Monitoring performance of predictive computer-implemented models

A monitoring and forecasting, computer technology, applied in computational models, computer-aided medical procedures, computing, etc., can solve problems affecting the predictive performance of predictive models, etc.

Pending Publication Date: 2022-05-13
KONINKLJIJKE PHILIPS NV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Of course, other types of changes can also occur that can affect the predictive performance of a predictive model

Method used

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  • Monitoring performance of predictive computer-implemented models
  • Monitoring performance of predictive computer-implemented models
  • Monitoring performance of predictive computer-implemented models

Examples

Experimental program
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Embodiment Construction

[0043] figure 1 The general principles of creating and using predictive models (PCIMs) in the monitoring and maintenance of systems are illustrated. Ability to use predictive models to monitor any type of system, for example, systems such as healthcare-based imaging systems including magnetic resonance imaging (MRI), computed tomography (CT), image Guided Therapy (IGT), etc. For ease of understanding, in this disclosure, the terms "predictive model," "predictive model," "predictive computer-implemented model," and "PCIM" all refer to device) state model. A PCIM can be any type of computer-implemented machine learning model, such as a support vector machine (SVM) model, a random forest model, or a logistic regression model, among others.

[0044] Data 2 comes from or is provided by the system and includes values ​​for a number of characteristics. Characteristics (or "system characteristics") can relate to various operational or functional aspects of the system, for example,...

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Abstract

According to one aspect, there is provided a computer-implemented method of monitoring performance of a predictive computer-implemented model (PCIM) for monitoring a state of a first system. The PCIM receives as input observed values for a plurality of features related to the first system, and the PCIM determines whether to issue a status alert based on the observed values. The method comprises: obtaining reference information for the PCIM, where the reference information for the PCIM comprises a first set of values for the plurality of features related to the first system in a first time period; determining a set of reference probability distributions from the first set of values, the set of reference probability distributions including a respective reference probability distribution for each of the features determined from the values of the respective features in the first set of values; obtaining operational information for the PCIM, where the operational information for the PCIM includes a second set of values for the plurality of features related to the first system in a second time period subsequent to the first time period; determining a set of operational probability distributions from the second set of values, the set of operational probability distributions including a respective operational probability distribution for each of the features determined from the values of the respective features in the second set of values; determining a drift metric for the PCIM, the drift metric representing a metric of performance drift of the PCIM between the first time period and the second time period, where the drift metric is based on a comparison of the set of reference probability distributions and the set of operational probability distributions; and outputting the drift metric.

Description

technical field [0001] The present disclosure relates to monitoring the performance of a predictive computer-implemented model (PCIM) for monitoring system status, and more particularly to a computer-implemented method, apparatus, and computer program product for monitoring the performance of a PCIM. Background technique [0002] Predictive computer-implemented models, PCIM, (also referred to herein as "predictive models" and "predictive models") are becoming more prevalent in many platforms and systems that aim to identify services or systems in advance possible disruptions and resolve the issue with minimal disruption to the service or end users of the system. In the context of healthcare-based imaging systems (e.g., magnetic resonance imaging (MRI), computed tomography (CT), image-guided therapy (IGT), etc.), construct a predictive model and use it to inform relevant parties (e.g., away from Service or system monitoring engineers and maintenance engineers) are alerted to...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G16H40/40G06F11/34G06N20/00
CPCG16H40/40G06F11/3476G06N20/00G16H50/20G06F7/584G06N3/08
Inventor R·B·帕蒂尔V·拉维M·L·H·布曼斯N·布萨
Owner KONINKLJIJKE PHILIPS NV
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