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- 1. UMHS Routing Examples There are several items in the proposal which dictate what units need to be involved in the routing and approval process: – Faculty Effort – Space – Administrative Home – Subaccounts (the old PAF P4) – Cost Sharing 1
- 2. 2 Pathology Example Project Title: – Support for Programmer and Server for Advanced A.P. Workflow Key Personnel: – Ul Balis, Pathology, PI Dept ID 251000 – Jeffery Myers, Pathology Dept ID 251000 – Yolanda Helfrich, Dermatology Dept ID 235000 – Anna Lok, Int Med - Gastroenterology Dept ID 239500 Research Administrator and Administrative Home: – Thad Schork / 251000 (Pathology) Other Information: – Somewhere on the PAF DeptIDs were listed for effort, space, subaccounts, cost sharing, IDC waiver, or any other reference that generates an approval needed.
- 3. 3 Pathology Example Departments Listed on PAF: – Pathology Dept ID 251000 – Dermatology Dept ID 235000 – Int Med - Gastroenterology Dept ID 239500 School/College Approval: Pathology 251000 Int Med-Gastroenterology 239500 Pathology Dept Roll-up 251099 Medical School 230000 Internal Medicine 236999 Dermatology 235000
- 4. First Scenario Dermatology signs off before Internal Medicine In Diagrams – Red Box – where the proposal is awaiting approval Blue Star – Approval given
- 5. 5 Pathology Example Departments Listed on PAF: – Pathology Dept ID 251000 – Dermatology Dept ID 235000 – Int Med - Gastroenterology Dept ID 239500 School/College Approval: Pathology 251000 Int Med-Gastroenterology 239500 Pathology Dept Roll-up 251099 Medical School 230000 Internal Medicine 236999 Dermatology 235000 No authorized signers named, so the proposal does not stop in 251000
- 6. 6 Pathology Example Departments Listed on PAF: – Pathology Dept ID 251000 – Dermatology Dept ID 235000 – Int Med - Gastroenterology Dept ID 239500 School/College Approval: Pathology 251000 Int Med-Gastroenterology 239500 Pathology Dept Roll-up 251099 Medical School 230000 Internal Medicine 236999 Dermatology 235000
- 7. 7 Pathology Example Departments Listed on PAF: – Pathology Dept ID 251000 – Dermatology Dept ID 235000 – Int Med - Gastroenterology Dept ID 239500 School/College Approval: Pathology 251000 Int Med-Gastroenterology 239500 Pathology Dept Roll-up 251099 Medical School 230000 Internal Medicine 236999 Dermatology 235000
- 8. 8 Pathology Example Departments Listed on PAF: – Pathology Dept ID 251000 – Dermatology Dept ID 235000 – Int Med - Gastroenterology Dept ID 239500 School/College Approval: Pathology 251000 Int Med-Gastroenterology 239500 Pathology Dept Roll-up 251099 Medical School 230000 Internal Medicine 236999 Dermatology 235000 Note: it does not arrive in Pathology until all other departments have signed because they are the administrative home.
