Construction of Diabetes Knowledge Graph Based on Deep Learning

Lu, Yonghe; Zhao, Ruifeng; Huang, Shan; Liu, Runjia

doi:10.1109/icnisc54316.2021.00181

Cited by 2 publications

(3 citation statements)

References 2 publications

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“…Therefore, given the large volume of medical images, automatically generating reports can improve current clinical practice in diagnostic radiology and assist radiologists in clinical decisionmaking. Thus, automatic report generation is receiving remarkable attention in both communities of artificial intelligence and clinical medicine 14,7,10,15,49,50,51 . To measure the performance of COVID-19 reporting, we select the widelyused natural language generation metrics, including BLEU-2, -3, -4 60 , ROUGE-L 61 , and CIDEr 62 , which are computed by a standard evaluation toolkit 63 automatically.…”

Section: Covid-19 Reportingmentioning

confidence: 99%

“…We further select existing state-of-the-art methods, including R2Gen 49 , PPKED 10 , KGAE 50 , and XProNet 51 , for comparison. We conduct retrospective studies on the COVID-19-CT dataset in Chinese and the BIMCV-COVID-19 dataset in Spanish.…”

Section: Retrospective Studiesmentioning

confidence: 99%

“…In particular, given a mini-batch of N randomly sampled images and texts, we adopt the MetaMap 86 to extract entities defined in Unified Medical Language System 45 from the i th medical text. Following previous works 87,88,80,50,28,29 , we focus on the 14 common radiographic entities, i.e., atelectasis, cardiomegaly, consolidation, edema, enlarged cardiomediastinum, fracture, lung lesion, lung opacity, no finding, pleural effusion, pleural other, pneumonia, pneumothorax, support devices. As a result, given the medical text, "A right pleural effusion.…”

Section: Soft Image-text Alignment (Sita)mentioning

confidence: 99%

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A Large Language Modelling Deep Learning Framework for the Next Pandemic

Zhu

Wang

Yang

et al. 2023

Preprint

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Deep neural networks have been integrated into the whole clinical decision procedure which can improve the efficiency of diagnosis and alleviate the heavy workload of physicians. Typical applications include 1) medical report generation, 2) disease classification, and 3) survival prediction. Since most neural networks are supervised, their quality heavily depends on the volume and quality of available labels. However, for novel diseases, e.g., new pandemics or new variants, there are few existing labels. In addition, the acquisition of new pandemic cases to collect sufficient labels for training is time-consuming and is typically unavailable at the early stage. To prepare neural networks for the next pandemic, in this paper, we propose a large language model - Unsupervised Learning from Unlabelled Medical Images and Text (ULUMIT) framework, which can learn broad medical knowledge (e.g., image understanding, text semantics, and clinical phenotypes) from unlabelled data. As a result, when encountering new pandemics, our framework can be rapidly deployed and easily adapted to them with extremely limited labels. Furthermore, ULUMIT supports medical data across visual modality (e.g., chest X-ray and CT) and textual modality (e.g., medical report and free-text clinical note); therefore, it can be used for any clinical task that involves both visual and textual medical data. We demonstrate the effectiveness of our ULUMIT by showing how it would perform using the COVID-19 pandemic ``in replay''. In particular, in the retrospective setting, we test the model on the early COVID-19 datasets; and in the prospective setting, we test the model on the new variant COVID-19-Omicron. The experiments are conducted on 1) three kinds of input medical data, image-only, text-only, and image-text; 2) three kinds of downstream tasks, medical reporting, diagnosis, and prognosis; 3) five public COVID-19 datasets; and 4) three different languages, i.e., English, Chinese, and Spanish. All experiments consistently show that our framework can make accurate and robust COVID-19 decision-support tasks with little labelled data (such as considering information from only one patient), providing an impact on medical data analysis during the early stage of the next pandemic. Besides COVID-19, our framework can be applied to identify 14 common thorax diseases and tuberculosis across five additional public datasets, demonstrating its robustness in generalization and transferability. In brief, our framework achieves state-of-the-art performances on ten datasets.

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