Abstract
With the rapid development of AI techniques, Computer-aided Diagnosis has attracted much attention and has been successfully deployed in many applications of health care and medical diagnosis. For some specific tasks, the learning-based system can compare with or even outperform human experts' performance. The impressive performance owes to the excellent expressiveness and scalability of the neural networks, although the models' intuition usually cannot be represented explicitly. Interpretability is, however, very important, even the same as the diagnosis precision, for computer-aided diagnosis. To fill this gap, our approach is intuitive to detect pneumonia interpretably. We first build a large dataset of community-acquired pneumonia consisting of 35389 cases (distinguished from nosocomial pneumonia) based on actual medical records. Second, we train a prediction model with the chest X-ray images in our dataset, capable of precisely detecting pneumonia. Third, we propose an intuitive approach to combine neural networks with an explainable model such as the Bayesian Network. The experiment result shows that our proposal further improves the performance by using multi-source data and provides intuitive explanations for the diagnosis results.
Highlights
Pneumonia is a respiratory infection caused by bacteria, viruses, or fungi, and it has been known as a quite common and potentially fatal disease in the past two centuries
3) We propose an approach to combine medical knowledge with a Bayesian Network, which constructs a reasonable Bayesian Network structure and improves pneumonia detection’s interpretability
We mainly focus on CAP, which is acquired in the community; we merely selected pneumonia cases from the respiratory medicine department and pediatric department
Summary
Pneumonia is a respiratory infection caused by bacteria, viruses, or fungi, and it has been known as a quite common and potentially fatal disease in the past two centuries. Real diagnosis procedure, a human physician uses not merely these images, and some observable clinical features as criteria. Symptoms such as fever, cough, and chest pain are very crucial to detect the disease. 2) We propose an intuitive method to integrate multisource data such as chest X-ray images and clinical reports in natural language to predict pneumonia 3) We propose an approach to combine medical knowledge with a Bayesian Network, which constructs a reasonable Bayesian Network structure and improves pneumonia detection’s interpretability (section V). We believe that our proposal is general enough to be used in other prediction models by fine-tuning, and is straightforward to be extended by using other explainable models such as Situation Calculus, Nonmonotonic Logics, Latent Trees, etc
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