Abstract
Many AI systems have been developed for clinical diagnoses, in which most of them lack interpretability in both knowledge representation and inference results. The newly developed Dynamic Uncertain Causality Graph (DUCG) is a probabilistic graphical model with strong interpretability. However, existing DUCG is mainly for fault diagnoses of large, complex industrial systems. In this paper, we extend DUCG for better application in general clinical diagnoses. Four extensions are introduced: (1) special logic gate and zoom function event variables to represent and quantify the influences of various risk factors on the morbidities of diseases. (2) Reversal logic gate to model the case that some diseases/causes may result in at least two simultaneous symptoms/consequences. (3) Disease-specific manifestation variable for special inference and easy understanding to diagnose a specific disease. (4) Event attention importance to count contributions of isolated state-abnormal variables in inference. To illustrate and verify the extended DUCG methodology, we performed a case study for diagnosing 25 diseases causing nasal obstruction. We tested 171 cases randomly selected from total 471 cases of discharged patients in the hospital information system of Xuanwu Hospital. The diagnosis precision of the extended DUCG was 100%. The diagnosis precision of the third-party verification performed by Suining Central Hospital was 98.86%, which exhibited the strong generalization ability of the extended DUCG.
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See Zhang (2012) for details.
Assumptions of DUCG are indexed in series in the DUCG papers.
The rules are indexed in series in the DUCG papers.
The rules are indexed in series in the DUCG papers.
Corollary 15: \(A_{{nk_{n} ;i}} V_{i} A_{{mk_{m} ;i}} V_{i} = \left( {A_{{nk_{n} ;i}} *A_{{mk_{m} ;i}} } \right)V_{i}\), in which
\(\left( {A_{{nk_{n} ;i}} *A_{{mk_{m} ;i}} } \right) \equiv \left( {\begin{array}{*{20}c} {A_{{nk_{n} ;i1}} A_{{mk_{m} ;i1}} } & {A_{{nk_{n} ;i2}} A_{{mk_{m} ;i2}} } & { \ldots } & {A_{{nk_{n} ;ij}} A_{{mk_{m} ;ij}} } & { \ldots } & {A_{{nk_{n} ;iJ}} A_{{mk_{m} ;iJ}} } \\ \end{array} } \right)\)
Correspondingly, \(a_{{nk_{n} ;i}} *a_{{mk_{m} ;i}} \equiv \left( {\begin{array}{*{20}c} {a_{{nk_{n} ;i1}} a_{{mk_{m} ;i1}} } & {a_{{nk_{n} ;i2}} a_{{mk_{m} ;i2}} } & { \ldots } & {a_{{nk_{n} ;ij}} a_{{mk_{m} ;ij}} } & { \ldots } & {a_{{nk_{n} ;iJ}} a_{{mk_{m} ;iJ}} } \\ \end{array} } \right)\)
where, “*” is an AND/multiplication matrix operator specially defined in DUCG. In format, the “*” operator is similar to Hadamard product.
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This research was fully supported by Beijing Tsingrui Intelligence Technology Co., Ltd.
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Appendix
Appendix
Rules 1-10 presented in Zhang (2012) and Rule 16 presented in Zhang (2015a, b), Zhang and Geng (2015), Zhang and Zhang (2015):
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Rule 1: “If E shows that Zn;i is not met, Fn;i or Pn;i is eliminated from the DUCG. If E shows that Zn;i is met, the conditional Fn;i or Pn;i becomes the ordinary Fn;i or Pn;i.”
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Rule 2: “If E shows that Vij,V∈{B, X}, is true while Vij is not a parent event of Xn, Fn;i or Pn;i is eliminated from the DUCG.”
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Rule 3: “If E shows that Xnk is true while Xnk cannot be caused by any states of Vij, V∈{B, X, G}, Fn;i or Pn;i is eliminated from the DUCG, except that Vi is included in a hypothesis, or is a descendant of an event included in a hypothesis, and the causality chain between them is not blocked by an known event.”
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Rule 4: “If Ε shows that Xnk and Vij, V∈{B, X}, are true while Xnk cannot be caused by Vij, Fn;i or Pn;i is eliminated from the DUCG.”
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Rule 5: “If the state unknown Xn without input variable or Gn without input variable is encountered, Xn or Gn and its output directed arcs are eliminated from the DUCG.”
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Rule 6: “If Gi without any output is encountered for any reason, Gi is eliminated from the DUCG.”
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Rule 7: “If 1) the state of Xn is unknown, 2) Xn does not have any output, and 3) Xn is not predetermined in concern, Xn and all its input directed arcs are eliminated from the DUCG.”
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Rule 8: “If E shows that Xnk and Vij, V∈{B, X}, are true and Xnk appears earlier than Vij, which means that Vij cannot be the cause of Xnk, the F or P type variables (they are the members of the causality chain from Vij to Xnk and are not related to any other upstream causality chain of Xnk) are eliminated from the DUCG.”
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Rule 9: “If there is such a group of variables (named as the independent group) that have no causal connection with those variables related to E, and no variable in this group is predetermined in concern, this independent group of variables can be eliminated from the DUCG.”
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Rule 10: “If E shows Xnk is true while Xnk does not have any input due to any reason, add a virtual parent event Dn to Xnk with ank;nD = 1 and ank’;nD = 0, k ≠ k’. rn;D can be any value. The added virtual Dn can be drawn as
in the simplified graph.” -
Rule 16: “If (a) E indicates a group of normal state events Xnη,where n∈SI and SI denotes the index set of the variables of this group, (b) Xnη, n∈SI, have no output to other variables, (c) Xnη, n∈SI, are connected with none or a group of state unknown {B-, X-, G-, D-, F-, P-}-type variables given that this group of state unknown variables are isolated by Xnη, n∈SI, and (d) this isolated group of variables are not predetermined in concern, then this isolated group of variables and Xnη, n∈SI, are eliminated from the DUCG, except Xnη that is the descendant of the hypothesis in concern without events in E to block the connection between them.” In this paper, η = 0.
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Zhang, Q., Bu, X., Zhang, M. et al. Dynamic uncertain causality graph for computer-aided general clinical diagnoses with nasal obstruction as an illustration. Artif Intell Rev 54, 27–61 (2021). https://doi.org/10.1007/s10462-020-09871-0
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DOI: https://doi.org/10.1007/s10462-020-09871-0