- 9. 9 Pathology Example Departments Listed on PAF: – Pathology Dept ID 251000 – Dermatology Dept ID 235000 – Int Med - Gastroenterology Dept ID 239500 School/College Approval: Pathology 251000 Int Med-Gastroenterology 239500 Pathology Dept Roll-up 251099 Medical School 230000 Internal Medicine 236999 Dermatology 235000
- 10. Next Scenario Dermatology signs after Internal Medicine
- 11. 11 Pathology Example Departments Listed on PAF: – Pathology Dept ID 251000 – Dermatology Dept ID 235000 – Int Med - Gastroenterology Dept ID 239500 School/College Approval: Pathology 251000 Int Med-Gastroenterology 239500 Pathology Dept Roll-up 251099 Medical School 230000 Internal Medicine 236999 Dermatology 235000
- 12. 12 Pathology Example Departments Listed on PAF: – Pathology Dept ID 251000 – Dermatology Dept ID 235000 – Int Med - Gastroenterology Dept ID 239500 School/College Approval: Pathology 251000 Int Med-Gastroenterology 239500 Pathology Dept Roll-up 251099 Medical School 230000 Internal Medicine 236999 Dermatology 235000
- 13. 13 Pathology Example Departments Listed on PAF: – Pathology Dept ID 251000 – Dermatology Dept ID 235000 – Int Med - Gastroenterology Dept ID 239500 School/College Approval: Pathology 251000 Int Med-Gastroenterology 239500 Pathology Dept Roll-up 251099 Medical School 230000 Internal Medicine 236999 Dermatology 235000
- 14. 14 Pathology Example Departments Listed on PAF: – Pathology Dept ID 251000 – Dermatology Dept ID 235000 – Int Med - Gastroenterology Dept ID 239500 School/College Approval: Pathology 251000 Int Med-Gastroenterology 239500 Pathology Dept Roll-up 251099 Medical School 230000 Internal Medicine 236999 Dermatology 235000
- 15. 15 Pathology Example Departments Listed on PAF: – Pathology Dept ID 251000 – Dermatology Dept ID 235000 – Int Med - Gastroenterology Dept ID 239500 School/College Approval: Pathology 251000 Int Med-Gastroenterology 239500 Pathology Dept Roll-up 251099 Medical School 230000 Internal Medicine 236999 Dermatology 235000
Notas do Editor
- Here is the outline of this talk.The focus of this talk will be on epilepsy and brain disordersFirst I will try to convince the audience why this problem is important and those patients need our helpThen I will identify the research goals, then I’ll talk about how to acquire and process the data from the brain – specifically try to predict seizuresThe second research challenge is how to use optimization and data mining techniques to recognize/or classify normal and abnormal brain data – this framework can be applied to other medical data or data in other real life problems.
- m = number of samples for class 1n = number of samples for class 2Bradley, Fung and Mangasarian revamped this idea – using this robust optimization model – it is very fast and scalable
- For multidimensional time series, it is ideal to do multivariate analysis – but it is computationally impossible in our applicationIn our work , we use univariate analysis – perform classification on each electrode at a time.Then we use the idea of ensemble classification to make the final decision.
- Most ensemble deal with how to sample the data Bagging, Bootstrapping Boosting, - here we use the idea of voting and averaging/or accumulating prediction scoreHere I give an example why we use ensemble classification
- Today about 3 million americans and other 60 million people worldwide have epilepsy. Epilepsy is the second most common brain disorder after stroke. It causes recurrent seizures, which appear to occur spontaneously and randomly.What happens when someone has a seizure – in his or her brain, there is a massive group of neurons hypersynchronized in a highly organized rhythmic patterns – which lasts about 20 seconds to a few minsThis brain disease causes our country so much money – in 1995 estimate, it imposes an economic burden of $12.5 billions – no just healthcare cost - including job loss, productivityPer patient, the healthcare cost ranged from 4k to almost 140k per year – and these numbers are more than 10 years ago.By now I hope I’ve convinced the audience that we should do something about this disease – next I will discuss standard diagnosis, treatment, (acquired data) and how we can help these patients.
- Given multi-dimensional time series and a set of events/episodes (if you will). How can we predict the eventClassification of medical data (normal and abnormal) for guiding the future diagnosisFeature selection -> initiating events – most differentiable
- First we implement a modified support vector machine, which is one of the most commonly used classification technique. The main idea is to
- Overfitting the dataSample sizeCPU time
- The issue is not just to get 100% classification – rather we focus more on why we get that kind of results and understand the data.For example, we look at the selected electrodes that help in distinguishing epilepsy and non-epilepsy patients. We found 3 electrodes that play a major role – when we went back to the neurologists and talked to him. He was very surprised to see.One would not expect to see that the selected electrodes would be involved in epilepsy mechanisms.Again it could be the scalp electrode – one focus on the left but electrodes on the right pick up first.
- We envision the outcome of our research in medical diagnosis as a tool or apparatus to process medical data signalThis is just my vision but we still have a long way to go.We have started off with neurophysiological signals like electroencephalograms or fMRI –Then use the tools developed over the course of my research as an automated decision support systems for physicians to helpRecognize abnormal data or abnormal patterns in medical dataTry to localize the source of abnormalityRecommend the diagnosis outcome – rather improve the confidence in the diagnosis