Evaluation of different machine learning approaches and input text representations for multilingual classification of tweets for disease surveillance in the social web

Magumba, Mark Abraham; Nabende, Peter

doi:10.1186/s40537-021-00528-5

Cited by 5 publications

(5 citation statements)

References 14 publications

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“…These results indicate that classification machine learning models can be used to map the different predictors to their respective classes. Likewise, these capabilities have been reported in similar rather challenging scenarios like predicting viral failure (53), tweets classification for disease surveillance (59), identification of HIV predictors for screening (60), and dermatology conditions (67). Therefore, this demonstrates the potential and applicability of machine learning algorithms to provide insights in scenarios where human decision-making would be limited.…”

Section: Discussionmentioning

confidence: 60%

“…Lastly, this study notes the increasing use of ML for specific customer loan default predictions in the banking sector (58), multilingual tweets classification for disease surveillance (59), and predicting an individual HIV/AIDs patient likely not to adhere to treatment (60). Similarly, this could be replicated in TB management and research.…”

Section: (Which Was Not Certified By Peer Review)mentioning

confidence: 81%

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A machine learning model to explore individual risk factors for tuberculosis treatment non-adherence in Mukono district

Gichuhi

Magumba

Kumar

et al. 2022

Preprint

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Despite the availability and implementation of well-known efficacious interventions for Tuberculosis (TB) prevention and treatment by the Ministry of Health, Uganda (MoH), treatment non-adherence persists. Moreover, identifying a specific patient at risk of non-adherence is still a challenge. Thus, this study set out to utilize machine learning modeling to explore individual risk factors predictive of tuberculosis treatment non-adherence in the Mukono district. This was a retrospective study based on a record review of 838 TB patients enrolled in six health facilities (3 government, 3 private-not-for-profit) in the Mukono district. We developed five machine learning algorithms (Logistic regression (LR), artificial neural networks (ANN), support vector machines (SVM), Random Forest (RF), and AdaBoost) to explore the individual risk factors for tuberculosis treatment non-adherence. Also, we evaluated their accuracy by computing the positive predictive value (PPV) and negative predictive value (NPV) through the aid of a confusion matrix. Of the five developed and evaluated models, SVM performed the best with an accuracy of 91.28 % compared to RF (89.97%), LR (88.30%), ANN (88.30%), and AdaBoost (91.05%) respectively. Individual risk factors predictive of non-adherence included; TB type, GeneXpert results, sub-country, ART status, contacts below 5 years, health facility ownership, sputum test results at 2 months, treatment supporter, CPT Dapson status, risk group, patient age, gender, middle and upper arm circumference, referral, positive sputum test at 5 months and 6 months. This study shows that classification machine learning techniques can identify patient factors predictive of treatment non-adherence and accurately differentiate between adherent and non-adherent patients. Thus, TB program management should consider adopting the machine learning techniques evaluated in this study as a screening tool for both identity and target-suited interventions for these patients.

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Section: Discussionmentioning

confidence: 60%

Section: (Which Was Not Certified By Peer Review)mentioning

confidence: 81%

A machine learning model to explore individual risk factors for tuberculosis treatment non-adherence in Mukono district

Gichuhi

Magumba

Kumar

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…These results indicate that classification machine learning models can be used to map the different predictors to their respective classes. Likewise, these capabilities have been reported in similar rather challenging scenarios like predicting viral failure [ 51 ], tweets classification for disease surveillance [ 43 ], identification of HIV predictors for screening [ 52 ], and cancer [ 53 ]. Therefore, this demonstrates the potential and applicability of machine learning algorithms to provide insights in scenarios where human decision-making would be limited.…”

Section: Discussionmentioning

confidence: 94%

“…Lastly, this study notes the increasing use of machine learning for specific customer loan default predictions in the banking sector [ 42 ], multilingual tweets classification for disease surveillance [ 43 ], and predicting an individual HIV/AIDs patient likely not to adhere to treatment [ 44 ]. Similarly, this study set out to replicate this in tuberculosis management and research.…”

Section: Introductionmentioning

confidence: 99%

A machine learning approach to explore individual risk factors for tuberculosis treatment non-adherence in Mukono district

