Application of Machine Learning Based on Structured Medical Data in Gastroenterology

Kim, Hye-Jin; Gong, Eun-Jeong; Bang, Chang-Seok

doi:10.3390/biomimetics8070512

Cited by 8 publications

(2 citation statements)

References 56 publications

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“…As the application of machine learning expands across various domains such as weather prediction and recommendation engines, an increasing number of researchers recognize the potential to apply novel machine learning methods developed in other fields in medical area [44][45][46]. This has led to the emergence of a plethora of machine learning-based prognostic models.…”

Section: Discussionmentioning

confidence: 99%

Mime: A flexible machine-learning framework to construct and visualize models for clinical characteristics prediction and feature selection

Liu,

Zhang,

Zhang

et al. 2023

Preprint

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With the widespread use of high-throughput sequencing technologies, understanding biology and cancer heterogeneity has been revolutionized. Recently, several machine-learning models based on transcriptional data have been developed to accurately predict patient’s outcome and clinical response. However, an open-source R package covering state-of-the-art machine learning algorithms for user-friendly access has yet to be developed. Thus, we proposed a flexible computational framework to construct machine learning-based integration model with elegant performance (Mime). Mime streamlined the process of developing predictive models with high accuracy, leveraging complex datasets to identify critical genes associated with prognosis. An in silico combined model based on de novo PIEZO1-associated signatures constructed by Mime demonstrated high accuracy in predicting outcomes of patients compared with other published models. In addition, PIEZO1-associated signatures could also precisely infer immunotherapy response by applying different algorithms in Mime. Finally, SDC1 selected from PIEZO1-associated signatures presented high-potential role in glioma with targeted prospect. Taken together, our package provides a user-friendly solution for constructing machine learning-based integration models and will be greatly expanded to provide valuable insights into current fields.

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Section: Discussionmentioning

confidence: 99%

Mime: A flexible machine-learning framework to construct and visualize models for clinical characteristics prediction and feature selection

Liu,

Zhang,

Zhang

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…However, traditional ML models continue to hold significant potential to enhance healthcare efficiency, especially for structured data. While ML models have been widely applied in predicting diagnoses and prognoses for various diseases, their adoption in gastroenterology has been relatively limited compared to traditional statistical models or DL approaches [71].…”

Section: Diagnosis and Clinical Decision Makingmentioning

confidence: 99%

Transformative Potential of AI in Healthcare: Definitions, Applications, and Navigating the Ethical Landscape and Public Perspectives

Bekbolatova,

Mayer,

Ong

et al. 2024

Healthcare

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Artificial intelligence (AI) has emerged as a crucial tool in healthcare with the primary aim of improving patient outcomes and optimizing healthcare delivery. By harnessing machine learning algorithms, natural language processing, and computer vision, AI enables the analysis of complex medical data. The integration of AI into healthcare systems aims to support clinicians, personalize patient care, and enhance population health, all while addressing the challenges posed by rising costs and limited resources. As a subdivision of computer science, AI focuses on the development of advanced algorithms capable of performing complex tasks that were once reliant on human intelligence. The ultimate goal is to achieve human-level performance with improved efficiency and accuracy in problem-solving and task execution, thereby reducing the need for human intervention. Various industries, including engineering, media/entertainment, finance, and education, have already reaped significant benefits by incorporating AI systems into their operations. Notably, the healthcare sector has witnessed rapid growth in the utilization of AI technology. Nevertheless, there remains untapped potential for AI to truly revolutionize the industry. It is important to note that despite concerns about job displacement, AI in healthcare should not be viewed as a threat to human workers. Instead, AI systems are designed to augment and support healthcare professionals, freeing up their time to focus on more complex and critical tasks. By automating routine and repetitive tasks, AI can alleviate the burden on healthcare professionals, allowing them to dedicate more attention to patient care and meaningful interactions. However, legal and ethical challenges must be addressed when embracing AI technology in medicine, alongside comprehensive public education to ensure widespread acceptance.

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Advancements and challenges in gastrointestinal imaging

Gong,

Bang

2024

World J Clin Cases

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A recent review by Gulinac et al , provides an in-depth analysis of current clinical issues and challenges in gastrointestinal imaging. This editorial highlights the advancements in imaging techniques, including the integration of artificial intelligence and functional imaging modalities, and discusses the ongoing relevance of traditional nuclear medicine tests. The future of gastrointestinal imaging looks promising, with continuous improvements in resolution, enhanced ability to analyze color and texture beyond visual diagnosis, faster image processing, and the application of molecular imaging and nanoparticles expected to enhance diagnostic accuracy and clinical outcomes.

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Application of Machine Learning Based on Structured Medical Data in Gastroenterology

Cited by 8 publications

References 56 publications

Mime: A flexible machine-learning framework to construct and visualize models for clinical characteristics prediction and feature selection

Mime: A flexible machine-learning framework to construct and visualize models for clinical characteristics prediction and feature selection

Transformative Potential of AI in Healthcare: Definitions, Applications, and Navigating the Ethical Landscape and Public Perspectives

Advancements and challenges in gastrointestinal imaging

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