Method and system for detecting pseudomorphism in record of ICU (intensive care unit) patient based on compressed sensing

Abstract

The invention discloses a method for detecting pseudomorphism in record of an ICU (intensive care unit) patient based on compressed sensing. The method comprises the following steps of establishing a binary classifier by finding out sparse representation of a test set about a training set to realize the death judgment to the ICU patient. Based on the method for detecting the pseudomorphism in the record of the ICU patient, a statistics model does not need to be pre-established, and the classification can be realized only by finding out the sparse representation of a test sample about the training set, so that the use of the method is flexible; compared with an illness state evaluation model based on hypothesis test, the method disclosed by the invention has the advantage that the degree of physical indexes deviating from a normal range does not need to be classified so that the subjective influence of a researcher is basically eliminated. The invention further discloses a system realizing the method, wherein the system can effectively solve the non-specificity problem of the current risk prediction model; compared with the current model, the accuracy and the specificity of predictionare improved by the system disclosed by the invention.

Description

technical field [0001] The invention belongs to the technical field of medical detection, and in particular relates to a method and system for detecting artifacts in ICU patient records based on compressed sensing. Background technique [0002] The limited medical resources and high costs of the intensive care unit (ICU) determine that not all patients can receive special care. Sensing technologies (such as ECG, blood pressure, blood oxygen, respiration, body temperature, etc.) are widely used in ICU. The expert system processes monitoring signals from various sensors, accurately assesses the patient's state, and then decides whether special care is needed. Accurate evaluation of the patient's condition and reliable risk prediction are important basis for deciding whether the patient needs intensive care and controlling the quality of care. An effective condition evaluation system can save medical costs to the greatest extent and benefit more patients in real need under th...

AI Technical Summary

Problems solved by technology

However, the addition of these factors is bound to significantly increase the complexity of the model, posing great challenges to both the system builder and the user.

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