ArticleFrontiers in public health2021
Blockchain-Secured Recommender System for Special Need Patients Using Deep Learning.
Article in Frontiers in public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed.
- Comparison of AI-generated renal diets by different large language models: a guideline-based evaluation with expert input.BMC nephrology · 2026Article
- Blockchain Technology in Digital Health and Medical Technologies.Blockchain in healthcare today · 2025Article
- Development and Evaluation of Health Recommender Systems: Systematic Scoping Review and Evidence Mapping.Journal of medical Internet research · 2023Article
- Commentary: Integrated blockchain-deep learning approach for analyzing the electronic health records recommender system.Frontiers in public health · 2023Article
- MR-KPA: medication recommendation by combining knowledge-enhanced pre-training with a deep adversarial network.BMC bioinformatics · 2022Article
- PROTEIN AI Advisor: A Knowledge-Based Recommendation Framework Using Expert-Validated Meals for Healthy Diets.Nutrients · 2022Article
- Integrated Blockchain-Deep Learning Approach for Analyzing the Electronic Health Records Recommender System.Frontiers in public health · 2022Article
- NextGen Public Health Surveillance and the Internet of Things (IoT).Frontiers in public health · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recommender systems offer several advantages to hospital data management units and patients with special needs. These systems are more dependent on the extreme subtle hospital-patient data. Thus, disregarding the confidentiality of patients with special needs is not an option. In recent times, several proposed techniques failed to cryptographically guarantee the data privacy of the patients with special needs in the diet recommender systems (RSs) deployment. In order to tackle this pitfall, this paper incorporates a blockchain privacy system (BPS) into deep learning for a diet recommendation system for patients with special needs. Our proposed technique allows patients to get notifications about recommended treatments and medications based on their personalized data without revealing their confidential information. Additionally, the paper implemented machine and deep learning algorithms such as RNN, Logistic Regression, MLP, etc., on an Internet of Medical Things (IoMT) dataset acquired
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.