Fuzzy naive bayesian model for medical diagnostic decision support

Abstract Background Computer-based medical diagnostic decision support systems have been used for decades, initially as stand-alone applications. More recent versions have been tested for their effectiveness in enhancing the diagnostic ability of clinicians. Objective To determine if viewing a rank-ordered list of diagnostic possibilities from a medical diagnostic decision support system improves residents' differential diagnoses or management plans. Method Twenty first-year internal medicine residents at Massachusetts General Hospital viewed 3 deidentified case descriptions of real patients. All residents completed a web-based questionnaire, entering the differential diagnosis and management plan before and after seeing the diagnostic decision support system's suggested list of diseases. In all 3 exercises, the actual case diagnosis was first on the system's list. Each resident served as his or her own control (pretest/posttest). Results For all 3 cases, a substantial percentage of residents changed their primary considered diagnosis after reviewing the system's suggested diagnoses, and a number of residents who had not initially listed a “further action” (laboratory test, imaging study, or referral) added or changed their management options after using the system. Many residents (20% to 65% depending on the case) improved their differential diagnosis from before to after viewing the system's suggestions. The average time to complete all 3 cases was 15.4 minutes. Most residents thought that viewing the medical diagnostic decision support system's list of suggestions was helpful. Conclusion Viewing a rank-ordered list of diagnostic possibilities from a diagnostic decision support tool had a significant beneficial effect on the quality of first-year medicine residents' differential diagnoses and management plans.

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Sentiment analysis of comments of wooden furniture based on naive Bayesian model

Progress in Artificial Intelligence ◽

10.1007/s13748-020-00221-3 ◽

2020 ◽

Author(s):

Jie Ji ◽

Haiyan Wang ◽

Shasha Song ◽

Jingwen Pi

Keyword(s):

Sentiment Analysis ◽

Bayesian Model ◽

Naive Bayesian ◽

Naïve Bayesian ◽

Wooden Furniture

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Crash Risk Assessment of Off-Ramps, Based on the Gaussian Mixture Model Using Video Trajectories

Sustainability ◽

10.3390/su12083076 ◽

2020 ◽

Vol 12 (8) ◽

pp. 3076

Author(s):

Ting Xu ◽

Yanjun Hao ◽

Shichao Cui ◽

Xingqi Wu ◽

Zhishun Zhang ◽

...

Keyword(s):

Logistic Regression ◽

Regression Model ◽

Bayesian Model ◽

Logistic Regression Model ◽

Gaussian Mixture ◽

Ordinal Logistic Regression ◽

Crash Risk ◽

Naive Bayesian ◽

Naïve Bayesian ◽

Ordinal Logistic Regression Model

The focus of this paper is the crash risk assessment of off-ramps in Xi’an. The time-to-collision (TTC) is used for the measurement and cross-comparison of the crash risk of each location. Five sites from the urban expressway in Xi’an were selected to explore the TTC distribution. An unmanned aerial vehicle and a camera were used to collect traffic flow data for 20 min at each site. The parameters, including speed, deceleration rate, truck percentage, traffic volume, and vehicle trajectories, were extracted from video images. The TTCs were calculated for each vehicle. The Gaussian mixture model (GMM) was proposed to predict the TTC probability density functions (PDFs) and cumulative density functions (CDFs) for five sites. The Kolmogorov–Smirnov (K-S) test indicated that the samples followed the estimated GMM distribution. The relationship between the crash risk level and influencing factors was studied by an ordinal logistic regression model and a naive Bayesian model. The results showed that the naive Bayesian model had an accuracy of 86.71%, while the ordinal logistic regression model had an accuracy of 84.81%. The naive Bayesian model outperformed the ordinal logistic regression model, and it could be applied to the real-time collision warning system.

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