373 related articles for article (PubMed ID: 36249690)
1. Deep Learning Approaches for Predicting Glaucoma Progression Using Electronic Health Records and Natural Language Processing.
Wang SY; Tseng B; Hernandez-Boussard T
Ophthalmol Sci; 2022 Jun; 2(2):100127. PubMed ID: 36249690
[TBL] [Abstract][Full Text] [Related]
2. Predicting near-term glaucoma progression: An artificial intelligence approach using clinical free-text notes and data from electronic health records.
Jalamangala Shivananjaiah SK; Kumari S; Majid I; Wang SY
Front Med (Lausanne); 2023; 10():1157016. PubMed ID: 37122330
[TBL] [Abstract][Full Text] [Related]
3. Looking for low vision: Predicting visual prognosis by fusing structured and free-text data from electronic health records.
Gui H; Tseng B; Hu W; Wang SY
Int J Med Inform; 2022 Mar; 159():104678. PubMed ID: 34999410
[TBL] [Abstract][Full Text] [Related]
4. Predicting Glaucoma Progression Requiring Surgery Using Clinical Free-Text Notes and Transfer Learning With Transformers.
Hu W; Wang SY
Transl Vis Sci Technol; 2022 Mar; 11(3):37. PubMed ID: 35353148
[TBL] [Abstract][Full Text] [Related]
5. Classifying social determinants of health from unstructured electronic health records using deep learning-based natural language processing.
Han S; Zhang RF; Shi L; Richie R; Liu H; Tseng A; Quan W; Ryan N; Brent D; Tsui FR
J Biomed Inform; 2022 Mar; 127():103984. PubMed ID: 35007754
[TBL] [Abstract][Full Text] [Related]
6. Predicting Glaucoma Progression to Surgery with Artificial Intelligence Survival Models.
Tao S; Ravindranath R; Wang SY
Ophthalmol Sci; 2023 Dec; 3(4):100336. PubMed ID: 37415920
[TBL] [Abstract][Full Text] [Related]
7. Automated extraction of ophthalmic surgery outcomes from the electronic health record.
Wang SY; Pershing S; Tran E; Hernandez-Boussard T
Int J Med Inform; 2020 Jan; 133():104007. PubMed ID: 31706228
[TBL] [Abstract][Full Text] [Related]
8. Identifying and Predicting Intentional Self-Harm in Electronic Health Record Clinical Notes: Deep Learning Approach.
Obeid JS; Dahne J; Christensen S; Howard S; Crawford T; Frey LJ; Stecker T; Bunnell BE
JMIR Med Inform; 2020 Jul; 8(7):e17784. PubMed ID: 32729840
[TBL] [Abstract][Full Text] [Related]
9. Automated Recognition of Visual Acuity Measurements in Ophthalmology Clinical Notes Using Deep Learning.
Bernstein IA; Koornwinder A; Hwang HH; Wang SY
Ophthalmol Sci; 2024; 4(2):100371. PubMed ID: 37868799
[TBL] [Abstract][Full Text] [Related]
10. Predicting Postoperative Mortality With Deep Neural Networks and Natural Language Processing: Model Development and Validation.
Chen PF; Chen L; Lin YK; Li GH; Lai F; Lu CW; Yang CY; Chen KC; Lin TY
JMIR Med Inform; 2022 May; 10(5):e38241. PubMed ID: 35536634
[TBL] [Abstract][Full Text] [Related]
11. Predicting Glaucoma Surgical Outcomes Using Neural Networks and Machine Learning on Electronic Health Records.
Barry S; Wang SY
Transl Vis Sci Technol; 2024 Jun; 13(6):15. PubMed ID: 38904612
[TBL] [Abstract][Full Text] [Related]
12. Prediction of multiclass surgical outcomes in glaucoma using multimodal deep learning based on free-text operative notes and structured EHR data.
Lin WC; Chen A; Song X; Weiskopf NG; Chiang MF; Hribar MR
J Am Med Inform Assoc; 2024 Jan; 31(2):456-464. PubMed ID: 37964658
[TBL] [Abstract][Full Text] [Related]
13. Development and evaluation of novel ophthalmology domain-specific neural word embeddings to predict visual prognosis.
Wang S; Tseng B; Hernandez-Boussard T
Int J Med Inform; 2021 Jun; 150():104464. PubMed ID: 33892445
[TBL] [Abstract][Full Text] [Related]
14. A comparative study on deep learning models for text classification of unstructured medical notes with various levels of class imbalance.
Lu H; Ehwerhemuepha L; Rakovski C
BMC Med Res Methodol; 2022 Jul; 22(1):181. PubMed ID: 35780100
[TBL] [Abstract][Full Text] [Related]
15. Natural Language Processing of Clinical Notes on Chronic Diseases: Systematic Review.
Sheikhalishahi S; Miotto R; Dudley JT; Lavelli A; Rinaldi F; Osmani V
JMIR Med Inform; 2019 Apr; 7(2):e12239. PubMed ID: 31066697
[TBL] [Abstract][Full Text] [Related]
16. Comparing Natural Language Processing and Structured Medical Data to Develop a Computable Phenotype for Patients Hospitalized Due to COVID-19: Retrospective Analysis.
Chang F; Krishnan J; Hurst JH; Yarrington ME; Anderson DJ; O'Brien EC; Goldstein BA
JMIR Med Inform; 2023 Aug; 11():e46267. PubMed ID: 37621195
[TBL] [Abstract][Full Text] [Related]
17. Artificial Intelligence Learning Semantics via External Resources for Classifying Diagnosis Codes in Discharge Notes.
Lin C; Hsu CJ; Lou YS; Yeh SJ; Lee CC; Su SL; Chen HC
J Med Internet Res; 2017 Nov; 19(11):e380. PubMed ID: 29109070
[TBL] [Abstract][Full Text] [Related]
18. Machine Learning-Based Predictive Modeling of Surgical Intervention in Glaucoma Using Systemic Data From Electronic Health Records.
Baxter SL; Marks C; Kuo TT; Ohno-Machado L; Weinreb RN
Am J Ophthalmol; 2019 Dec; 208():30-40. PubMed ID: 31323204
[TBL] [Abstract][Full Text] [Related]
19. A natural language processing and deep learning approach to identify child abuse from pediatric electronic medical records.
Annapragada AV; Donaruma-Kwoh MM; Annapragada AV; Starosolski ZA
PLoS One; 2021; 16(2):e0247404. PubMed ID: 33635890
[TBL] [Abstract][Full Text] [Related]
20. A nursing note-aware deep neural network for predicting mortality risk after hospital discharge.
Huang YZ; Chen YM; Lin CC; Chiu HY; Chang YC
Int J Nurs Stud; 2024 Aug; 156():104797. PubMed ID: 38788263
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
