Dr. Shiyu Zhou, Univ. of Wisconsin-Madison

Dr. Patricia Flatley Brennan, Univ. of Wisconsin-Madison

Dr. Yong Chen, University of Iowa

Smart Asthma Management:Statistical modeling, prognostics, and

intervention decision making

Research Approaches

o Motivation

• Asthma is a common lung disease impacting on a largenumber of people

• Smart Asthma Management (SAM) system makes thepatient-centered chronic asthma care possible

o Objectives

• Patient condition modeling and estimation at populationlevel

• Patient condition model updating and individualizedprognosis

• Clinical decision support using partially observable Markovdecision process

Accomplishment & Future Work

Case Study

SCH:EXP:Collaborative Research: Smart Asthma Management: Statistical modeling, prognostics, and intervention decision making (SCH-1343969) e-mail: szhou@engr.wisc.edu

Rescue inhaler usage0 5 10 15 20 25 30

250

300

350

400

450

500

550

Day

Peak F

low

( L

/min

)

Longitudinal biomarker(time-dependent covariate)

Mixed Effects Model for the

Time-dependent Covariate

(Continuous)

Progresses� Datasets from Propeller Health and Project HealthDesign. Some

will be publicly available in ICPSR for use by other investigators.

� Son, J., Brennan, P., and Zhou, S., “Rescue inhaler usage

prediction in smart asthma management systems using joint

mixed effects logistic regression model,” IIE Transactions, 2014,

submitted

� Jia, H., Brennan, P., Zhou, S., Son J., and Hung, Y.,

“Personalizing statistical models for asthma prognosis and

therapeutics,” Artificial Intelligence in Medical Applications (AIMA)

Annual Conference, 2014 (poster, accepted)

� �,� �,� �,� �∗

Response: (logit transformed) Probability of rescue

inhaler use at time t for the ith patient

(individual probability)Individual covariate model

� �∗

��

� �

Separate model for the

time dependent covariate

II. Joint Mixed Effects Logistic Regression

III. Individualizing the Model

(1) Updating the time-dependent covariate model

�∗ = �,� � �,�

�∗ = �,� � �,�

Historical

Database

(Population)

Updated

Model

(Individual)

Learn Update

New observations

from patient p

�Population-wise

Distribution

Distribution

for patient p

�

(2) Updating the logistic regression model

Distribution

for patient pHistorical

Database

(Population)

Updated

Model

(Individual)

Learn Update

����Population-wise

Distribution

USE DAY PF

Y 1 350

⋮ ⋮ ⋮

Y 10 250New data from

patient p

Goal: Predict the number of expected inhaler usage in the

immediate next week (response range: 0 to 7)

Method: Joint Mixed Effects Logistic Regression

Criteria: Cross-validation and mean absolute error (MAE)

2 20 40

01

23

45

67

(a) M1

Number of observations (m)

Absolu

te e

rror

2 20 40

01

23

45

67

(b) M2

Number of observations (m)

Absolu

te e

rror

2 20 40

01

23

45

67

(c) M3

Number of observations (m)

Absolu

te e

rror

Criteria MethodNumber of observations (m)

2 20 40

MAE

(SD)

M1: the model which assumes

a population-wise behavior

2.098

(1.326)

2.105

(1.282)

1.957

(1.399)

M2: proposed individualized

prognostic model

1.690

(1.521)

1.070

(1.010)

0.861

(0.790)

M3: individually fitted model2.720

(2.762)

1.312

(1.440)

1.180

(1.599)

Future work:� Underlying asthma condition change detection method based

on individual rescue inhaler usage profile

� Optimal clinical intervention decision making algorithm based on

the patient-level statistical models

I. Joint Modeling Framework

Rescue Inhaler Usage Prediction using Joint Mixed Effects Logistic Regression Model

Mixed Effects Logistic

Regression for Rescue

Inhaler Use (Binary)

Joint Mixed Effects

Logistic Regression

Model

Both models consider random

effects that can be considered

as individual effects

Joining

models

IV. Individualized Prediction

Number of rescue inhaler use in a week is a key

indicator of the asthma control level of patients

(National Heart, Lung, and Blood Institute)

Historical

dataTime

Collect data from the

new patient up to �∗

∗ ∗+7New patient

enrolls Individualize (update) the model

Prediction with

individualized model

Smart Asthma Management (SAM)

Motivation and Objectives