scholarly journals Supporting Risk-Aware Use of Online Translation Tools in Delivering Mental Healthcare Services among Spanish-Speaking Populations

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Wenxiu Xie ◽  
Meng Ji ◽  
Mengdan Zhao ◽  
Xiaobo Qian ◽  
Chi-Yin Chow ◽  
...  
Keyword(s):  
Machine Learning ◽  
Machine Translation ◽  
Mental Healthcare ◽  
Healthcare Services ◽  
Likelihood Ratios ◽  
Spanish Speaking ◽  
Healthcare Settings ◽  
Learning Classifier ◽  
Translation Tools ◽  
Bayesian Machine Learning

Neural machine translation technologies are having increasing applications in clinical and healthcare settings. In multicultural countries, automatic translation tools provide critical support to medical and health professionals in their interaction and exchange of health messages with migrant patients with limited or non-English proficiency. While research has mainly explored the usability and limitations of state-of-the-art machine translation tools in the detection and diagnosis of physical diseases and conditions, there is a persistent lack of evidence-based studies on the applicability of machine translation tools in the delivery of mental healthcare services for vulnerable populations. Our study developed Bayesian machine learning algorithms using relevance vector machine to support frontline health workers and medical professionals to make better informed decisions between risks and convenience of using online translation tools when delivering mental healthcare services to Spanish-speaking minority populations living in English-speaking countries. Major strengths of the machine learning classifier that we developed include scalability, interpretability, and adaptability of the classifier for diverse mental healthcare settings. In this paper, we report on the process of the Bayesian machine learning classifier development through automatic feature optimisation and the interpretation of the classifier-enabled assessment of the suitability of original English mental health information for automatic online translation. We elaborate on the interpretation of the assessment results in clinical settings using statistical tools such as positive likelihood ratios and negative likelihood ratios.

scholarly journals Recursive Machine Translation

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Joshua Evans
Keyword(s):  
Machine Learning ◽  
Machine Translation ◽  
Literary Culture ◽  
Literary Texts ◽  
Literary Value ◽  
Translation Tools ◽  
Methodological Reflection

Machine translation tools such as Google Translate are at best seen as useful approximators, rather than offering any literary potential. In this experiment and short methodological reflection, I use Google Translate to recursively translate Austrian poet Georg Trakl’s celebrated WWI poem, ‘Grodek’, between German and English, until the two versions stabilise. I am attentive to places in which the poem and its renderings are simplified and/or literary value may be lost, but also places in which new or unexpected renderings emerge. This is a preliminary foray, but I propose that the method of recursive machine translation offers a new way to explore the translation of literary texts—a timely proposal, given the increasing applications of computer programmes and machine learning both within the humanities and throughout wider literary culture.

scholarly journals Forecasting the Suitability of Online Mental Health Information for Effective Self-Care Developing Machine Learning Classifiers Using Natural Language Features

2021 ◽  
Vol 18 (19) ◽  
pp. 10048
Author(s):  
Meng Ji ◽  
Wenxiu Xie ◽  
Riliu Huang ◽  
Xiaobo Qian
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Health Information ◽  
Self Care ◽  
Health Resources ◽  
Health Issues ◽  
Mental Health Information ◽  
Learning Classifier ◽  
Mental Health Issues ◽  
Bayesian Machine Learning

