Mycoplasma pneumonitis It is an atypical intracellular pathogen that cannot be detected in standard blood or sp cultures, and is easily transmitted by droplets or direct contact in dense, confined, or poorly ventilated environments, making it a prominent cause of respiratory infections ranging from mild respiratory symptoms to severe pneumonia in pediatric populations.17,18. MP infections occur at varying degrees of prevalence worldwide, with local outbreaks occur every 3-7 years, each outbreak lasting 1-2 years19. The worsening of MP infection, hydrogen peroxide, and community acquired dyspnea syndrome toxin (card TX) can produce MP infection (TX) in children with respiratory symptoms as the main symptoms such as cough, sore throat, and coughing, which can thus lead to necrosis and the dissolution of intestinal gutis cells. It shows widespread inflammation in the image and can manifest as it progresses, with changes in Rober, changes in solids after fusion, and even death in necrotic pneumonia, lung abscess, chest abscess, respiratory failure, and even severe cases.1,20,21. Therefore, early identification and treatment of MP infection is essential and necessary for clinicians. There are many methods commonly used in the clinical diagnosis of MP, such as MP culture, serological antibody testing, MP nucleic acid testing, and MNGS testing.22,23,24. However, each of these tests has its own advantages and disadvantages. PCR tests are highly sensitive, but rely on specific environmental conditions. As a diagnostic “gold standard,” the MP culture test cycle is long and can delay treatment times. The BALF MNGS test is expensive and has specific sample collection requirements. Serological antibody testing is more practical in clinical practice, but should be performed a few days after infection. Therefore, early identification of MP and targeted therapy is necessary to stop the progression of pneumonia and improve the prognosis of children with segmental/Rober pneumonia.
With the development of artificial intelligence, machine learning-based prediction models have been widely used for medical risk prediction and auxiliary diagnosis, including predictive studies of mycoplasma in children via blood indicators and chest x-rays.12,13,25. These studies demonstrate that machine learning has advantages in building predictive models that are unparalleled with traditional statistical methods. Current research on prediction of MP infections focuses primarily on regression models. This model is a classic regression algorithm in which logistics are commonly used. Mycoplasma pneumonitis Pneumonia (MPP) prediction study. Although we mainly use nomograms to build MPP prediction models, it is difficult to effectively solve the problem of data imbalancestwenty five. In this study, we chose to build predictive models to compare and analyze and predict them in various machine learning models based on collected clinical data from large samples of children with segmental/Rober pneumonia. Mycoplasma pneumonitis Segment/Infection in children with Rober pneumonia. Through model selection and comparison, we identified the random forest algorithm as the best performance model and developed a predictive model to predict MP infection in childhood segmentation/Robert's pneumonia. In contrast, decision tree models tend to train overfits, while logistic regression models can mostly be applied to linear variables, whereas the variables in our study are primarily categorical variables, and the KNN algorithm is more adaptive, while the larger algorithms are easily influenced by outgoing people. Random Forest is an ensemble monitoring learning method consisting of multiple decision trees that correspond to various sub-data sets. Each tree calculates the results and obtains the average of the prediction results. This approach can reduce the variance of the decision tree9. Advanced machine learning techniques can be used to identify several important clinical factors associated with MP infection, including WBC, CRP, NLR, PLR, LDH, and complications of lung imaging. We developed a random forest prediction model for MP infection. Compared to previous logistic regression prediction models, our model is more suited to predictors, which are primarily categorical variables. These indicators included in model training and testing are clinically easy to obtain and are closely related to current clinical status. Mycoplasma pneumonitis Segment/Rober pneumonia infections can be used to use the model to more intuitively assess the risk of acquiring MP infection in Segment/Rober children.
This study found that age is an important factor in MP infection. The literature reports that MP infections are primarily seen in school-age children. Here, MP infections are prevalent due to the characteristics of the environment and contact population and are easily transmitted to each other.twenty one. Furthermore, the pathogenic mechanism of MP is complex and includes multiple mechanisms such as adhesion damage, membrane fusion damage, nutrient depletion, invasive damage, toxic damage, immune damage, and inflammatory damage.26. Clinically, elevated platelet counts have been observed in MP-infected children with pneumonia, and platelets have been shown to play an important role in infection and inflammation, in addition to their traditional role in thrombosis and hemostasis, and have been shown to contribute to the immune response to the potential source of infection by initiating an immune response via the interaction of neutron-induced interactions.27. PLR combines with platelet count and lymphocyte count to better reflect disease status, while WBC and CRP are the most commonly used indicators of the body's inflammatory response to infection and injurytwenty five. Serum LDH is a metabolic biomarker and a prognostic biomarker for immune monitoring that acts as a marker for severe inflammatory diseases. High levels of LDH are related to respiratory function28,29 Additionally, several clinical studies have shown a strong correlation between LDH and MPP severity.30. Therefore, in this study, several key variables of high rationality and clinical utility were screened by the Bruta algorithm for inclusion in predictive model training and testing of MP infection.
This study has several advantages and disadvantages. The clinical manifestations of MP infection are diverse and the poor timeliness and specificity of traditional diagnostic methods can lead to misdiagnosis or delayed diagnosis. Through machine learning techniques, we establish predictive models of MP infection to improve diagnostic efficiency and accuracy of MP infection, particularly in children with segmental/Rober pneumonia. Furthermore, using multiple machine learning methods in this study has the advantage that unique patterns and insights can be captured within the dataset. Various machine learning models have their own advantages and disadvantages. For example, the Random Forest model is more suitable for data with categorical variables. Logistic regression models are suitable for data with continuous variables. KNN has a slightly lower prediction rate when the data sample is unbalanced, its algorithm learning ability is slightly less, and the decision tree is above. Comparing results from different methods allows you to comprehensively understand your data and identify the best performance model. Finally, further comparison of the generalization effects of the model using decision curves allows for accurate selection of a more appropriate predictive model. Despite the promising results, our study also has some limitations. Relatively speaking, the AUC and ACC of the Random Forest Prediction Model were not perfect compared to the excellent model performance of other studies, such as the Light GBM model and the GBDT model (AUC = 0.975, 0.980; ACC = 0.928, 0.814).13,25 Although it was superior to the other three models in our study. Therefore, in the future, it is necessary to expand the inclusion of easily available variables in the early stages of the disease, which are closely related to MP infection and in order to continue to optimize training models and improve model sensitivity, such as the daily cough symptoms and the period of fever. Second, data from retrospective studies are always slightly biased, and the retrospective nature of the studies may introduce bias and limit generalization. At the same time, our study lacks external data validation compared to other excellent machine learning models, and in the future we will conduct prospective studies involving larger and diverse populations, verify the performance of predictive models in a variety of clinical settings, and introduce multicenter clinical data to improve model robustness and generalizability. Furthermore, neural networks and hybrid models have been gradually developed, and more powerful features such as the Corona Virus Disease 2019 (Covid-19) epidemic developed for research into Covid-19 infected neural networks.[31] IoT machine learning technology is also developed to adapt to the powerful features of medical data.32,33. In the future, we hope to establish powerful machine learning models, platforms, and other medical data processing technologies that will be applied to MP infection through deep learning and reopening.
