An automated medical scribe for documenting clinical encounters
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Gregory Finley, Erik Edwards, Amanda Robinson, Michael Brenndoerfer, Najmeh Sadoughi, James Fone, Nico Axtmann, Mark Miller, David Suendermann-Oeft. Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations. 2018.
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Having multiple speakers participating in the conversation and differentiating them in the audio adds a level of complexity and potential errors to ASR.
“Having multiple speakers participating in the conversation and differentiating them in the audio (speaker diarization) also adds a level of complexity and potential errors to ASR.7”
From Challenges of developing a digital scribe to reduce clinical documentation burden (2019) · cites this paper as [7]
After conversion from speech to text, NLP techniques that perform well on grammatically correct sentences break down with conversational speech because of the lack of punctuation and sentence boundaries, grammatical differences between spoken and written language, and lack of structure.
“After conversion from speech to text, NLP techniques that perform well on grammatically correct sentences break down with conversational speech because of the lack of punctuation and sentence boundaries, grammatical differences between spoken and written language, and lack of structure.7,24,25”
From Challenges of developing a digital scribe to reduce clinical documentation burden (2019) · cites this paper as [7]
Generating a medical summary from a clinician-patient conversation can be cast as a supervised learning task, where an ML algorithm is trained with a large set of past medical conversation transcripts along with the gold standard summary associated with each conversation.
“Generating a medical summary from a clinician–patient conversation can be cast as a supervised learning task,32 where an ML algorithm is trained with a large set of past medical conversation transcripts along with the gold standard summary associated with each conversation.7,33”
From Challenges of developing a digital scribe to reduce clinical documentation burden (2019) · cites this paper as [7]
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Cites
Kaldi Speech Recognition Toolkit
Povey, D. (2024) · Infoscience (Ecole Polytechnique Fédérale de Lausanne) · doi:10.57702/jb3fvbn9 · Cited by 5,270 · see citing works
Hierarchical Attention Networks for Document Classification
Yang, Z., Yang, D., Dyer, C., He, X., Smola, A. J., & Hovy, E. H. (2016) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n16-1174 · Cited by 4,874 · see citing works
Automatic sense disambiguation using machine readable dictionaries
Lesk, M. (1986) · ACM International Conference on Systems Documentation (SIGDOC) · doi:10.1145/318723.318728 · Cited by 1,787 · see citing works
Arndt, B. G., Beasley, J. W., Watkinson, M. D., Temte, J. L., Tuan, W.-J., Sinsky, C. A., & Gilchrist, V. J. (2017) · The Annals of Family Medicine · doi:10.1370/afm.2121 · Cited by 972 · cited 1× · see citing works
“A recent study from the University of Wisconsin concluded that primary care physicians spend almost two hours on tasks related to electronic medical record (EMR) systems for every one hour of direct patient care (Arndt et al., 2017).”
BackgroundWeighted finite-state transducers in speech recognition
Mohri, M., Pereira, F. M. B., & Riley, M. (2002) · Computer Speech & Language · doi:10.1006/csla.2001.0184 · Cited by 896 · see citing works
Speaker Diarization: A Review of Recent Research
Anguera, X., Bozonnet, S., Evans, N., Fredouille, C., Friedland, G. H., & Vinyals, O. (2012) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/tasl.2011.2125954 · Cited by 671 · see citing works
OpenFst: A General and Efficient Weighted Finite-State Transducer Library
Allauzen, C., Riley, M., Schalkwyk, J., Skut, W., & Mohri, M. (2007) · Springer eBooks · doi:10.1007/978-3-540-76336-9_3 · Cited by 541 · see citing works
HL7 FHIR: An Agile and RESTful approach to healthcare information exchange
Bender, D., & Sartipi, K. (2013) · Proceedings of the 26th IEEE International Symposium on Computer-Based Medical Systems · doi:10.1109/cbms.2013.6627810 · Cited by 540 · see citing works
Real versus Template-Based Natural Language Generation: A False Opposition?
