Skip to content

BIDS-Xu-Lab/UNER_Prompt_Library

BranchesTags

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

30 Commits
Extracted prompt
Extracted prompt
 
 
Original paper
Original paper
 
 
README.md
README.md
 
 

Repository files navigation

A Prompt Library for Efficient Clinical Entity Recognition Using Large Language Models

Framework Overview

Original paper: This folder include all 70 articles in PDF format.

Extracted prompt: This folder includes the prompts using extracted information from the 70 articles.

Table 1. Summary of collected articles categorized by disease focused with reference

Disease Category Included Diseases Included Clinical Note Type Included Entities Number of Papers
Neurological Diseases Subdural hematoma (SDH)1, Acute Stroke2, Stroke3, Brain Injuries4, Alzheimer's Disease5 Clinical Letters, Radiology Reports, Clinical trial notes Stroke Attributes, Brain Injury Characteristics, Alzheimer's Disease Factors, Clinical and Diagnostic Parameters 5
Cardiovascular Diseases Heart Disease6-9, Cardiac Function10, Peripheral Arterial Disease11 Clinical Reports, Discharge Documents, Radiology Notes, CT reports Cardiovascular Risk Factors, ARDS Treatment and Management, PAD Status, Cardiac Function Measures 6
Cancers Lung Cancer12, Melanoma13, Breast Cancer14, 15, Prostate Cancer16, Liver cancer17-19, Metastatic Disease20, Gastroesophageal cancer21, Mutli-cancer22, General cancer23, 24 Pathology reports, Drug Notes, Electronic Health Records (EHR), Radiology Reports, Operation Notes Cancer Type and Location, Diagnostic and Prognostic Markers, Treatment and Genetic Information, Metastasis and Histological Details 13
Respiratory Diseases Acute Respiratory Distress Syndrome (ARDS)25, Pulmonary nodules26 CT reports, Discharge documents Pulmonary Nodule, ARDS, Mechanical Ventilation, ICU Admission, PICS Symptoms 2
Substance-Related Disorders Substance use27-29, Smoking status30 Clinical Reports, Medical discharge records Alcohol Use, Drug Use, Nicotine Use, Smoking Status, Non-Smoker, Current Smoker, Unknown 4
Injuries and Related Conditions Bone Fractures31, Wound Information32, Fall-related information33 Radiology Reports, Clinical Notes Fracture Specifics, Wound Care Attributes, Fall Prevention Measures 3
Social and Economic Issues Social Determinants of Health (SDoH)34, 35 Clinical Notes, Social history sections Alcohol, Drug, Tobacco, Employment, Living Status, Substance Use, Employment, Living Status 2
Blood and Circulatory System Disorders Venous thromboembolisms (VTE)36, Bleeding events37 Narrative radiology reports, EHR Thrombosis and Pulmonary Embolism Details, Bleeding Event Characteristics 2
Eye Diseases Diabetic Retinopathy38, Glaucoma39, 40 Radiological Reports, Clinical Progress Notes, Ophthalmology Notes Eye Disease Indicators, Treatment and Medication Adherence 3
Infectious Diseases COVID-1941, Invasive fungal infection42, 43 Radiological Reports COVID-19 diagnostics and symptoms, infection, infection risk factors, abnormalities 3
Radiation and Related Treatments Radiation Therapy51 Clinical Texts Radiation Therapy Parameters 1
Other Specific Conditions Preterm birth risk44, Craniofacial and oral phenotypes45, Colonoscopy46, 47, Thyroid Nodules48, Geriatric syndromes49, Cartilage diseases50 Medical notes, Clinical narratives, Colonoscopy Reports, Radiology Reports, Electronic Medical Records, Knee MRI Pregnancy Risks, Craniofacial and Oral Health, Colonoscopy Results, Thyroid and Cartilage Condition Details, Geriatric Syndromes 7
Others General52-70 Clinical Reports, Radiology Reports, HER, Hospital Discharge Summaries, Operative Notes, Radiology Reports, EHR, Physician’s free-text notes Identifications, Disorder, Medicine related, Observation uncertainty, Problems, Treatments, Tests, Drugs 19

