An open natural language processing (NLP) framework for EHR-based clinical research: a case demonstration using the National COVID Cohort Collaborative (N3C).
An open natural language processing (NLP) framework for EHR-based clinical research: a case demonstration using the National COVID Cohort Collaborative (N3C).
复制标题
基于EHR的临床研究的开放式自然语言处理(NLP)框架：使用国家COVID队列协作(N3C)的案例演示。
DOI：
10.1093/jamia/ocad134
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发表时间：
2023-11-17
影响因子：
6.4
通讯作者：
Liu, Hongfang
中科院分区：
文献类型：
--
作者：
Liu, Sijia;Wen, Andrew;Wang, Liwei;He, Huan;Fu, Sunyang;Miller, Robert;Williams, Andrew;Harris, Daniel;Kavuluru, Ramakanth;Liu, Mei;Abu-el-Rub, Noor;Schutte, Dalton;Zhang, Rui;Rouhizadeh, Masoud;Osborne, John D.;He, Yongqun;Topaloglu, Umit;Hong, Stephanie S.;Saltz, Joel H.;Schaffter, Thomas;Pfaff, Emily;Chute, Christopher G.;Duong, Tim;Haendel, Melissa A.;Fuentes, Rafael;Szolovits, Peter;Xu, Hua;Liu, Hongfang
关键词：
尽管最近在临床自然语言处理（NLP）方面取得了方法学上的进步，但在翻译研究领域内采用临床NLP模型仍然受到过程异质性和人为因素变化的阻碍。同时，这些因素也大大增加了在多站点环境中开发NLP模型的难度，这对于算法的鲁棒性和通用性是必要的。在这里，我们报告了我们在开放的NLP框架中为2019冠状病毒病（COVID-19）体征和症状提取开发NLP解决方案的经验，该解决方案来自参与国家COVID队列（N3 C）的一部分站点。然后，我们从经验上强调了多站点数据对符号和统计方法的好处，并强调了联合注释和评估的必要性，以解决在这些努力过程中遇到的几个陷阱。
Despite recent methodology advancements in clinical natural language processing (NLP), the adoption of clinical NLP models within the translational research community remains hindered by process heterogeneity and human factor variations. Concurrently, these factors also dramatically increase the difficulty in developing NLP models in multi-site settings, which is necessary for algorithm robustness and generalizability. Here, we reported on our experience developing an NLP solution for Coronavirus Disease 2019 (COVID-19) signs and symptom extraction in an open NLP framework from a subset of sites participating in the National COVID Cohort (N3C). We then empirically highlight the benefits of multi-site data for both symbolic and statistical methods, as well as highlight the need for federated annotation and evaluation to resolve several pitfalls encountered in the course of these efforts.
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影响因子：
9.9
作者：
Kent, David M.;Leung, Lester Y.;Chen, Wansu
通讯作者：
Chen, Wansu
影响因子：
14.9
作者：
Köhler S;Gargano M;Matentzoglu N;Carmody LC;Lewis-Smith D;Vasilevsky NA;Danis D;Balagura G;Baynam G;Brower AM;Callahan TJ;Chute CG;Est JL;Galer PD;Ganesan S;Griese M;Haimel M;Pazmandi J;Hanauer M;Harris NL;Hartnett MJ;Hastreiter M;Hauck F;He Y;Jeske T;Kearney H;Kindle G;Klein C;Knoflach K;Krause R;Lagorce D;McMurry JA;Miller JA;Munoz-Torres MC;Peters RL;Rapp CK;Rath AM;Rind SA;Rosenberg AZ;Segal MM;Seidel MG;Smedley D;Talmy T;Thomas Y;Wiafe SA;Xian J;Yüksel Z;Helbig I;Mungall CJ;Haendel MA;Robinson PN
通讯作者：
Robinson PN
DOI：
10.1093/jamia/ocaa196
发表时间：
2021-03-01
影响因子：
6.4
作者：
Haendel, Melissa A.;Chute, Christopher G.;Gersing, Ken R.
通讯作者：
Gersing, Ken R.
影响因子：
1.5
作者：
Goodlett, Dana;Hung, Anna;Mullins, C. Daniel
通讯作者：
Mullins, C. Daniel
影响因子：
3.5
作者：
Fu, Sunyang;Leung, Lester Y.;Liu, Hongfang
通讯作者：
Liu, Hongfang