Application of Machine Learning for the Prediction of Etiological Types of Classic Fever of Unknown Origin

Yan, Yongjie; Chen, Chongyuan; Liu, Yunyu; Zhang, Zuyue; Lin, Xu; Pu, Kexue

doi:10.3389/fpubh.2021.800549

Cited by 6 publications

(3 citation statements)

References 46 publications

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“…LightGBM is another framework that implements the GBDT algorithm, which supports efficient parallel training, and has faster training speed, lower memory consumption and better accuracy [ 34 ]. This method has been applied to the interpretability of classification, as evidenced by previous studies [ 35 ].…”

Section: Datamentioning

confidence: 99%

A score-based method of immune status evaluation for healthy individuals with complete blood cell counts

Zhang,

Zhao,

Cheng

et al. 2023

BMC Bioinformatics

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Background With the COVID-19 outbreak, an increasing number of individuals are concerned about their health, particularly their immune status. However, as of now, there is no available algorithm that effectively assesses the immune status of normal, healthy individuals. In response to this, a new score-based method is proposed that utilizes complete blood cell counts (CBC) to provide early warning of disease risks, such as COVID-19. Methods First, data on immune-related CBC measurements from 16,715 healthy individuals were collected. Then, a three-platform model was developed to normalize the data, and a Gaussian mixture model was optimized with expectation maximization (EM-GMM) to cluster the immune status of healthy individuals. Based on the results, Random Forest (RF), Light Gradient Boosting Machine (LightGBM) and Extreme Gradient Boosting (XGBoost) were used to determine the correlation of each CBC index with the immune status. Consequently, a weighted sum model was constructed to calculate a continuous immunity score, enabling the evaluation of immune status. Results The results demonstrated a significant negative correlation between the immunity score and the age of healthy individuals, thereby validating the effectiveness of the proposed method. In addition, a nonlinear polynomial regression model was developed to depict this trend. By comparing an individual’s immune status with the reference value corresponding to their age, their immune status can be evaluated. Conclusion In summary, this study has established a novel model for evaluating the immune status of healthy individuals, providing a good approach for early detection of abnormal immune status in healthy individuals. It is helpful in early warning of the risk of infectious diseases and of significant importance.

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Section: Datamentioning

confidence: 99%

A score-based method of immune status evaluation for healthy individuals with complete blood cell counts

Zhang,

Zhao,

Cheng

et al. 2023

BMC Bioinformatics

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“…The distribution of these causative agents varies both temporally and geographically, necessitating comprehensive and in-depth investigations to accurately determine the underlying cause of the disease. Consequently, identifying the cause of FUO poses a significant challenge within the medical field [6] . In the diagnosis of febrile illness, doctors need to conduct a thorough evaluation and examination based on the patient's symptoms, signs and possible causes to determine the final diagnosis and treatment plan.…”

Section: Introduction Backgroundmentioning

confidence: 99%

A Pathological Diagnosis Method for FUO based on Multi-Path Hierarchical Classification: Model Design and Validation (Preprint)

Du,

Ding,

et al. 2024

Preprint

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BACKGROUND Fever of Unknown Origin (FUO) is a significant challenge for the medical community due to its association with a wide range of diseases, the complexity of diagnosis, and the likelihood of misdiagnosis. Machine learning can extract valuable information from the extensive data of patient indicators, aiding doctors in diagnosing the underlying cause of FUO. OBJECTIVE A hierarchical classification method based on multipath feature selection was proposed to address the problem that the causes of FUO are diverse and difficult to diagnose accurately. METHODS The dataset was obtained from the clinical diagnostic records of patients with FUO who were admitted to the First Affiliated Hospital of Xi'an Jiaotong University between the years 2011 and 2020. The dataset contains 564 samples, with 5 broad categories of etiologies and subcategories, totaling 16 precise etiologies. An intelligent diagnostic algorithm for FUO was constructed based on hierarchical classification to identify 16 precise etiologies and two improvements were made: (1) A multi-path prediction model to minimize the probability of error propagation; (2) A utilization of L1,2 regularization constraint to effectively identify the most suitable subset of features to eliminate redundancy and interference in the hierarchical classification process. To confirm the proposed method's validity, we conducted ablation experiments and compared it with other flat and hierarchical classification algorithms. RESULTS According to the ablation experiments, the proposed method achieves the optimal result when the intermediate path is 3, with an accuracy of 72.35%, FH of 85.01%, and FLCA of 83.29%. This performance is superior to the traditional single-path hierarchical classification, showing improvements of 5.69%, 2.89%, and 3.39%, respectively. The feature selection based on the L1,2 regularization constraint can further enhance the model's performance. The best results were achieved by filtering 25% of the features, with an accuracy of 76.08%, FH of 86.72%, and FLCA of 85.39%. Based on the comparison experiments, the proposed method outperforms the seven flat algorithms and four hierarchical classification algorithms that were compared. For example, the accuracy can be improved by at least 5.39% compared to ELM and up to 24.7% compared to KNN. CONCLUSIONS The proposed algorithm greatly improves the classification performance compared to existing machine learning algorithms. It can better predict the cause of FUO, assisting physicians in their work. CLINICALTRIAL

