An Efficient Pattern Recognition Based Method for Drug-Drug Interaction Diagnosis

Javed, Rabia; Saba, Tanzila; Humdullah, Salman; Jamail, Nor Shahida Mohd; Awan, Mazhar Javed

doi:10.1109/caida51941.2021.9425062

Cited by 22 publications

(7 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Thus, to perform an all-encompassing analysis of drug trends, we have two potential options. Firstly, we could apply more sophisticated drug recognition techniques that require advanced programming, or secondly, we could engage in a manual curation or “cleaning” process of the ICTRP, DrugBank, and NIH DIP ( Piliouras et al, 2013 ; Javed et al, 2021 ). However, this is a complex task that requires significant effort.…”

Section: Discussionmentioning

confidence: 99%

The international clinical trials registry platform (ICTRP): data integrity and the trends in clinical trials, diseases, and drugs

Namiot,

Smirnovová,

Sokolov

et al. 2023

Front. Pharmacol.

View full text Add to dashboard Cite

Introduction: Clinical trials are the gold standard for testing new therapies. Databases like ClinicalTrials.gov provide access to trial information, mainly covering the US and Europe. In 2006, WHO introduced the global ICTRP, aggregating data from ClinicalTrials.gov and 17 other national registers, making it the largest clinical trial platform by June 2019. This study conducts a comprehensive global analysis of the ICTRP database and provides framework for large-scale data analysis, data preparation, curation, and filtering.Materials and methods: The trends in 689,793 records from the ICTRP database (covering trials registered from 1990 to 2020) were analyzed. Records were adjusted for duplicates and mapping of agents to drug classes was performed. Several databases, including DrugBank, MESH, and the NIH Drug Information Portal were used to investigate trends in agent classes.Results: Our novel approach unveiled that 0.5% of the trials we identified were hidden duplicates, primarily originating from the EUCTR database, which accounted for 82.9% of these duplicates. However, the overall number of hidden duplicates within the ICTRP seems to be decreasing. In total, 689 793 trials (478 345 interventional) were registered in the ICTRP between 1990 and 2020, surpassing the count of trials in ClinicalTrials.gov (362 500 trials by the end of 2020). We identified 4 865 unique agents in trials with DrugBank, whereas 2 633 agents were identified with NIH Drug Information Portal data. After the ClinicalTrials.gov, EUCTR had the most trials in the ICTRP, followed by CTRI, IRCT, CHiCTR, and ISRCTN. CHiCTR displayed a significant surge in trial registration around 2015, while CTRI experienced rapid growth starting in 2016.Conclusion: This study highlights both the strengths and weaknesses of using the ICTRP as a data source for analyzing trends in clinical trials, and emphasizes the value of utilizing multiple registries for a comprehensive analysis.

show abstract

Section: Discussionmentioning

confidence: 99%

The international clinical trials registry platform (ICTRP): data integrity and the trends in clinical trials, diseases, and drugs

Namiot,

Smirnovová,

Sokolov

et al. 2023

Front. Pharmacol.

View full text Add to dashboard Cite

show abstract

“…Many diseases can be predicted through machine learning and deep learning models (Aftab et al, 2021; Javed et al, 2021). There are additional regression models used to evaluate healthcare, notably polynomial regression, decision trees regression, and random forest regression (Awan et al, 2019; Awan, Rahim, Salim, et al, 2021; Gupta et al, 2021).…”

Section: Literature Reviewmentioning

confidence: 99%

Privacy Prevention of Big Data Applications: A Systematic Literature Review

et al. 2022

View full text Add to dashboard Cite

This paper focuses on privacy and security concerns in Big Data. This paper also covers the encryption techniques by taking existing methods such as differential privacy, k-anonymity, T-closeness, and L-diversity. Several privacy-preserving techniques have been created to safeguard privacy at various phases of a large data life cycle. The purpose of this work is to offer a comprehensive analysis of the privacy preservation techniques in Big Data, as well as to explain the problems for existing systems. The advanced repository search option was utilized for the search of the following keywords in the search: “Cyber security” OR “Cybercrime”) AND ((“privacy prevention”) OR (“Big Data applications”)). During Internet research, many search engines and digital libraries were utilized to obtain information. The obtained findings were carefully gathered out of which 103 papers from 2,099 were found to gain the best information sources to address the provided study subjects. Hence a systemic review of 32 papers from 103 found in major databases (IEEExplore, SAGE, Science Direct, Springer, and MDPIs) were carried out, showing that the majority of them focus on the privacy prediction of Big Data applications with a contents-based approach and the hybrid, which address the major security challenge and violation of Big Data. We end with a few recommendations for improving the efficiency of Big Data projects and provide secure possible techniques and proposed solutions and model that minimizes privacy violations, showing four different types of data protection violations and the involvement of different entities in reducing their impacts.

show abstract

“…There is always a risk to medical and healthcare practices falling behind the competition if attention to innovation in technological developments and medical software is not paid [12]. Recently, a lot of research is related to diagnosing the various diseases through machine and deep learning approaches deployed in E-healthcare sector [13][14][15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Software Measurement by Using Artificial Intelligence

Tariq

Awan

Alshudukhi

et al. 2022

Journal of Nanomaterials

View full text Add to dashboard Cite

Artificial intelligence (AI) is a subfield of computer science concerned with developing intelligent machines capable of performing tasks similar to those performed by humans. This human-created intelligence began more than 60 years ago. The goal of previous generations of applications was to demonstrate generic human-like behaviour. The goal has expanded with the advancement and increased compliance of this technology. It includes areas such as healthcare, gaming, and smart devices. The COVID-19 epidemic has posed a significant barrier to maintaining a sustainable strategy for mental health support clients with major mental illnesses and clinicians who have had to shift delivery modes quickly. In this study, we have conducted a systematic literature review (SLR) to provide an overview of the current state of the literature related to software measurement of healthcare using artificial intelligence. The study followed a secondary research strategy. The systematic literature review aim was to analyze software measurement of mental health illness in terms of previous literature. This study screened out of 28 research papers out of 1076 initial searches. We used Science Direct, IEEE Xplore, Springer Link, ACM, and Hindawi as database search engines. The research objective was to explore the needs of software applications and automation in the healthcare sector to bring efficiency to the systems. The research concluded that the healthcare setting crucially requires the implementation of software automation.

show abstract

An Efficient Pattern Recognition Based Method for Drug-Drug Interaction Diagnosis

Cited by 22 publications

References 20 publications

The international clinical trials registry platform (ICTRP): data integrity and the trends in clinical trials, diseases, and drugs

The international clinical trials registry platform (ICTRP): data integrity and the trends in clinical trials, diseases, and drugs

Privacy Prevention of Big Data Applications: A Systematic Literature Review

Software Measurement by Using Artificial Intelligence

Contact Info

Product

Resources

About