Flexible NLP toolkit for automatic curation of outcomes for breast cancer patients

灵活的 NLP 工具包，用于自动治疗乳腺癌患者的结果

• 批准号: 10675009
• 负责人: Imon Banerjee
• 金额: $ 54.09万
• 依托单位: MAYO CLINIC ARIZONA
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-01 至 2027-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10675009
• 关键词: Adherence; Adopted; Age; Anxiety; Biological; Black Populations; Breast; Breast Cancer Patient; Breast Cancer Risk Factor; Breast Cancer Treatment; California; Cancer Burden; Cancer Patient; Cessation of life; Clinic; Clinical; Collaborations; Communication; Computerized Medical Record; Computers; Data; Data Collection; Databases; Detection; Development; Diagnosis; Diagnostic Neoplasm Staging; Disease; Disparity; Distant Metastasis; Early Diagnosis; Epidemiologist; Ethnic Origin; Evaluation; Fatigue; Funding; Future; Guidelines; Health system; Healthcare; Hispanic; Hispanic Populations; Hospitals; Hour; Human; Immunotherapy; Informatics; Institution; Insurance Coverage; Intervention; Lead; Learning; Malignant Neoplasms; Malignant neoplasm of prostate; Manuals; Medical center; Mental Depression; Methodology; Methods; Mission; Modeling; Morbidity - disease rate; Natural Language Processing; Nausea; Neoplasm Metastasis; Not Hispanic or Latino; Oncologist; Operative Surgical Procedures; Outcome; Paper; Pathology; Pathology Report; Patient-Focused Outcomes; Patients; Pattern; Performance; Population; Population Heterogeneity; Primary Neoplasm; Process; Prognosis; Psyche structure; Quality of life; Race; Radiation; Radiology Specialty; Recording of previous events; Recurrence; Recurrent Malignant Neoplasm; Registries; Reporting; Research Personnel; Role; Running; Scientist; Site; Software Tools; Stage at Diagnosis; Structure; Team Nursing; Technology; Testing; Text; Time; Tumor Subtype; Tumor stage; University Hospitals; Validation; Visit; Woman; anticancer research; artificial intelligence algorithm; biological systems; breast cancer survival; cancer classification; cancer prevention; cancer recurrence; cancer site; cancer survival; cancer therapy; chemotherapy; clinical center; clinical encounter; comorbidity; cost; data curation; data integration; experience; flexibility; follow-up; hormone therapy; informatics tool; low income country; malignant breast neoplasm; molecular subtypes; multidisciplinary; multimodal data; neoplasm registry; open source; outcome prediction; patient population; physical conditioning; population based; radiologist; relational database; socioeconomic disparity; surveillance data; survival disparity; tool; treatment and outcome; treatment planning; trend

Project summary/Abstract Breast cancer has the largest number of new cases in world (11.7%). Although the prognosis of breast cancer patients is generally favorable due to early detection and comprehensive treatment, 20%–30% of patients will still develop distant metastases and cases with progressive stage only have a median two-year survival time. Breast cancer is widely recognized as a heterogeneous disease in the sense of both primary tumor metastatic capacity and time to metastatic spread of disease. High-quality population-based cancer surveillance data are needed to: (1) describe cancer burden, patterns, and outcomes in order to (2) inform cancer prevention, detection and control activities; and (3) evaluate interventions on the basis of past and future trends so that optimal approaches to alleviate burden and suffering from cancer can be adopted. However, the laborious manual curation process makes the population wise surveillance data collection challenging. It has been shown in studies that a large percentage of total registry cost is devoted to labor for data curation, even in the low-income countries. In this project, our mission is to build a flexible NLP toolset that can be executed locally at the institution level and will curate the clinical and patient-centered outcomes of breast cancer patients by parsing longitudinally acquired clinic notes, radiology and pathology reports. In order to test the generalizability of the tools and to initiate their deployment for data collection, we will partner with both Georgia SEER and California state cancer registry and will curate the outcome data of past 10-years breast cancer patients from two institutions across US representing diverse patient populations - Emory University hospital (Georgia) and Stanford Medical Center (California). We will leverage the previously developed tools and technologies and extend them to automatically curate the clinical and patient- centered outcome data – recurrence date and site of recurrence, treatment administered, mental and physical outcomes – from clinic notes and convert these into structured and query-able format. The NLP tools will be dockerized and run locally at the hospital registry level for automated outcome curation. Finally, the NLP extracted outcomes will be shared with State Cancer registry for evaluation. From a methodological perspective, the framework and the open-source software tools developed can be employed for cancer research beyond the scope of our project for curating outcomes regardless of the problem domain.

项目总结/文摘

项目成果

A large language model-based generative natural language processing framework fine-tuned on clinical notes accurately extracts headache frequency from electronic health records.

基于大型语言模型的生成自然语言处理框架根据临床记录进行微调，可以从电子健康记录中准确提取头痛频率。

• DOI: 10.1111/head.14702
• 发表时间: 2024
• 期刊: Headache
• 影响因子: 5
• 作者: Chiang,Chia-Chun;Luo,Man;Dumkrieger,Gina;Trivedi,Shubham;Chen,Yi-Chieh;Chao,Chieh-Ju;Schwedt,ToddJ;Sarker,Abeed;Banerjee,Imon
• 通讯作者: Banerjee,Imon

A Large Language Model-Based Generative Natural Language Processing Framework Finetuned on Clinical Notes Accurately Extracts Headache Frequency from Electronic Health Records.

基于大型语言模型的生成自然语言处理框架根据临床记录进行微调，可从电子健康记录中准确提取头痛频率。

• DOI: 10.1101/2023.10.02.23296403
• 发表时间: 2023
• 期刊: medRxiv : the preprint server for health sciences
• 作者: Chiang,Chia-Chun;Luo,Man;Dumkrieger,Gina;Trivedi,Shubham;Chen,Yi-Chieh;Chao,Chieh-Ju;Schwedt,ToddJ;Sarker,Abeed;Banerjee,Imon
• 通讯作者: Banerjee,Imon

Graph convolutional network-based fusion model to predict risk of hospital acquired infections.

基于图卷积网络的融合模型来预测医院获得性感染的风险。

• DOI: 10.1093/jamia/ocad045
• 发表时间: 2023
• 期刊: Journal of the American Medical Informatics Association : JAMIA
• 作者: Tariq,Amara;Lancaster,Lin;Elugunti,Praneetha;Siebeneck,Eric;Noe,Katherine;Borah,Bijan;Moriarty,James;Banerjee,Imon;Patel,BhavikN
• 通讯作者: Patel,BhavikN