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“…The pathophysiology, treatment, and prognosis of these categories differ markedly [ 7 ]. Early identification of the underlying category is important to optimize treatment strategies for patients with FUO [ 8 – 10 ]. Currently, 18 F-fluorodeoxyglucose positron emission tomography/computed tomography ( 18 F-FDG PET/CT) is considered one of the most promising diagnostic tools for FUO [ 11 – 29 ].…”

Section: Introductionmentioning

confidence: 99%

Diagnostic models for fever of unknown origin based on 18F-FDG PET/CT: a prospective study in China

Chen

Xing

et al. 2022

EJNMMI Res

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Background This study aims to analyze the 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) characteristics of different causes of fever of unknown origin (FUO) and identify independent predictors to develop a suitable diagnostic model for distinguishing between these causes. A total of 524 patients with classical FUO who underwent standard diagnostic procedures and PET/CT were prospectively studied. The diagnostic performance of PET/CT imaging was analyzed, and relevant clinical parameters that could improve diagnostic efficacy were identified. The model was established using the data of 369 patients and the other 155 patients comprised the validation cohort for verifying the diagnostic performance of the model. Results The metabolic characteristics of the “hottest” lesion, the spleen, bone marrow, and lymph nodes varied for various causes. PET/CT combined with clinical parameters achieved better discrimination in the differential diagnosis of FUO. The etiological diagnostic models included the following factors: multisite metabolic characteristics, blood cell counts, inflammatory indicators (erythrocyte sedimentation rate, C-reactive protein, serum ferritin, and lactate dehydrogenase), immunological indicators (interferon gamma release assay, antinuclear antibody, and anti-neutrophil cytoplasm antibody), specific signs (weight loss, rash, and splenomegaly), and age. In the testing cohort, the AUCs of the infection prediction model, the malignancy diagnostic model, and the noninfectious inflammatory disease prediction model were 0.89 (95% CI 0.86–0.92), 0.94 (95% CI 0.92–0.97), and 0.95 (95% CI 0.93–0.97), respectively. The corresponding AUCs for the validation cohort were 0.88 (95% CI 0.82–0.93), 0.93 (95% CI 0.89–0.98), and 0.95 (95% CI 0.92–0.99), respectively. Conclusions 18F-FDG PET/CT has a certain level of sensitivity and accuracy in diagnosing FUO, which can be further improved by combining it with clinical parameters. Diagnostic models based on PET/CT show excellent performance and can be used as reliable tools to discriminate the cause of FUO. Trial registration This study (a two-step method apparently improved the physicians’ level of diagnosis decision-making for adult patients with FUO) was registered on the website http://www.clinical-trials.gov on January 14, 2014, with registration number NCT02035670.

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Application of Machine Learning for the Prediction of Etiological Types of Classic Fever of Unknown Origin

Cited by 6 publications

References 46 publications

A score-based method of immune status evaluation for healthy individuals with complete blood cell counts

A score-based method of immune status evaluation for healthy individuals with complete blood cell counts

A Pathological Diagnosis Method for FUO based on Multi-Path Hierarchical Classification: Model Design and Validation (Preprint)

Diagnostic models for fever of unknown origin based on 18F-FDG PET/CT: a prospective study in China

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