88 related articles for article (PubMed ID: 30875704)
1. Leveraging Electronic Dental Record Data to Classify Patients Based on Their Smoking Intensity.
Patel J; Siddiqui Z; Krishnan A; Thyvalikakath TP
Methods Inf Med; 2018 Nov; 57(5-06):253-260. PubMed ID: 30875704
[TBL] [Abstract][Full Text] [Related]
2. Natural language processing and machine learning to enable automatic extraction and classification of patients' smoking status from electronic medical records.
Caccamisi A; Jørgensen L; Dalianis H; Rosenlund M
Ups J Med Sci; 2020 Nov; 125(4):316-324. PubMed ID: 32696698
[TBL] [Abstract][Full Text] [Related]
3. Identification of patients' smoking status using an explainable AI approach: a Danish electronic health records case study.
Ebrahimi A; Henriksen MBH; Brasen CL; Hilberg O; Hansen TF; Jensen LH; Peimankar A; Wiil UK
BMC Med Res Methodol; 2024 May; 24(1):114. PubMed ID: 38760718
[TBL] [Abstract][Full Text] [Related]
4. Data mining to retrieve smoking status from electronic health records in general practice
de Boer AR; de Groot MCH; Groenhof TKJ; van Doorn S; Vaartjes I; Bots ML; Haitjema S
Eur Heart J Digit Health; 2022 Sep; 3(3):437-444. PubMed ID: 36712169
[TBL] [Abstract][Full Text] [Related]
5. Accuracy of Parental Self-Report of Medical History in a Dental Setting: Integrated Electronic Health Record and Nonintegrated Dental Record.
Claman DB; Molina JL; Peng J; Fischbach H; Casamassimo PS
Pediatr Dent; 2021 May; 43(3):230-236. PubMed ID: 34172118
[No Abstract] [Full Text] [Related]
6. Screening Electronic Health Record-Related Patient Safety Reports Using Machine Learning.
Marella WM; Sparnon E; Finley E
J Patient Saf; 2017 Mar; 13(1):31-36. PubMed ID: 24721977
[TBL] [Abstract][Full Text] [Related]
7. A machine learning-based framework to identify type 2 diabetes through electronic health records.
Zheng T; Xie W; Xu L; He X; Zhang Y; You M; Yang G; Chen Y
Int J Med Inform; 2017 Jan; 97():120-127. PubMed ID: 27919371
[TBL] [Abstract][Full Text] [Related]
8. How Do Dental Clinicians Obtain Up-To-Date Patient Medical Histories? Modeling Strengths, Drawbacks, and Proposals for Improvements.
Li S; Rajapuri AS; Felix Gomez GG; Schleyer T; Mendonca EA; Thyvalikakath TP
Front Digit Health; 2022; 4():847080. PubMed ID: 35419556
[TBL] [Abstract][Full Text] [Related]
9. Documentation of the patient's smoking status in common chronic diseases - analysis of medical narrative reports using the ULMFiT based text classification.
Hirvonen E; Karlsson A; Saaresranta T; Laitinen T
Eur Clin Respir J; 2021; 8(1):2004664. PubMed ID: 34868489
[TBL] [Abstract][Full Text] [Related]
10. Prediction of Sjögren's disease diagnosis using matched electronic dental-health record data.
Mao J; Gomez GGF; Wang M; Xu H; Thyvalikakath TP
BMC Med Inform Decis Mak; 2024 Feb; 24(1):43. PubMed ID: 38336735
[TBL] [Abstract][Full Text] [Related]
11. Mining fall-related information in clinical notes: Comparison of rule-based and novel word embedding-based machine learning approaches.
Topaz M; Murga L; Gaddis KM; McDonald MV; Bar-Bachar O; Goldberg Y; Bowles KH
J Biomed Inform; 2019 Feb; 90():103103. PubMed ID: 30639392
[TBL] [Abstract][Full Text] [Related]
12. An automated pipeline for analyzing medication event reports in clinical settings.
Zhou S; Kang H; Yao B; Gong Y
BMC Med Inform Decis Mak; 2018 Dec; 18(Suppl 5):113. PubMed ID: 30526590
[TBL] [Abstract][Full Text] [Related]
13. Identifying and extracting patient smoking status information from clinical narrative texts in Spanish.
Figueroa RL; Soto DA; Pino EJ
Annu Int Conf IEEE Eng Med Biol Soc; 2014; 2014():2710-3. PubMed ID: 25570550
[TBL] [Abstract][Full Text] [Related]
14. Data mining information from electronic health records produced high yield and accuracy for current smoking status.
Groenhof TKJ; Koers LR; Blasse E; de Groot M; Grobbee DE; Bots ML; Asselbergs FW; Lely AT; Haitjema S; ;
J Clin Epidemiol; 2020 Feb; 118():100-106. PubMed ID: 31730918
[TBL] [Abstract][Full Text] [Related]
15. Assessing document section heterogeneity across multiple electronic health record systems for computational phenotyping: A case study of heart-failure phenotyping algorithm.
Moon S; Liu S; Kshatriya BSA; Fu S; Moser ED; Bielinski SJ; Fan J; Liu H
PLoS One; 2023; 18(3):e0283800. PubMed ID: 37000801
[TBL] [Abstract][Full Text] [Related]
16. Text mining electronic hospital records to automatically classify admissions against disease: Measuring the impact of linking data sources.
Kocbek S; Cavedon L; Martinez D; Bain C; Manus CM; Haffari G; Zukerman I; Verspoor K
J Biomed Inform; 2016 Dec; 64():158-167. PubMed ID: 27742349
[TBL] [Abstract][Full Text] [Related]
17. Using natural language processing methods to classify use status of dietary supplements in clinical notes.
Fan Y; Zhang R
BMC Med Inform Decis Mak; 2018 Jul; 18(Suppl 2):51. PubMed ID: 30066648
[TBL] [Abstract][Full Text] [Related]
18. 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]
19. LATTE: A knowledge-based method to normalize various expressions of laboratory test results in free text of Chinese electronic health records.
Jiang K; Yang T; Wu C; Chen L; Mao L; Wu Y; Deng L; Jiang T
J Biomed Inform; 2020 Feb; 102():103372. PubMed ID: 31901507
[TBL] [Abstract][Full Text] [Related]
20. Discovering and identifying New York heart association classification from electronic health records.
Zhang R; Ma S; Shanahan L; Munroe J; Horn S; Speedie S
BMC Med Inform Decis Mak; 2018 Jul; 18(Suppl 2):48. PubMed ID: 30066653
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]