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System and method for predicting types of pathogens in patients with septicemia

A technology of prediction system and prediction method, applied in the field of bacterial species prediction system of sepsis, can solve the problem that no one correctly predicts infection pathogenic bacteria and so on.

Inactive Publication Date: 2020-07-07
陈柏龄
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  • Abstract
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  • Claims
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Problems solved by technology

[0002] Sepsis is the leading cause of death among hospitalized patients. Immediate administration of effective antibiotics can reduce the mortality of patients with sepsis. However, there is currently no test method for correctly predicting the infection of pathogenic bacteria before the results of pathogen culture are released. Therefore, clinicians usually rely on Antibiotics are given to patients based on personal judgment. Therefore, how to determine whether a patient is infected and what kind of pathogenic bacteria is infected before the results of pathogenic bacteria culture are released is an issue that technicians in this field are concerned about.

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  • System and method for predicting types of pathogens in patients with septicemia
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  • System and method for predicting types of pathogens in patients with septicemia

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[0022] The terms "first", "second" and the like used herein do not specifically refer to a sequence or sequence, but are only used to distinguish elements or operations described with the same technical terms.

[0023] figure 1 It is a schematic diagram illustrating a sepsis strain prediction system according to an embodiment. Please refer to figure 1 , the strain prediction system 100 includes a plurality of sensors 110 , a processor 120 , a communication module 130 and a display 140 . The sensor 110 can be used to sense physiological data such as body temperature, blood pressure (including diastolic and systolic pressure), pulse, heart rate, etc. Those skilled in the art can select appropriate sensors, such as using an infrared thermometer to sense body temperature. The processor 120 may be a central processing unit, a microprocessor, a microcontroller, a signal processor, an application-specific integrated circuit, and the like. Communication module 130 can be wired or w...

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Abstract

A system for predicting types of pathogens in patients with septicemia is provided. The system includes at least one sensor and a processor. The sensor is used to sense current physiological data including at least one of body temperature, blood pressure, and pulse. The processor is configured to calculate at least one feature value according to the current physiological data, and input the feature value into a machine learning model to determine one of categories including at least two of uninfected, fungal infected, contaminated bacteria infected, Gram-negative infected, and Gram-positive infected.

Description

technical field [0001] The invention relates to a system and method for predicting bacterial strains of sepsis, which can predict the types of pathogenic bacteria before the culture results of the pathogenic bacteria are released. Background technique [0002] Sepsis is the leading cause of death among hospitalized patients. Immediate administration of effective antibiotics can reduce the mortality of patients with sepsis. However, there is currently no test method for correctly predicting the infection of pathogenic bacteria before the results of pathogen culture are released. Therefore, clinicians usually rely on Antibiotics are given to patients based on personal judgment. Therefore, how to determine whether a patient is infected and what kind of pathogenic bacteria is infected before the results of pathogenic bacteria culture are released is a topic of concern to those skilled in the art. Contents of the invention [0003] In order to solve the above problems, the embo...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): A61B5/00A61B5/0205A61B5/01
CPCA61B5/412A61B5/02055A61B5/01A61B5/0002G16H50/20G16H50/50G06N20/20G06N5/01G06N7/01G06N3/045A61B5/11A61B2018/00791
Inventor 陈柏龄蔡承谕吕秉泽舒宇宸柯乃熒叶俊吟柯文谦庄坤达高宏宇
Owner 陈柏龄
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