Christian Brieghel, Mikkel Werling, Casper Møller Frederiksen, Mehdi Parviz, Caspar da Cunha-Bang, Tereza Faitova, Rebecca Svanberg Teglgaard, Noomi Vainer, Thomas Lacoppidan, Emelie Rotbain, Rudi Agius, Carsten Utoft Niemann
{"title":"丹麦淋巴癌研究（DALY-CARE）数据资源：发展数据驱动血液学的基础","authors":"Christian Brieghel, Mikkel Werling, Casper Møller Frederiksen, Mehdi Parviz, Caspar da Cunha-Bang, Tereza Faitova, Rebecca Svanberg Teglgaard, Noomi Vainer, Thomas Lacoppidan, Emelie Rotbain, Rudi Agius, Carsten Utoft Niemann","doi":"10.1101/2024.04.11.24305663","DOIUrl":null,"url":null,"abstract":"Lymphoid-lineage cancers (LC: lymphoma, chronic lymphocytic leukemia, multiple myeloma, and their precursors) share many epidemiological and clinical features. To develop data-driven hematology, we gathered electronic health data and created open-source data processing pipelines to create a comprehensive data resource for Danish LC Research (DALY-CARE) approved for epidemiological, molecular, and data-driven research. We included all Danish adults registered with LC diagnoses since 2002 (n=65,774) and combined 10 nationwide registers, electronic health records (EHR), and laboratory data on a high-powered cloud-computer to develop a secure research environment. We herein exemplify how DALY-CARE has been used to develop novel prognostic markers using biobank data, real-world evidence to evaluate the efficacy of care, and medical artificial intelligence algorithms deployed directly into EHR systems. The DALY-CARE data resource allows for development of both near real-time decision-support tools and extrapolation of clinical trial results to clinical practice, thereby improving care for patients with LC.","PeriodicalId":501203,"journal":{"name":"medRxiv - Hematology","volume":"30 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"The Danish Lymphoid Cancer Research (DALY-CARE) data resource: the basis for developing data-driven hematology\",\"authors\":\"Christian Brieghel, Mikkel Werling, Casper Møller Frederiksen, Mehdi Parviz, Caspar da Cunha-Bang, Tereza Faitova, Rebecca Svanberg Teglgaard, Noomi Vainer, Thomas Lacoppidan, Emelie Rotbain, Rudi Agius, Carsten Utoft Niemann\",\"doi\":\"10.1101/2024.04.11.24305663\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Lymphoid-lineage cancers (LC: lymphoma, chronic lymphocytic leukemia, multiple myeloma, and their precursors) share many epidemiological and clinical features. To develop data-driven hematology, we gathered electronic health data and created open-source data processing pipelines to create a comprehensive data resource for Danish LC Research (DALY-CARE) approved for epidemiological, molecular, and data-driven research. We included all Danish adults registered with LC diagnoses since 2002 (n=65,774) and combined 10 nationwide registers, electronic health records (EHR), and laboratory data on a high-powered cloud-computer to develop a secure research environment. We herein exemplify how DALY-CARE has been used to develop novel prognostic markers using biobank data, real-world evidence to evaluate the efficacy of care, and medical artificial intelligence algorithms deployed directly into EHR systems. The DALY-CARE data resource allows for development of both near real-time decision-support tools and extrapolation of clinical trial results to clinical practice, thereby improving care for patients with LC.\",\"PeriodicalId\":501203,\"journal\":{\"name\":\"medRxiv - Hematology\",\"volume\":\"30 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-04-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"medRxiv - Hematology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1101/2024.04.11.24305663\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"medRxiv - Hematology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1101/2024.04.11.24305663","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The Danish Lymphoid Cancer Research (DALY-CARE) data resource: the basis for developing data-driven hematology
Lymphoid-lineage cancers (LC: lymphoma, chronic lymphocytic leukemia, multiple myeloma, and their precursors) share many epidemiological and clinical features. To develop data-driven hematology, we gathered electronic health data and created open-source data processing pipelines to create a comprehensive data resource for Danish LC Research (DALY-CARE) approved for epidemiological, molecular, and data-driven research. We included all Danish adults registered with LC diagnoses since 2002 (n=65,774) and combined 10 nationwide registers, electronic health records (EHR), and laboratory data on a high-powered cloud-computer to develop a secure research environment. We herein exemplify how DALY-CARE has been used to develop novel prognostic markers using biobank data, real-world evidence to evaluate the efficacy of care, and medical artificial intelligence algorithms deployed directly into EHR systems. The DALY-CARE data resource allows for development of both near real-time decision-support tools and extrapolation of clinical trial results to clinical practice, thereby improving care for patients with LC.
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