van Deemter, K., Theune, M., & Krahmer, E. (2005) · Computational Linguistics · doi:10.1162/0891201053630291 · Cited by 207 · see citing works
Segregation of speakers for speech recognition and speaker identification
Gish, H., Siu, M.-H., & Rohlicek, R. (1991) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.1991.150477 · Cited by 205 · see citing works
Unsupervised Speech Activity Detection Using Voicing Measures and Perceptual Spectral Flux
Sadjadi, S. O., & Hansen, J. H. L. (2013) · IEEE Signal Processing Letters · doi:10.1109/lsp.2013.2237903 · Cited by 182 · see citing works
A review on speaker diarization systems and approaches
Moattar, M. H., & Homayounpour, M. M. (2012) · Speech Communication · doi:10.1016/j.specom.2012.05.002 · Cited by 102 · see citing works
Scribes in an Ambulatory Urology Practice: Patient and Physician Satisfaction
Koshy, S., Feustel, P. J., Hong, M., & Kogan, B. A. (2010) · The Journal of Urology · doi:10.1016/j.juro.2010.03.040 · Cited by 83 · see citing works
A Statistical NLG Framework for Aggregated Planning and Realization
Kondadadi, R., Howald, B. S., & Schilder, F. (2013) · Cited by 75 · see citing works
An ED scribe program is able to improve throughput time and patient satisfaction
Bastani, A., Shaqiri, B., Palomba, K., Bananno, D., & Anderson, W. C. (2014) · The American Journal of Emergency Medicine · doi:10.1016/j.ajem.2013.03.040 · Cited by 73 · see citing works
An economic evaluation of the costs of training a medical scribe to work in Emergency Medicine
Walker, K., Dunlop, W. A., Liew, D., Staples, M., Johnson, M., Ben‐Meir, M., Rodda, H., Turner, I., & Phillips, D. G. (2016) · Emergency Medicine Journal · doi:10.1136/emermed-2016-205934 · Cited by 35 · see citing works
Medical Speech Recognition: Reaching Parity with Humans
Edwards, E., Salloum, W., Finley, G. P., Fone, J., Cardiff, G., Miller, M., & Suendermann‐Oeft, D. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-66429-3_51 · Cited by 35 · see citing works
Self-citationCan scribes boost FPs' efficiency and job satisfaction?
Earls, S. T., Savageau, J. A., Begley, S., Saver, B. G., Sullivan, K., & Chuman, A. (2017) · PubMed · Cited by 31 · see citing works
Deep Learning for Punctuation Restoration in Medical Reports
Salloum, W., Finley, G. P., Edwards, E., Miller, M., & Suendermann‐Oeft, D. (2017) · doi:10.18653/v1/w17-2319 · Cited by 27 · see citing works
Self-citationExtracting Social Networks and Biographical Facts From Conversational Speech Transcripts
Jing, H., Kambhatla, N., & Roukos, S. (2007) · Cited by 25 · see citing works
Diverse Keyword Extraction from Conversations
Habibi, M., & Popescu-Belis, A. (2013) · Infoscience (Ecole Polytechnique Fédérale de Lausanne) · Cited by 18 · see citing works
Virtual medical scribes: making electronic medical records work for you.
Brady, K., & Shariff, A. (2014) · PubMed · Cited by 14 · see citing works
Automated Identification of Synonyms in Biomedical Acronym Sense Inventories
Melton, G. B., Moon, S., McInnes, B. T., & Pakhomov, S. (2010) · Cited by 8 · see citing works
Seamless navigation in audio files
Wellekens, C. J. (2001) · Odyssey · Cited by 3 · see citing works
Lin, S., Osborn, K., Sattler, A. L., Nelligan, I., Svec, D., Aaronson, A. J., & Schillinger, E. (2016) · Education for Primary Care · doi:10.1080/14739879.2016.1259965 · Cited by 3 · see citing works
Flagged first, then by citations
Research works printed but not linked: 2
- ACL.
- ACL.