Background: Online mental health information represents important resources for people living with mental health issues. Suitability of mental health information for effective self-care remains understudied, despite the increasing needs for more actionable mental health resources, especially among young people. Objective: We aimed to develop Bayesian machine learning classifiers as data-based decision aids for the assessment of the actionability of credible mental health information for people with mental health issues and diseases. Methods: We collected and classified creditable online health information on mental health issues into generic mental health (GEN) information and patient-specific (PAS) mental health information. GEN and PAS were both patient-oriented health resources developed by health authorities of mental health and public health promotion. GENs were non-classified online health information without indication of targeted readerships; PASs were developed purposefully for specific populations (young, elderly people, pregnant women, and men) as indicated by their website labels. To ensure the generalisability of our model, we chose to develop a sparse Bayesian machine learning classifier using Relevance Vector Machine (RVM). Results: Using optimisation and normalisation techniques, we developed a best-performing classifier through joint optimisation of natural language features and min-max normalisation of feature frequencies. The AUC (0.957), sensitivity (0.900), and specificity (0.953) of the best model were statistically higher (p < 0.05) than other models using parallel optimisation of structural and semantic features with or without feature normalisation. We subsequently evaluated the diagnostic utility of our model in the clinic by comparing its positive (LR+) and negative likelihood ratios (LR−) and 95% confidence intervals (95% C.I.) as we adjusted the probability thresholds with the range of 0.1 and 0.9. We found that the best pair of LR+ (18.031, 95% C.I.: 10.992, 29.577) and LR− (0.100, 95% C.I.: 0.068, 0.148) was found when the probability threshold was set to 0.45 associated with a sensitivity of 0.905 (95%: 0.867, 0.942) and specificity of 0.950 (95% C.I.: 0.925, 0.975). These statistical properties of our model suggested its applicability in the clinic. Conclusion: Our study found that PAS had significant advantage over GEN mental health information regarding information actionability, engagement, and suitability for specific populations with distinct mental health issues. GEN is more suitable for general mental health information acquisition, whereas PAS can effectively engage patients and provide more effective and needed self-care support. The Bayesian machine learning classifier developed provided automatic tools to support decision making in the clinic to identify more actionable resources, effective to support self-care among different populations.

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.

scholarly journals Mental healthcare in Kenya: Exploring optimal conditions for capacity building

2014 ◽  
Vol 6 (1) ◽  
Author(s):  
Elijah Marangu ◽  
Natisha Sands ◽  
John Rolley ◽  
David Ndetei ◽  
Fethi Mansouri
Keyword(s):  
Mental Health ◽  
Capacity Building ◽  
Low Income ◽  
Mental Healthcare ◽  
Healthcare Services ◽  
World Health ◽  
Low Income Countries ◽  
Healthcare Settings ◽  
Global Action ◽  
Health Organization

The global burden of disease related to mental disorders is on the increase, with the World Health Organization (WHO) estimating that over 450 million people are affected worldwide. The Mental Health Global Action Program (mhGAP) was launched by the WHO in 2002 in order to address the widening gap in access to mental healthcare in low-income countries. Despite these efforts, access to mental healthcare in low-income countries remains poor and is often described as inadequate, inefficient and inequitable, with an 85% estimated treatment gap in low-income countries, as compared with 35% to 50% in high-income countries.In this article, the authors argue that integrating mental health services into primary healthcare settings through capacity building is vital with regard to achieving mhGAP goals. The article explores the challenges to and potential enablers for the improvement of the delivery of broad-based mental healthcare services in Kenya. The authors propose the integration of the conceptual dimensions of both the cosmopolitanism and capabilities approaches as a combined strategy for dealing with capacity building in heterogeneous settings such as Kenya.

scholarly journals Detecting Symptom Errors in Neural Machine Translation of Patient Health Information on Depressive Disorders: Developing Interpretable Bayesian Machine Learning Classifiers

2021 ◽  
Vol 12 ◽  
Author(s):  
Wenxiu Xie ◽  
Meng Ji ◽  
Mengdan Zhao ◽  
Tianqi Zhou ◽  
Fan Yang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Health Information ◽  
Machine Translation ◽  
Posterior Probability ◽  
Depressive Disorders ◽  
Diagnostic Utility ◽  
Neural Machine Translation ◽  
Machine Learning Classifiers ◽  
Patient Health ◽  
Bayesian Machine Learning

