Development of an artificial intelligence diagnostic model based on dynamic uncertain causality graph for the differential diagnosis of dyspnea

doi:10.1007/s11684-020-0762-0

Frontiers of Medicine

2020, Vol. 14

Issue (4): 488-497 https://doi.org/10.1007/s11684-020-0762-0

本期目录

Development of an artificial intelligence diagnostic model based on dynamic uncertain causality graph for the differential diagnosis of dyspnea

Yang Jiao¹(

), Zhan Zhang², Ting Zhang³, Wen Shi⁴, Yan Zhu⁵, Jie Hu⁶, Qin Zhang⁷(

)

¹. Department of General Internal Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China
². Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing 100084, China
³. Department of Pulmonary and Critical Care Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China
⁴. Department of Gastroenterology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China
⁵. Institute of Internet Industry, Tsinghua University, Beijing 100084, China
⁶. Department of Medical Administration, Suining Central Hospital, Suining 629000, China
⁷. Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China

全文: PDF(1800 KB) HTML

Abstract：

Dyspnea is one of the most common manifestations of patients with pulmonary disease, myocardial dysfunction, and neuromuscular disorder, among other conditions. Identifying the causes of dyspnea in clinical practice, especially for the general practitioner, remains a challenge. This pilot study aimed to develop a computer-aided tool for improving the efficiency of differential diagnosis. The disease set with dyspnea as the chief complaint was established on the basis of clinical experience and epidemiological data. Differential diagnosis approaches were established and optimized by clinical experts. The artificial intelligence (AI) diagnosis model was constructed according to the dynamic uncertain causality graph knowledge-based editor. Twenty-eight diseases and syndromes were included in the disease set. The model contained 132 variables of symptoms, signs, and serological and imaging parameters. Medical records from the electronic hospital records of Suining Central Hospital were randomly selected. A total of 202 discharged patients with dyspnea as the chief complaint were included for verification, in which the diagnoses of 195 cases were coincident with the record certified as correct. The overall diagnostic accuracy rate of the model was 96.5%. In conclusion, the diagnostic accuracy of the AI model is promising and may compensate for the limitation of medical experience.

Key words： knowledge representation uncertain causality graphical model artificial intelligence diagnosis dyspnea

收稿日期: 2019-08-08 出版日期: 2020-08-26

Corresponding Author(s): Yang Jiao,Qin Zhang

引用本文:

. [J]. Frontiers of Medicine, 2020, 14(4): 488-497.
Yang Jiao, Zhan Zhang, Ting Zhang, Wen Shi, Yan Zhu, Jie Hu, Qin Zhang. Development of an artificial intelligence diagnostic model based on dynamic uncertain causality graph for the differential diagnosis of dyspnea. Front. Med., 2020, 14(4): 488-497.

链接本文:

https://academic.hep.com.cn/fmd/CN/10.1007/s11684-020-0762-0
https://academic.hep.com.cn/fmd/CN/Y2020/V14/I4/488

