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Mobile Data Mining for Intelligent Healthcare Support

By: Pari Delir Haghighi, Arkady Zaslavsky, Shonali Krishnaswamy, Mohamed Medhat GaberCenter for Distributed Systems and Software EngineeringMonash University, Australia

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An Overview

• Introduction• The State-of-the-Art • Situation-Aware Adaptive Processing (SAAP) of

Data Streams • Fuzzy Situation Inference (FSI)• Adaptation Engine (AE) • Implementation• Evaluation• Future Work• Conclusion

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Introduction

Mobile healthcare services: • provide a convenient, safe and constant way of

monitoring of vital signs • development of mobile healthcare applications

encouraged by– innovations in mobile communications – low-cost of wireless biosensors

• the issues:– maintaining continuity of running applications on mobile

devices– enabling real-time analysis of data and decision making

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The State-of-the-Art (1)

• recent works in mobile healthcare – mostly focused on using, enhancing or combining existing

technologies> projects: EPI-MEDICS [RFN05],MobiHealth [MWH07]

– limited use of context-awareness – lack of resource-aware data analysis techniques

• a need for a general approach:– performing smart and cost-efficient analysis of data

in real-time– providing a general model for representation of

real-world and health-related situations

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The State-of-the-Art (2)

Ubiquitous Data Stream Mining (UDM) – real-time analysis of data streams on-board

small/mobile devices > techniques and algorithms for resource-aware data

stream mining [GKZ05]

• However, to perform smart and intelligent analysis of data on mobile devices

– imperative to factor in contextual information

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Situation-aware Adaptive Processing (SAAP) of Data Streams

SAAP:

1. incorporates situation-awareness into data stream mining

2. performing situation-aware adaptation of data streaming parameters according to occurring situations and available resources

3. situation-awareness achieved by Fuzzy Situation Inference (FSI) model– FSI combines fuzzy logic principles with the

Context Spaces (CS) model> a general context modeling and reasoning approach for

pervasive computing environments

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The Framework of SAAP

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Fuzzy Situation Inference (FSI)

• FSI inspired by the Context Spaces (CS) Model [PAD04]• The CS model

advantages:> deals with uncertainty associated with sensors’

inaccuracies

disadvantages:> does not deal with other aspect of uncertainty related to

human concepts and real-world situations

• FSI integrates fuzzy logic principles into the CS model FSI– enables representation of vague situations – reflects minor and delta changes in the inference results

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FSI: Situation Modeling

• linguistic variables: e.g. heart rate• terms/Fuzzy sets: e.g. low, normal, fast• membership functions to map input data into fuzzy sets

• A FSI Rule defines a situation– consists of multiple conditions joined with the AND operator

> each condition can be a disjunction of conditions

e.g. if Room-Temperature is ‘hot’ and Heart-Rate is ‘fast’ and ( Age is ‘middle-aged’ or ‘old) then situation is ’heat stroke’ ’

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SAAP

• Fuzzy Situation Inference (FSI) Engine• Adaptation Engine (AE)

– Resource-aware strategies– Situation-aware strategies– Hybrid strategies

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Adaptation Engine (AE)

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The Controller

Cases Adaptation Strategy

1 – R at safe level and S at safe Level Situation-aware

2 – R at safe level and S at medium level Situation-aware

3 – R at safe level and S at critical level Situation-aware

4 – R at medium level and S at safe level Resource-aware

5 – R at medium level and S at medium level Hybrid

6 – R at medium level and S at critical level Hybrid

7 – R at critical level and S at safe level

8 – R at critical level and S at medium level

9 – R at critical level and S at critical level

Other strategies e.g. migration

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• Lightweight data stream mining algorithms– Adjusting mining parameters according to resource

availability – E.g: LWC (LightWeight Clustering) [GKZ05]

> considers a threshold distance measure for clustering

> Increasing the threshold discourages forming of new clusters

– in turn reduces memory consumption

Resource-aware Adaptation

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Situation-aware Adaptation

• based on the concept of resource-aware adaptation

• but adjustment of parameters according to results of situation inference (FSI engine)

• starts with pre-set values of parameters for each situation

• at run-time based on degree of fuzziness of each situation these parameters adjusted

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/ˆ µ: degree of fuzziness of each situation

p: parameter value

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Hybrid Adaptation

• when both resources and situations are getting critical

• a trade-off between the results of these two strategies

• hybrid method combines resource-aware and situation-aware strategies and deals with the trade-off:

SR

SSRRI ycriticalitycriticalit

ycriticalitpycriticalitpp

).ˆ().ˆ(

ˆcriticality of resources and situations represented by a value between 0 and 1

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Implementation

• healthcare monitoring application

• Implemented in J2ME

• deployed on a Nokia N95 mobile phone

• situations: ‘normal’, ‘Pre-Hypotension’, ‘Hypotension’, ‘Hypertension’ and ‘Pre-Hypertension’

• context: SBP, DBP and HR

• using a Bluetooth-enabled ECG sensor

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Evaluation of FSI

A Comparative Evaluation • The reasoning approaches

– FSI– CS– Dempster-Shafer (DS)