Cited by
Sorted by citations
Neural Natural Language Processing for unstructured data in electronic health records: A review
Li, I., Pan, J. N., Goldwasser, J., Verma, N., Wong, W. P., Nuzumlalı, M. Y., Rosand, B., Li, Y., Zhang, M. M., Chang, D., et al. (2022) · Computer Science Review · doi:10.1016/j.cosrev.2022.100511 · Cited by 262 · see citing works
Artificial Intelligence for the Otolaryngologist: A State of the Art Review
Bur, A. M., Shew, M. A., & New, J. (2019) · Otolaryngology · doi:10.1177/0194599819827507 · Cited by 172 · see citing works
Challenges of developing a digital scribe to reduce clinical documentation burden
Quiroz, J. C., Laranjo, L., Kocaballi, A. B., Berkovsky, S., Rezazadegan, D., & Coiera, E. (2019) · npj Digital Medicine · doi:10.1038/s41746-019-0190-1 · Cited by 162 · see citing works
Strategies for improving physician documentation in the emergency department: a systematic review
Lorenzetti, D., Quan, H., Lucyk, K., Cunningham, C., Hennessy, D. A., Jiang, J., & Beck, C. A. (2018) · BMC Emergency Medicine · doi:10.1186/s12873-018-0188-z · Cited by 106 · see citing works
The Impact of AI Scribes on Streamlining Clinical Documentation: A Systematic Review
Sasseville, M., Yousefi, F., Ouellet, S., Naye, F., Stefan, T., Carnovale, V., Bergeron, F., Ling, L., Gheorghiu, B., Hagens, S., et al. (2025) · Healthcare · doi:10.3390/healthcare13121447 · Cited by 86 · see citing works
Digital scribe utility and barriers to implementation in clinical practice: a scoping review
Ghatnekar, S., Faletsky, A., & Nambudiri, V. E. (2021) · Health and Technology · doi:10.1007/s12553-021-00568-0 · Cited by 73 · see citing works
Extracting Symptoms and their Status from Clinical Conversations
Du, N., Chen, K., Kannan, A., Tran, L., Chen, Y., & Shafran, I. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1087 · Cited by 66 · see citing works
Generating Medical Reports from Patient-Doctor Conversations Using Sequence-to-Sequence Models
Enarvi, S., Amoia, M., Teba, M. D.-A., Delaney, B., Diehl, F., Hahn, S., Harris, K., McGrath, L., Pan, Y., Pinto, J., et al. (2020) · doi:10.18653/v1/2020.nlpmc-1.4 · Cited by 64 · see citing works
MIE: A Medical Information Extractor towards Medical Dialogues
Zhang, Y., Jiang, Z., Zhang, T., Liu, S., Cao, J., Liu, K., Liu, S., & Zhao, J. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.576 · Cited by 55 · see citing works
Coiera, E. W., & Liu, S. (2022) · Cell Reports Medicine · doi:10.1016/j.xcrm.2022.100860 · Cited by 52 · see citing works
A dataset of simulated patient-physician medical interviews with a focus on respiratory cases
Fareez, F., Parikh, T., Wavell, C., Shahab, S., Chevalier, M., Good, S., De Blasi, I., Rhouma, R., McMahon, C., Lam, J.-P., et al. (2022) · Scientific Data · doi:10.1038/s41597-022-01423-1 · Cited by 39 · see citing works
Learning to Infer Entities, Properties and their Relations from Clinical Conversations
Du, N., Wang, M., Tran, L. Q., Lee, G., & Izhak Shafran, J. (2019) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d19-1503 · Cited by 27 · see citing works
Giorgi, J., Toma, A., Xie, R., Chen, S., An, K., Zheng, G., & Wang, B. (2023) · Clinical Natural Language Processing Workshop (ClinicalNLP) · doi:10.18653/v1/2023.clinicalnlp-1.36 · Cited by 31 · see citing works
Deep Learning based Transcribing and Summarizing Clinical Conversations
Menon, N. G., Shrivastava, A., Bhavana, N., & Simon, J. (2021) · International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC) · doi:10.1109/i-smac52330.2021.9640683 · Cited by 25 · see citing works
PriMock57: A Dataset Of Primary Care Mock Consultations
Papadopoulos Korfiatis, A., Moramarco, F., Sarac, R., & Savkov, A. (2022) · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) · doi:10.18653/v1/2022.acl-short.65 · Cited by 23 · see citing works
Automating Clinical Documentation with Digital Scribes: Understanding the Impact on Physicians
Li, B., Crampton, N. H., Yeates, T., Xia, Y., Tian, X., & Truong, K. N. (2021) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445172 · Cited by 24 · see citing works
Saadat, S., Darounkolaei, M. K., Qorbani, M., Hemmat, A., & Hariri, S. (2025) · InfoScience Trends · doi:10.61186/ist.202502.01.01 · Cited by 22 · see citing works
Medication Regimen Extraction from Medical Conversations
Selvaraj, S. P. P., & Konam, S. (2020) · Studies in computational intelligence · doi:10.1007/978-3-030-53352-6_18 · Cited by 23 · see citing works
Evaluating an artificial intelligence scribe for clinical documentation
