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www.monash.edu.au
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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Reasoning technique 1Heuristics: weight and contribution
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Reasoning technique 2Heuristics: sensors’ inaccuracy
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Reasoning technique 3 and 4Heuristics: Symmetric and Asymmetric context attributes, partial and complete containment
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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
n
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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
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0.8
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1 11 21 31 41 51 61 71 81 91 101 111 121 131
Data Rows
Lev
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FSI_N
CS_N
DS_N
Comparison of DS, CS and FSI for Hypertension
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1 11 21 31 41 51 61 71 81 91 101 111 121 131
Data Rows
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FS_Hyper
CS_Hyper
DS_Hyper
Comparison of DS, CS and FSI Hypotension
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1 11 21 31 41 51 61 71 81 91 101 111 121 131
Data RowsL
evel
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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
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26 26 26 29 32 42 42 35 35 29 10 10 10 10
Data Stream Algorithm Threshold
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of
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?