Abstract
Artificial intelligence (AI)-aided general clinical diagnosis is helpful to primary clinicians. Machine learning approaches have problems of generalization, interpretability, etc. Dynamic Uncertain Causality Graph (DUCG) based on uncertain casual knowledge provided by clinical experts does not have these problems. This paper extends DUCG to include the representation and inference algorithm for non-causal classification relationships. As a part of general clinical diagnoses, six knowledge bases corresponding to six chief complaints (arthralgia, dyspnea, cough and expectoration, epistaxis, fever with rash and abdominal pain) were constructed through constructing subgraphs relevant to a chief complaint separately and synthesizing them together as the knowledge base of the chief complaint. A subgraph represents variables and causalities related to a single disease that may cause the chief complaint, regardless of which hospital department the disease belongs to. Verified by two groups of third-party hospitals independently, total diagnostic precisions of the six knowledge bases ranged in 96.5–100%, in which the precision for every disease was no less than 80%.
Highlights
Artificial intelligence (AI)-aided clinical diagnosis can help clinicians working at primary hospitals and clinics to avoid or reduce misdiagnoses and missing diagnoses
The ML models based on processed big data are well known, e.g., convolutional neural network (CNN), deep neural network (DNN), recurrent neural network (RNN) and Bayesian network (BN) (Fukushima and Neocognitron 1982; Lo et al 1995; Russakovsky et al 2015; Szegedy et al 2015; Brosch et al 2016; Shin et al 2016; Duraisamy and Emperumal 2017; Bardou et al 2018; Christodoulidis et al 2017; Lin et al 2018; Er et al 2016; Ceccon et al 2014), etc
It is reasonable to doubt the generalization ability of the two models described in Wu et al (2018) and (Liang et al 2019), because the essence of deep learning is to establish a nonlinear mapping between the input and output by adjusting the structure and parameters of the neural network
Summary
AI-aided clinical diagnosis can help clinicians working at primary hospitals and clinics to avoid or reduce misdiagnoses and missing diagnoses. References (Wu et al 2018) and (Liang et al 2019) report two deep learning models that can perform general clinical diagnoses. It is not clear whether or not they have the same precisions when being applied in different application scenarios as being achieved in the testing dataset, which is called the generalization problem, some comparisons between the models and clinicians have been made. When the actual application scenario is different from the dataset in terms of sample space, which is common, the precision may drop, leading to the generalization problem.
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