Gichuhi

Magumba

Kumar

et al. 2023

PLOS Glob Public Health

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Despite the availability and implementation of well-known efficacious interventions for tuberculosis treatment by the Ministry of Health, Uganda (MoH), treatment non-adherence persists. Moreover, identifying a specific tuberculosis patient at risk of treatment non-adherence is still a challenge. Thus, this retrospective study, based on a record review of 838 tuberculosis patients enrolled in six health facilities, presents, and discusses a machine learning approach to explore the individual risk factors predictive of tuberculosis treatment non-adherence in the Mukono district, Uganda. Five classification machine learning algorithms, logistic regression (LR), artificial neural networks (ANN), support vector machines (SVM), random forest (RF), and AdaBoost were trained, and evaluated by computing their accuracy, F1 score, precision, recall, and the area under the receiver operating curve (AUC) through the aid of a confusion matrix. Of the five developed and evaluated algorithms, SVM (91.28%) had the highest accuracy (AdaBoost, 91.05% performed better than SVM when AUC is considered as evaluation parameter). Looking at all five evaluation parameters globally, AdaBoost is quite on par with SVM. Individual risk factors predictive of non-adherence included tuberculosis type, GeneXpert results, sub-country, antiretroviral status, contacts below 5 years, health facility ownership, sputum test results at 2 months, treatment supporter, cotrimoxazole preventive therapy (CPT) dapsone status, risk group, patient age, gender, middle and upper arm circumference, referral, positive sputum test at 5 and 6 months. Therefore, machine learning techniques, specifically classification types, can identify patient factors predictive of treatment non-adherence and accurately differentiate between adherent and non-adherent patients. Thus, tuberculosis program management should consider adopting the classification machine learning techniques evaluated in this study as a screening tool for identifying and targeting suited interventions to these patients.

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“…However, these studies frequently rely on learning representations of vision-based or natural language-based data through weight updates. Subsequently, a supervised learning model that can be resource-intensive is constructed [19], [20] for dealing with various tasks from videos [21], [22]. On the contrary, our approach entails automatically generating annotations for frames in videos to facilitate comprehension.…”

Section: A Multi-input Learningmentioning

confidence: 99%

On the Combination of Multi-Input and Self-Attention for Sign Language Recognition

Hoai,

Van,

Anh

2024

IJACSA

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Sign language recognition can be considered as a branch of human action recognition. The deaf-muted community utilizes upper body gestures to convey sign language words. With the rapid development of intelligent systems based on deep learning models, video-based sign language recognition models can be integrated into services and products to improve the quality of life for the deaf-muted community. However, comprehending the relationship between different words within videos is a complex and challenging task, particularly in understanding sign language actions in videos, further constraining the performance of previous methods. Recent methods have been explored to generate video annotations to address this challenge, such as creating questions and answers for images. An optimistic approach involves fine-tuning autoregressive language models trained using multiinput and self-attention mechanisms to facilitate understanding of sign language in videos. We have introduced a bidirectional transformer language model, MISA (multi-input self-attention), to enhance solutions for VideoQA (video question and answer) without relying on labeled annotations. Specifically, (1) one direction of the model generates descriptions for each frame of the video to learn from the frames and their descriptions, and (2) the other direction generates questions for each frame of the video, then integrates inference with the first aspect to produce questions that effectively identify sign language actions. Our proposed method has outperformed recent techniques in VideoQA by eliminating the need for manual labeling across various datasets, including CSL-Daily, PHOENIX14T, and PVSL (our dataset). Furthermore, it demonstrates competitive performance in lowdata environments and operates under supervision.

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Evaluation of different machine learning approaches and input text representations for multilingual classification of tweets for disease surveillance in the social web

Cited by 5 publications

References 14 publications

A machine learning model to explore individual risk factors for tuberculosis treatment non-adherence in Mukono district

A machine learning model to explore individual risk factors for tuberculosis treatment non-adherence in Mukono district

A machine learning approach to explore individual risk factors for tuberculosis treatment non-adherence in Mukono district

On the Combination of Multi-Input and Self-Attention for Sign Language Recognition

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