126 related articles for article (PubMed ID: 35985621)
21. Extracting social determinants of health from electronic health records using natural language processing: a systematic review.
Patra BG; Sharma MM; Vekaria V; Adekkanattu P; Patterson OV; Glicksberg B; Lepow LA; Ryu E; Biernacka JM; Furmanchuk A; George TJ; Hogan W; Wu Y; Yang X; Bian J; Weissman M; Wickramaratne P; Mann JJ; Olfson M; Campion TR; Weiner M; Pathak J
J Am Med Inform Assoc; 2021 Nov; 28(12):2716-2727. PubMed ID: 34613399
[TBL] [Abstract][Full Text] [Related]
22. Comparison of Natural Language Processing of Clinical Notes With a Validated Risk-Stratification Tool to Predict Severe Maternal Morbidity.
Clapp MA; Kim E; James KE; Perlis RH; Kaimal AJ; McCoy TH; Easter SR
JAMA Netw Open; 2022 Oct; 5(10):e2234924. PubMed ID: 36197662
[TBL] [Abstract][Full Text] [Related]
23. Precision Assessment of COVID-19 Phenotypes Using Large-Scale Clinic Visit Audio Recordings: Harnessing the Power of Patient Voice.
Barr PJ; Ryan J; Jacobson NC
J Med Internet Res; 2021 Feb; 23(2):e20545. PubMed ID: 33556031
[TBL] [Abstract][Full Text] [Related]
24. PhenoGO: assigning phenotypic context to gene ontology annotations with natural language processing.
Lussier Y; Borlawsky T; Rappaport D; Liu Y; Friedman C
Pac Symp Biocomput; 2006; ():64-75. PubMed ID: 17094228
[TBL] [Abstract][Full Text] [Related]
25. Towards building a disease-phenotype knowledge base: extracting disease-manifestation relationship from literature.
Xu R; Li L; Wang Q
Bioinformatics; 2013 Sep; 29(17):2186-94. PubMed ID: 23828786
[TBL] [Abstract][Full Text] [Related]
26. HPO2Vec+: Leveraging heterogeneous knowledge resources to enrich node embeddings for the Human Phenotype Ontology.
Shen F; Peng S; Fan Y; Wen A; Liu S; Wang Y; Wang L; Liu H
J Biomed Inform; 2019 Aug; 96():103246. PubMed ID: 31255713
[TBL] [Abstract][Full Text] [Related]
27. Natural Language Processing for EHR-Based Computational Phenotyping.
Zeng Z; Deng Y; Li X; Naumann T; Luo Y
IEEE/ACM Trans Comput Biol Bioinform; 2019; 16(1):139-153. PubMed ID: 29994486
[TBL] [Abstract][Full Text] [Related]
28. Adverse drug events and medication relation extraction in electronic health records with ensemble deep learning methods.
Christopoulou F; Tran TT; Sahu SK; Miwa M; Ananiadou S
J Am Med Inform Assoc; 2020 Jan; 27(1):39-46. PubMed ID: 31390003
[TBL] [Abstract][Full Text] [Related]
29. Identifying Goals of Care Conversations in the Electronic Health Record Using Natural Language Processing and Machine Learning.
Lee RY; Brumback LC; Lober WB; Sibley J; Nielsen EL; Treece PD; Kross EK; Loggers ET; Fausto JA; Lindvall C; Engelberg RA; Curtis JR
J Pain Symptom Manage; 2021 Jan; 61(1):136-142.e2. PubMed ID: 32858164
[TBL] [Abstract][Full Text] [Related]
30. Mimvec: a deep learning approach for analyzing the human phenome.
Gan M; Li W; Zeng W; Wang X; Jiang R
BMC Syst Biol; 2017 Sep; 11(Suppl 4):76. PubMed ID: 28950906
[TBL] [Abstract][Full Text] [Related]
31. PISTON: Predicting drug indications and side effects using topic modeling and natural language processing.
Jang G; Lee T; Hwang S; Park C; Ahn J; Seo S; Hwang Y; Yoon Y
J Biomed Inform; 2018 Nov; 87():96-107. PubMed ID: 30268842
[TBL] [Abstract][Full Text] [Related]
32. Using Natural Language Processing and Machine Learning to Preoperatively Predict Lymph Node Metastasis for Non-Small Cell Lung Cancer With Electronic Medical Records: Development and Validation Study.
Hu D; Li S; Zhang H; Wu N; Lu X
JMIR Med Inform; 2022 Apr; 10(4):e35475. PubMed ID: 35468085
[TBL] [Abstract][Full Text] [Related]
33. Information extraction from multi-institutional radiology reports.
Hassanpour S; Langlotz CP
Artif Intell Med; 2016 Jan; 66():29-39. PubMed ID: 26481140
[TBL] [Abstract][Full Text] [Related]
34. Machine Learning, Natural Language Processing, and the Electronic Health Record: Innovations in Mental Health Services Research.
Edgcomb JB; Zima B
Psychiatr Serv; 2019 Apr; 70(4):346-349. PubMed ID: 30784377
[TBL] [Abstract][Full Text] [Related]
35. Subgraph augmented non-negative tensor factorization (SANTF) for modeling clinical narrative text.
Luo Y; Xin Y; Hochberg E; Joshi R; Uzuner O; Szolovits P
J Am Med Inform Assoc; 2015 Sep; 22(5):1009-19. PubMed ID: 25862765
[TBL] [Abstract][Full Text] [Related]
36. 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]
37. Machine Learning and Natural Language Processing in Mental Health: Systematic Review.
Le Glaz A; Haralambous Y; Kim-Dufor DH; Lenca P; Billot R; Ryan TC; Marsh J; DeVylder J; Walter M; Berrouiguet S; Lemey C
J Med Internet Res; 2021 May; 23(5):e15708. PubMed ID: 33944788
[TBL] [Abstract][Full Text] [Related]
38. Challenges in clinical natural language processing for automated disorder normalization.
Leaman R; Khare R; Lu Z
J Biomed Inform; 2015 Oct; 57():28-37. PubMed ID: 26187250
[TBL] [Abstract][Full Text] [Related]
39. Telmisartan/hydrochlorothiazide: a new fixed dose combination.
Maillard M; Burnier M
Expert Rev Cardiovasc Ther; 2005 May; 3(3):375-86. PubMed ID: 15889965
[TBL] [Abstract][Full Text] [Related]
40. Incorporating natural language processing to improve classification of axial spondyloarthritis using electronic health records.
Zhao SS; Hong C; Cai T; Xu C; Huang J; Ermann J; Goodson NJ; Solomon DH; Cai T; Liao KP
Rheumatology (Oxford); 2020 May; 59(5):1059-1065. PubMed ID: 31535693
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]