References

  1. Pruitt P, Naidech A, Van Ornam J, Borczuk P, Thompson W. A natural language processing algorithm to extract characteristics of subdural hematoma from head CT reports. Emergency Radiology. 2019;26:301-6.
  2. Cutforth M, Watson H, Brown C, Wang C, Thomson S, Fell D, et al. Acute stroke CDS: automatic retrieval of thrombolysis contraindications from unstructured clinical letters. Frontiers in Digital Health. 2023;5:1186516.
  3. Alex B, Grover C, Tobin R, Sudlow C, Mair G, Whiteley W. Text mining brain imaging reports. Journal of biomedical semantics. 2019;10:1-11.
  4. Torres-Lopez VM, Rovenolt GE, Olcese AJ, Garcia GE, Chacko SM, Robinson A, et al. Development and validation of a model to identify critical brain injuries using natural language processing of text computed tomography reports. JAMA network open. 2022;5(8):e2227109-e.
  5. Sun Z, Tao C, editors. Named entity recognition and normalization for alzheimer’s disease eligibility criteria. 2023 IEEE 11th International Conference on Healthcare Informatics (ICHI); 2023: IEEE.
  6. Yang H, Garibaldi JM. A hybrid model for automatic identification of risk factors for heart disease. Journal of biomedical informatics. 2015;58:S171-S82.
  7. Chen Q, Li H, Tang B, Wang X, Liu X, Liu Z, et al. An automatic system to identify heart disease risk factors in clinical texts over time. Journal of biomedical informatics. 2015;58:S158-S63.
  8. Karystianis G, Dehghan A, Kovacevic A, Keane JA, Nenadic G. Using local lexicalized rules to identify heart disease risk factors in clinical notes. Journal of biomedical informatics. 2015;58:S183-S8.
  9. Kim Y, Garvin JH, Goldstein MK, Hwang TS, Redd A, Bolton D, et al. Extraction of left ventricular ejection fraction information from various types of clinical reports. Journal of biomedical informatics. 2017;67:42-8.
  10. Pandey M, Xu Z, Sholle E, Maliakal G, Singh G, Fatima Z, et al. Extraction of radiographic findings from unstructured thoracoabdominal computed tomography reports using convolutional neural network based natural language processing. PLoS One. 2020;15(7):e0236827.
  11. Savova GK, Fan J, Ye Z, Murphy SP, Zheng J, Chute CG, et al., editors. Discovering peripheral arterial disease cases from radiology notes using natural language processing. AMIA Annual Symposium Proceedings; 2010.
  12. Hu D, Zhang H, Li S, Wang Y, Wu N, Lu X. Automatic extraction of lung cancer staging information from computed tomography reports: deep learning approach. JMIR medical informatics. 2021;9(7):e27955.
  13. Kang H, Li J, Wu M, Shen L, Hou L. Building a pharmacogenomics knowledge model toward precision medicine: case study in melanoma. JMIR Medical Informatics. 2020;8(10):e20291.
  14. Zhou S, Wang N, Wang L, Liu H, Zhang R. CancerBERT: a cancer domain-specific language model for extracting breast cancer phenotypes from electronic health records. Journal of the American Medical Informatics Association. 2022;29(7):1208-16.
  15. Zhang X, Zhang Y, Zhang Q, Ren Y, Qiu T, Ma J, et al. Extracting comprehensive clinical information for breast cancer using deep learning methods. International journal of medical informatics. 2019;132:103985.
  16. Leyh-Bannurah S-R, Tian Z, Karakiewicz PI, Wolffgang U, Sauter G, Fisch M, et al. Deep learning for natural language processing in urology: state-of-the-art automated extraction of detailed pathologic prostate cancer data from narratively written electronic health records. JCO clinical cancer informatics. 2018;2:1-9.
  17. Yim W-w, Kwan SW, Yetisgen M. Tumor reference resolution and characteristic extraction in radiology reports for liver cancer stage prediction. Journal of biomedical informatics. 2016;64:179-91.
  18. Yim W-w, Denman T, Kwan SW, Yetisgen M. Tumor information extraction in radiology reports for hepatocellular carcinoma patients. AMIA Summits on Translational Science Proceedings. 2016;2016:455.