Tab.1

Fig.1

Tab.2

Fig.2

Fig.3

Tab.3

Fig.4

Fig.5

Fig.6

1	MB Parshall, RM Schwartzstein, L Adams, RB Banzett, HL Manning, J Bourbeau, PM Calverley, AG Gift, A Harver, SC Lareau, DA Mahler, PM Meek, DE O’Donnell; American Thoracic Society Committee on Dyspnea. An official American Thoracic Society statement: update on the mechanisms, assessment, and management of dyspnea. Am J Respir Crit Care Med 2012; 185(4): 435–452 https://doi.org/10.1164/rccm.201111-2042ST pmid: 22336677
2	GR Pesola, V Terla, N Malik, H Ahsan. Chronic dyspnoea: finding the cause to reduce mortality. J Thorac Dis 2018; 10(Suppl 33): S4057–S4060 https://doi.org/10.21037/jtd.2018.09.60 pmid: 30631554
3	B Ehteshami Bejnordi, M Veta, P Johannes van Diest, B van Ginneken, N Karssemeijer, G Litjens, JAWM van der Laak; the CAMELYON16 Consortium, M Hermsen, QF Manson, M Balkenhol, O Geessink, N Stathonikos, MC van Dijk, P Bult, F Beca, AH Beck, D Wang, A Khosla, R Gargeya, H Irshad, A Zhong, Q Dou, Q Li, H Chen, HJ Lin, PA Heng, C Haß, E Bruni, Q Wong, U Halici, MÜ Öner, R Cetin-Atalay, M Berseth, V Khvatkov, A Vylegzhanin, O Kraus, M Shaban, N Rajpoot, R Awan, K Sirinukunwattana, T Qaiser, YW Tsang, D Tellez, J Annuscheit, P Hufnagl, M Valkonen, K Kartasalo, L Latonen, P Ruusuvuori, K Liimatainen, S Albarqouni, B Mungal, A George, S Demirci, N Navab, S Watanabe, S Seno, Y Takenaka, H Matsuda, H Ahmady Phoulady, V Kovalev, A Kalinovsky, V Liauchuk, G Bueno, MM Fernandez-Carrobles, I Serrano, O Deniz, D Racoceanu, R Venâncio. Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer. JAMA 2017; 318(22): 2199–2210 https://doi.org/10.1001/jama.2017.14585 pmid: 29234806
4	MR Daliri. A hybrid automatic system for the diagnosis of lung cancer based on genetic algorithm and fuzzy extreme learning machines. J Med Syst 2012; 36(2): 1001–1005 https://doi.org/10.1007/s10916-011-9806-y pmid: 22113438
5	D Salas-Gonzalez, JM Górriz, J Ramírez, M López, I Alvarez, F Segovia, R Chaves, CG Puntonet. Computer-aided diagnosis of Alzheimer’s disease using support vector machines and classification trees. Phys Med Biol 2010; 55(10): 2807–2817 https://doi.org/10.1088/0031-9155/55/10/002 pmid: 20413829
6	Q Zhang. Dynamic uncertain causality graph for knowledge representation and probabilistic reasoning: directed cyclic graph and joint probability distribution. IEEE Trans Neural Netw Learn Syst 2015; 26(7): 1503–1517 https://doi.org/10.1109/TNNLS.2015.2402162 pmid: 25781960
7	Q Zhang, Q Yao. Dynamic uncertain causality graph for knowledge representation and reasoning: utilization of statistical data and domain knowledge in complex cases. IEEE Trans Neural Netw Learn Syst 2018; 29(5): 1637–1651 https://doi.org/10.1109/TNNLS.2017.2673243 pmid: 28328514
8	C Dong, Y Wang, Q Zhang, N Wang. The methodology of dynamic uncertain causality graph for intelligent diagnosis of vertigo. Comput Methods Programs Biomed 2014; 113(1): 162–174 https://doi.org/10.1016/j.cmpb.2013.10.002 pmid: 24176413
9	SR Hao, SC Geng, LX Fan, JJ Chen, Q Zhang, LJ Li. Intelligent diagnosis of jaundice with dynamic uncertain causality graph model. J Zhejiang Univ Sci B 2017; 18(5): 393–401 https://doi.org/10.1631/jzus.B1600273 pmid: 28471111
10	Q Zhang. Dynamic uncertain causality graph for knowledge representation and reasoning: discrete DAG cases. J Comput Sci Technol 2012; 27(1): 1–23
11	Q Zhang, CL Dong, Y Cui, ZH Yang. Dynamic uncertain causality graph for knowledge representation and probabilistic reasoning: statistics base, matrix and fault diagnosis. IEEE Trans Neural Netw Learn Syst 2014; 25(4): 645–663
12	Q Zhang. Dynamic uncertain causality graph for knowledge representation and probabilistic reasoning: directed cyclic graph and joint probability distribution. IEEE Trans Neural Netw Learn Syst 2015; 26(7): 1503–1517
13	C Dong, Y Zhao, Q Zhang. Assessing the influence of an individual event in complex fault spreading network based on dynamic uncertain causality graph. IEEE Trans Neural Netw Learn Syst 2016; 27(8): 1615–1630 https://doi.org/10.1109/TNNLS.2016.2547339 pmid: 27101619
14	S Ceccon, DF Garway-Heath, DP Crabb, A Tucker. Exploring early glaucoma and the visual field test: classification and clustering using Bayesian networks. IEEE J Biomed Health Inform 2014; 18(3): 1008–1014 https://doi.org/10.1109/JBHI.2013.2289367 pmid: 24808230
15	RH Lin, CL Chuang. A hybrid diagnosis model for determining the types of the liver disease. Comput Biol Med 2010; 40(7): 665–670 https://doi.org/10.1016/j.compbiomed.2010.06.002 pmid: 20591425

Viewed

Full text

Abstract

Cited

Shared

Discussed