Conversational stories &amp; self organizing maps: Innovations for the scalable study of uncertainty in healthcare communication

Gramling, Robert; Javed, Ali; Durieux, Brigitte N.; Clarfeld, Laurence A.; Matt, Jeremy E.; Rizzo, Donna M.; Wong, April S. Y.; Braddish, Tess; Gramling, Cailin J.; Wills, Joseph; Arnoldy, Francesca; Straton, Jack C.; Cheney, Nicholas; Eppstein, Margaret J.; Gramling, David

doi:10.1016/j.pec.2021.07.043

Cited by 5 publications

(8 citation statements)

References 29 publications

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“…Clinical notes and electronic medical records were the most common primary data sources, used in 57 studies (69.5%). 21,23–27,29,30,33–36,40,42–46,48–52,54,55,57–64,66–68,70–73,75,79–93,95 Other primary sources included audio recordings ( n = 6, 7.3%), 6,28,32,38,39,65 administrative data ( n = 5, 6.1%), 37,47,53,77,...…”

Section: Resultsmentioning

confidence: 99%

“…We identified a trend in recent years where natural language processing was frequently used in analyzing clinical serious illness conversations extracted from audio recordings, particularly by the same research group (Gramling et al). 6,28,32,38,39,65 These in-depth analyses included concepts like conversational stories, conversational pauses, shapes of stories, connectional silence, analysis of prosody, turn length, turn-taking, and analyses of the lexicon of uncertainty.…”

Section: Discussionmentioning

confidence: 99%

“…Brizzi et al, 25 Kern et al, 49 Lau et al, 53 Lee et al, 54,55 Lindvall et al, 58 Poort et al, 71 Udelsman et al 84 Conversation analysis/conversational dynamics during serious illness conversations 7 (8.5) van den Broek-Altenburg et al, 6 Clarfeld et al, 28 Durieux et al, 32 Gramling et al, 38,39 Manukyan et al, 65 Ross et al 72 Symptom identification 6 (7.3) DiMartino et al, 30 Forsyth et al, 35 Heintzelman et al, 43 López-Torrecilla et al, 60 Taggart et al, 83 Yang et al 91 Decision-making 4 (4.8) Barrett et al, 24 Ouchi et al, 70 Saeed et al, 73 Almasalha et al 85 Identification of patients likely to benefit from palliative care or palliative needs or palliative status 4 (4.8) Murphree et al, 68 Sandham et al, 75 Song et al, 79 Zhang et al 97 Clinical phenotypes identification 3 (3.6) Ernecoff et al, 34 Jay et al, 45 natural language processing software to complete data analysis. This suggests that each software has its own strengths, and that weaknesses may be overcome by the combined use of more than one software solution to achieve the desired goal of the analyses.…”

Section: Discussionmentioning

confidence: 99%

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The use of natural language processing in palliative care research: A scoping review

et al. 2022

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Background: Natural language processing has been increasingly used in palliative care research over the last 5 years for its versatility and accuracy. Aim: To evaluate and characterize natural language processing use in palliative care research, including the most commonly used natural language processing software and computational methods, data sources, trends in natural language processing use over time, and palliative care topics addressed. Design: A scoping review using the framework by Arksey and O’Malley and the updated recommendations proposed by Levac et al. was conducted. Sources: PubMed, Web of Science, Embase, Scopus, and IEEE Xplore databases were searched for palliative care studies that utilized natural language processing tools. Data on study characteristics and natural language processing instruments used were collected and relevant palliative care topics were identified. Results: 197 relevant references were identified. Of these, 82 were included after full-text review. Studies were published in 48 different journals from 2007 to 2022. The average sample size was 21,541 (median 435). Thirty-two different natural language processing software and 33 machine-learning methods were identified. Nine main sources for data processing and 15 main palliative care topics across the included studies were identified. The most frequent topic was mortality and prognosis prediction. We also identified a trend where natural language processing was frequently used in analyzing clinical serious illness conversations extracted from audio recordings. Conclusions: We found 82 papers on palliative care using natural language processing methods for a wide-range of topics and sources of data that could expand the use of this methodology. We encourage researchers to consider incorporating this cutting-edge research methodology in future studies to improve published palliative care data.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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The use of natural language processing in palliative care research: A scoping review

et al. 2022

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“…This represents a helpful starting point for serious illness communication research. Nonetheless, applying new methods to the study of communication necessitates innovative adoption and articulation of theoretical frameworks [63][64][65]. For example, Mishel's [66] theory of uncertainty in illness may be appropriate if interested in investigating managing uncertainty.…”

Section: Theorymentioning

confidence: 99%

Toward a basic science of communication in serious illness

Tarbi

Blanch‐Hartigan

Vliet³

et al. 2022

Patient Education and Counseling

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“…Our previous work suggests that conversational narrative analysis offers a clinically meaningful framework for understanding serious illness conversations (Ross et al, 2020;Gramling et al, 2021), and others have demonstrated that unsupervised machine learning can identify "types of stories" using time-series analysis of lexicon (Reagan et al, 2016). One core feature of conversational narrative, called temporal reference, characterizes how participants organize their conversations about things that happened in the past, are happening now, or may happen in the future (Romaine, 1983).…”

Section: Need For Scalability In Health Care Communication Sciencementioning

confidence: 99%

SOMTimeS: Self Organizing Maps for Time Series Clustering and its Application to Serious Illness Conversations

Ali¹,

Rizzo²,

Lee³

et al. 2021

Preprint

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There is an increasing demand for scalable algorithms capable of clustering and analyzing large time series datasets. The Kohonen self-organizing map (SOM) is a type of unsupervised artificial neural network for visualizing and clustering complex data, reducing the dimensionality of data, and selecting influential features. Like all clustering methods, the SOM requires a measure of similarity between input data (in this work time series). Dynamic time warping (DTW) is one such measure, and a top performer given that it accommodates the distortions when aligning time series. Despite its use in clustering, DTW is limited in practice because it is quadratic in runtime complexity with the length of the time series data.To address this, we present a new DTW-based clustering method, called SOMTimeS (a Self-Organizing Map for TIME Series), that scales better and runs faster than other DTW-based clustering algorithms, and has similar performance accuracy. The computational performance of SOMTimeS stems from its ability to prune unnecessary DTW computations during the SOM's training phase. We also implemented a similar pruning strategy for K-means for comparison with one of the top performing clustering algorithms. We evaluated the pruning effectiveness, accuracy, execution time and scalability on 112 benchmark time series datasets from the University of California, Riverside classification archive. We showed that for similar accuracy, the speed-up achieved for SOMTimeS and K-means was 1.8x on average; however, rates varied between 1x and 18x depending on the dataset. SOMTimeS and K-means pruned

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Conversational stories & self organizing maps: Innovations for the scalable study of uncertainty in healthcare communication

Cited by 5 publications

References 29 publications

The use of natural language processing in palliative care research: A scoping review

The use of natural language processing in palliative care research: A scoping review

Toward a basic science of communication in serious illness

SOMTimeS: Self Organizing Maps for Time Series Clustering and its Application to Serious Illness Conversations

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