Compound-level identification of sasang constitution type-specific personalized herbal medicine using data science approach

Park, Sa-Yoon; Kim, Young Woo; Song, Yu Rim; Bak, Seon Been; Jang, Young Pyo; Kim, IlKon; Kim, Ji‐Hwan; Kim, Chang-Eop

doi:10.1016/j.heliyon.2023.e13692

Cited by 3 publications

(1 citation statement)

References 28 publications

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“…These studies demonstrated robust and efficient performance of models with AUC ranging from 0.75–0.92 ( 41 - 44 ). It is well known that commonly used metrics for evaluating machine learning algorithms are accuracy, precision, and recall ( 45 ). In large-sample machine learning in general recall and precision show a significant negative correlation ( 46 ).…”

Section: Discussionmentioning

confidence: 99%

Imaging features and deep learning for prediction of pulmonary epithelioid hemangioendothelioma in CT images

Huang,

Xie,

Huang

et al. 2024

J Thorac Dis

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Background Pulmonary epithelioid hemangioendothelioma (PEH) is a rare vascular tumour, and its early diagnosis remains challenging. This study aims to comprehensively analyse the imaging features of PEH and develop a model for predicting PEH. Methods Retrospective and pooled analyses of imaging findings were performed in PEH patients at our center (n=25) and in published cases (n=71), respectively. Relevant computed tomography (CT) images were extracted and used to build a deep learning model for PEH identification and differentiation from other diseases. Results In this study, bilateral multiple nodules/masses (n=19) appeared to be more common with most nodules less than 2 cm. In addition to the common types and features, the pattern of mixed type (n=4) and isolated nodules (n=4), punctate calcifications (5/25) and lymph node enlargement were also observed (10/25). The presence of pleural effusion is associated with a poor prognosis in PEH. The deep learning model, with an area under the receiver operating characteristic curve (AUC) of 0.71 [95% confidence interval (CI): 0.69–0.72], has a differentiation accuracy of 100% and 74% for the training and test sets respectively. Conclusions This study confirmed the heterogeneity of the imaging findings in PEH and showed several previously undescribed types and features. The current deep learning model based on CT has potential for clinical application and needs to be further explored in the future.

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Section: Discussionmentioning

confidence: 99%

Imaging features and deep learning for prediction of pulmonary epithelioid hemangioendothelioma in CT images

Huang,

Xie,

Huang

et al. 2024

J Thorac Dis

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A practical guide to implementing artificial intelligence in traditional East Asian medicine research

Bae,

Park,

Kim

2024

Integrative Medicine Research

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Data-Driven Future Trends and Innovation in Telemedicine

Shafik

2024

Advances in Medical Technologies and Clinical Practice

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This study examines the future of telemedicine by investigating the influence of developing technologies on healthcare. It emphasizes the need to comprehend the impact of these advancements on healthcare provision, given their swift progression. The debate encompasses a range of technologies, including internet of things (IoT) devices, artificial intelligence (AI), augmented reality (AR), robotics, blockchain, virtual reality (VR), genomics, and wearable tech within healthcare settings. It highlights the capacity for patient monitoring, diagnosis, tailored therapy, and improved access to healthcare. Furthermore, it tackles the legal, privacy, and ethical issues linked to these breakthroughs and emphasizes the need for ongoing study, collaboration, and strong regulation to exploit their capabilities fully.

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Compound-level identification of sasang constitution type-specific personalized herbal medicine using data science approach

Cited by 3 publications

References 28 publications

Imaging features and deep learning for prediction of pulmonary epithelioid hemangioendothelioma in CT images

Imaging features and deep learning for prediction of pulmonary epithelioid hemangioendothelioma in CT images

A practical guide to implementing artificial intelligence in traditional East Asian medicine research

Data-Driven Future Trends and Innovation in Telemedicine

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