Alpert, J. M., Saper, R. B., Boose, E., Ruff, J. M., Hopkins, K., Gaskins, D., Guo, N., Schneider, J. P., Scibona, A. G., Gutierrez, J., et al. (2025) · Digital Health · doi:10.1177/20552076251395588 · Cited by 13 · see citing works
CSDM: A context-sensitive deep matching model for medical dialogue information extraction
Zeng, D., Peng, R., Jiang, C., Li, Y., & Dai, J. (2022) · Information Sciences · doi:10.1016/j.ins.2022.05.099 · Cited by 14 · see citing works
Towards an Automated SOAP Note: Classifying Utterances from Medical ConversationsPreprint
Schloss, B., & Konam, S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.08749 · Cited by 13 · see citing works
Neupane, S., Tripathi, H., Mitra, S., Bozorgzad, S., Mittal, S., Rahimi, S., & Amirlatifi, A. (2024) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata62323.2024.10825266 · Cited by 11 · see citing works
Khosla, S., Vashishth, S., Lehman, J. F., & Rosé, C. P. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.626 · Cited by 10 · see citing works
Medical Term and Status Generation From Chinese Clinical Dialogue With Multi-Granularity Transformer
Li, M., Xiang, L., Kang, X., Zhao, Y., Yu, Z., & Zong, C. (2021) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2021.3122301 · Cited by 10 · see citing works
Leveraging Large Text Corpora For End-To-End Speech Summarization
Matsuura, K., Ashihara, T., Moriya, T., Tanaka, T., Ogawa, A., Delcroix, M., & Masumura, R. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10094993 · Cited by 11 · see citing works
Using cTAKES to Build a Simple Speech Transcriber Plugin for an EMR
Wenceslao, S. J. M., & Estuar, M. R. J. E. (2019) · International Conference on Medical and Health Informatics (ICMHI) · doi:10.1145/3340037.3340044 · Cited by 9 · see citing works
Electronic health record data mining for artificial intelligence healthcare
Lin, A. L., Chen, W. C., & Hong, J. C. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-821259-2.00008-9 · Cited by 10 · see citing works
The Medical Scribe: Corpus Development and Model Performance AnalysesPreprint
Izhak Shafran, J., Du, N., Tran, L. Q., Perry, A., Keyes, L., Knichel, M., Domin, A., Huang, L., Chen, Y., Li, G., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.11531 · Cited by 9 · see citing works
‘Beach’ to ‘Bitch’: Inadvertent Unsafe Transcription of Kids’ Content on YouTube
Ramesh, K., KhudaBukhsh, A. R., & Kumar, S. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i11.21470 · Cited by 7 · see citing works
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Edwards, E., Brenndoerfer, M., Robinson, A., Sadoughi, N., Finley, G. P., Korenevsky, M. L., Axtmann, N., Miller, M. A., & Suendermann‐Oeft, D. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-99579-3_13 · Cited by 7 · see citing works
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Du, N., Wang, M., Tran, L. Q., Li, G., & Izhak Shafran, J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1908.11536 · Cited by 7 · see citing works
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Hu, Z., Ni, Z., Shi, J. Y., Xu, S., & Xu, B. (2024) · Machine Intelligence Research · doi:10.1007/s11633-023-1461-5 · Cited by 6 · see citing works
BERT-Driven Automation in Electronic Health Record Management System
Khan, F. A., & Hajiarbabi, M. (2024) · IEEE SoutheastCon · doi:10.1109/southeastcon52093.2024.10500252 · Cited by 6 · see citing works
Landsman, D., Abdelbasit, A., Wang, C., Guerzhoy, M., Joshi, U., Mathew, S., Pou-Prom, C., Dai, D., Pequegnat, V., Murray, J., et al. (2021) · PLoS ONE · doi:10.1371/journal.pone.0247872 · Cited by 5 · see citing works
Consultation Checklists: Standardising the Human Evaluation of Medical Note Generation
Savkov, A., Moramarco, F., Korfiatis, A. P., Perera, M., Belz, A., & Reiter, E. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-industry.10 · Cited by 5 · see citing works
Quiroz, J. C., Laranjo, L., Tufănaru, C., Kocaballi, A. B., Rezazadegan, D., Berkovsky, S., & Coiera, E. W. (2020) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2020.104324 · Cited by 4 · see citing works
Alignment Annotation for Clinic Visit Dialogue to Clinical Note Sentence Language Generation
Yim, W., Yetişgen, M., Huang, J., & Grossman, M. (2020) · Language Resources and Evaluation · Cited by 3 · see citing works
KNSE: A Knowledge-aware Natural Language Inference Framework for Dialogue Symptom Status Recognition
Chen, W., Wei, S., Wei, Z., & Huang, X. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.652 · Cited by 3 · see citing works
Development of an ASR System for Medical Conversations