  19. Ping X-O, Tseng Y-J, Chung Y, Wu Y-L, Hsu C-W, Yang P-M, et al. Information extraction for tracking liver cancer patients' statuses: from mixture of clinical narrative report types. TELEMEDICINE and e-HEALTH. 2013;19(9):704-10.
  20. Tay SB, Low GH, Wong GJE, Tey HJ, Leong FL, Li C, et al. Use of natural language processing to infer sites of metastatic disease from radiology reports at scale. JCO Clinical Cancer Informatics. 2024;8:e2300122.
  21. Oliwa T, Maron SB, Chase LM, Lomnicki S, Catenacci DV, Furner B, et al. Obtaining knowledge in pathology reports through a natural language processing approach with classification, named-entity recognition, and relation-extraction heuristics. JCO clinical cancer informatics. 2019;3:1-8.
  22. Gao S, Young MT, Qiu JX, Yoon H-J, Christian JB, Fearn PA, et al. Hierarchical attention networks for information extraction from cancer pathology reports. Journal of the American Medical Informatics Association. 2018;25(3):321-30.
  23. Ashish N, Dahm L, Boicey C. University of California, Irvine–Pathology Extraction Pipeline: The pathology extraction pipeline for information extraction from pathology reports. Health informatics journal. 2014;20(4):288-305.
  24. Sugimoto K, Takeda T, Oh J-H, Wada S, Konishi S, Yamahata A, et al. Extracting clinical terms from radiology reports with deep learning. Journal of Biomedical Informatics. 2021;116:103729.
  25. Weissman GE, Harhay MO, Lugo RM, Fuchs BD, Halpern SD, Mikkelsen ME. Natural language processing to assess documentation of features of critical illness in discharge documents of acute respiratory distress syndrome survivors. Annals of the American Thoracic Society. 2016;13(9):1538-45.
  26. Mojibian A, Jaskolka J, Ching G, Lee B, Myers R, Devine C, et al. The Efficacy of a Named Entity Recognition AI Model for Identifying Incidental Pulmonary Nodules in CT Reports. Canadian Association of Radiologists Journal. 2025;76(1):68-75.
  27. Wang Y, Chen ES, Pakhomov S, Arsoniadis E, Carter EW, Lindemann E, et al., editors. Automated extraction of substance use information from clinical texts. AMIA Annual Symposium Proceedings; 2015.
  28. Poulsen M, Troiani V, Freda P, Mowery D, Davoudi A. Annotation dataset of problematic opioid use and related contexts from MIMIC-III Critical Care Database discharge summaries.
  29. Lybarger K, Ostendorf M, Yetisgen M. Annotating social determinants of health using active learning, and characterizing determinants using neural event extraction. Journal of Biomedical Informatics. 2021;113:103631.
  30. Uzuner Ö, Goldstein I, Luo Y, Kohane I. Identifying patient smoking status from medical discharge records. Journal of the American Medical Informatics Association. 2008;15(1):14-24.
  31. Dai Z, Li Z, Han L, editors. Bonebert: A bert-based automated information extraction system of radiology reports for bone fracture detection and diagnosis. Advances in Intelligent Data Analysis XIX: 19th International Symposium on Intelligent Data Analysis, IDA 2021, Porto, Portugal, April 26–28, 2021, Proceedings 19; 2021: Springer.
  32. Topaz M, Lai K, Dowding D, Lei VJ, Zisberg A, Bowles KH, et al. Automated identification of wound information in clinical notes of patients with heart diseases: Developing and validating a natural language processing application. International journal of nursing studies. 2016;64:25-31.
  33. Topaz M, Murga L, Gaddis KM, McDonald MV, Bar-Bachar O, Goldberg Y, et al. Mining fall-related information in clinical notes: Comparison of rule-based and novel word embedding-based machine learning approaches. Journal of biomedical informatics. 2019;90:103103.
  34. Zhao X, Rios A. A marker-based neural network system for extracting social determinants of health. Journal of the American Medical Informatics Association. 2023;30(8):1398-407.
  35. Lituiev DS, Lacar B, Pak S, Abramowitsch PL, De Marchis EH, Peterson TA. Automatic extraction of social determinants of health from medical notes of chronic lower back pain patients. Journal of the American Medical Informatics Association. 2023;30(8):1438-47.
