207 related articles for article (PubMed ID: 33345951)
1. Identifying and Characterizing a Chronic Cough Cohort Through Electronic Health Records.
Weiner M; Dexter PR; Heithoff K; Roberts AR; Liu Z; Griffith A; Hui S; Schelfhout J; Dicpinigaitis P; Doshi I; Weaver JP
Chest; 2021 Jun; 159(6):2346-2355. PubMed ID: 33345951
[TBL] [Abstract][Full Text] [Related]
2. Development of a natural language processing algorithm to detect chronic cough in electronic health records.
Bali V; Weaver J; Turzhitsky V; Schelfhout J; Paudel ML; Hulbert E; Peterson-Brandt J; Currie AG; Bakka D
BMC Pulm Med; 2022 Jun; 22(1):256. PubMed ID: 35764999
[TBL] [Abstract][Full Text] [Related]
3. Applying interpretable deep learning models to identify chronic cough patients using EHR data.
Luo X; Gandhi P; Zhang Z; Shao W; Han Z; Chandrasekaran V; Turzhitsky V; Bali V; Roberts AR; Metzger M; Baker J; La Rosa C; Weaver J; Dexter P; Huang K
Comput Methods Programs Biomed; 2021 Oct; 210():106395. PubMed ID: 34525412
[TBL] [Abstract][Full Text] [Related]
4. Prescriptions of opioid-containing drugs in patients with chronic cough.
Weiner M; Liu Z; Schelfhout J; Dexter P; Roberts AR; Griffith A; Bali V; Weaver J
Ther Adv Respir Dis; 2024; 18():17534666241259373. PubMed ID: 38877686
[TBL] [Abstract][Full Text] [Related]
5. Augmented intelligence with natural language processing applied to electronic health records for identifying patients with non-alcoholic fatty liver disease at risk for disease progression.
Van Vleck TT; Chan L; Coca SG; Craven CK; Do R; Ellis SB; Kannry JL; Loos RJF; Bonis PA; Cho J; Nadkarni GN
Int J Med Inform; 2019 Sep; 129():334-341. PubMed ID: 31445275
[TBL] [Abstract][Full Text] [Related]
6. Challenges of Developing a Natural Language Processing Method With Electronic Health Records to Identify Persons With Chronic Mobility Disability.
Agaronnik ND; Lindvall C; El-Jawahri A; He W; Iezzoni LI
Arch Phys Med Rehabil; 2020 Oct; 101(10):1739-1746. PubMed ID: 32446905
[TBL] [Abstract][Full Text] [Related]
7. Using natural language processing to identify opioid use disorder in electronic health record data.
Singleton J; Li C; Akpunonu PD; Abner EL; Kucharska-Newton AM
Int J Med Inform; 2023 Feb; 170():104963. PubMed ID: 36521420
[TBL] [Abstract][Full Text] [Related]
8. Natural Language Processing for Improved Characterization of COVID-19 Symptoms: Observational Study of 350,000 Patients in a Large Integrated Health Care System.
Malden DE; Tartof SY; Ackerson BK; Hong V; Skarbinski J; Yau V; Qian L; Fischer H; Shaw SF; Caparosa S; Xie F
JMIR Public Health Surveill; 2022 Dec; 8(12):e41529. PubMed ID: 36446133
[TBL] [Abstract][Full Text] [Related]
9. Using natural language processing to identify problem usage of prescription opioids.
Carrell DS; Cronkite D; Palmer RE; Saunders K; Gross DE; Masters ET; Hylan TR; Von Korff M
Int J Med Inform; 2015 Dec; 84(12):1057-64. PubMed ID: 26456569
[TBL] [Abstract][Full Text] [Related]
10. Moving Biosurveillance Beyond Coded Data Using AI for Symptom Detection From Physician Notes: Retrospective Cohort Study.
