Mining Discriminative Patterns to Predict Health Status for Cardiopulmonary Patients

Transcription

1 Mining Discriminative Patterns to Predict Health Status for Cardiopulmonary Patients Qian Cheng, Jingbo Shang, Joshua Juen, Jiawei Han and Bruce Schatz Department of Computer Science Department of Electrical and Computer Engineering Department of Medical InformaAon Science InsAtute of Genomic Biology University of Illinois at Urbana-Champaign, Urbana, IL

2 Mobile Health: Detection of Abnormal Health Why Smartphones? Smartphones vs. Medical Devices Ubiquitous device, solware only Set of sensors (GPS, accelerometer, gyroscope, etc.), medical qualified High level framework and expandable operaang system Data transmission via 4G LTE or even higher capability in the future, no locaaon restricaon.

3 Why Mining Discriminative Patterns? Pros and Cons of State-of-arts Methods: StaAsAcal Learning Models Good performance Lack of interpretaaon Tree-structure Models Easy interpretable Limited predicang power Mining DiscriminaAve PaVerns Maintaining high quality performance ConstrucAng a new feature space with discriminaave paverns of input features The top paverns providing interpretable insights

4 Experimental Setup Subjects 59 cardiopulmonary paaents perform 6MWT. PaAents also take pulmonary funcaon tests to obtain the GOLD status. GOLD: Global IniAaAve for Chronic ObstrucAve Lung Disease. We define healthy subject as GOLD 0. GOLD 4 (more severe) paaents can barely walk so not included. MoveSense, the solware we developed to monitor COPD severity, running on the Android phone. Phones are fixed on the lower back (L3) posiaon with a fanny pack.

5 Methodology Input Features Demographic InformaAon: Age, Sex, Height and Weight Cadence Selected 8 Gait Parameters: MeanAcc, StdAcc, AC, CV, MCR, RMS, PF, Entropy Learning Methods Linear SVM RBF SVM Decision Tree DPClass Model ValidaAon 10-fold Cross ValidaAon

6 *Shang J. et al. DPClass: An effecave but concise discriminaave pavern-based classificaaon framework. SDM 16. DPClass: An Effective but Concise Discriminative Pattern-based ClassiCication Framework DPClass is a hybrid machine learning approach applying mul6-tree based model and pa9ern based classifica6on framework. Candidate DiscriminaAve PaVerns PaVern Space Top-k DiscriminaAve PaVerns Final MulA-class ClassificaAon Training Dataset Multiple Tree-based Model Top-k Discriminative Patterns Testing Dataset Discriminative Patterns Generation Efficient Testing c... b e d a f g... i h j k m n Compressed Model * a b - 1 * a g * a b d f * h i j l Top-k Discriminative Patterns Selection Generalized Linear Model Training a a b a g d j f Two possible discriminative patterns A non-leaf node. The prefix path from its root to it forms a discriminative pattern A selected discriminative pattern b a i h Figure. An Overview of DPClass Model

7 Performance Comparison Model Valida4on 10-fold cross validaaon. Each column represents the predicaon accuracy for samples in the corresponding category. Detailed confusion matrix is in the paper. GOLD 0 GOLD 1 GOLD 2 GOLD 3 Overall Linear-SVM RBF-SVM Decision Tree DPClass

8 Decision Tree Model Structure Root Age < 49.5 GOLD 0 MeanAcc <9.79 Age < 60.5 Weight <60.78 Weight <96.09 RMS<0.98 Age<77.5 RMS<1.05 GOLD 2 GOLD 3 GOLD 0 GOLD 3 GOLD 3 RMS<1.01 Age<81.5 GOLD 2 GOLD 2 GOLD 3 GOLD 1 GOLD 2

9 DPClass Model Structure A DiscriminaAve PaVern is a conjuncave clause containing several condiaons. E.g. (Age < 80.5) && (Height > 1.5) && (MeanAcc>=9.76) && (RMS< 1.05) is a discriminaave pavern ProporAon of Each Demographic and Gait Feature in Top 30 discriminaave paverns

10 Model Interpretation Both decision tree model and DPClass model provides significant informaaon to interpret the model mechanism. Different features dominates different status: Age dominates GOLD 0 and GOLD 1 BMI disanguishes demographic cohorts MeanAcc separates GOLD 1 and GOLD 2 over cadence, represents 6MWT performance RMS reflects risks, differenaaang GOLD 2 and GOLD 3

11 Model Interpretation Age dominates healthy and unhealthy status classificaaon Age is a component in 24/30 discriminaave paverns. Tree structure: Root Age < 49.5 GOLD 0 MeanAcc <9.79 Age < 60.5 Weight <60.78 Weight <96.09 RMS<0.98 Age<77.5 RMS<1.05 GOLD 2 GOLD 3 GOLD 0 GOLD 3 GOLD 3 RMS<1.01 Age<81.5 GOLD 2 GOLD 2 GOLD 3 GOLD 1 GOLD 2

12 Model Interpretation BMI significantly disanguishes demographic cohorts CombinaAon of height and weight. Height (12/30) and weight (18/30) appear frequently. Sex/gender (2/30) does not: Sex/gender does not affect 6MWDs (p > 0.05). Pulmonary funcaon tests has already adjusted the predicted FEV1 % by gender.

13 Model Interpretation Mean AcceleraAon is more important than Cadence Cadence the number of strides per minute An essenaal parameter in gait analysis Both decision tree structure and discriminaave paverns show that cadence is not as significant as mean acceleraaon. Cadence does not dominate tree node but mean acceleraaon does Cadence only contributes to 2/30 DPs but mean acceleraaon contributes to 15/30 DPs.

14 Model Interpretation RMS is more important than Standard DeviaAon Instability reflects risks Both Standard deviaaon and root mean square of acceleraaon reflect stability of walking. RMS dominates two tree nodes in decision tree model while standard deviaaon not. RMS contributes to 11/30 DPs but StdAcc only contributes to 5/30. Nishiguchi at el. claimed that RMS is a bever measure for stability for human walking. * Nishiguchi at el. Reliability and validity of gait analysis by android-based smartphone. Telemedicine and e-health, 2012.

15 Future Work Passive Monitoring PaAent can take the device back home and capture their daily moaons. The passive monitor can select good walking pieces out of the collecaon Health status can be predicted by the selected good walking samples in a daily frequency. Longitudinal Study With daily walking feed back, we are able to assess paaent s walking longitudinally, so that it could be possible that we capture bever metrics for measuring risk of health status variaaon Approaches for comprehensive cohort analysis For larger populaaon, simple demographic cohorts may not work. PaAents EMR contains high dimensional features. Sufficient IR techniques to select feature set from whole EMR.

16 Acknowledgement Thank you to the InsAtute of Genomic Biology for office space and computaaonal resources Thank you rthshore University HealthSystem for coordinaang the clinical trials and experiments at Chicago Thank you Carle FoundaAon Hospital for coordinaang the clinical trials and experiments at Urbana-Champaign area Thank you every one for supporang the project under IRB approvals

17 QuesAons? THANK YOU!