  36. Tian Z, Sun S, Eguale T, Rochefort CM. Automated extraction of VTE events from narrative radiology reports in electronic health records: a validation study. Medical care. 2017;55(10):e73-e80.
  37. Mitra A, Rawat BPS, McManus D, Kapoor A, Yu H, editors. Bleeding entity recognition in electronic health records: a comprehensive analysis of end-to-end systems. AMIA Annual Symposium Proceedings; 2021.
  38. Yu Z, Yang X, Sweeting GL, Ma Y, Stolte SE, Fang R, et al. Identify diabetic retinopathy-related clinical concepts and their attributes using transformer-based natural language processing methods. BMC medical informatics and decision making. 2022;22(Suppl 3):255.
  39. Lin W-C, Chen JS, Kaluzny J, Chen A, Chiang MF, Hribar MR, editors. Extraction of active medications and adherence using natural language processing for glaucoma patients. AMIA Annual Symposium Proceedings; 2022.
  40. Wang SY, Huang J, Hwang H, Hu W, Tao S, Hernandez-Boussard T. Leveraging weak supervision to perform named entity recognition in electronic health records progress notes to identify the ophthalmology exam. International journal of medical informatics. 2022;167:104864.
  41. Rozova V, Khanina A, Teng JC, Teh JS, Worth LJ, Slavin MA, et al. Detecting evidence of invasive fungal infections in cytology and histopathology reports enriched with concept-level annotations. Journal of Biomedical Informatics. 2023;139:104293.
  42. Rozova V, Khanina, A., Ong, J., Alipour, R., Worth, L., Slavin, M., Thursky, K., & Verspoor, K. PIFIR: PET-CT Invasive Fungal Infection Reports (version 1.0.0). PhysioNet. 2025.
  43. Rozova V, Khanina, A., Teng, J., Teh, J., Worth, L., Slavin, M., thursky, k., & Verspoor, K. CHIFIR: Cytology and Histopathology Invasive Fungal Infection Reports. PhysioNet. 2024.
  44. Sterckx L, Vandewiele G, Dehaene I, Janssens O, Ongenae F, De Backere F, et al. Clinical information extraction for preterm birth risk prediction. Journal of Biomedical Informatics. 2020;110:103544.
  45. Mishra R, Burke A, Gitman B, Verma P, Engelstad M, Haendel MA, et al. Data-driven method to enhance craniofacial and oral phenotype vocabularies. The Journal of the American Dental Association. 2019;150(11):933-9. e2.
  46. Seong D, Choi YH, Shin S-Y, Yi B-K. Deep learning approach to detection of colonoscopic information from unstructured reports. BMC Medical Informatics and Decision Making. 2023;23(1):28.
  47. Denny JC, Peterson JF, Choma NN, Xu H, Miller RA, Bastarache L, et al. Extracting timing and status descriptors for colonoscopy testing from electronic medical records. Journal of the American Medical Informatics Association. 2010;17(4):383-8.
  48. Pathak A, Yu Z, Paredes D, Monsour EP, Rocha AO, Brito JP, et al., editors. Extracting thyroid nodules characteristics from ultrasound reports using transformer-based natural language processing methods. AMIA Annual Symposium Proceedings; 2024.
  49. Chen T, Dredze M, Weiner JP, Hernandez L, Kimura J, Kharrazi H. Extraction of geriatric syndromes from electronic health record clinical notes: assessment of statistical natural language processing methods. JMIR medical informatics. 2019;7(1):e13039.
  50. Valente AS, Trunfio TA, Aiello M, Baldi D, Baldi M, Imbò S, et al. Text mining approach for feature extraction and cartilage disease grade classification using knee MRI radiology reports. Computational and Structural Biotechnology Journal. 2024;24:622-9.
  51. Bitterman DS, Goldner E, Finan S, Harris D, Durbin EB, Hochheiser H, et al. An end-to-end natural language processing system for automatically extracting radiation therapy events from clinical texts. International Journal of Radiation Oncology* Biology* Physics. 2023;117(1):262-73.
  52. Kormilitzin A, Vaci N, Liu Q, Nevado-Holgado A. Med7: A transferable clinical natural language processing model for electronic health records. Artificial Intelligence in Medicine. 2021;118:102086.