Renato, A. C., Luna, D. R., & Benítez, S. (2024) · Studies in health technology and informatics · doi:10.3233/shti231048 · Cited by 3 · see citing works
Symbolic and Statistical Learning Approaches to Speech Summarization: A Scoping Review
Rezazadegan, D., Berkovsky, S., Quiroz, J. C., Kocaballi, A. B., Wang, Y., Laranjo, L., & Coiera, E. W. (2021) · Computer Speech & Language · doi:10.1016/j.csl.2021.101305 · Cited by 3 · see citing works
A Knowledge-aware Machine Reading Comprehension Framework for Dialogue Symptom Diagnosis
Zhao, X., Cheng, Y., Xiang, W., Wang, X., Han, L., Shang, J., & Peng, S. (2021) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm52615.2021.9669717 · Cited by 3 · see citing works
Automatically Extracting Information in Medical Dialogue: Expert System and Attention for Labelling
Wang, X., & Tang, X. (2023) · Studies in computational intelligence · doi:10.1007/978-3-031-36938-4_12 · Cited by 3 · see citing works
How are you? Introducing stress-based text tailoringPreprint
Balloccu, S., Reiter, E., Johnstone, A. M., & Fyfe, C. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.09970 · Cited by 3 · see citing works
Document-aware Information Extractor for Chinese Medical Dialogue
He, Y., Li, Y., & Hou, S. (2021) · International Workshop on Artificial Intelligence and Education (WAIE) · doi:10.1109/waie54146.2021.00021 · Cited by 2 · see citing works
Beyond calories: evaluating how tailored communication reduces emotional load in diet-coaching
Balloccu, S., & Reiter, E. (2022) · doi:10.18653/v1/2022.humeval-1.5 · Cited by 2 · see citing works
Zhang, C., Chen, T., Chen, J., Wang, H., Shi, J., Luo, Z., & Zhang, M. (2024) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3627673.3679671 · Cited by 2 · see citing works
DIPE: a diagnosis-assisted inquiry point extractor towards medical dialogues
Li, Q., Huang, F., Ge, L., & Zhao, J. (2024) · Applied Intelligence · doi:10.1007/s10489-024-06138-x · Cited by 2 · see citing works
Clinical note section classification on doctor-patient conversations in low-resourced settings
Chen, Z., Kim, J., Liu, Y., & Narayanan, S. S. (2023) · doi:10.18653/v1/2023.nlpmc-1.1 · Cited by 1 · see citing works
Oncology field diagnosis and treatment report generation
Dianzhen, L., & Yao, F. (2025) · IEEE International Conference on Pattern Recognition and Machine Learning (PRML) · doi:10.1109/prml66062.2025.11159685 · Cited by 1 · see citing works
A Language Model for Pediatric Occupational Therapy Documentation: Model Development and Pilot Study
DiMaio, R., Tuinstra, T., Yu, T., Koshy, I., Wylie-Toal, B., & Tripp, B. (2026) · JMIR AI · doi:10.2196/73274 · Cited by 1 · see citing works
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Krishna, N. M. S., Priyakanth, R., Sharma, C. S., Aalla, C. B., Kolluru, S., Yalavarthy, G., & Maheswari, K. S. U. (2024) · Lecture notes in electrical engineering · doi:10.1007/978-981-97-0767-6_32 · Cited by 1 · see citing works
Speaker Diarization: A Top-Down Approach Using Syllabic Phonology
Edwards, E., Robinson, A., Sadoughi, N., Finley, G. P., Korenevsky, M. L., Brenndoerfer, M., Axtmann, N., Miller, M., & Suendermann‐Oeft, D. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-99579-3_14
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Ren, J., Cao, T., Yang, Y., Zhang, Y., Chen, X., Feng, T., Chang, B., Sui, Z., Zhao, R., Zheng, Y., et al. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-10986-7_17
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- Authors
- Finley, G., Edwards, E., Robinson, A., Brenndoerfer, M., Sadoughi, N., Fone, J., Axtmann, N., Miller, M., & Suendermann-Oeft, D.
- Affiliations
- University of California, Berkeley (US)
- Baden-Wuerttemberg Cooperative State University (DE)
- Corresponding author
- Finley, Gregory
- ORCID
- Miller, Mark
- Venue
- Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations
- Published
- 2018
- Pages
- 11-15
- OpenAlex
- W2806237610
- Cited by
- 61
- References
- 25
- Citations by year
- 2026: 1 · 2025: 6 · 2024: 10 · 2023: 7 · 2022: 10 · 2021: 8 · 2020: 10 · 2019: 6 · 2018: 3
- FWCI
- 7.86
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) (publishedVersion, cc-by)
- Topics
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
- Health Sciences › Nursing › Issues, ethics and legal aspects › Nursing Diagnosis and Documentation
- Keywords
- medical scribes, clinical documentation, automated documentation, clinical text generation, clinical natural language processing
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