max{h } ij similarity(t , t )...

Computational Algorithms for Predictive Health Assessment M. Popescu M. Skubic & J. Keller R. Koopman

Health Management & Informatics Electrical & Computer Engineering School of Medicine

University of Missouri , Columbia, MO, USA

More details @ www.eldertech.missouri.edu Funded by the NSF SHWB grant, award #: IIS-1115956

1. Popescu M, Chronis G, Ohol R, Skubic M, Rantz M, "An Eldercare Electronic Health Record System for Predictive HealthAssessment," IEEE Int. Conf. on Health Communication 2011, Columbia, MO, June 13-15, 2011, pp 193-196.

2. Rantz M, Marek K, Aud M, Tyrer H, Skubic M, Demiris G & Hussam A, "A Technology and Nursing Collaboration to Help OlderAdults Age in Place," Nursing Outlook, vol. 53, no. 1, pp. 40-45, January-February, 2005.

3. Z. Hajihashemi, M. Popescu, “Improving Health Pattern Recognition Using Smith Waterman Algorithm and NLP, AMIA FallSymposium, Washington Dc., Nov. 13-16 2013.

4. Z. Hajihashemi, M. Popescu,” Detection of Abnormal Sensor Patterns in Eldercare”, E-Health and Bioengineering Conference(EHB), 21-23 Nov. 2013, Iasi, Romania, 2013, pp. 1-4.

5. Z. Hajihashemi, M. Yefimova, M. Popescu, “Detecting Daily Routines of Older Adults Using Sensor Time Series Clustering”,submitted to EMBC 2014, Chicago, IL.

6. Z. Hajihashemi, M. Yefimova, M. Popescu, “A New Illness Recognition Framework Using Frequent Temporal Pattern Mining”,submitted to SmartHealthSys 2014, Seattle, WA.

Apartment

Wireless sensor network(bed, motion, stove and other sensors)

Computational

algorithms

Predictive

health

assessment(fall risk,

depression, UTI,

etc.)

Electronic

health records

(EHR)

Daily activity summaries obtained by annotation of FDA

or activity chunks using a home-grown nursing electronic

health record (EHR) system [1] and NLP [2] using a

bioinformatics approach (“guilt by association”)

Current trend

Aimed trend (with technology)

Functional Decline [2]

Time

1. Predictive health assessment framework

based on detection of missing frequent daily

activities [6] (as opposed to finding abnormal

patterns)

Unsupervised algorithms for finding FREQUENT

daily activities (FDA) [5], [6] (activities performed

at least once a day in a given period of time)

A bioinformatics motif finding algorithm (MEME)

A modified behavioral science algorithm (THEME)

based on time and symbol distribution in

sequences

TigerPlace aging-in-place facility, Columbia, MO

UserID SensorID Year Month Day Hour Minute Second

3 3 2005 10 5 12 34 38

3 2 2005 10 5 12 36 52

3 2 2005 10 5 12 37 04

3 2 2005 10 5 12 37 11

3 1 2005 10 5 12 37 26

3 1 2005 10 5 12 37 28

3 2 2005 10 5 12 37 32

3 2 2005 10 5 12 41 18

3 2 2005 10 5 12 41 11

3 2 2005 10 5 12 41 4

3 5 2005 10 5 12 42 40

3 5 2005 10 5 12 42 58

Discrete sequence representation

55 apartments monitored

2. Predictive health assessment framework

based on the detection of abnormal patterns (not

similar enough to previous patterns) [4]

Day chunking: find all activity chunks in a day

Compute a distribution of the activity chunk

similarity in a given time interval (2 weeks)

Send an alarm if a new activity is very

dissimilar to previous ones

Use a sensor sequence similarity, temporal

Smith-Waterman [3,4], to compute the similarity

between two activity patterns (sequences) T1, T2

Distribution of chunk similarities for 2 weeks

Hi0=H0j , iϵ[1,n] and jϵ[1,m]

Hij=max{0, Hi-1,j-1+ S(C1i, C2j), maxk≥1{ Hi-k,j - Wt), maxk≥1 { Hi, j-k - Wt}}

WΔt = g + c|t1i-t2j|

m}Min{n,

}Max{H)T,(TSimilarity

ij21

T1={(C11,t11), (C12, t12)…, (C1m,t1m)} T2={(C21,t21), (C22, t12)…, (C2n,t2n)}

Results: better than the typical Gaussian approach