Classifications

• • G—PHYSICS
  • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
  • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
  • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
  • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
• • G—PHYSICS
  • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
  • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
  • G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
  • G16H40/60—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices
  • G16H40/67—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for remote operation

Definitions

• the invention relates to the field of process monitoring that generates data repetitively according to predictive time rules, so as to anticipate the occurrence of events (or data) and / or to facilitate decision-making.
• an event may consist of the collection of data or a set, optionally ordered data.
• a set of appropriate temporal predictive rules As known to the man of art, when we want to carry out effective monitoring data generating process, it is imperative to have a set of appropriate temporal predictive rules.
• the definition of such a set is usually done in two phases. In a first phase, harvest a data set which characterizes the operation of a process to be monitored. In a second phase, the harvested data set is analyzed to extract information necessary for the construction of predictive temporal rules.
• the object of the invention is in particular to improve the situation, and in particular to allow rapid convergence towards time rules making it possible to anticipate the occurrence of events (or data) and possibly to provide non-specialist users in real time tips for solving problems, possibly complex.
• a second step in which it is determined in real time whether data newly generated by the process satisfy determined time rules associated with a quality indicator value greater than a first chosen threshold, and if not, modifies each untested time rule or the associated quality indicator value and / or create at least one new time rule for the set, while in the affirmative generate a first message containing a proposed action (s) to achieve due to the verification of at least one time rule by newly generated data.
• the monitoring method according to the invention may comprise other characteristics that can be taken separately or in combination, and in particular:
• each time ruler can be determined as a function of at least one selected constraint (e.g. based on a structural description and / or functional sub monitoring process);
• this datum in the first step, in the case of generation of a datum of the quantitative type, this datum can be transformed into a datum of qualitative type;
• each quality indicator can be chosen from (at least) a confidence rate representative of the probability that an ordered sequence of selected data will lead to the generation of another chosen data item, and a support rate representative of the number of occurrences of a selected datum resulting from the occurrence of an ordered sequence of other selected data;
• an unverified time rule can be deleted when the associated quality indicator value is strictly less than a second threshold (parameter which is for example related to the requirements of the application concerned and / or the job);
• the set may not include any rules temporal
• each temporal rule can be integrated into the set after being validated (for example by human reasoning).
• the invention also provides a computer program product comprising a set of instructions which, when executed by the processing means, is peculiar to implement a type of monitoring method from the one shown above to watch a data generating process.
• the invention also proposes a device, intended to monitor a data generating process, and comprising:
• the analysis means arranged to analyze data generated by the process to determine a set of time rule generation prediction of future data subsequent to the generation of deleted data, each temporal rule being associated with at least one value of at least one quality indicator, and
• monitoring means arranged to determine in real time whether data newly generated by the process checks determined time rules associated with a quality indicator value higher than a first chosen threshold, and if not to modify each time rule not checked or the associated quality indicator value and / or create at least one new time rule for the set, or if so to generate a first message containing a proposed action (s) to achieve due to the checking at least one time rule with the newly generated data.
• the invention also proposes electronic equipment comprising a monitoring device of the type of that presented above.
• FIG. 1 diagrammatically and functionally illustrates an electronic equipment equipped with an exemplary embodiment of a monitoring device according to the invention and coupled to a communication network to which communication terminals are also coupled,
• FIG. 2 illustrates an example of an algorithm implementing a method of monitoring according to the invention
• FIG. 3 illustrates an example of spatial discretization of input data.
• the object of the invention is in particular to propose a monitoring method, and an associated DS monitoring device, for enabling the monitoring of a data generating process (or events).
• the data generating process is a medical application providing data relating to a person suffering from at least one pathology, such as for example diabetes arterial tension or respiratory failure.
• the invention can, for example, allow a home telemonitoring to inform real-time health status of a person (possibly interactively) others chosen, such as doctors, nursing staff, nurses, physiotherapists.
• the invention is not limited to this type of process. It concerns indeed any type of process generating data (or events). Thus, it also concerns, in particular, industrial processes, and in particular production systems, financial processes, and more generally any physical system that can generate data.
• a datum can be in various forms. Thus, it may be a qualitative or categorical data, which may be ordinal or nominal, such as an event, or quantitative, numerical, discrete or continuous data.
• an event may be an exceeding of a threshold, opening a door, a change in value of a variable (or a parameter), pressing a button of an interface man / machine, or any other binary event.
• a monitoring method according to the invention comprises at least two steps that can be implemented by means of a monitoring device DS.
• this monitoring device DS may, for example, be installed in electronic equipment EE.
