Nataly Zhukova - Conceptual Model for Routine Measurements Analyses in Semantic Web Applications

AIST 2016, 7-9 April 2016, Yekaterinburg

Conceptual Model for Routine Measurements Processing and Analyses in Adaptive Intelligent Information Systems

Maxim Lapaev, Alexander Vodyaho, Nataly Zhukovam.lapaev@corp.ifmo.ru, aivodyajo@mail.ru, nazhukova@mail.ru

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Motivation and objectives

The motivation is to provide a user with tools to solve domain-specific tasks

Users are not specialized in data processing issues, especially ones related totemporal measurements as a result of massive data: data is too big; too many interrelations between data pieces; numerous processing methods; methods are specific.

Measurements are the major part of data requiring temporal synchronization (common time scale). Furthermore, measurement data contains noise to be analyzed and eliminated.

A wide diversity of ways to obtain measurements exists nowadays to collectmeasurement data of high quality (precise tools and measuring devices),which provides all the raw data for solving application problems.

IntroductionCurrent stateRequired featuresOur approach• Baseline• From object to model• Generalization

Model viewsTechnological baseCase study: Botkin’s sheetConclusion

Motivation and objectives

Currents stateTypical workflow in Federal Almazov North-West Medical Research Center

Required featuresTasks: To justify expectations an AIIS has to solve following tasks: 1. reduce amount of data; 2. build linked data and information space; 3. enrich data, information and knowledge; 4. provide machine-based applied problems solutions.

Properties: An AIIS must possess following properties: 1. accumulating by gathering all objective and subjective data, information and

knowledge; 2. resource saving; 3. accessibility; 4. theoretical background.

Features:1. intelligence;2. automation 3. dynamism;4. ability to process historical data.

Our approach: baseline

The proposed concept model is based on a number of general ideas:1. feasible way to investigate the real world through dealing with measurements –

gathering, storing, processing, analyzing;2. capability of consuming measurements is achievable if based on consequent

measurement transformations;3. real-world objects are too complex for modeling, but their numerous views have

simple models;4. real-world processes are poorly predictable, too complex to be formalized, but

well-decomposable into sub-processes.

Our approach: from object to model

Model principals (a total of 13 principals). Some of the principals are:1. the main value is knowledge; it is vital to operate with knowledge in each case;2. any data can be meaningful; thus, all data is supposed to be carefully processed;3. models and processes must be adaptable at the level of structure and contents level.

Our approach: from model to general model

To build models we use general models and knowledge domain. Target users are domain experts, end users, researchers and sponsors (model producers)

Model views

Technological base1. Transformation technologies: defined for JDL-models for measurements processing.2. Semantic Web technologies: to build interpretable and human- and machine-

comprehensible giant global graph for machine solution of end-users problems. 3. IT technologies: for system design and support using agile technologies provided by IT.

Case study: Botkin’s temporal sheet

SMDA system prototype for Almazov medical center: http://islegiaa.bget.ru/

Stages:1. obtaining raw measurement data from devices;2. processing separate values and timelines corresponding to value sequences;3. construction of sparse temporal matrix;4. processing sparse matrix to gain event timeline;5. masking sparse matrix by event timeline;6. matrix compression to produce a uniform matrix and event-based intervals;7. calculation of integral patient’s state indicator.

Results:8. a way to asses the correspondence of measurement values with events (medicine

prescriptions, manipulations on patient);9. association of measurements with expected results (typical treatment regimes);10. recommendations for end user (doctor).

Case study: time- and event-based processing

Case study: Botkin’s temporal sheet prototype

Conclusion and future workAlready achieved:1. specification of general models;2. medicine domain-oriented specification;3. a prototype of the system is designed, implemented and passed to domain experts;4. two scenarios are supported: medical (Botkin’s sheet) and managerial (matching

objective (measurements) and subjective (medical notes) data).

Future work: design and implementation of a framework for specialists and non-specialists in domain to deal with models

Thanks for attention

Maxim Lapaev, Alexander Vodyaho, Nataly Zhukovam.lapaev@corp.ifmo.ru, aivodyajo@mail.ru, nazhukova@mail.ru

http://www.ifmo.ru/49, Kronverksky Pr., St. Petersburg, 197101, Russia

AIST 2016, 7-9 April 2016, Yekaterinburg