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Matt JE, Rizzo DM, Javed A, Eppstein MJ, Manukyan V, Gramling C, Dewoolkar AM, Gramling R. An Acoustical and Lexical Machine-Learning Pipeline to Identify Connectional Silences. J Palliat Med 2023; 26:1627-1633. [PMID: 37440175 DOI: 10.1089/jpm.2023.0087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/14/2023] Open
Abstract
Context: Developing scalable methods for conversation analytics is essential for health care communication science and quality improvement. Purpose: To assess the feasibility of automating the identification of a conversational feature, Connectional Silence, which is associated with important patient outcomes. Methods: Using audio recordings from the Palliative Care Communication Research Initiative cohort study, we develop and test an automated measurement pipeline comprising three machine-learning (ML) tools-a random forest algorithm and a custom convolutional neural network that operate in parallel on audio recordings, and subsequently a natural language processing algorithm that uses brief excerpts of automated speech-to-text transcripts. Results: Our ML pipeline identified Connectional Silence with an overall sensitivity of 84% and specificity of 92%. For Emotional and Invitational subtypes, we observed sensitivities of 68% and 67%, and specificities of 95% and 97%, respectively. Conclusion: These findings support the capacity for coordinated and complementary ML methods to fully automate the identification of Connectional Silence in natural hospital-based clinical conversations.
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Affiliation(s)
- Jeremy E Matt
- Graduate Program in Complex Systems and Data Science, College of Engineering and Mathematical Sciences, University of Vermont, Burlington, Vermont, USA
| | - Donna M Rizzo
- Department of Civil and Environmental Engineering, University of Vermont, Burlington, Vermont, USA
| | - Ali Javed
- Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford University, Stanford, California, USA
| | - Margaret J Eppstein
- Department of Computer Science, University of Vermont, Burlington, Vermont, USA
| | | | - Cailin Gramling
- Graduate Program in Complex Systems and Data Science, College of Engineering and Mathematical Sciences, University of Vermont, Burlington, Vermont, USA
| | - Advik Mandar Dewoolkar
- Department of Electrical and Biomedical Engineering, University of Vermont, Burlington, Vermont, USA
| | - Robert Gramling
- Department of Family Medicine, University of Vermont, Burlington, Vermont, USA
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Applications of Machine Learning in Palliative Care: A Systematic Review. Cancers (Basel) 2023; 15:cancers15051596. [PMID: 36900387 PMCID: PMC10001037 DOI: 10.3390/cancers15051596] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 02/24/2023] [Accepted: 03/02/2023] [Indexed: 03/08/2023] Open
Abstract
Objective: To summarize the available literature on using machine learning (ML) for palliative care practice as well as research and to assess the adherence of the published studies to the most important ML best practices. Methods: The MEDLINE database was searched for the use of ML in palliative care practice or research, and the records were screened according to PRISMA guidelines. Results: In total, 22 publications using machine learning for mortality prediction (n = 15), data annotation (n = 5), predicting morbidity under palliative therapy (n = 1), and predicting response to palliative therapy (n = 1) were included. Publications used a variety of supervised or unsupervised models, but mostly tree-based classifiers and neural networks. Two publications had code uploaded to a public repository, and one publication uploaded the dataset. Conclusions: Machine learning in palliative care is mainly used to predict mortality. Similarly to other applications of ML, external test sets and prospective validations are the exception.
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Sarmet M, Kabani A, Coelho L, Dos Reis SS, Zeredo JL, Mehta AK. The use of natural language processing in palliative care research: A scoping review. Palliat Med 2023; 37:275-290. [PMID: 36495082 DOI: 10.1177/02692163221141969] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
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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Affiliation(s)
- Max Sarmet
- Tertiary Referral Center of Neuromuscular Diseases, Hospital de Apoio de Brasília, Brazil.,Graduate Department of Health Science and Technology, University of Brasília, Brazil
| | - Aamna Kabani
- Johns Hopkins University, School of Medicine, USA
| | - Luis Coelho
- Center of Innovation in Engineering and Industrial Technology, Polytechnic of Porto - School of Engineering (ISEP), Portugal
| | - Sara Seabra Dos Reis
- Center of Innovation in Engineering and Industrial Technology, Polytechnic of Porto - School of Engineering (ISEP), Portugal
