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702 result(s) for 'natural language processing' within BMC Medical Informatics and Decision Making

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  1. The text descriptions in electronic medical records are a rich source of information. We have developed a Health Information Text Extraction (HITEx) tool and used it to extract key findings for a research stud...

    Authors: Qing T Zeng, Sergey Goryachev, Scott Weiss, Margarita Sordo, Shawn N Murphy and Ross Lazarus
    Citation: BMC Medical Informatics and Decision Making 2006 6:30
  2. Efficient exploration of knowledge for the treatment of recurrent glioblastoma (GBM) is critical for both clinicians and researchers. However, due to the large number of clinical trials and published articles, se...

    Authors: Bum-Sup Jang, Andrew J. Park and In Ah Kim
    Citation: BMC Medical Informatics and Decision Making 2022 22:267
  3. In this study, we examined two state-of-the-art transformer-based natural language processing (NLP) models, including BERT and RoBERTa...

    Authors: Zehao Yu, Xi Yang, Gianna L. Sweeting, Yinghan Ma, Skylar E. Stolte, Ruogu Fang and Yonghui Wu
    Citation: BMC Medical Informatics and Decision Making 2022 22(Suppl 3):255

    This article is part of a Supplement: Volume 22 Supplement 3

  4. Important clinical information of patients is present in unstructured free-text fields of Electronic Health Records (EHRs). While this information can be extracted using clinical Natural Language Processing (cNLP...

    Authors: Guillermo Argüello-González, José Aquino-Esperanza, Daniel Salvador, Rosa Bretón-Romero, Carlos Del Río-Bermudez, Jorge Tello and Sebastian Menke
    Citation: BMC Medical Informatics and Decision Making 2023 23:216
  5. The identification of patients who pose an epidemic hazard when they are admitted to a health facility plays a role in preventing the risk of hospital acquired infection. An automated clinical decision support sy...

    Authors: Solweig Gerbier, Olga Yarovaya, Quentin Gicquel, Anne-Laure Millet, Véronique Smaldore, Véronique Pagliaroli, Stefan Darmoni and Marie-Hélène Metzger
    Citation: BMC Medical Informatics and Decision Making 2011 11:50
  6. Modernizing medical education by using artificial intelligence and other new technologies to improve the clinical thinking ability of medical students is an important research topic in recent years. Prominent med...

    Authors: Mengying Wang, Zhen Sun, Mo Jia, Yan Wang, Heng Wang, Xingxing Zhu, Lianzhong Chen and Hong Ji
    Citation: BMC Medical Informatics and Decision Making 2022 22:60
  7. We presented RegEMR, an artificial intelligence tool composed of a rule-based natural language processing (NLP) extractor and a knowledge-based...

    Authors: Jie Cai, Shenglin Chen, Siyun Guo, Suidong Wang, Lintong Li, Xiaotong Liu, Keming Zheng, Yudong Liu and Shiling Chen
    Citation: BMC Medical Informatics and Decision Making 2023 23:126
  8. Cardiac magnetic resonance (CMR) imaging is important for diagnosis and risk stratification of hypertrophic cardiomyopathy (HCM) patients. However, collection of information from large numbers of CMR reports by m...

    Authors: Nakeya Dewaswala, David Chen, Huzefa Bhopalwala, Vinod C. Kaggal, Sean P. Murphy, J. Martijn Bos, Jeffrey B. Geske, Bernard J. Gersh, Steve R. Ommen, Philip A. Araoz, Michael J. Ackerman and Adelaide M. Arruda-Olson
    Citation: BMC Medical Informatics and Decision Making 2022 22:272
  9. Kampo medicine is widely used in Japan; however, most physicians and pharmacists have insufficient knowledge and experience in it. Although a chatbot-style system using machine learning and natural language processing

    Authors: Ayako Maeda-Minami, Tetsuhiro Yoshino, Tetsuro Yumoto, Kayoko Sato, Atsunobu Sagara, Kenjiro Inaba, Hidenori Kominato, Takao Kimura, Tetsuya Takishita, Gen Watanabe, Tomonori Nakamura, Yasunari Mano, Yuko Horiba, Kenji Watanabe and Junzo Kamei
    Citation: BMC Medical Informatics and Decision Making 2023 23:119
  10. Evaluating patients’ experiences is essential when incorporating the patients’ perspective in improving healthcare. Experiences are mainly collected using closed-ended questions, although the value of open-ended ...

    Authors: Marieke M. van Buchem, Olaf M. Neve, Ilse M. J. Kant, Ewout W. Steyerberg, Hileen Boosman and Erik F. Hensen
    Citation: BMC Medical Informatics and Decision Making 2022 22:183
  11. Extracting metastatic information from previous radiologic-text reports is important, however, laborious annotations have limited the usability of these texts. We developed a deep-learning model for extracting...