  53. Kusa W, Mendoza ÓE, Knoth P, Pasi G, Hanbury A. Effective matching of patients to clinical trials using entity extraction and neural re-ranking. Journal of biomedical informatics. 2023;144:104444.
  54. Yang H. Automatic extraction of medication information from medical discharge summaries. Journal of the American Medical Informatics Association. 2010;17(5):545-8.
  55. Cho M, Ha J, Park C, Park S. Combinatorial feature embedding based on CNN and LSTM for biomedical named entity recognition. Journal of biomedical informatics. 2020;103:103381.
  56. Polignano M, de Gemmis M, Semeraro G, editors. Comparing Transformer-based NER approaches for analysing textual medical diagnoses. CLEF (Working Notes); 2021.
  57. Li F, Liu W, Yu H. Extraction of information related to adverse drug events from electronic health record notes: design of an end-to-end model based on deep learning. JMIR medical informatics. 2018;6(4):e12159.
  58. Dandala B, Joopudi V, Tsou C-H, Liang JJ, Suryanarayanan P. Extraction of information related to drug safety surveillance from electronic health record notes: Joint modeling of entities and relations using knowledge-aware neural attentive models. JMIR medical informatics. 2020;8(7):e18417.
  59. Suresh S, Tavabi N, Golchin S, Gilreath L, Garcia-Andujar R, Kim A, et al., editors. Intermediate domain finetuning for weakly supervised domain-adaptive clinical NER. The 22nd Workshop on Biomedical Natural Language Processing and BioNLP Shared Tasks; 2023.
  60. Thukral A, Dhiman S, Meher R, Bedi P. Knowledge graph enrichment from clinical narratives using NLP, NER, and biomedical ontologies for healthcare applications. International Journal of Information Technology. 2023;15(1):53-65.
  61. Xu H, Stenner SP, Doan S, Johnson KB, Waitman LR, Denny JC. MedEx: a medication information extraction system for clinical narratives. Journal of the American Medical Informatics Association. 2010;17(1):19-24.
  62. Yehia E, Boshnak H, AbdelGaber S, Abdo A, Elzanfaly DS. Ontology-based clinical information extraction from physician’s free-text notes. Journal of biomedical informatics. 2019;98:103276.
  63. Platas A, Zotova E, Martínez-Arias P, López-Linares K, Cuadros M. Synthetic Annotated Data for Named Entity Recognition in Computed Tomography Scan Reports. 2024.
  64. Jain S, Agrawal A, Saporta A, Truong SQ, Duong DN, Bui T, et al. Radgraph: Extracting clinical entities and relations from radiology reports. arXiv preprint arXiv:210614463. 2021.
  65. Bear Don't Walk IV O, Pichon, A., Reyes Nieva, H., Sun, T., Lı, J., Joseph, J. W., Kinberg, S., Richter, L. R., Crusco, S., Kulas, K., Ahmed, S., Snyder, D., Rahbari, A., Ranard, B., Juneja, P., Demner-Fushman, D., & Elhadad, N. C-REACT: Contextualized Race and Ethnicity Annotations for Clinical Text (version 1.0.0). PhysioNet. 2024.
  66. Goel A, Gueta A, Gilon O, Erell S, Feder A. Medication Extraction Labels for MIMIC-IV-Note Clinical Database. PhysioNet; 2023.
  67. Patrick J, Li M. High accuracy information extraction of medication information from clinical notes: 2009 i2b2 medication extraction challenge. Journal of the American Medical Informatics Association. 2010;17(5):524-7.
  68. Uzuner Ö, South BR, Shen S, DuVall SL. 2010 i2b2/VA challenge on concepts, assertions, and relations in clinical text. Journal of the American Medical Informatics Association. 2011;18(5):552-6.
  69. Stubbs A, Kotfila C, Uzuner Ö. Automated systems for the de-identification of longitudinal clinical narratives: Overview of 2014 i2b2/UTHealth shared task Track 1. Journal of biomedical informatics. 2015;58:S11-S9.
  70. Pradhan S, Elhadad N, Chapman W, Manandhar S, Savova G, editors. Semeval-2014 task 7: Analysis of clinical text. Proceedings of the 8th international workshop on semantic evaluation (SemEval 2014); 2014.

About

No description, website, or topics provided.

Resources

Readme
Activity
Custom properties

Stars

0 stars

Watchers

0 watching

Forks

0 forks
Report repository

Releases

No releases published

Packages

No packages published