AUTONOMIC MODEL FOR MANAGING COMPLEX HEALTHCARE APPLICATIONS

Transcription

1 AUTONOMIC MODEL FOR MANAGING COMPLEX HEALTHCARE APPLICATIONS Prepared by Faisal Sibai fr CS 895 Original paper Wail M. Omar, K. Samir, and A. Taleb-Bendiab Autnmic Mdel fr Managing Cmplex Healthcare Applicatins. In Prceedings f the Furth IEEE Internatinal Wrkshp n Engineering f Autnmic and Autnmus Systems (EASE '07). IEEE Cmputer Sciety, Washingtn, DC, USA, DOI= /EASE

2 Presentatin Agenda Intrductin Backgrund E-Health Autnmic Cmputing Actin Case Study Cnclusin

3 Intrductin Sme previus wrk in this area: Grid Overlay fr Remte E-Health Mnitring. Service Oriented Architecture fr Remte E-Health Mnitring System E-Health Supprt Services Based On Service Oriented Architecture Autnmic apprach t guarantee the delivery f safe and high quality f prcesses by embedding a validated dmain knwledge in the meta-cntrller. Why using an Autnmic system?: High-assurance f medical infrmatin systems Guarantee delivery f high quality f prcess using adjustable gvernance fr the interactins between cnsumers and patents, as well as decisins fr cmpliance.

4 Backgrund

5 E-Health Autnmic Cmputing in Actin Request frm health cnsumer (hspital) fr required sensrs, target and authrized agent Cnsumer prvides system with medical schema fr tests Upladed t knwledge layer autnmic health system Knwledge layer prvides cnsumer with wealthy infrmatin (service, Infrastructure..) Ontlgy interacts with ther cmpnents (infrmatin assists in well interactin) Mnitring system receives requests fr cllecting readings, incrprates with the knwledge layer searching fr the adequate medical sensrs in rder t cllect infrmatin frm the targets.

6 E-Health Autnmic Cmputing in Actin Mnitring system Injects sensrs int target t gather infrmatin (duratin fr test) Mnitring system stres in patient (target) lg file in the lgger (Distributed DB) Later Mnitring system sends them t the Health Mnitring System Health Mnitring System receives messages frm Mnitring System and hspital HMS+MS= Medical Mnitring System (MMS) fr the autnmic cmputing mdel. MMS prvides the Medical Analyzer Service (MAS), the Predictin Service. Histry data and cncepts behind it are prvided by the medical schema. Predictin service uses the infrmatin fr finding the mdel fr each mnitred case accrding t the bundaries (test levels) stated in the medical schema

7 E-Health Autnmic Cmputing in Actin Predicatin results is the diagnsis fr the mnitred cases The Diagnsis is sent t the medical plan (treatment plan) service fr curse f actin (patient need t be taken t the hspital) Suggested plan s are passed t Medical Executin fr implementatin

8 Mnitring System Reuse previus wrk - intrduced a mnitring framewrk fr different types f enterprise applicatins (Sensr and Actuatr Framewrk) Deplying, discvering, invking and managing mnitring resurces frm sensrs, actuatrs and analyzers Mnitring resurces are deplyed with this framewrk, such as medical sensrs, memry sensrs, prcessrs sensrs etc. Tw descriptin languages are used with this framewrk t supprt the pen standard frmat Sensr and Actuatr Descriptin Language (SADL)- deplying and discvering mnitring resurces Mnitring and Sessin Descriptin Language (MSDL)- passing the request frm the cnsumers in standard frmat

9 Mnitring System

10 Predictin Services as web service Intelligent part f the system Predicts diagnsis by frm gathering data at the patient side Algrithms prpsed: Machine learning Data mining Analysis algrithm Analysis algrithms: Prved their ability t implement different types f classificatin and predictin prcesses depending n mathematic analysis Regressin Analysis: predict the value f a dependent r respnse variable frm the values f at least ne explanatry r independent variable (als called predictrs r factrs) Dr. Daniel A. Menasce, Linear Regressin Mutiple Regressin: Use t predict the value f the respnse variable as functin f k predictr variables x1,, xn. Dr. Daniel A. Menasce, Linear Regressin Why use it: Helps us understand hw the typical value f the dependent variable changes when any ne f the independent variables is varied, while the ther independent variables are held fixed Wikipedia Multiple regressin analysis is designed as web service in rder t be integrated with ther services t frm the enterprise applicatins.

11 Mathematical mdel

12 Mathematical mdel Implemented as server side that receives the training data frm the cnsumer as XML in rder t train the system and generate the mdel (Y) fr the requested predictin task. Training data is included in the medical schema, which fr instance prvides histry data fr the threshld and bundaries f the cllected readings,

13 Treatment Plan Healthcare applicatins invlve many tasks t be carried ut Prviding planes fr executing the jb and mnitring the perfrmance during the executin. The classical planning prblem can be defined as fllws [Weld DS, Recent advances in AI planning]: Descriptin f the knwn part f the initial state f the wrld (in a frmal language, usually prpsitinal lgic). Descriptin f the gal (i.e., a set f gal states). Descriptin f the pssible (atmic) actins that can be perfrmed. Summarizing the abve :A plan is a sequence f actins that transfrms each f the states fitting the initial cnfiguratin f the wrld int ne f the gal states

14 Case Study - diabetes Sending request t Mnitring System using MSDL

15 Case Study - diabetes Multiple tests are prpsed t be used t cllect the readings frm patient. Selected type f the required sensrs (test in ur case) is decided based n the crpratin between the knwledge system and the mnitring system Knwledge system ffers the infrmatin regarding each sensr, patients infrmatin. Mnitring sensrs decides which ne is mre adequate t be used. After the selectin sensr starts t cllect and saves int lgger. Hspital prvides the system with the schema ( as we have seen previusly) Ranges: nrmal, critical r emergency (MSDL example) Readings delivered t predictin service t generate predictin mdel (next page)

16 Case Study - diabetes Predicted diagnsis fr the mnitred case is frwarded t the planning services. Planning service prduces plans as fllw: 1. N medicine plan 2. Give medicine plan 3. Hspitalizatin plan

17 Cnclusins and future wrk Autnmic cmputing mdel fr gverning healthcare enterprise applicatin. Results shw significant imprvement Mdel has been tested n mnitring the diabetes level at the patient side, predicts the diagnsis and suggested the plans f actins Mrever, different types f predictin services can be used fr anticipating the diagnsis, such as machine learning. My cmments: Overall gd framewrk N details fr the predictins service in case study, mre r less repeating what was said in the framewrk with sme extra snapshts fr the system. It wuld be interesting if they wuld have shwn hw they wuld apply real data t the predictin service using multiple regressin analysis.