Background: Due to its convenience, wide availability, low usage cost, neural machine translation (NMT) has increasing applications in diverse clinical settings and web-based self-diagnosis of diseases. Given the developing nature of NMT tools, this can pose safety risks to multicultural communities with limited bilingual skills, low education, and low health literacy. Research is needed to scrutinise the reliability, credibility, usability of automatically translated patient health information.Objective: We aimed to develop high-performing Bayesian machine learning classifiers to assist clinical professionals and healthcare workers in assessing the quality and usability of NMT on depressive disorders. The tool did not require any prior knowledge from frontline health and medical professionals of the target language used by patients.Methods: We used Relevance Vector Machine (RVM) to increase generalisability and clinical interpretability of classifiers. It is a typical sparse Bayesian classifier less prone to overfitting with small training datasets. We optimised RVM by leveraging automatic recursive feature elimination and expert feature refinement from the perspective of health linguistics. We evaluated the diagnostic utility of the Bayesian classifier under different probability cut-offs in terms of sensitivity, specificity, positive and negative likelihood ratios against clinical thresholds for diagnostic tests. Finally, we illustrated interpretation of RVM tool in clinic using Bayes' nomogram.Results: After automatic and expert-based feature optimisation, the best-performing RVM classifier (RVM_DUFS12) gained the highest AUC (0.8872) among 52 competing models with distinct optimised, normalised features sets. It also had statistically higher sensitivity and specificity compared to other models. We evaluated the diagnostic utility of the best-performing model using Bayes' nomogram: it had a positive likelihood ratio (LR+) of 4.62 (95% C.I.: 2.53, 8.43), and the associated posterior probability (odds) was 83% (5.0) (95% C.I.: 73%, 90%), meaning that approximately 10 in 12 English texts with positive test are likely to contain information that would cause clinically significant conceptual errors if translated by Google; it had a negative likelihood ratio (LR-) of 0.18 (95% C.I.: 0.10,0.35) and associated posterior probability (odds) was 16% (0.2) (95% C.I: 10%, 27%), meaning that about 10 in 12 English texts with negative test can be safely translated using Google.

scholarly journals Using explainable machine learning to characterise data drift and detect emergent health risks for emergency department admissions during COVID-19

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Christopher Duckworth ◽  
Francis P. Chmiel ◽  
Dan K. Burns ◽  
Zlatko D. Zlatev ◽  
Neil M. White ◽  
...  
Keyword(s):  
Machine Learning ◽  
Emergency Department ◽  
Clinical Outcomes ◽  
Health Risks ◽  
Concept Drift ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Time Data ◽  
Healthcare Settings ◽  
Learning Classifier

AbstractA key task of emergency departments is to promptly identify patients who require hospital admission. Early identification ensures patient safety and aids organisational planning. Supervised machine learning algorithms can use data describing historical episodes to make ahead-of-time predictions of clinical outcomes. Despite this, clinical settings are dynamic environments and the underlying data distributions characterising episodes can change with time (data drift), and so can the relationship between episode characteristics and associated clinical outcomes (concept drift). Practically this means deployed algorithms must be monitored to ensure their safety. We demonstrate how explainable machine learning can be used to monitor data drift, using the COVID-19 pandemic as a severe example. We present a machine learning classifier trained using (pre-COVID-19) data, to identify patients at high risk of admission during an emergency department attendance. We then evaluate our model’s performance on attendances occurring pre-pandemic (AUROC of 0.856 with 95%CI [0.852, 0.859]) and during the COVID-19 pandemic (AUROC of 0.826 with 95%CI [0.814, 0.837]). We demonstrate two benefits of explainable machine learning (SHAP) for models deployed in healthcare settings: (1) By tracking the variation in a feature’s SHAP value relative to its global importance, a complimentary measure of data drift is found which highlights the need to retrain a predictive model. (2) By observing the relative changes in feature importance emergent health risks can be identified.

