McNamara DS - Search Results

1

Natural language processing in an intelligent writing strategy tutoring system.

McNamara DS, Crossley SA, Roscoe R. McNamara DS, et al. Behav Res Methods. 2013 Jun;45(2):499-515. doi: 10.3758/s13428-012-0258-1. Behav Res Methods. 2013. PMID: 23055164

2

The tool for the automatic analysis of text cohesion (TAACO): Automatic assessment of local, global, and text cohesion.

Crossley SA, Kyle K, McNamara DS. Crossley SA, et al. Among authors: mcnamara ds. Behav Res Methods. 2016 Dec;48(4):1227-1237. doi: 10.3758/s13428-015-0651-7. Behav Res Methods. 2016. PMID: 26416138

3

Sentiment Analysis and Social Cognition Engine (SEANCE): An automatic tool for sentiment, social cognition, and social-order analysis.

Crossley SA, Kyle K, McNamara DS. Crossley SA, et al. Among authors: mcnamara ds. Behav Res Methods. 2017 Jun;49(3):803-821. doi: 10.3758/s13428-016-0743-z. Behav Res Methods. 2017. PMID: 27193159

4

Challenges and solutions to employing natural language processing and machine learning to measure patients' health literacy and physician writing complexity: The ECLIPPSE study.

Brown W 3rd, Balyan R, Karter AJ, Crossley S, Semere W, Duran ND, Lyles C, Liu J, Moffet HH, Daniels R, McNamara DS, Schillinger D. Brown W 3rd, et al. Among authors: mcnamara ds. J Biomed Inform. 2021 Jan;113:103658. doi: 10.1016/j.jbi.2020.103658. Epub 2020 Dec 11. J Biomed Inform. 2021. PMID: 33316421 Free PMC article.

5

Age of Exposure 2.0: Estimating word complexity using iterative models of word embeddings.

Botarleanu RM, Dascalu M, Watanabe M, Crossley SA, McNamara DS. Botarleanu RM, et al. Among authors: mcnamara ds. Behav Res Methods. 2022 Dec;54(6):3015-3042. doi: 10.3758/s13428-022-01797-5. Epub 2022 Feb 15. Behav Res Methods. 2022. PMID: 35167112

6

Using natural language processing and machine learning to classify health literacy from secure messages: The ECLIPPSE study.

Balyan R, Crossley SA, Brown W 3rd, Karter AJ, McNamara DS, Liu JY, Lyles CR, Schillinger D. Balyan R, et al. Among authors: mcnamara ds. PLoS One. 2019 Feb 22;14(2):e0212488. doi: 10.1371/journal.pone.0212488. eCollection 2019. PLoS One. 2019. PMID: 30794616 Free PMC article.

7

Employing computational linguistics techniques to identify limited patient health literacy: Findings from the ECLIPPSE study.

Schillinger D, Balyan R, Crossley SA, McNamara DS, Liu JY, Karter AJ. Schillinger D, et al. Among authors: mcnamara ds. Health Serv Res. 2021 Feb;56(1):132-144. doi: 10.1111/1475-6773.13560. Epub 2020 Sep 23. Health Serv Res. 2021. PMID: 32966630 Free PMC article.

8

Developing and Testing Automatic Models of Patient Communicative Health Literacy Using Linguistic Features: Findings from the ECLIPPSE study.

Crossley SA, Balyan R, Liu J, Karter AJ, McNamara D, Schillinger D. Crossley SA, et al. Health Commun. 2021 Jul;36(8):1018-1028. doi: 10.1080/10410236.2020.1731781. Epub 2020 Mar 2. Health Commun. 2021. PMID: 32114833 Free PMC article.

9

Predicting the readability of physicians' secure messages to improve health communication using novel linguistic features: Findings from the ECLIPPSE study.

Crossley SA, Balyan R, Liu J, Karter AJ, McNamara D, Schillinger D. Crossley SA, et al. J Commun Healthc. 2020;13(4):1-13. doi: 10.1080/17538068.2020.1822726. Epub 2020 Sep 24. J Commun Healthc. 2020. PMID: 34306181 Free PMC article.

10

Precision communication: Physicians' linguistic adaptation to patients' health literacy.

Schillinger D, Duran ND, McNamara DS, Crossley SA, Balyan R, Karter AJ. Schillinger D, et al. Among authors: mcnamara ds. Sci Adv. 2021 Dec 17;7(51):eabj2836. doi: 10.1126/sciadv.abj2836. Epub 2021 Dec 17. Sci Adv. 2021. PMID: 34919437 Free PMC article.

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