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timeline [2014/01/13 12:44] stamang |
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- | ====== Denmark Project Timeline ====== | + | {{ ::patient-timeline.png? |
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- | =====Fall quarter 2012===== | + | |
- | 1 - implemented the trajectory stitching using simulated cost data | + | |
- | 2 - implemented 3 different matching mechanisms for trajectory stitching | + | |
- | 3 - presented results at end of quarter (Dec 12th) | + | |
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- | =====Winter quarter 2013===== | + | |
- | 1 - Engaged with Tommy (first meeting Jan 15th) | + | |
- | 2 - decision to proceed with the " | + | |
- | 3 - started quest to get actual cost data, and data dictionaries from Aarhus (Feb 5th) | + | |
- | * requested 1-year bins on demographics file instead of current 3-year bins | + | |
- | * started getting cost information for drugs and admissions (possibly DRG-classified costs) | + | |
- | 4 - visited Denmark / Lars to actually get the data we wanted (we hoped to do it remotely, but nothing beats sitting side by side) | + | |
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- | =====Spring quarter 2013===== | + | |
- | 1 - started on shopping cart models | + | |
- | 2 - Both patient-level and population-level validation runs for a test set of 1000 patients (validation, | + | |
- | 3 - Also tried, probabilistic approach to trajectory stitching (didn' | + | |
- | 4 - decided to focus on a certain set of diseases going forward (diabetes / CHF / CAD / COPD) | + | |
- | 5 - started clustering and template matching as other alternative methods (Ken Jung's work) | + | |
- | 6 - focused all methods on one goal: find " | + | |
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- | As of May 15th, we have narrowed the scope to: | + | |
- | 1. simply identifying patients with some positive control conditions .. where we believe that bifurcations should exist. | + | |
- | 2. finding patients of interest (with respect to their cost trajectory) | + | |
- | 3. given 2, determining to first order (i.e., without trying to find intervention points) what drives higher costs in patients with breakpoints in their trajectories | + | |
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- | =====Summer quarter 2013===== | + | |
- | 1. Characterized cost patterns and concluded that outside of end of life, high costs are concentrated in acute episodes rather than chronic elevated outpatient costs. | + | |
- | 2. Identified high cost subset of patients (defined as >= 90th percentile of total expense) and high cost episodes (defined as >= 90th percentile of annual costs). | + | |
- | 3. Ran association rule mining (apriori) to identify simple combinations of codes that predict high costs (either definition). | + | |
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- | // | + |