    Authors: Hyung Jun Park, Namu Park, Jang Ho Lee, Myeong Geun Choi, Jin-Sook Ryu, Min Song and Chang-Min Choi
    Citation: BMC Medical Informatics and Decision Making 2022 22:229
  12. In our study, we created a natural language processing (NLP) workflow to analyze electronic medical ... domain criteria using a pre-trained transformer-based natural language model, all-mpnet-base-v2. We...

    Authors: Oshin Miranda, Sophie Marie Kiehl, Xiguang Qi, M. Daniel Brannock, Thomas Kosten, Neal David Ryan, Levent Kirisci, Yanshan Wang and LiRong Wang
    Citation: BMC Medical Informatics and Decision Making 2024 24:154
  13. A systematic approach to MRI protocol assignment is essential for the efficient delivery of safe patient care. Advances in natural language processing (NLP) allow for the development of...

    Authors: Yeshwant Reddy Chillakuru, Shourya Munjal, Benjamin Laguna, Timothy L. Chen, Gunvant R. Chaudhari, Thienkhai Vu, Youngho Seo, Jared Narvid and Jae Ho Sohn
    Citation: BMC Medical Informatics and Decision Making 2021 21:213
  14. Natural language processing (NLP) tools can facilitate the extraction...

    Authors: Jacqueline Peng, Mengge Zhao, James Havrilla, Cong Liu, Chunhua Weng, Whitney Guthrie, Robert Schultz, Kai Wang and Yunyun Zhou
    Citation: BMC Medical Informatics and Decision Making 2020 20(Suppl 11):322

    This article is part of a Supplement: Volume 20 Supplement 11

  15. The Institute of Medicine has identified patient safety as a key goal for health care in the United States. Detecting vaccine adverse events is an important public health activity that contributes to patient safe...

    Authors: Herman D Tolentino, Michael D Matters, Wikke Walop, Barbara Law, Wesley Tong, Fang Liu, Paul Fontelo, Katrin Kohl and Daniel C Payne
    Citation: BMC Medical Informatics and Decision Making 2007 7:3
  16. Differentiating between ulcerative colitis (UC), Crohn’s disease (CD) and intestinal tuberculosis (ITB) using endoscopy is challenging. We aimed to realize automatic differential diagnosis among these diseases...

    Authors: Yuanren Tong, Keming Lu, Yingyun Yang, Ji Li, Yucong Lin, Dong Wu, Aiming Yang, Yue Li, Sheng Yu and Jiaming Qian
    Citation: BMC Medical Informatics and Decision Making 2020 20:248
  17. In Chile, a patient needing a specialty consultation or surgery has to first be referred by a general practitioner, then placed on a waiting list. The Explicit Health Guarantees (GES in Spanish) ensures, by la...

    Authors: Fabián Villena, Jorge Pérez, René Lagos and Jocelyn Dunstan
    Citation: BMC Medical Informatics and Decision Making 2021 21:208

    The Correction to this article has been published in BMC Medical Informatics and Decision Making 2021 21:220

  18. Systemic lupus erythematosus (SLE) is a rare autoimmune disorder characterized by an unpredictable course of flares and remission with diverse manifestations. Lupus nephritis, one of the major disease manifestati...

    Authors: Yu Deng, Jennifer A. Pacheco, Anika Ghosh, Anh Chung, Chengsheng Mao, Joshua C. Smith, Juan Zhao, Wei-Qi Wei, April Barnado, Chad Dorn, Chunhua Weng, Cong Liu, Adam Cordon, Jingzhi Yu, Yacob Tedla, Abel Kho…
    Citation: BMC Medical Informatics and Decision Making 2024 22(Suppl 2):348

    This article is part of a Supplement: Volume 22 Supplement 2

  19. Herein, we describe a system that securely gathers microbiology data from the Department of Veterans Affairs (VA) network of databases. Using natural language processing methods, we applied an information extract...

    Authors: Makoto Jones, Scott L DuVall, Joshua Spuhl, Matthew H Samore, Christopher Nielson and Michael Rubin
    Citation: BMC Medical Informatics and Decision Making 2012 12:34
  20. For this project, 80 medical problems were selected for their frequency of use in our future clinical field of evaluation (cardiovascular). We have developed an Automated Problem List system composed of two main ...

    Authors: Stephane Meystre and Peter J Haug
    Citation: BMC Medical Informatics and Decision Making 2005 5:30
  21. Twitter messages (tweets) contain various types of topics in our daily life, which include health-related topics. Analysis of health-related tweets would help us understand health conditions and concerns encounte...

