Nataly Zhukova - Conceptual Model for Routine Measurements Analyses in Semantic Web Applications
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Transcript of 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 [email protected], [email protected], [email protected]
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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 [email protected], [email protected], [email protected]
http://www.ifmo.ru/49, Kronverksky Pr., St. Petersburg, 197101, Russia
AIST 2016, 7-9 April 2016, Yekaterinburg