• Such EE electronic equipment may, for example, be a computer (possibly communicating), a microcomputer (possibly communicating) or an electronic tablet. But this is not mandatory. Indeed, the monitoring device DS could itself be in the form of an electronic equipment, possibly dedicated. Therefore, a surveillance device DS, according to the invention, can be realized either in the form of software modules (or computer (or "software”)); it is then in the presence of a computer program product comprising a set of instructions which, when executed by processing means such as electronic circuits (or "hardware"), is adapted to implement the method monitoring, either in the form of a combination of software modules and electronic circuits.
• EE electronic equipment has a communication module MC that allows it to connect to a communications network RC (possibly by waves), in order to be able to transmit information relating to this person to at least one communication terminal TCj.
• the index j is equal to 1 or 2, by way of purely illustrative example.
• the EA electronic equipment includes a medical application providing data relating to the aforementioned person based on measurements transmitted by radio by sensors coupled to that person or located in the close environment of that person.
• the electronic equipment EE also includes an IH man / machine interface intended to allow its control by people and the control of the DS (monitoring) device that it includes.
• Some authorized persons may possibly partially or completely control the application (here medical) via their communication terminal TCj. It is also possible to provide an interface application with a central information system enabling authorized individuals to connect to this system in order to create / update / view medical information and patient records.
• (MA monitoring means of the (monitoring) device DS) analyzes (s) so automated data that has already been generated by the process (here the medical application) to determine a set of prediction time rules for generating future data subsequent to the generation of prior data.
• Each time ruler so determined is associated with at least one value of at least one quality indicator.
• This first step corresponds to substeps 10 to 60 of the exemplary algorithm of FIG.
• the data which are analyzed are, for example, stored, in correspondence of the times when they were respectively generated, in first storage means B1 which may be part of the electronic equipment EE, as shown in non-limiting manner in FIG.
• first B1 storage means may, e.g., be formed as a first database. It will be understood that these stored data have been accumulated during the life of the process that generates them.
• the generated data can be qualitative or quantitative type
• the means of analysis MA extract from the first storage means B1 a quantitative type data they can first perform a test for determine if its type is qualitative. This test corresponds to the substep 10 of the example algorithm of FIG. 2. If the result of the test is negative (and therefore if the datum is of the quantitative type), then the analysis means MA transform this datum. in a qualitative type of data, for example by cutting into classes at intervals or constant strengths or by thresholding. This transformation corresponds to the substep 20 of the exemplary algorithm of FIG.
• the objective of this transformation of a quantitative data into a qualitative data is to obtain a spatial and temporal discretization of the input data.
• This treatment can be simply segmenting a signal from the derivative of order one (1). It is also possible to perform a thresholding based on the dispersion of the measurements, then to segment the signal according to the crossing of recorded thresholds. Thresholds created are made optimal with respect to a chosen criterion, which can be (but not limited to) the respective maximization and minimization of inter-threshold and intra-threshold dispersions.
• An example of spatial discretization is given in Figure 3. The segments (or events) from the same threshold are then grouped according to their distance two-to-two. This frees up event groups with common trends.
• a unique reference time signature corresponding to the characteristic change of a signal over a given time interval can be calculated. It is then possible to characterize the data by a limited set of classes, the set of classes obtained making it possible to reconstruct the entirety of the initial signal.
• each temporal rule is constructed according to the behavioral modelings of the data which concern it.
• An ordered sequence of events can, for example, be represented by d1 ⁇ d2 ⁇ d3 or d1 ⁇ d3 ⁇ d4 ⁇ d7.
• the arrow (or arc) between two successive events of a sequence represents the temporal constraint that binds these two events.
• an event may correspond to a set, possibly ordered, of collected data. For example, if a first event d1 is generated and the second event d2 is generated within the next 10 minutes then there is X% probability that a third event d3 will be generated in the next 15 minutes and 15 seconds.
• a sequence therefore has a prognostic power and / or a diagnostic power which is / are characterized by a quality indicator (s).
• each quality indicator can be chosen from a confidence level tdc (prognostic power) and a support rate tds (diagnostic power).
• the confidence rate tdc is representative of the probability that an ordered sequence of selected data will lead to the generation of another chosen data item. For example, if one has the ordered sequence "d1 ⁇ d2 ⁇ d3", tdc is equal to the ratio of the total number of occurrences of the ordered sequence "d1 ⁇ d2 ⁇ d3" to the total number of occurrences of the ordered sequence "d1 ⁇ d2".
• the support rate tds is representative of the number of occurrences of a chosen datum resulting from the occurrence of an ordered sequence of other selected data. For example, if we have the ordered sequence "d1 ⁇ d2 ⁇ d3", tds is equal to the ratio between the total number of occurrences of the sequence ordered "d1 ⁇ d2 ⁇ d3" and the total number of occurrences of the data d3.
• each time ruler may be optionally determined as a function of at least one selected constraint.
• at least one flow constraint which sets the flow order of the data flow (in order to fix the causal order of the data generated in a multi-model approach)
• at least one function constraint which sets functional relationships between variables
• a constraint may be a flow sensor or implantation model indicating material flow / energy or data flow indication.
