The US health care system

Transcription

1 Theodora Brisimi

2 The US health care system $3 T (2014) annually for healthcare in the US (17% of GPD, ~2.5x OECD avg). 31% of health care is hospital care (AHRQ): 4.4M preventable hospital admissions=$30.8 B. 31% of those admissions, or $9 B, were heart-related. 19% of those admissions, or $5.8 B, were diabetes-related. Enablers: Electronic Health Records (EHRs) Systems approach: Analytics (algorithms, inference, decision making).

3 Focus on predicting hospitalizations? Average cost per hospital stay (2010, AHRQ): 49,700. Acute Myocardial Infarction (AMI): $18,200. Coronary atherosclerosis and other heart disease: $16,500. Congestive heart failure: $10,500. Cardiac dysrhythmias: $9,500. Complications of surgical procedures: $12,500. Diabetes with complications: $9,500 but with among the largest increases since 1997 (4.1%) Average lifetime cost of AMI: $1 M (2010). Cost of outpatient care (BCBS): Office visit: $200-$240. ECG: $40-$100. Echo: $75-$225. Hematology: $15-$ day supply of blood pressure or cholesterol Rx: $16-$50. Cardiac catheterization: $500-$2,500.

4 The data Electronic Health Records from Boston Medical Center Two populations: patients with heart-related diagnosis between and patients with diabetes between Medical factors we extract as history since 2001 include: Demographics, Diagnoses, Procedures, diastolic and systolic blood pressure, tobacco use, visits to the Emergency Room, Admissions. E.g., medical factors for the heart study: Demographics Sex, Age, Race, Zipcode Diagnoses e.g., AMI, Heart failure, Cardiac dysrhythmias, Diabetes mellitus with complications, obesity Tobacco use Visits to the ER Lab tests CPK, CRP cardio, Direct LDL, HDL Procedures e.g., Cardiovascular procedures, Cardiac stresstest and pacemaker checks, surgical procedures on the arteries and vein Vitals Diastolic and systolic blood pressure Admissions e.g., Heart transplant or implant of heart assist system, Coronary bypass, Heart failure and shock

5 Preprocessing and feature organization Time aspect in the model History Summary Summary Summary Hospitalized? Pre 3 Years 3 Years Before 2 Years Before 1 Year Before Target Year Selection of target year Single hospitalization: set its year as the target year Multiple hospitalizations: select one year randomly between first and last hospitalization No hospitalization: set the latest year (2010 for heart study and 2012 for diabetes study) as the target year (to have richer history). Patients with no records before the target year are removed.

6 Prediction as a classification problem (Moderately) high-dimensional feature vector ( ). Discriminative Methods: Support Vector Machines (SVM). Sparse SVM. AdaBoost with trees as the weak learner. Logistic Regression. L1-reguralized logistic regression. Generative Methods: Naïve Bayes Event Model: every patient modeled as a sequence of events (summaries for each time period). K-Likelihood Ratio Test (K-LRT). Novel!

7 K-Likelihood Ratio Test (K-LRT) K-LRT: a variation of LRT Model: n features z=(z 1, z 2,, z n ) with label y {0,1} Assumption: features independently distributed, discretized p(z 1 y), P(z 2 y),, p(z n y). Testing: z t =(z t 1, z t 12, z t n ), LRT(z t )= i P(z i t 1) P(z i 0t ) Calculate P(z i t 1) P(z i 0t ) and order them. Select K largest ratios for each patient and threshold the product of these values.

8 Framingham Heart Study (FHS) based methods MDs have been doing this empirically for some time Framingham Heart Study: a set of risk factors for developing heart diseases. The most related one: 10-year risk of general cardiovascular disease. FHS predictors: Age, Diabetes, Smoking, Blood Pressure, Total cholesterol, HDL. Classifiers using the FHS predictors: Calculate the FHS risk factor for each patient and threshold it. Applied AdaBoost with trees on the features used by the FHS 10-year metric.

9 Heart study predictions All methods perform consistently. AdaBoost is the best. K-LRT performs remarkably well. FHS-based methods are weaker (the whole EHR useful after all!) "Prediction of hospitalization due to heart diseases by supervised learning methods". W. Dai, T. S. Brisimi, W. Adams, F. Mela, I. Ch. Paschalidis, Intl. J. Medical Informatics, 2014.

10 Diabetes study predictions AdaBoost gives best performance. With a 25% miss detection rate, we can achieve a false alarm rate as low as 30%. Sparse SVM comparable to RBF SVM (except for small miss detection rates). Big advantage: interpretability! Work to be submitted.

11 Joint clustering and classification For each patient, sparse set of relevant features. Feature set different for each patient. Clustering? Clear coupling between clustering and classification. Use the label in clustering too! Sparse linear SVM classifiers for each cluster. Novel!

12 Problem formulation x i +, y i +, i = 1,, N +, x j, y j, j = 1,, N. L hidden clusters, mapping l i, classifiers β l, β 0 l.

13 Notes on the formulation l 1 penalty to induce a sparse classifier per cluster. Gaining in ability to interpret the results. Loosing ability to kernelize. Imbalanced training set (larger negative class), so different penalties for + and slack variables. Can add regularizers to induce more homogeneous clustering. The problem can be formulated as Quadratic Integer Programming (QIP). Can solve small instances (e.g., N=200).

14 Alternating Clustering and Classification (ACC) Fixing the clusters => L SLSVM problems=>l classifiers A Joint Clustering and Classification Approach for Healthcare Predictive Analytics. W. Dai, T. S. Brisimi, T. Xu, T. Wang, V. Saligrama, I. Ch. Paschalidis, AMIA workshop 2015, NIPS workshop 2015

15 Re-clustering positive samples Can use projection on an even lower-dimensional diagnostic set of features. Stopping criterion: clusters remain unchanged or no cost decrease.

16 Predicting heart-related hospitalizations Accuracy Informative clusters of heart diseases: Cluster membership can provide guidance! A Joint Clustering and Classification Approach for Healthcare Predictive Analytics. W. Dai, T. S. Brisimi, T. Xu, T. Wang, V. Saligrama, I. Ch. Paschalidis, AMIA workshop 2015, NIPS workshop 2015

17 Predicting diabetes-related Accuracy hospitalizations Cluster 1: Various complications (skin, cerebrovascular, etc.) Cluster 2: Gestational diabetes Cluster 3: Onset of diabetes Work to be submitted.

18 Final thoughts Health care is becoming increasingly data-driven (technology, PHR, data deluge, inability of MDs to process so much information). Quality/efficiency metrics are being introduced and taken seriously because of financial implications. Tremendous opportunities for algorithmic approaches in making the healthcare system more efficient, less prone to errors, and more cost effective!