| | - Jorge L Zeredo
- Graduate Department of Health Science and Technology, University of Brasília, Brazil
| | - Ambereen K Mehta
- Palliative Care Program, Division of General Internal Medicine, Johns Hopkins Bayview Medical Center, Johns Hopkins University, School of Medicine, USA
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Gramling CJ, Durieux BN, Clarfeld LA, Javed A, Matt JE, Manukyan V, Braddish T, Wong A, Wills J, Hirsch L, Straton J, Cheney N, Eppstein MJ, Rizzo DM, Gramling R. Epidemiology of Connectional Silence in specialist serious illness conversations. PATIENT EDUCATION AND COUNSELING 2022; 105:2005-2011. [PMID: 34799186 DOI: 10.1016/j.pec.2021.10.032] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/22/2021] [Revised: 10/27/2021] [Accepted: 10/28/2021] [Indexed: 06/13/2023]
Abstract
CONTEXT Human connection can reduce suffering and facilitate meaningful decision-making amid the often terrifying experience of hospitalization for advanced cancer. Some conversational pauses indicate human connection, but we know little about their prevalence, distribution or association with outcomes. PURPOSE To describe the epidemiology of Connectional Silence during serious illness conversations in advanced cancer. METHODS We audio-recorded 226 inpatient palliative care consultations at two academic centers. We identified pauses lasting 2+ seconds and distinguished Connectional Silences from other pauses, sub-categorized as either Invitational (ICS) or Emotional (ECS). We identified treatment decisional status pre-consultation from medical records and post-consultation via clinicians. Patients self-reported quality-of-life before and one day after consultation. RESULTS Among all 6769 two-second silences, we observed 328 (4.8%) ECS and 240 (3.5%) ICS. ECS prevalence was associated with decisions favoring fewer disease-focused treatments (ORadj: 2.12; 95% CI: 1.12, 4.06). Earlier conversational ECS was associated with improved quality-of-life (p = 0.01). ICS prevalence was associated with clinicians' prognosis expectations. CONCLUSIONS Connectional Silences during specialist serious illness conversations are associated with decision-making and improved patient quality-of-life. Further work is necessary to evaluate potential causal relationships. PRACTICE IMPLICATIONS Pauses offer important opportunities to advance the science of human connection in serious illness decision-making.
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Affiliation(s)
| | | | | | - Ali Javed
- Department of Computer Science, University of Vermont, Burlington, VT, USA
| | - Jeremy E Matt
- Complex Systems & Data Science, University of Vermont, Burlington, VT, USA
| | | | - Tess Braddish
- Department of Family Medicine, University of Vermont, Burlington, VT, USA
| | - Ann Wong
- University of Vermont, Burlington, VT, USA
| | | | | | | | - Nicholas Cheney
- Department of Computer Science, University of Vermont, Burlington, VT, USA
| | | | - Donna M Rizzo
- Department of Civil Engineering, University of Vermont, Burlington, VT, USA
| | - Robert Gramling
- Department of Family Medicine, University of Vermont, Burlington, VT, USA.
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Gramling R, Javed A, Durieux BN, Clarfeld LA, Matt JE, Rizzo DM, Wong A, Braddish T, Gramling CJ, Wills J, Arnoldy F, Straton J, Cheney N, Eppstein MJ, Gramling D. Conversational stories & self organizing maps: Innovations for the scalable study of uncertainty in healthcare communication. PATIENT EDUCATION AND COUNSELING 2021; 104:2616-2621. [PMID: 34353689 DOI: 10.1016/j.pec.2021.07.043] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/24/2021] [Accepted: 07/27/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND Understanding uncertainty in participatory decision-making requires scientific attention to interaction between what actually happens when patients, families and clinicians engage one another in conversation and the multi-level contexts in which these occur. Achieving this understanding will require conceptually grounded and scalable methods for use in large samples of people representing diversity in cultures, speaking and decision-making norms, and clinical situations. DISCUSSION Here, we focus on serious illness and describe Conversational Stories as a scalable and conceptually grounded framework for characterizing uncertainty expression in these clinical contexts. Using actual conversations from a large direct-observation cohort study, we demonstrate how natural language processing and unsupervised machine learning methods can reveal underlying types of uncertainty stories in serious illness conversations. CONCLUSIONS Conversational Storytelling offers a meaningful analytic framework for scalable computational methods to study uncertainty in healthcare conversations.
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Affiliation(s)
- Robert Gramling
- University of Vermont, Department of Family Medicine, Burlington, VT, USA.