• to highlight the benefits of the FSI for reasoning about uncertain situations

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FSI Evaluation: Dataset

• The dataset:– generated continuously (data rate is 30 records/minute) in ascending

order– 131 context states– used our data synthesizer

> to represent the different events (of the DS model) – contribute to the occurrence of each pre-defined situation as well as the

uncertain situations

Context attribute scales Corresponding DS events SBP:40-65, DBP: 20-45, HR: 20-45 SBPLow, DBPLow, HRSlow SBP:66-80, DBP: 46-60, HR: 46-60 SBPLow, DBPLow, HRMed SBP:81-85, DBP: 61-65, HR: 61-65 SBPLow, DBPMed, HRMed SBP:86-105, DBP: 66-85, HR: 66-85 SBPMed, DBPMed, HRMed SBP:106-130, DBP: 86-110, HR: 86-110 SBPMed, DBPMed, HRHigh SBP:131-135, DBP: 111-115, HR: 111-115 SBPLow, DBPHigh, HRHigh SBP:136-170, DBP: 116-150, HR: 116-150 SBPHigh, DBPHigh, HRHigh

FSI Evaluation: Results

Comparison of DS, CS and FSI for Normal

0

0.2

0.4

0.6

0.8

1

1.2

1 11 21 31 41 51 61 71 81 91 101 111 121 131

Data Rows

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fid

ence

FSI_N

CS_N

DS_N

Comparison of DS, CS and FSI for Hypertension

0

0.2

0.4

0.6

0.8

1

1.2

1 11 21 31 41 51 61 71 81 91 101 111 121 131

Data Rows

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fid

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FS_Hyper

CS_Hyper

DS_Hyper

Comparison of DS, CS and FSI Hypotension

0

0.2

0.4

0.6

0.8

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1.2

1 11 21 31 41 51 61 71 81 91 101 111 121 131

Data RowsL

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of

Co

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FS_Hypo

CS_Hypo

DS_Hypo

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FSI Evaluation: Results

• when situations are stable and pre-defined (not vague) – all have a relatively similar trend– more noticeable with the CS and FSI models

• when situations change and evolve – the CS and DS methods show sudden rises and falls with

sharp edges> not matching the real-life situations

– Yet FSI reflects very minor changes between situations> represent changes in a more gradual and smooth manner

> more appropriate approach for health monitoring applications

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Evaluation of Situation-aware Adaptation

• Data stream mining algorithm used – the LWC algorithm

• situations– ‘normal’, ‘hypertension’ and ‘hypotension’ – situations’ importance: 0.1, 0.9 and 0.5– parameter set values: 42 (normal), 10 (hypertension) and 26

(hypotension) – context attributes: SBP, DBP and HR

• Dataset– the same used in the FSI evaluation

> 131 context states (rows)

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SA Evaluation: Results

0

0.2

0.4

0.6

0.8

1

1.2

26 26 26 29 32 42 42 35 35 29 10 10 10 10

Data Stream Algorithm Threshold

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Sit

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FSI_N

FS_Hypo

FS_Hyper

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SA Evaluation: Results

• threshold value automatically adjusted according to the fuzziness and membership degree of each situation

• when situations are normal, threshold increases– increasing the threshold value for normal situations

decreases the mining output – reduces resource consumption

• when situation get critical, threshold decreases – increases the number of the output (clusters) and

accuracy level of results that is required for closer monitoring

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Future work

• currently finalizing implementation and evaluation of hybrid adaptation using RA-Cluster

• using RA-Cluster enables adaptation of sampling rate according to battery charge

• integrating time-constraint into adaptation of battery usage

• working on testing of our prototype in real-world situation in conjunction with relevant healthcare professionals

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References

[GZK04] Gaber MM, Zaslavsky A, Krishnaswamy S (2004), A Cost-Efficient Model for Ubiquitous Data Stream Mining, Proceedings of the Tenth International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, Perugia Italy.

[GKZ05]Gaber MM, Krishnaswamy S, Zaslavsky A (2005) On-board Mining of Data Streams in Sensor Networks”, A Book Chapter in Advanced Methods of Knowledge Discovery from Complex Data, (Eds.) S. Badhyopadhyay, U. Maulik, L. Holder and D. Cook, Springer Ver-lag.

[MWH07] Mei, H., Widya, I., Halteren, A.V., and Erfianto, B., A Flexible Vital Sign Representation Framework for Mobile Healthcare. 2007.

[PLZ05] Padovitz, A., Loke, S.W., Zaslavsky, A., Burg, B. and Bartolini, C.: An Approach to Data Fusion for Context-Awareness. Fifth International Conference on Modeling and Using Context, CONTEXT’05, Paris, France (2005).

[PZL06] Padovitz, A., Zaslavsky, A. and Loke, S.W.:. A Unifying Model for Representing and Reasoning About Context under Uncertainty, 11th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU), July 2006, Paris, France (2006).

[RFN05] Rubel, P., Fayn, J., Nollo, G., Assanelli, D., Li, B., Restier, L., Adami, S., Arod, S.,Atoui, H., Ohlsson, M., Simon-Chautemps, L., Te´lisson, D., Malossi, C., Ziliani, G., Galassi, A., Edenbrandt, L., and Chevalier, Ph., Toward Personal eHealth in Cardiology: Results from the EPI-MEDICS Telemedicine Project. Journal of Electrocardiology 2005. 38: p. 100-106

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Thank you

Questions?