In recent years, many hospitals have built clinical big data research platforms and specialized disease databases, focusing on improving the efficiency of doctors to carry out clinical scientific research, and then improving scientific research results. Based on the self-developed smart scientific research series products, Yidu Cloud has carried out scientific research cooperation with clinical experts in multiple disease fields, and in the first three quarters of 2023, it has helped the PI team to publish dozens of scientific research papers, and the results have been remarkable.

Products enable the efficient output of scientific research results

Based on leading data governance technology, excellent quality of governance results, abundant and easy-to-use scientific research tools and shorter delivery cycles, Yidu Cloud's scientific research products have always been one of the first choices for medical institutions to build scientific research platforms and special disease databases.

Since 2023, using the scientific research data platform built by Yidu Cloud, the PI team has published dozens of scientific research papers. In these works, there are nearly 30 papers that have been co-signed with Yidu Yun or acknowledged Yidu Yun in articles.

Among them, the "Xinjiang Key Laboratory of Cardiovascular Homeostasis and Regenerative Medicine" of the People's Hospital of Xinjiang Uygur Autonomous Region has completed a number of related studies on cardiovascular diseases with the help of the "Yiduyun Cardiovascular Disease Database" undertaken by Yiduyun, and the laboratory has recently been successfully selected as the Key Laboratory of Xinjiang Uygur Autonomous Region. In addition, the hospital's clinicians and nursing team have published more than a dozen articles using the hypertension database built by Yidu Cloud, and a total of 8 SCI papers have been published in the first 10 months of 2023 alone, of which 3 have an impact factor of more than 8 points.

These teams can publish a large number of research results in a short period of time, which is closely related to the empowerment and efficiency improvement of Yidu Cloud's scientific research products. As we all know, real-world clinical diagnosis and treatment data is a treasure trove of clinical research, but the raw massive data accumulated in clinical practice for a long time cannot "automatically" become high-quality real-world big data available for research. The objective status quo of multi-source heterogeneous data, lack of structure of content, and difficulty in standardizing terminology greatly restricts the efficiency of PI to carry out high-quality research.

Yidu Cloud helps hospitals build a scientific research data platform, helping them process and govern the hard-to-use raw data into data that is easy to use in scientific research, so that researchers can collect, search and analyze data more efficiently on the platform, and focus their limited energy on scientific problem thinking, so as to support PI to improve scientific research efficiency and improve the quality of scientific research results.

Medical-engineering cooperation produces high-quality scientific research results

On the basis of delivering and operating scientific research data platform products, Yidu Cloud has built a data science team that is familiar with the interdisciplinary research of medical and engineering. By jointly carrying out collaborative research on practical scientific research topics with PI, the data science team not only produces high-quality results, but also refines product improvement directions through real needs in the research process, forming a closed loop from the data platform to the iterative promotion of scientific research results.

Since 2023, Yidu Cloud's data science team and clinical PI have produced high-quality results in multiple disease directions, some of which are typical as follows:

In the field of critical illness risk modeling, he collaborated with Professor Zhou Xiang and other teams from Peking Union Medical College Hospital to publish the results of dynamic risk modeling of COVID-19 patient risk in Infectious Disease Modelling (Q1) [1].

In the field of endocrinology clinical research, in collaboration with Professor Gao Zhengnan and other teams from Dalian Central Hospital, based on the in-hospital data platform jointly built by the two parties, two papers were published in The Journal of Clinical Endocrinology & Metabolism (Q1), an authoritative journal in the field of endocrinology, with research topics related to subtype cluster analysis of kidney disease risk in patients with type 2 diabetes [2] and intraoperative risk modeling of pheochromocytoma [3].

In the field of big data knowledge mining, he collaborated with the team of Professor Xia Yunlong from the First Affiliated Hospital of Dalian Medical University to publish medical knowledge mining research results in the IEEE Journal of Biomedical and Health Informatics (Q1) [4].

As a leading enterprise in the field of medical intelligence and one of the industry's leading scientific research platform manufacturers, Yidu Cloud has built a hospital-wide scientific research platform, 200+ special disease databases, and 10+ national/regional multi-center disease data centers for nearly 100 leading hospitals in China, covering a wide range of clinical research fields.

In the future, Yidu Cloud will continue to strive to improve the quality and service level of the data platform and provide customers with high-quality data governance services. We hope to have the opportunity to work with more hospitals and expert teams to jointly promote the improvement of clinical research.

References：

1.Chen, Yujie, Yao Wang, Jieqing Chen, Xudong Ma, Longxiang Su, Yuna Wei, Linfeng Li et al. "Multidimensional dynamic prediction model for hospitalized patients with the omicron variant in China." Infectious Disease Modelling (2023).

2.Shen Li, Mengxuan Cui, Yingshu Liu, Xuhan Liu, Lan Luo, Wei Zhao, Xiaolan Gu, Linfeng Li, Chao Liu, Lan Bai, Di Li, Bo Liu, Defei Che, Xinyu Li, Yao Wang, Zhengnan Gao, Metabolic profiles of type 2 diabetes and their association with renal complications, The Journal of Clinical Endocrinology & Metabolism, 2023;, dgad643, 

3.Liu, Yingshu, Chao Liu, Yao Wang, Shen Li, Xinyu Li, Xuhan Liu, Bing Wang et al. "Nomogram for Predicting Intraoperative Hemodynamic Instability in Patients With Normotensive Pheochromocytoma." The Journal of Clinical Endocrinology & Metabolism 108, no. 7 (2023): 1657-1665.

4.Wang, Shaobo, Xinhui Du, Guangliang Liu, Hang Xing, Zengtao Jiao, Jun Yan, Youjun Liu, Haichen Lv, and Yunlong Xia. "An Interpretable Data-driven Medical Knowledge Discovery Pipeline Based on Artificial Intelligence." IEEE Journal of Biomedical and Health Informatics (2023).