Explainable Artificial Intelligence (XAI) Model for the Diagnosis of Urinary Tract Infections in Emergency Care Patients
Bibliographic record
Abstract
Significance of machine learning (ML), deep learning (DL) techniques and the availability of Electronic Health Records (EHR) has motivated the need of automated diagnosis system. Furthermore, this development has transformed the health care systems. Recently, several ML and DL models has been proposed for various diseases and has shown the significant outcomes as well. Unfortunately, Urinary tract infections (UTI) is among the minor diseases that is not investigated a lot interms of diagnosing using computation intelligence techniques. However, these models lack the reliability due to the black box nature of the highly complex logic model. Therefore, we attempt to develop an interpretable deep learning (DL) model for the diagnosis of UTI using the dataset of emergency department (ED) patients from UK. Several sets of experiments were conducted using complete dataset, reduced attribute set identified using recursive feature elimination (RFE) and using the attributes identified by the baseline study. The proposed DL model has improved the baseline study accuracy from 0.875 to 0.9275 for 184 feature and 0.859 to 0.943 for the reduced feature. Furthermore, the model has outperformed interms of sensitivity and specificity as well. Due to the data imbalance positive predicted value (PPV), negative predicted value (NPV) and Youden Index was also used for evaluating the performance of the model. The proposed DL model has achieved the highest outcome using 18 attributes selected with RFE technique. The proposed model will produce reliability in the diagnosis made by the model and provide confidence to the doctors to adopt the system in the real life.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".