Making Use of Comparable Health Data to Improve Quality of Care and Outcomes in Diabetes: the EUBIROD Review of Diabetes Registries and Data Sources in Europe
Frontiers in Clinical Diabetes and Healthcare(2021)
Univ Bologna | Univ Med Ctr Ljubljana | Univ Dundee | Croatian Inst Publ Hlth | Larnaca Hosp Cyprus | Univ Malta | Sciensano | Univ Debrecen | Haraldsplass Deaconess Hosp | Med Univ Silesia | Silesian Tech Univ | Telemed Consulting | Univ Oxford | St Anna Hosp Ferrara | Assoc Med Diabetol AMD | Robert Koch Inst | Minist Hlth | Ctr Dis Prevent & Control Latvia | Steno Diabet Ctr Copenhagen | Finnish Inst Hlth & Welf THL | Univ Gothenburg | Serectrix snc | Int Diabet Federat European Reg | Amsterdam Publ Hlth Res Inst
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

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