Artificial Intelligence-Based Medical Data Mining

Zia, Amjad; Aziz, Muzzamil; Popa, Ionel; Khan, Sabih Ahmed; Hamedani, Amirreza Fazely; Asif, Abdul R.

doi:10.3390/jpm12091359

Cited by 22 publications

(11 citation statements)

References 105 publications

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“…The Pandas library provides a comprehensive set of data structures and functions for working with structured data, while the NumPy library offers fast numerical operations on large arrays. To scrape data from the website, Scrapy with Python was used, which is considered a fast and powerful Python-based web crawling framework in comparison to other tools [ 7 ].…”

Section: Methodsmentioning

confidence: 99%

Automated Retrieval of Heterogeneous Proteomic Data for Machine Learning

Rafay

Aziz

Zia³

et al. 2023

JPM

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Proteomics instrumentation and the corresponding bioinformatics tools have evolved at a rapid pace in the last 20 years, whereas the exploitation of deep learning techniques in proteomics is on the horizon. The ability to revisit proteomics raw data, in particular, could be a valuable resource for machine learning applications seeking new insight into protein expression and functions of previously acquired data from different instruments under various lab conditions. We map publicly available proteomics repositories (such as ProteomeXchange) and relevant publications to extract MS/MS data to form one large database that contains the patient history and mass spectrometric data acquired for the patient sample. The extracted mapped dataset should enable the research to overcome the issues attached to the dispersions of proteomics data on the internet, which makes it difficult to apply emerging new bioinformatics tools and deep learning algorithms. The workflow proposed in this study enables a linked large dataset of heart-related proteomics data, which could be easily and efficiently applied to machine learning and deep learning algorithms for futuristic predictions of heart diseases and modeling. Data scraping and crawling offer a powerful tool to harvest and prepare the training and test datasets; however, the authors advocate caution because of ethical and legal issues, as well as the need to ensure the quality and accuracy of the data that are being collected.

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Section: Methodsmentioning

confidence: 99%

Automated Retrieval of Heterogeneous Proteomic Data for Machine Learning

Rafay

Aziz

Zia³

et al. 2023

JPM

Self Cite

View full text Add to dashboard Cite

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“…AI algorithms can be used to identify potential drug targets and develop new therapies based on genomic data. AI can be used to help predict the potential effects of genomic editing techniques, such as CRISPR-Cas9, and improve the precision and accuracy [15]. AI algorithms can be used to develop new diagnostic tools for genetic diseases, enabling earlier and more accurate diagnosis.…”

Section: Methodologies Of Ai In Genomic Researchmentioning

confidence: 99%

Artificial Intelligence in the Genomics Era: A Blessing or a Curse?

Reddy

2023

J Regen Biol Med.

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As it offers tremendous potential for genomic advancements and has become an invaluable tool in understanding and harnessing the power of genetic information, artificial intelligence has revolutionised genomics by enabling researchers to analyse complex genomic data, unravel biological insights, and accelerate discoveries in personalised medicine, disease diagnosis, drug development, and gene editing. Artificial intelligence can be considered both a boon and a bane in the field of genomics, depending on how it is applied and its implications. Artificial intelligence has the potential to bring significant benefits to genomics, including improved data analysis, precision medicine, and drug discovery. However, ethical considerations, interpretability challenges, biases, and the need for human expertise must be carefully addressed to fully harness the potential of artificial intelligence in genomics and ensure its responsible and beneficial application. The purpose of this narrative review is to shed light on the various applications of genomics that take advantage of technological advancements in artificial intelligence, and to identify its benefits and limitations.

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“…The data collected by AI‐powered self‐diagnosis tools hold significant potential for analysis and research. By anonymizing and aggregating user data, these tools create valuable datasets that can be analysed to identify patterns, trends, and emerging health concerns (Zeng et al., 2021; Zia et al., 2022). Researchers and healthcare professionals can gain insights from this analysis, leading to the development of more effective treatments, preventive strategies, and a deeper understanding of various health conditions (Bajwa et al., 2021; Wani et al., 2022; Zia et al., 2022).…”

Section: Benefits and Limitations Of Ai In The Self‐diagnosis Of Ment...mentioning

confidence: 99%

Critical review of self‐diagnosis of mental health conditions using artificial intelligence

Wimbarti,

Kairupan,

Tallei

2024

Int J Mental Health Nurs

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The advent of artificial intelligence (AI) has revolutionised various aspects of our lives, including mental health nursing. AI‐driven tools and applications have provided a convenient and accessible means for individuals to assess their mental well‐being within the confines of their homes. Nonetheless, the widespread trend of self‐diagnosing mental health conditions through AI poses considerable risks. This review article examines the perils associated with relying on AI for self‐diagnosis in mental health, highlighting the constraints and possible adverse outcomes that can arise from such practices. It delves into the ethical, psychological, and social implications, underscoring the vital role of mental health professionals, including psychologists, psychiatrists, and nursing specialists, in providing professional assistance and guidance. This article aims to highlight the importance of seeking professional assistance and guidance in addressing mental health concerns, especially in the era of AI‐driven self‐diagnosis.

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Artificial Intelligence-Based Medical Data Mining

Cited by 22 publications

References 105 publications

Automated Retrieval of Heterogeneous Proteomic Data for Machine Learning

Automated Retrieval of Heterogeneous Proteomic Data for Machine Learning

Artificial Intelligence in the Genomics Era: A Blessing or a Curse?

Critical review of self‐diagnosis of mental health conditions using artificial intelligence

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