Using Natural Language Processing to Classify Serious Illness Communication with Oncology Patients

Davoudi, Anahita; Tissot, Hegler; Doucette, Abigail; Gabriel, Peter; Parikh, Ravi B.; Mowery, Danielle L.; Miranda, Stephen P.

doi:10.1101/2021.08.20.21262082

Cited by 9 publications

(10 citation statements)

References 36 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%

“…These applications offer the possibility of several toolkits for natural language processing and also the possibility to develop codes based on the researchers' needs. Another interesting finding was that many of the studies used a combination of more than one 27 Elhazmi et al, 33 Ganguli et al, 36 George et al, 37 Hu et al, 44 Kehl et al, 48 Laios et al, 50 Lin et al, 57 Manz et al, 66 Agarwal et al, 74 Santos et al, 77 Sung et al, 82 Ye et al 93 Assessment of the impact of interventions 9 (10.9) Ando et al, 22 Greer et al, 42 Lakin et al, 51 Lefèvre et al, 56 Macieira et al, 64 Santarpia et al, 76 Steiner et al, 80 Udelsman et al, 87 Uyeda et al 89 Social and spiritual health 8 (9.7) Gray et al, 40 Johnson et al, 46,47 Masukawa et al, 67 Yoon et al, 94 Ando et al, 96 Ando et al 99 Topic identification 8 (9.7) Sarmet et al, 5 Ando et al, 21 Chan et al, 26 Davoudi et al, 29 Agaronnik et al, 52 Lucini et al, 61 Seale et al, 78 Wang et al 90 Advance care planning/EOL process measures/Code-status clarification/Goals of care documentation 8 (9.7)…”

Section: Discussionmentioning

confidence: 99%

“…Clinical notes and electronic medical records were the most common primary data sources, used in 57 studies (69.5%). 21,[23][24][25][26][27]29,30,[33][34][35][36]40,[42][43][44][45][46][48][49][50][51][52]54,55,[57][58][59][60][61][62][63][64][66][67][68][70][71][72][73]75,[79][80][81][82][83][84][85][86][87][88][89]…”

Section: Sources Considered For Data Processingmentioning

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: Sources Considered For Data Processingmentioning

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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“…In this study, relying on Natural Language Processing (NLP) methods, we present an automatic classifier to retrieve GOC discussions in the EHR that may facilitate both clinicians’ point-of-care access to their patients’ documented goals (or lack thereof) and arduous data extraction processes for research. Recent studies have employed various NLP methods to identify GOC discussions in the EHR Chan et al (2019); Lee et al (2021); Davoudi et al (2022) in different care settings and seriously ill populations. All of the studies reported good overall model performance in internal validation cohorts and a substantial reduction in human annotation time for EHR review.…”

Section: Significance and Backgroundmentioning

confidence: 99%

Detecting Goals of Care Conversations in Clinical Notes with Active Learning

Weissenbacher,

Courtright,

Rawal

et al. 2024

Preprint

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ObjectiveGoals Of Care (GOC) discussions are an increasingly used quality metric in serious illness care and research. Wide variation in documentation practices within the Electronic Health Record (EHR) presents challenges for reliable measurement of GOC discussions. Novel natural language processing approaches are needed to capture GOC discussions documented in real-world samples of seriously ill hospitalized patients’ EHR notes, a corpus with a very low event prevalence.MethodsTo automatically detect utterances documenting GOC discussions outside of dedicated GOC note types, we proposed an ensemble of classifiers aggregating the predictions of rule-based, feature-based, and three transformers-based classifiers. We trained our classifier on 600 manually annotated EHR notes among patients with serious illnesses. Our corpus exhibited an extremely imbalanced ratio between utterances discussing GOC and utterances that do not. This ratio challenges standard supervision methods to train a classifier. Therefore, we trained our classifier with active learning.ResultsUsing active learning, we reduced the annotation cost to fine-tune our ensemble by 70% while improving its performance in our test set of 176 EHR notes, with 0.557 F1-score for utterance classification and 0.629 for note classification.ConclusionWhen classifying notes, with a true positive rate of 72% (13/18) and false positive rate of 8% (13/158), our performance may be sufficient for deploying our classifier in the EHR to facilitate point-of-care access to GOC conversations documented outside of dedicated notes types, without overburdening clinicians with false positives. Improvements are needed before using it to enrich trial populations or as an outcome measure.

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“…NLP is ubiquitous in consumer life, with uses ranging from predictive text on our smartphones and emails to automated voice assistants such as Siri and Alexa. We have only begun exploring uses of NLP in healthcare; so far, its applications include predicting admissions of patients in the emergency department, identifying patients suitable for clinical trials, mining medical records to predict treatment efficacy and adverse events, and analyzing the quality and comprehensiveness of clinical documentation, to name a few ( 8 – 11 ).…”

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confidence: 99%

The Artificially Intelligent Teacher: Applying Natural Language Processing to Critical Care Education

2022

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Using Natural Language Processing to Classify Serious Illness Communication with Oncology Patients

Cited by 9 publications

References 36 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

Detecting Goals of Care Conversations in Clinical Notes with Active Learning

The Artificially Intelligent Teacher: Applying Natural Language Processing to Critical Care Education

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