دورية أكاديمية
أعرض في EDS

Algorithms for Empathy: Using Machine Learning to Categorize Common Empathetic Traits Across Professional and Peer-Based Conversations.

التفاصيل البيبلوغرافية
العنوان: Algorithms for Empathy: Using Machine Learning to Categorize Common Empathetic Traits Across Professional and Peer-Based Conversations.
المؤلفون: Provence S; Founder, Versant Metrics, Duvall, USA., Forcehimes AA; Board Member, Versant Metrics, Paradise Valley, USA.
المصدر: Cureus [Cureus] 2024 Apr 06; Vol. 16 (4), pp. e57719. Date of Electronic Publication: 2024 Apr 06 (Print Publication: 2024).
نوع المنشور: Journal Article
اللغة: English
بيانات الدورية: Publisher: Cureus, Inc Country of Publication: United States NLM ID: 101596737 Publication Model: eCollection Cited Medium: Print ISSN: 2168-8184 (Print) Linking ISSN: 21688184 NLM ISO Abbreviation: Cureus Subsets: PubMed not MEDLINE
أسماء مطبوعة: Original Publication: Palo Alto, CA : Cureus, Inc.
مستخلص: Introduction In this article, we describe our creation of a machine-learning model that uses a combination of rule-based and natural language processing (NLP) algorithms. We show how this "Empathy Algorithm" was developed and how its results compare to three datasets of professional counseling and peer-led conversations.  Methods These conversation datasets were rated by people with varying degrees of empathetic expertise (from counselors to student volunteers) and labeled as either low- or high-quality empathy. Our methodology involved running both these "low-empathy" and "high-empathy" conversations through our algorithm and then looking for a correlation between conversations labeled "high empathy" and an increased presence of six empathy skills flagged by our algorithm.  Results We found positive correlations between four of the six skills that our algorithm measures (i.e., four empathizing skills showed up the same or more in each of the "high-empathy" conversations within the three datasets). This suggests that certain empathizing skills are not only consistently present in effective conversations but also quantifiable enough to be measured by today's machine-learning models. Conclusion While limitations of language, binary classifications, and non-verbal cues remain as opportunities for further development, using algorithms to objectively assess empathic skills represents an important step to improve client outcomes and refine communication practices for today's healthcare professionals.
Competing Interests: The authors have declared financial relationships, which are detailed in the next section.
(Copyright © 2024, Provence et al.)
References: Psychother Res. 2023 Sep;33(7):957-973. (PMID: 37306165)
J Subst Abuse Treat. 2016 Jun;65:43-50. (PMID: 26944234)
Psychotherapy (Chic). 2011 Mar;48(1):43-9. (PMID: 21401273)
J Consult Clin Psychol. 1992 Jun;60(3):441-9. (PMID: 1619098)
Contemp Clin Trials. 2017 Oct;61:29-32. (PMID: 28732758)
PeerJ Comput Sci. 2016 Apr;2:. (PMID: 28286867)
J Consult Clin Psychol. 2016 Mar;84(3):221-9. (PMID: 26795938)
Health Commun. 2004;16(2):159-82. (PMID: 15090283)
J Subst Abuse Treat. 2005 Jan;28(1):19-26. (PMID: 15723728)
Psychother Res. 2020 Jul;30(6):693-705. (PMID: 31519140)
J Med Internet Res. 2019 Jul 15;21(7):e12529. (PMID: 31309929)
Psychotherapy (Chic). 2019 Jun;56(2):318-328. (PMID: 30958018)
فهرسة مساهمة: Keywords: ai-based education; artificial intelligence; education; empathy; language and communication; machine learning
تواريخ الأحداث: Date Created: 20240507 Latest Revision: 20240508
رمز التحديث: 20240508
مُعرف محوري في PubMed: PMC11070898
DOI: 10.7759/cureus.57719
PMID: 38711721
قاعدة البيانات: MEDLINE
الوصف
تدمد:2168-8184
DOI:10.7759/cureus.57719