A Pathological Diagnosis Method for Fever of Unknown Origin Based on Multipath Hierarchical Classification: Model Design and Validation
Bibliographic record
Abstract
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: The study aims to design a multipath hierarchical classification algorithm to diagnose FUO due to the hierarchical structure of the etiology of FUO. In addition, to improve the diagnostic performance of the model, a mechanism for feature selection is added to the model. Methods: The case data of patients with FUO admitted to the First Affiliated Hospital of Xi'an Jiaotong University between 2011 and 2020 in China were used as the dataset for model training and validation. The hierarchical structure tree was then characterized according to etiology. The structure included 3 layers, with the top layer representing the FUO, the middle layer dividing the FUO into 5 categories of etiology (bacterial infection, viral infection, other infection, autoimmune diseases, and other noninfection), and the last layer further refining them to 16 etiologies. Finally, ablation experiments were set to determine the optimal structure of the proposed method, and comparison experiments were to verify the diagnostic performance. Results: According to ablation experiments, the model achieved the best performance with an accuracy of 76.08% when the number of middle paths was 3%, and 25% of the features were selected. According to comparison experiments, the proposed model outperformed the comparison methods, both from the perspective of feature selection methods and hierarchical classification methods. Specifically, brucellosis had an accuracy of 100%, and liver abscess, viral infection, and lymphoma all had an accuracy of more than 80%. Conclusions: In this study, a novel multipath feature selection and hierarchical classification model was designed for the diagnosis of FUO and was adequately evaluated quantitatively. Despite some limitations, this model enriches the exploration of FUO in machine learning and assists physicians in their work.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".