Predicting Undesired Treatment Outcome in Mental Healthcare: Machine Learning Study (Preprint)

2019 ◽  
Author(s):  
Kasper Van Mens ◽  
Joran Lokkerbol ◽  
Richard Janssen ◽  
Robert de Lange ◽  
Bea Tiemens
Keyword(s):  
Machine Learning ◽  
Treatment Outcome ◽  
Mental Health Treatment ◽  
Mental Healthcare ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
Trade Off ◽  
Trade Offs ◽  
Outcome Monitoring ◽  
Extreme Gradient Boosting

BACKGROUND It remains a challenge to predict which treatment will work for which patient in mental healthcare. OBJECTIVE In this study we compare machine algorithms to predict during treatment which patients will not benefit from brief mental health treatment and present trade-offs that must be considered before an algorithm can be used in clinical practice. METHODS Using an anonymized dataset containing routine outcome monitoring data from a mental healthcare organization in the Netherlands (n = 2,655), we applied three machine learning algorithms to predict treatment outcome. The algorithms were internally validated with cross-validation on a training sample (n = 1,860) and externally validated on an unseen test sample (n = 795). RESULTS The performance of the three algorithms did not significantly differ on the test set. With a default classification cut-off at 0.5 predicted probability, the extreme gradient boosting algorithm showed the highest positive predictive value (ppv) of 0.71(0.61 – 0.77) with a sensitivity of 0.35 (0.29 – 0.41) and area under the curve of 0.78. A trade-off can be made between ppv and sensitivity by choosing different cut-off probabilities. With a cut-off at 0.63, the ppv increased to 0.87 and the sensitivity dropped to 0.17. With a cut-off of at 0.38, the ppv decreased to 0.61 and the sensitivity increased to 0.57. CONCLUSIONS Machine learning can be used to predict treatment outcomes based on routine monitoring data.This allows practitioners to choose their own trade-off between being selective and more certain versus inclusive and less certain.

A Machine Learning Application for Raising WASH Awareness in the Times of COVID-19 Pandemic (Preprint)

2020 ◽  
Author(s):  
Rohan Pandey ◽  
Vaibhav Gautam ◽  
Ridam Pal ◽  
Harsh Bandhey ◽  
Lovedeep Singh Dhingra ◽  
...  
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Machine Translation ◽  
Language Processing ◽  
User Feedback ◽  
Who Guidelines ◽  
The Times ◽  
The Right ◽  
Local Languages

BACKGROUND The COVID-19 pandemic has uncovered the potential of digital misinformation in shaping the health of nations. The deluge of unverified information that spreads faster than the epidemic itself is an unprecedented phenomenon that has put millions of lives in danger. Mitigating this ‘Infodemic’ requires strong health messaging systems that are engaging, vernacular, scalable, effective and continuously learn the new patterns of misinformation. OBJECTIVE We created WashKaro, a multi-pronged intervention for mitigating misinformation through conversational AI, machine translation and natural language processing. WashKaro provides the right information matched against WHO guidelines through AI, and delivers it in the right format in local languages. METHODS We theorize (i) an NLP based AI engine that could continuously incorporate user feedback to improve relevance of information, (ii) bite sized audio in the local language to improve penetrance in a country with skewed gender literacy ratios, and (iii) conversational but interactive AI engagement with users towards an increased health awareness in the community. RESULTS A total of 5026 people who downloaded the app during the study window, among those 1545 were active users. Our study shows that 3.4 times more females engaged with the App in Hindi as compared to males, the relevance of AI-filtered news content doubled within 45 days of continuous machine learning, and the prudence of integrated AI chatbot “Satya” increased thus proving the usefulness of an mHealth platform to mitigate health misinformation. CONCLUSIONS We conclude that a multi-pronged machine learning application delivering vernacular bite-sized audios and conversational AI is an effective approach to mitigate health misinformation. CLINICALTRIAL Not Applicable

scholarly journals Location Detection Method of Detector in Pipeline Using VMD Algorithm and Machine Learning Classifier

