Deep Neural Network
- 20. •Learning Health Systems •What is machine learning? •Example current applications of machine learning in health •Challenges and Open Issues
- 23. April 14, 2007 CHIEF COMPLAINT: Shortness of breath. HISTORY OF PRESENT ILLNESS: This 68-year-old female presents to the emergency department with shortness of breath that has gone on for 4-5 days, progressively getting worse. It comes on with any kind of activity whatsoever. She has had a nonproductive cough. She has not had any chest pain. She has had chills but no fever. EMERGENCY DEPARTMENT COURSE: The patient was admitted. She has had intermittent episodes of severe dyspnea. Lungs were clear. These would mildly respond to breathing treatments and morphine. Her D‐dimer was positive. We cannot scan her chest; therefore, a nuclear V/Q scan has been ordered. However, after consultation with Dr. C, it is felt that she is potentially too unstable to go for this. Given the positive D‐dimer and her severe dyspnea, we have weighed the risks and benefits of anticoagulation with her heme-positive stools. She states that she has been constipated lately and doing a lot of straining. Given the possibility of a PE, it was felt like anticoagulation was very important at this time period; therefore, she was anticoagulated. The patient will be admitted to the hospital under Dr. C.
- 24. April 14, 2007 CHIEF COMPLAINT: Shortness of breath HISTORY OF PRESENT ILLNESS: This 68-year-old female presents to the emergency department with shortness of breath that has gone on for 4-5 days, progressively getting worse. It comes on with any kind of activity whatsoever. She has had a nonproductive cough. She has not had any chest pain. She has had chills but no fever. EMERGENCY DEPARTMENT COURSE: The patient was admitted. She has had intermittent episodes of severe dyspnea. Lungs were clear. These would mildly respond to breathing treatments and morphine. Her D‐dimer was positive. We cannot scan her chest; therefore, a nuclear V/Q scan has been ordered. However, after consultation with Dr. C, it is felt that she is potentially too unstable to go for this. Given the positive D‐dimer and her severe dyspnea, we have weighed the risks and benefits of anticoagulation with her heme-positive stools. She states that she has been constipated lately and doing a lot of straining. Given the possibility of a PE, it was felt like anticoagulation was very important at this time period; therefore, she was anticoagulated. The patient will be admitted to the hospital under Dr. C.
- 25. (symptoms) shortness of breath dyspnea cough chills constipated (test results) D‐dimer was positive positive D‐dimer (demographics and status) 68-year-old female admitted (therapies) breathing treatments morphine anticoagulated Text analysis supporting structured (coded) representation of clinical texts.
- 26. April 14, 2007 CHIEF COMPLAINT: Shortness of breath HISTORY OF PRESENT ILLNESS: This 68-year-old female presents to the emergency department with shortness of breath that has gone on for 4-5 days, progressively getting worse. It comes on with any kind of activity whatsoever. She has had a nonproductive cough. She has not had any chest pain. She has had chills but no fever. EMERGENCY DEPARTMENT COURSE: The patient was admitted. She has had intermittent episodes of severe dyspnea. Lungs were clear. These would mildly respond to breathing treatments and morphine. Her D‐dimer was positive. We cannot scan her chest; therefore, a nuclear V/Q scan has been ordered. However, after consultation with Dr. C, it is felt that she is potentially too unstable to go for this. Given the positive D‐dimer and her severe dyspnea, we have weighed the risks and benefits of anticoagulation with her heme-positive stools. She states that she has been constipated lately and doing a lot of straining. Given the possibility of a PE, it was felt like anticoagulation was very important at this time period; therefore, she was anticoagulated. The patient will be admitted to the hospital under Dr. C.
- 27. Fungal infection surveillance by classifyingCT scan reports Extracting key information from pathology reports Work by or with Lawrence Cavedon, David Martinez, and others, starting at NICTA Retrieval of disease-related records based on unstructured data (pathology reports, radiology reports, clinical notes)
- 29. https://shahlab.stanford.edu/greenbutton clinical situation RCT guideline available? use guideline cohort of similar patients no query medical record db machine learning: analysis, prediction, hypothesis testingdecision!
- 30. •Learning Health Systems •What is machine learning? •Example current applications of machine learning in health •Challenges and Open Issues
- 33. CT-scan 85% chance tumor is cancerous Recommend radiation treatment • Do I trust the prediction? • How was this conclusion reached? • Are there limitations in the reasoning? • Can the prediction be re-done, placing more weight on the patient’s family history?Dr Smart