McMurry AJ; Zipursky AR; Geva A; Olson KL; Jones JR; Ignatov V; Miller TA; Mandl KD
J Med Internet Res; 2024 Apr; 26():e53367. PubMed ID: 38573752
[TBL] [Abstract][Full Text] [Related]
11. The Value of Unstructured Electronic Health Record Data in Geriatric Syndrome Case Identification.
Kharrazi H; Anzaldi LJ; Hernandez L; Davison A; Boyd CM; Leff B; Kimura J; Weiner JP
J Am Geriatr Soc; 2018 Aug; 66(8):1499-1507. PubMed ID: 29972595
[TBL] [Abstract][Full Text] [Related]
12. Getting More Out of Large Databases and EHRs with Natural Language Processing and Artificial Intelligence: The Future Is Here.
Khosravi B; Rouzrokh P; Erickson BJ
J Bone Joint Surg Am; 2022 Oct; 104(Suppl 3):51-55. PubMed ID: 36260045
[TBL] [Abstract][Full Text] [Related]
13. Using Clinical Notes and Natural Language Processing for Automated HIV Risk Assessment.
Feller DJ; Zucker J; Yin MT; Gordon P; Elhadad N
J Acquir Immune Defic Syndr; 2018 Feb; 77(2):160-166. PubMed ID: 29084046
[TBL] [Abstract][Full Text] [Related]
14. Determining Multiple Sclerosis Phenotype from Electronic Medical Records.
Nelson RE; Butler J; LaFleur J; Knippenberg K; C Kamauu AW; DuVall SL
J Manag Care Spec Pharm; 2016 Dec; 22(12):1377-1382. PubMed ID: 27882837
[TBL] [Abstract][Full Text] [Related]
15. The prevalence of problem opioid use in patients receiving chronic opioid therapy: computer-assisted review of electronic health record clinical notes.
Palmer RE; Carrell DS; Cronkite D; Saunders K; Gross DE; Masters E; Donevan S; Hylan TR; Von Kroff M
Pain; 2015 Jul; 156(7):1208-1214. PubMed ID: 25760471
[TBL] [Abstract][Full Text] [Related]
16. Natural Language Processing Improves Detection of Nonsevere Hypoglycemia in Medical Records Versus Coding Alone in Patients With Type 2 Diabetes but Does Not Improve Prediction of Severe Hypoglycemia Events: An Analysis Using the Electronic Medical Record in a Large Health System.
Misra-Hebert AD; Milinovich A; Zajichek A; Ji X; Hobbs TD; Weng W; Petraro P; Kong SX; Mocarski M; Ganguly R; Bauman JM; Pantalone KM; Zimmerman RS; Kattan MW
Diabetes Care; 2020 Aug; 43(8):1937-1940. PubMed ID: 32414887
[TBL] [Abstract][Full Text] [Related]
17. 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]
18. Development and Evaluation of a Natural Language Processing Annotation Tool to Facilitate Phenotyping of Cognitive Status in Electronic Health Records: Diagnostic Study.
Noori A; Magdamo C; Liu X; Tyagi T; Li Z; Kondepudi A; Alabsi H; Rudmann E; Wilcox D; Brenner L; Robbins GK; Moura L; Zafar S; Benson NM; Hsu J; R Dickson J; Serrano-Pozo A; Hyman BT; Blacker D; Westover MB; Mukerji SS; Das S
J Med Internet Res; 2022 Aug; 24(8):e40384. PubMed ID: 36040790
[TBL] [Abstract][Full Text] [Related]
19. Natural language processing of clinical notes for identification of critical limb ischemia.
Afzal N; Mallipeddi VP; Sohn S; Liu H; Chaudhry R; Scott CG; Kullo IJ; Arruda-Olson AM
Int J Med Inform; 2018 Mar; 111():83-89. PubMed ID: 29425639
[TBL] [Abstract][Full Text] [Related]
20. Natural language processing of electronic health records is superior to billing codes to identify symptom burden in hemodialysis patients.
Chan L; Beers K; Yau AA; Chauhan K; Duffy Á; Chaudhary K; Debnath N; Saha A; Pattharanitima P; Cho J; Kotanko P; Federman A; Coca SG; Van Vleck T; Nadkarni GN
Kidney Int; 2020 Feb; 97(2):383-392. PubMed ID: 31883805
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