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1436
Author(s):  
Tuoru Li ◽  
Senxiang Lu ◽  
Enjie Xu
Keyword(s):  
Machine Learning ◽  
Magnetic Flux ◽  
Magnetic Flux Leakage ◽  
Intrinsic Mode Functions ◽  
Detection Range ◽  
Vibration Signals ◽  
Feature Vectors ◽  
Learning Classifier ◽  
Mode Decomposition ◽  
Flux Leakage

The internal detector in a pipeline needs to use the ground marker to record the elapsed time for accurate positioning. Most existing ground markers use the magnetic flux leakage testing principle to detect whether the internal detector passes. However, this paper uses the method of detecting vibration signals to track and locate the internal detector. The Variational Mode Decomposition (VMD) algorithm is used to extract features, which solves the defect of large noise and many disturbances of vibration signals. In this way, the detection range is expanded, and some non-magnetic flux leakage internal detectors can also be located. Firstly, the extracted vibration signals are denoised by the VMD algorithm, then kurtosis value and power value are extracted from the intrinsic mode functions (IMFs) to form feature vectors, and finally the feature vectors are input into random forest and Multilayer Perceptron (MLP) for classification. Experimental research shows that the method designed in this paper, which combines VMD with a machine learning classifier, can effectively use vibration signals to locate the internal detector and has the characteristics of high accuracy and good adaptability.

scholarly journals Exploring patterns in psychiatric outpatients’ preferences for involvement in decision-making: a latent class analysis approach

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Ingunn Mundal ◽  
Mariela Loreto Lara-Cabrera ◽  
Moisés Betancort ◽  
Carlos De las Cuevas
Keyword(s):  
Decision Making ◽  
Depressive Disorder ◽  
Latent Class ◽  
Psychiatric Patients ◽  
Mental Healthcare ◽  
Psychological Reactance ◽  
Psychiatric Outpatients ◽  
Public And Private ◽  
Level Of Education ◽  
Healthcare Settings

Abstract Background Shared decision-making (SDM), a collaborative approach that includes and respects patients’ preferences for involvement in decision-making about their treatment, is increasingly advocated. However, in the practice of clinical psychiatry, implementing SDM seems difficult to accomplish. Although the number of studies related to psychiatric patients’ preferences for involvement is increasing, studies have largely focused on understanding patients in public mental healthcare settings. Thus, investigating patient preferences for involvement in both public and private settings is of particular importance in psychiatric research. The objectives of this study were to identify different latent class typologies of patient preferences for involvement in the decision-making process, and to investigate how patient characteristics predict these typologies in mental healthcare settings. Methods We conducted latent class analysis (LCA) to identify groups of psychiatric outpatients with similar preferences for involvement in decision-making to estimate the probability that each patient belonged to a certain class based on sociodemographic, clinical and health belief variables. Results The LCA included 224 consecutive psychiatric outpatients’ preferences for involvement in treatment decisions in public and private psychiatric settings. The LCA identified three distinct preference typologies, two collaborative and one passive, accounting for 78% of the variance. Class 1 (26%) included collaborative men aged 34–44 years with an average level of education who were treated by public services for a depressive disorder, had high psychological reactance, believed they controlled their disease and had a pharmacophobic attitude. Class 2 (29%) included collaborative women younger than 33 years with an average level of education, who were treated by public services for an anxiety disorder, had low psychological reactance or health control belief and had an unconcerned attitude toward medication. Class 3 (45%) included passive women older than 55 years with lower education levels who had a depressive disorder, had low psychological reactance, attributed the control of their disease to their psychiatrists and had a pharmacophilic attitude. Conclusions Our findings highlight how psychiatric patients vary in pattern of preferences for treatment involvement regarding demographic variables and health status, providing insight into understanding the pattern of preferences and comprising a significant advance in mental healthcare research.

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