Research Based on Multimodal Deep Feature Fusion for the Auxiliary Diagnosis Model of Infectious Respiratory Diseases

Liyan Yu,Jingyuan Zhao,Qingchen Zhang,Zhuo Liu

doi:10.1155/2021/5576978

Liyan Yu, Jingyuan Zhao + Show 2 more

Open Access

https://doi.org/10.1155/2021/5576978

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Abstract

Pulmonary infection is a common clinical respiratory tract infectious disease with a high incidence rate and a severe mortality rate as high as 30%–50%, which seriously threatens human life and health. Accurate and timely anti-infective treatment is the key to improving the cure rate. NGS technology provides a new, fast, and accurate method for pathogenic diagnosis, which can provide effective clues to the clinic, but determining the true pathogenic bacteria is a problem that needs to be solved urgently, and a comprehensive judgment must be made by the clinician combining the laboratory results, clinical information, and epidemiology. This paper intends to effectively collect and process the missing values of NGS data, clinical manifestations, laboratory test results, imaging test results, and other multimodal data of patients with infectious respiratory diseases. It also studies the deep feature fusion algorithm of multimodal data, couples the private and shared features of different modal data of infectious respiratory diseases, and digs into the hidden information of different modalities to obtain efficient and robust shared features that are conducive to auxiliary diagnosis. The establishment of an auxiliary diagnosis model for the infectious respiratory diseases can intelligentize and automate the diagnosis process of infectious respiratory, which has important significance and application value when applied to clinical practice.

Highlights

With the advent of the big data era, data has flooded all aspects of society
We design and establish research based on high-throughput pathogen detection system of the artificial intelligence high-performance computing platform of the sequencing platform which establishes a high-order tensor database for infectious respiratory diseases and a multimodal database that combines imaging, laboratory examination results and clinical manifestations, based on artificial intelligence for disease exploring and unifying the treatments of patients and establishing a treatment query system. is paper aims to study the combination of NGS data with clinical data and epidemiological data with the help of in-depth calculation models, in the diagnosis and treatment of infectious respiratory diseases’ application
Pulmonary infection is a common respiratory infectious disease in clinical practice with a high incidence. It ranks the first cause of death in countryside and the third in urban areas of China, especially severe pneumonia. It has an increasing trend in recent years, the treatment methods have great progress than before, its fatality rate is still as high as 30% to 50%, which seriously threatens human life and health. e rapid and accurate diagnosis of pathogenic bacteria of respiratory tract infection is the key to treatment, which can help clinicians to optimize the use of antibacterial drugs in a timely manner, thereby speeding up recovery, increasing cure rate and improving prognosis

Summary

Introduction

With the advent of the big data era, data has flooded all aspects of society. For modern medicine, the human body has become a big database, and various medical data make modern medicine show obvious data characteristics. Pulmonary infection is a common respiratory infectious disease in clinical practice with a high incidence. It ranks the first cause of death in countryside and the third in urban areas of China, especially severe pneumonia. It has an increasing trend in recent years, the treatment methods have great progress than before, its fatality rate is still as high as 30% to 50%, which seriously threatens human life and health. Compared with traditional pathogenic microorganism detection, mNGS has high sensitivity and large amount of information It can detect pathogens early, guide the precise selection of antibacterial drugs, reduce the use of antibacterial drugs, reduce the mortality of patients, and can identify new/known pathogens infection and mixed infection

Current Research Status

Preprocessing of Multimodal Clinical Data of Infectious Respiratory Diseases

Findings

Discussion