    Authors: Son Doan, Elly W. Yang, Sameer S. Tilak, Peter W. Li, Daniel S. Zisook and Manabu Torii
    Citation: BMC Medical Informatics and Decision Making 2019 19(Suppl 3):79

    This article is part of a Supplement: Volume 19 Supplement 3

  22. In this study, we developed a rule-based natural language processing (NLP) algorithm for identification of twenty...

    Authors: Yanshan Wang, Saeed Mehrabi, Sunghwan Sohn, Elizabeth J. Atkinson, Shreyasee Amin and Hongfang Liu
    Citation: BMC Medical Informatics and Decision Making 2019 19(Suppl 3):73

    This article is part of a Supplement: Volume 19 Supplement 3

  23. Lung cancer is the second most common cancer for men and women; the wide adoption of electronic health records (EHRs) offers a potential to accelerate cohort-related epidemiological studies using informatics appr...

    Authors: Liwei Wang, Lei Luo, Yanshan Wang, Jason Wampfler, Ping Yang and Hongfang Liu
    Citation: BMC Medical Informatics and Decision Making 2019 19(Suppl 5):239

    This article is part of a Supplement: Volume 19 Supplement 5

  24. Accurate information in provider directories are vital in health care including health information exchange, health benefits exchange, quality reporting, and in the reimbursement and delivery of care. Maintaining...

    Authors: Matthew J. Cook, Lixia Yao and Xiaoyan Wang
    Citation: BMC Medical Informatics and Decision Making 2019 19(Suppl 3):80

    This article is part of a Supplement: Volume 19 Supplement 3

  25. Despite widespread use, the safety of dietary supplements is open to doubt due to the fact that they can interact with prescribed medications, leading to dangerous clinical outcomes. Electronic health records ...

    Authors: Yadan Fan and Rui Zhang
    Citation: BMC Medical Informatics and Decision Making 2018 18(Suppl 2):51

    This article is part of a Supplement: Volume 18 Supplement 2

  26. Patients and their loved ones often report symptoms or complaints of cognitive decline that clinicians note in free clinical text, but no structured screening or diagnostic data are recorded. These symptoms/compl...

    Authors: Robert B. Penfold, David S. Carrell, David J. Cronkite, Chester Pabiniak, Tammy Dodd, Ashley MH Glass, Eric Johnson, Ella Thompson, H. Michael Arrighi and Paul E. Stang
    Citation: BMC Medical Informatics and Decision Making 2022 22:129
  27. We examined the comparative performance of structured, diagnostic codes vs. natural language processing (NLP) of unstructured text for screening...

    Authors: Qiu-Yue Zhong, Elizabeth W. Karlson, Bizu Gelaye, Sean Finan, Paul Avillach, Jordan W. Smoller, Tianxi Cai and Michelle A. Williams
    Citation: BMC Medical Informatics and Decision Making 2018 18:30
  28. This paper presents a portable phenotyping system that is capable of integrating both rule-based and statistical machine learning based approaches.

    Authors: Himanshu Sharma, Chengsheng Mao, Yizhen Zhang, Haleh Vatani, Liang Yao, Yizhen Zhong, Luke Rasmussen, Guoqian Jiang, Jyotishman Pathak and Yuan Luo
    Citation: BMC Medical Informatics and Decision Making 2019 19(Suppl 3):78

    This article is part of a Supplement: Volume 19 Supplement 3

  29. Diagnostic accuracy might be improved by algorithms that searched patients’ clinical notes in the electronic health record (EHR) for signs and symptoms of diseases such as multiple sclerosis (MS). The focus th...

    Authors: Herbert S. Chase, Lindsey R. Mitrani, Gabriel G. Lu and Dominick J. Fulgieri
    Citation: BMC Medical Informatics and Decision Making 2017 17:24
  30. The aim of the present study is to investigate the use of semantics for biomedical resources annotation with domain specific ontologies and exploit Natural Language Processing methods in empowering the non-Inform...

    Authors: Pepi Sfakianaki, Lefteris Koumakis, Stelios Sfakianakis, Galatia Iatraki, Giorgos Zacharioudakis, Norbert Graf, Kostas Marias and Manolis Tsiknakis
    Citation: BMC Medical Informatics and Decision Making 2015 15:77
  31. Natural language processing (NLP) has become an increasingly significant ... of NLP methods and applications for medical information processing are available. It is of great significance...

    Authors: Xieling Chen, Haoran Xie, Fu Lee Wang, Ziqing Liu, Juan Xu and Tianyong Hao
    Citation: BMC Medical Informatics and Decision Making 2018 18(Suppl 1):14

    This article is part of a Supplement: Volume 18 Supplement 1

  32. Falls among older adults are both a common reason for presentation to the emergency department, and a major source of morbidity and mortality. It is critical to identify fall patients quickly and reliably duri...

