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Pattern discovery visual analytics system to analyze characteristics of clinical data and generate patient cohorts

A pattern discovery, patient technology, applied in clinical diagnosis and monitoring, medical field, can solve the problem of limiting the amount of information

Active Publication Date: 2018-12-21
KONINKLJIJKE PHILIPS NV
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

This limits the amount of information available to select patient cohorts for clinical trials

Method used

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  • Pattern discovery visual analytics system to analyze characteristics of clinical data and generate patient cohorts
  • Pattern discovery visual analytics system to analyze characteristics of clinical data and generate patient cohorts
  • Pattern discovery visual analytics system to analyze characteristics of clinical data and generate patient cohorts

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Embodiment Construction

[0017] The pattern discovery visual analysis techniques disclosed herein exploit the insight that information about individual patients (samples) is acquired over time in a clinical process (or any other stage-based process). For example, percutaneous coronary intervention (PCI) procedures in cardiology progressively generate more patient information in time-oriented stages, starting with historical and demographic information collected during patient admission, through pre-procedural laboratory tests generated Results, followed by in-procedure measurements of lesions and devices, followed by post-procedure lab test results, discharge status and medications, and more. Identifying groups of patients (cohorts) that satisfy criteria of a certain time-bound constraint based on selected prediction targets is meaningful for early intervention of high-risk patients to improve quality of care. Such patient cohort selection is also valuable for clinical research, such as selecting pati...

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Abstract

In pattern discovery visual analytics, a patient data table (14) is generated that tabulates, for each patient, attribute values for a set of attributes. A positive or negative prediction is generatedfor each patient for a target value of a target attribute using a prediction pattern (20) of attribute values for w attributes (22). The prediction is positive if at least a threshold fraction (26) of the w attributes of the patient match the prediction pattern, is negative otherwise. Patients are grouped into a selected proportion of a confusion matrix (30) in accord with the positive or negative predictions and actual values of the target attribute T in the patient data table. A display component (4) displays a representation (42) of patient statistics for the selected proportion of the confusion matrix on a per-attribute basis for attributes of the w attributes. A patient cohort (44) is identified using the representation.

Description

technical field [0001] The following generally relate to the field of medicine, the field of oncology, the field of clinical trial design, the field of clinical diagnosis and monitoring, and related fields. Background technique [0002] A wide range of medical information systems are employed in the healthcare industry, ranging from general-purpose electronic health record (EHR) systems or electronic medical record (EMR) systems to more specialized information systems such as cardiovascular information system (CVIS) deployments. These information systems store a variety of patient information in a wide variety of formats. The information may include, for example: demographic data such as gender (with values ​​of "male" or "female"), race (with various designations) and age in years; vital sign readings such as, Heart rate in beats / min, respiration in breaths / min, blood pressure in mmHg, SpO in percent 2 ; blood test results in units of mmol / L, mg / L, etc.; genomic data; qua...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G16H50/70G16B40/20G16B45/00G16B50/30
CPCG16H50/70G16B45/00G16B40/00G16B50/00G16B50/30G16B40/20G16H50/20
Inventor T·M·陈周子捷N·R·罗特甘斯N·劳特J·C·格斯林克J·M·德邦特
Owner KONINKLJIJKE PHILIPS NV
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