scholarly journals Detecting Critical Conceptual Mistakes in Google Translated Medical Information on Infectious Diseases: using Bayesian Machine Learning Classifiers (Preprint)

10.2196/31743 ◽  
2021 ◽  
Author(s):  
Wenxiu Xie ◽  
Meng Ji ◽  
Tianyong Hao ◽  
Chi-Yin Chow
Keyword(s):  
Machine Learning ◽  
Infectious Diseases ◽  
Medical Information ◽  
Machine Learning Classifiers ◽  
Learning Classifiers ◽  
Bayesian Machine Learning

Detecting Critical Conceptual Mistakes in Google Translated Medical Information on Infectious Diseases: using Bayesian Machine Learning Classifiers (Preprint)

2021 ◽  
Author(s):  
Wenxiu Xie ◽  
Meng Ji ◽  
Tianyong Hao ◽  
Chi-Yin Chow
Keyword(s):  
Machine Learning ◽  
Health Education ◽  
Infectious Diseases ◽  
Machine Translation ◽  
Semantic Features ◽  
Machine Learning Classifiers ◽  
Combined Features ◽  
Sensitivity Specificity ◽  
Translation Errors ◽  
Bayesian Machine Learning

UNSTRUCTURED Objective: To determine the linguistic/textual features of English health educational materials for predicting the probabilistic distribution of critical conceptual mistakes in neural machine translations (Google Translate: English to Chinese) of public-oriented online health resources on infectious diseases and viruses. Methods: We collected 200 English source texts on infectious diseases and their human translations to Chinese from HON. Net certified health education websites. Human translations were compared with machine translations (Google Translate) by native Chinese speakers to identify critical conceptual mistakes. To overcome overfitting issues of machine learning with small, high-dimensional datasets, Bayesian machine learning classifiers (relevance vector machine, RVM) was trained (70% and 30% train/test data split; 5-fold cross-validation) on English source texts classified as linked or not with machine translation outputs containing critical conceptual mistakes, to identify possible source text features causing clinically significant machine translation errors. We compared the performance of RVM with the combined features through separate optimization (CFSO: 21), to RVM trained on the original combined features (OCF: 135) (20 structural; 115 semantic features), combined features through joint optimization (CFJO: 48); optimized structural features (OTF: 5), and optimized semantic features (OSF: 16). In addition, RVM (CFSO) was compared to classifiers using individual standard (currently available) parameters to measure English complexity (Flesch Reading Ease FRE; Gunning Fog Index - GFI; SMOG Readability Index-SMOG). Results: The AUC, sensitivity, specificity and accuracy of RVM MLCs trained on different features sets were: CFSO (AUC: 0.685; sensitivity: 0.73, specificity: 0.63; accuracy: 0.68); OCF (AUC: 0.7; sensitivity: 0.42, specificity: 0.8; accuracy: 0.625); CFJO (AUC: 0.690; sensitivity: 0.54, specificity: 0.73; accuracy: 0.64); OTF (AUC: 0.587; sensitivity: 0.58, specificity: 0.53; accuracy: 0.55); OSF (AUC: 0.679; sensitivity: 0.58, specificity: 0.67; accuracy: 0.625). The best-performing model was RVM trained on the combined features through separate optimisation (CFSO) (16% of the original combined features). RVM (CFSO) outperformed binary classifiers (BCs) using standard English readability tests. The accuracy, sensitivity, specificity of the three BCs were FRE (accuracy 0.457; sensitivity 0.903, specificity 0.011); GFI (accuracy 0.5735; sensitivity 0.685, specificity 0.462); SMOG (accuracy 0.568; sensitivity 0.674, specificity 0.462). Conclusion: Our study found that machine-generated Chinese medical translation errors were not caused by difficult medical jargon or a lack of readability of source language information. It was certain English structures (passive voices; sentences starting with conjunctions), semantic polysemy (different meanings of a word when used in common versus specialized domains) which tend to cause critical conceptual mistakes in neural machine translation systems (English to Chinese) of health education information on infectious diseases.

Predicting Infectious Diseases by Using Machine Learning Classifiers

2020 ◽  
pp. 590-599
Author(s):  
Juan A. Gómez-Pulido ◽  
José M. Romero-Muelas ◽  
José M. Gómez-Pulido ◽  
José L. Castillo Sequera ◽  
José Sanz Moreno ◽  
...  
Keyword(s):  
Machine Learning ◽  
Infectious Diseases ◽  
Machine Learning Classifiers ◽  
Learning Classifiers

scholarly journals Evaluating Information-Retrieval Models and Machine-Learning Classifiers for Measuring the Social Perception towards Infectious Diseases

2019 ◽  
Vol 9 (14) ◽  
pp. 2858 ◽  
Author(s):  
Oscar Apolinardo-Arzube ◽  
José Antonio García-Díaz ◽  
José Medina-Moreira ◽  
Harry Luna-Aveiga ◽  
Rafael Valencia-García
Keyword(s):  
Machine Learning ◽  
Infectious Diseases ◽  
Research Field ◽  
Online Information ◽  
Surveillance Systems ◽  
Retrieval Models ◽  
Machine Learning Classifiers ◽  
Detection Systems ◽  
Learning Classifiers ◽  
The One

Recent outbreaks of infectious diseases remind us the importance of early-detection systems improvement. Infodemiology is a novel research field that analyzes online information regarding public health that aims to complement traditional surveillance methods. However, the large volume of information requires the development of algorithms that handle natural language efficiently. In the bibliography, it is possible to find different techniques to carry out these infodemiology studies. However, as far as our knowledge, there are no comprehensive studies that compare the accuracy of these techniques. Consequently, we conducted an infodemiology-based study to extract positive or negative utterances related to infectious diseases so that future syndromic surveillance systems can be improved. The contribution of this paper is two-fold. On the one hand, we use Twitter to compile and label a balanced corpus of infectious diseases with 6164 utterances written in Spanish and collected from Central America. On the other hand, we compare two statistical-models: word-grams and char-grams. The experimentation involved the analysis of different gram sizes, different partitions of the corpus, and two machine-learning classifiers: Random-Forest and Sequential Minimal Optimization. The results reach a 90.80% of accuracy applying the char-grams model with five-char-gram sequences. As a final contribution, the compiled corpus is released.

Bayesian Machine Learning Classifiers for Combining Structural and Functional Measurements to Classify Healthy and Glaucomatous Eyes

2008 ◽  
Vol 49 (3) ◽  
pp. 945 ◽  
Author(s):  
Christopher Bowd ◽  
Jiucang Hao ◽  
Ivan M. Tavares ◽  
Felipe A. Medeiros ◽  
Linda M. Zangwill ◽  
...  
Keyword(s):  
Machine Learning ◽  
Machine Learning Classifiers ◽  
Learning Classifiers ◽  
Bayesian Machine Learning
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