ArticleJournal of the American Medical Informatics Association : JAMIA2020
Spoken words as biomarkers: using machine learning to gain insight into communication as a predictor of anxiety.
Article in Journal of the American Medical Informatics Association : JAMIA, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed, 16 citations in OpenAlex.
- Natural language processing of biomedical text to map and prioritize protein-disease associations in HFpEF.Computers in biology and medicine · 2026Article
- A novel approach to anxiety level prediction using small sets of judgment and survey variables.Npj mental health research · 2024Article
- Wearable and Mobile Technologies for the Evaluation and Treatment of Obsessive-Compulsive Disorder: Scoping Review.JMIR mental health · 2023Article
- Methodological and Quality Flaws in the Use of Artificial Intelligence in Mental Health Research: Systematic Review.JMIR mental health · 2023Review
- Aberrated Multidimensional EEG Characteristics in Patients with Generalized Anxiety Disorder: A Machine-Learning Based Analysis Framework.Sensors (Basel, Switzerland) · 2022Article
- Examining spoken words and acoustic features of therapy sessions to understand family caregivers' anxiety and quality of life.International journal of medical informatics · 2022Article
- Lacking Communication Would Increase General Symptom Index Scores of Medical Team Members During COVID-19 Pandemic in China: A Retrospective Cohort Study.Inquiry : a journal of medical care organization, provision and financingArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 3 institutions in 1 country.
Funding
Abstract
objectiveThe goal of this study was to explore whether features of recorded and transcribed audio communication data extracted by machine learning algorithms can be used to train a classifier for anxiety. MATERIALS AND
methodsWe used a secondary data set generated by a clinical trial examining problem-solving therapy for hospice caregivers consisting of 140 transcripts of multiple, sequential conversations between an interviewer and a family caregiver along with standardized assessments of anxiety prior to each session; 98 of these transcripts (70%) served as the training set, holding the remaining 30% of the data for evaluation.
resultsA classifier for anxiety was developed relying on language-based features. An 86% precision, 78% recall, 81% accuracy, and 84% specificity were achieved with the use of the trained classifiers. High anxiety inflections were found among recently bereaved caregivers and were usually connected to issues related to transitioning out of the caregiving role. This analysis highlighted the impact of lowering anxiety by increasing reciprocity between interviewers and caregivers.
conclusionVerbal communication can provide a platform for machine learning tools to highlight and predict behavioral health indicators and trends.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.