    Authors: Brian W. Patterson, Gwen C. Jacobsohn, Manish N. Shah, Yiqiang Song, Apoorva Maru, Arjun K. Venkatesh, Monica Zhong, Katherine Taylor, Azita G. Hamedani and Eneida A. Mendonça
    Citation: BMC Medical Informatics and Decision Making 2019 19:138
  33. Manual coding of phenotypes in brain radiology reports is time consuming. We developed a natural language processing (NLP) algorithm to enable automatic identification...

    Authors: Emily Wheater, Grant Mair, Cathie Sudlow, Beatrice Alex, Claire Grover and William Whiteley
    Citation: BMC Medical Informatics and Decision Making 2019 19:184
  34. There are often multiple lesions in breast magnetic resonance imaging (MRI) reports and radiologists usually focus on describing the index lesion that is most crucial to clinicians in determining the management a...

    Authors: Yi Liu, Qing Liu, Chao Han, Xiaodong Zhang and Xiaoying Wang
    Citation: BMC Medical Informatics and Decision Making 2019 19:288
  35. The medical subdomain of a clinical note, such as cardiology or neurology, is useful content-derived metadata for developing machine learning downstream applications. To classify the medical subdomain of a note a...

    Authors: Wei-Hung Weng, Kavishwar B. Wagholikar, Alexa T. McCray, Peter Szolovits and Henry C. Chueh
    Citation: BMC Medical Informatics and Decision Making 2017 17:155
  36. To examine the association between the medical imaging utilization and information related to patients’ socioeconomic, demographic and clinical factors during the patients’ ED visits; and to develop predictive...

    Authors: Xingyu Zhang, M. Fernanda Bellolio, Pau Medrano-Gracia, Konrad Werys, Sheng Yang and Prashant Mahajan
    Citation: BMC Medical Informatics and Decision Making 2019 19:287
  37. Patient experience surveys often include free-text responses. Analysis of these responses is time-consuming and often underutilized. This study examined whether Natural Language Processing (NLP) techniques could ...

    Authors: Simone A. Cammel, Marit S. De Vos, Daphne van Soest, Kristina M. Hettne, Fred Boer, Ewout W. Steyerberg and Hileen Boosman
    Citation: BMC Medical Informatics and Decision Making 2020 20:97
  38. Accurate identification of venous thromboembolism (VTE) is critical to develop replicable epidemiological studies and rigorous predictions models. Traditionally, VTE studies have relied on international classific...

    Authors: Jeffrey Wang, Joao Souza de Vale, Saransh Gupta, Pulakesh Upadhyaya, Felipe A. Lisboa, Seth A. Schobel, Eric A. Elster, Christopher J. Dente, Timothy G. Buchman and Rishikesan Kamaleswaran
    Citation: BMC Medical Informatics and Decision Making 2023 23:262
  39. Radiology reports are a rich resource for biomedical research. Prior to utilization, trained experts must manually review reports to identify discrete outcomes. The Audiological and Genetic Database (AudGenDB)...

    Authors: Aaron J. Masino, Robert W. Grundmeier, Jeffrey W. Pennington, John A. Germiller and E. Bryan Crenshaw III
    Citation: BMC Medical Informatics and Decision Making 2016 16:65
  40. We outline the fundamental differences between Medical Informatics and theoretical sciences and propose that Medical Informatics research should advance individual knowledge processing (case-based reasoning) and ...

    Authors: Stefan V Pantazi, José F Arocha and Jochen R Moehr
    Citation: BMC Medical Informatics and Decision Making 2004 4:19
  41. Transformer...is an attention-based architecture proven the state-of-the-art model in natural language processing (NLP). To reduce the difficulty of...transformer-based models in medical language understanding an...

    Authors: Feihong Yang, Xuwen Wang, Hetong Ma and Jiao Li
    Citation: BMC Medical Informatics and Decision Making 2021 21(Suppl 2):90

    This article is part of a Supplement: Volume 21 Supplement 2

  42. Clinical trial protocols are the foundation for advancing medical sciences, however, the extraction of accurate and meaningful information from the original clinical trials is very challenging due to the complex ...

    Authors: Jianfu Li, Qiang Wei, Omid Ghiasvand, Miao Chen, Victor Lobanov, Chunhua Weng and Hua Xu
    Citation: BMC Medical Informatics and Decision Making 2022 22(Suppl 3):235

    This article is part of a Supplement: Volume 22 Supplement 3

  43. Natural language processing (NLP) tasks in the health domain ... task at hand. Recently, pretrained large-scale language models such as the Bidirectional Encoder Representations...

    Authors: Xuedong Li, Walter Yuan, Dezhong Peng, Qiaozhu Mei and Yue Wang
    Citation: BMC Medical Informatics and Decision Making 2022 21(Suppl 9):377

    This article is part of a Supplement: Volume 21 Supplement 9

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