• Each time rule is constructed from a target event that is the starting point for establishing a tree of the most likely causes that caused a problem.
• the method of discovering the most probable and temporal constraints may be as follows: from the data segmentation performed in the previous step, the sequences of events are compared for each variable. The temporal correlations between two (or more) variables are highlighted by comparing the number and frequency of transitions between the events identified, as well as the average time between these transitions. It is also possible to quantify the impact of carrying out an event on a related event with the help of an appropriate measure.
• the determination of the most likely causes that explain a particular problem can then be done through a tree that we pruned to determine / the rule (s) that will (have) to better anticipate the problem.
• only the time rules associated with at least one quality indicator value that is greater than a first threshold s1 are retained. It will be noted that the value of the first threshold s1 may vary from one quality indicator to another.
• the analysis means MA can perform a test to determine whether the quality indicator value associated with a given time rule is greater than the first threshold s1. This test corresponds to the sub-step 40 of the algorithm of example in Figure 2.
• the analysis means MA reject the temporal rule and therefore do not store it. Indeed, the value of the quality indicator evolves as and as new data is generated, and as new events arrive. If the rule is not effective and the value of the associated indicator falls below a certain threshold, it is revoked. This rejection corresponds to the substep 50 of the exemplary algorithm of FIG. 2.
• the means of analysis MA store the temporal rule, corresponding to each associated quality indicator value, in second storage means B2 which may be part of the device DS, as shown in non-limiting manner in FIG.
• This temporal rule becomes part of the overall usable temporal rules.
• These second storage means B2 may, for example, be arranged in the form of a second database. This storage corresponds to substep 60 of the exemplary algorithm of FIG. 2.
• each time rule is integrated into the set of time rules stored in the second storage means B2 after being validated or approved.
• This validation is preferably performed by an authorized person following a reasoning. But it could be done by the analysis means MA or by other means of dedicated DS device.
• the set of time rules may be empty (and thus may not include any temporal rules).
• a second step, the method according to the invention, is then performed in real time on the new data generated by the process. It is therefore a step of monitoring.
• This second stage corresponds to the sub-steps 70-1 10 of the algorithm example in Figure 2.
• monitoring means MS determines in real time whether the data newly generated by the process check time rules previously determined by the analysis means MA and which are associated with a quality indicator value that is greater than the first threshold chosen s1. In other words, the monitoring means MS check during test (s) whether each time rule of the stored set is satisfied by one or more newly generated data. This check corresponds to the sub-step 70 of the exemplary algorithm of FIG. 2.
• the MS monitoring means modifies each untested time rule or the indicator value. associated quality and / or create at least one new time rule for the set.
• This modification and / or creation is the sub-stage 80 of the example algorithm of Figure 2. It is for example carried out by means of analysis MA at the request of MS surveillance.
• the modified and / or created temporal rule is then integrated into the set of temporal rules in the second storage means B2 (possibly after validation), during a substep similar to the substep 60 previously described.
• MS monitoring means If the result of verification is positive (and therefore whether newly generated data satisfy a temporal rule), we (MS monitoring means) generate (s) a first message containing a proposal for action (s) to achieve the makes the verification of at least one time rule by newly generated data. This generation is the sub-step 90 of the algorithm of example in Figure 2.
• This first message may be of textual or audio type, and may possibly be encrypted. Depending on the application concerned, the first message may be displayed and / or broadcast either by the electronic equipment EE or by at least one communication terminal TCj after being transmitted via an RC communication network by the electronic equipment EE.
• this first message is intended to warn at least one person that one or more actions must be considered in view of the latest data generated by the process.
• An action can be an alarm trigger or a proposal for intervention, possibly in an emergency.
• bit (s) may then optionally generating a second message containing a justification for a reasoning which led to the proposed action (s) to achieve contained in the first message. This generation corresponds to sub-step 100 of the exemplary algorithm of FIG 2.
• This second message may be of textual or audio type, and may possibly be encrypted. Depending on the application concerned, the second message may be displayed and / or broadcast either by the electronic equipment EE or by at least one communication terminal TCj after having been transmitted via an RC communication network by the electronic equipment EE. It will be understood that this second message is intended to provide at least one person with an explanation (or justification) of the reasons which led to proposing one or more actions.
• an unverified time rule (previously stored in the second storage means B2) can be deleted when the associated quality indicator value has become strictly less than a second threshold s2.
• This second threshold s2 is preferably lower than the first threshold s1. But it could be equal to the latter (s1). It will be understood that it is no longer justified to use a temporal rule when its predictive interest with respect to the process becomes too low. This deletion is for example carried out by the analysis means MA automatically, or at the request of the monitoring means MS.

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• 2014
  • 2014-12-19 FR FR1462903A patent/FR3030815A1/fr active Pending
• 2015
  • 2015-12-15 EP EP15817178.5A patent/EP3234830A1/de not_active Ceased
  • 2015-12-15 WO PCT/EP2015/079843 patent/WO2016096886A1/fr active Application Filing

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