Deep-Learning-Based Drug Recommendation and ADR Detection Healthcare Model on Social Media

Dongre, Swati; Agrawal, Jitendra

doi:10.1109/tcss.2022.3231701

Cited by 10 publications

(2 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This study contributes to understanding the underlying patterns in social media content, which is crucial for effective moderation. In a similar vein, Dongre and Agrawal [4] focused on deep-learning-based drug recommendation and Adverse Drug Reaction (ADR) detection in healthcare models on social media, illustrating the application of deep learning in specific content domains.…”

Section: Related Studymentioning

confidence: 99%

MSCMGTB: A Novel Approach for Multimodal Social Media Content Moderation Using Hybrid Graph Theory and Bio-Inspired Optimization

Arya,

Pandey,

Gopal Krishna Patro

et al. 2024

IEEE Access

View full text Add to dashboard Cite

In an era where social media platforms burgeon with diverse content, effective content moderation becomes imperative to filter harmful materials. Traditional methods often grapple with the dual challenges of accuracy and computational efficiency levels. These conventional approaches typically rely on either text-based or image-based analysis, neglecting the complex interplay of multimodal content prevalent in social media scenarios. This limitation leads to suboptimal content filtering, often missing contextually nuanced or visually deceptive harmful content sets. Addressing these challenges, the proposed work introduces an innovative, hybrid deep learning model that integrates Convolutional Neural Networks (CNNs) and Transformers for nuanced visual content extraction from social media posts. CNNs adeptly handle image features, while Transformers enrich the analysis with contextual textual understanding, thereby enhancing accuracy in identifying harmful contents. Additionally, a Bi-directional Attention Mechanism (BAM) is employed for efficient text-visual fusion, dynamically prioritizing relevant information and reducing computational loads. The fusion process is further refined using Genetic Algorithms (GAs) for hyperparameter optimization, streamlining the model's performance levels. The model's robustness is demonstrated through its ability to discern complex intra and inter-modal relationships within social media content, facilitated by Graph Neural Networks (GNNs). Moreover, the model's scalability and deployment efficiency on social media platforms are ensured through quantization and pruning techniques, balancing accuracy with computational practicality levels. Empirical testing on datasets from Google, Facebook, and Kaggle reveals the model's superior performance over existing methods, with notable improvements in precision, accuracy, recall, AUC, specificity, and response delay in detecting harmful contents. The model also excels in preempting potential harmful content posters, offering enhanced pre-emption metrics.

show abstract

Section: Related Studymentioning

confidence: 99%

MSCMGTB: A Novel Approach for Multimodal Social Media Content Moderation Using Hybrid Graph Theory and Bio-Inspired Optimization

Arya,

Pandey,

Gopal Krishna Patro

et al. 2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Logistic Regression with Bi-grams and Support Vector Machine with tf-idf achieved the highest accuracy in an 80:20 data split scenario, while the combination of Tf-idf with Bi-grams performed the worst. [14] reviews clinical recommendation systems and ADR categorization using various models, with deep neural networks (DNN) showing the best ADR detection performance. SVM exhibits significant improvement post-preprocessing and with clinical vector space integration, achieving 86% accuracy in ADR classification with the SIDER dataset.…”

Section: Related Workmentioning

confidence: 99%

Adverse Drug Reactions Detection from Social Media: an Empirical Evaluation of Machine Learning Techniques

Elbiach,

Grissette,

Nfaoui

2023

2023 14th International Conference on Intelligent Systems: Theories and Applications (SITA)

View full text Add to dashboard Cite

This research addresses the critical concern of Adverse Drug Reactions (ADRs), emphasizing the need for their timely detection to safeguard patient well-being. Detecting ADRs within sentences is pivotal for effective public health monitoring. The study's primary objective is to assess whether sentences contain ADR references, a crucial step in identifying potential ADRs early. Timely recognition can mitigate patient harm and enhance drug development processes. The rise of patient engagement on social media has turned it into a valuable real-time resource for ADR-related information. Patients increasingly share their personal narratives about drug usage on these platforms, creating an innovative avenue for gathering firsthand accounts of ADRs. This evolving trend has transformed social media into an indispensable source of information. The study aims to compare machine learning algorithms in classifying sentences as containing ADRs or not. Three diverse datasets-CADEC, TwiMed (PubMed), and ADE-are used to train and evaluate models. Rigorous experimentation highlights the superiority of the Naive Bayes classifier over other methods. Notably, this classifier achieves remarkable accuracy rates of 94.29%, 78.76%, and 64.93%, on the CADEC, ADE, and PubMed datasets, respectively. This comparative study demonstrates the effectiveness of machine learning in identifying ADRs within sentences and underscores the Naive Bayes classifier's consistently impressive performance across different datasets.

show abstract

Big data analysis on medical field for drug recommendation using apriori algorithm and deep learning

Dasgupta,

Saha

2024

Multimed Tools Appl

View full text Add to dashboard Cite

Deep-Learning-Based Drug Recommendation and ADR Detection Healthcare Model on Social Media

Cited by 10 publications

References 26 publications

MSCMGTB: A Novel Approach for Multimodal Social Media Content Moderation Using Hybrid Graph Theory and Bio-Inspired Optimization

MSCMGTB: A Novel Approach for Multimodal Social Media Content Moderation Using Hybrid Graph Theory and Bio-Inspired Optimization

Adverse Drug Reactions Detection from Social Media: an Empirical Evaluation of Machine Learning Techniques

Big data analysis on medical field for drug recommendation using apriori algorithm and deep learning

Contact Info

Product

Resources

About