| | - Ali Javed
- University of Vermont, Department of Computer Science, Burlington, VT, USA
| | | | | | - Jeremy E Matt
- University of Vermont, Complex Systems & Data Science, USA
| | - Donna M Rizzo
- University of Vermont, Department of Civil & Environmental Engineering, Burlington, VT, USA
| | - Ann Wong
- University of Vermont, Burlington, VT, USA
| | - Tess Braddish
- University of Vermont, Department of Family Medicine, Burlington, VT, USA
| | | | | | - Francesca Arnoldy
- University of Vermont, Continuing and Distance Education, Burlington, VT, USA
| | | | - Nicholas Cheney
- University of Vermont, Department of Computer Science, Burlington, VT, USA
| | | | - David Gramling
- University of British Columbia, Department of Central, Eastern and Northern European Studies, Vancouver, BC, Canada
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van den Broek-Altenburg E, Gramling R, Gothard K, Kroesen M, Chorus C. Using natural language processing to explore heterogeneity in moral terminology in palliative care consultations. BMC Palliat Care 2021; 20:23. [PMID: 33494745 PMCID: PMC7836473 DOI: 10.1186/s12904-021-00716-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2020] [Accepted: 01/15/2021] [Indexed: 11/22/2022] Open
Abstract
Background High quality serious illness communication requires good understanding of patients’ values and beliefs for their treatment at end of life. Natural Language Processing (NLP) offers a reliable and scalable method for measuring and analyzing value- and belief-related features of conversations in the natural clinical setting. We use a validated NLP corpus and a series of statistical analyses to capture and explain conversation features that characterize the complex domain of moral values and beliefs. The objective of this study was to examine the frequency, distribution and clustering of morality lexicon expressed by patients during palliative care consultation using the Moral Foundations NLP Dictionary. Methods We used text data from 231 audio-recorded and transcribed inpatient PC consultations and data from baseline and follow-up patient questionnaires at two large academic medical centers in the United States. With these data, we identified different moral expressions in patients using text mining techniques. We used latent class analysis to explore if there were qualitatively different underlying patterns in the PC patient population. We used Poisson regressions to analyze if individual patient characteristics, EOL preferences, religion and spiritual beliefs were associated with use of moral terminology. Results We found two latent classes: a class in which patients did not use many expressions of morality in their PC consultations and one in which patients did. Age, race (white), education, spiritual needs, and whether a patient was affiliated with Christianity or another religion were all associated with membership of the first class. Gender, financial security and preference for longevity-focused over comfort focused treatment near EOL did not affect class membership. Conclusions This study is among the first to use text data from a real-world situation to extract information regarding individual foundations of morality. It is the first to test empirically if individual moral expressions are associated with individual characteristics, attitudes and emotions.
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Affiliation(s)
| | - Robert Gramling
- University of Vermont, Robert Larner, M.D. College of Medicine, 89 Beaumont Avenue, Burlington, VT, 05405, USA
| | - Kelly Gothard
- University of Vermont, Robert Larner, M.D. College of Medicine, 89 Beaumont Avenue, Burlington, VT, 05405, USA
| | - Maarten Kroesen
- Delft University of Technology, Stevinweg 1, Delft, CB, 2628, The Netherlands
| | - Caspar Chorus
- Delft University of Technology, Stevinweg 1, Delft, CB, 2628, The Netherlands
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Durieux BN, Gramling CJ, Manukyan V, Eppstein MJ, Rizzo DM, Ross LM, Ryan AG, Niland MA, Clarfeld LA, Alexander SC, Gramling R. Identifying Connectional Silence in Palliative Care Consultations: A Tandem Machine-Learning and Human Coding Method. J Palliat Med 2018; 21:1755-1760. [PMID: 30328760 DOI: 10.1089/jpm.2018.0270] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Background: Systematic measurement of conversational features in the natural clinical setting is essential to better understand, disseminate, and incentivize high quality serious illness communication. Advances in machine-learning (ML) classification of human speech offer exceptional opportunity to complement human coding (HC) methods for measurement in large scale studies. Objectives: To test the reliability, efficiency, and sensitivity of a tandem ML-HC method for identifying one feature of clinical importance in serious illness conversations: Connectional Silence. Design: This was a cross-sectional analysis of 354 audio-recorded inpatient palliative care consultations from the Palliative Care Communication Research Initiative multisite cohort study. Setting/Subjects: Hospitalized people with advanced cancer. Measurements: We created 1000 brief audio "clips" of randomly selected moments predicted by a screening ML algorithm to be two-second or longer pauses in conversation. Each clip included 10 seconds of speaking before and 5 seconds after each pause. Two HCs independently evaluated each clip for Connectional Silence as operationalized from conceptual taxonomies of silence in serious illness conversations. HCs also evaluated 100 minutes from 10 additional conversations having unique speakers to identify how frequently the ML screening algorithm missed episodes of Connectional Silence. Results: Connectional Silences were rare (5.5%) among all two-second or longer pauses in palliative care conversations. Tandem ML-HC demonstrated strong reliability (kappa 0.62; 95% confidence interval: 0.47-0.76). HC alone required 61% more time than the Tandem ML-HC method. No Connectional Silences were missed by the ML screening algorithm. Conclusions: Tandem ML-HC methods are reliable, efficient, and sensitive for identifying Connectional Silence in serious illness conversations.
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Affiliation(s)
| | - Cailin J Gramling
- School of Arts and Sciences, University of Vermont, Burlington, Vermont
| | | | | | - Donna M Rizzo
- Department of Civil and Environmental Engineering, University of Vermont, Burlington, Vermont
| | - Lindsay M Ross
- School of Engineering, University of Vermont, Burlington, Vermont
| | - Aidan G Ryan
- School of Engineering, University of Vermont, Burlington, Vermont
| | | | | | - Stewart C Alexander
- Department of Consumer Science and Public Health, Purdue University, West Lafayette, Indiana
| | - Robert Gramling
- Department of Family Medicine, University of Vermont, Burlington, Vermont
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