Overview

Quality of Life (QoL) is a multidimensional, patient-centered construct reflecting how health, functional ability, psychological state, and social context shape an individual’s lived experience and overall well-being. Despite its central role in preventive care, mental health, chronic disease management, and complementary and integrative medicine, QoL remains poorly represented in structured health records. The most meaningful QoL indicators such as functional decline, emotional distress, pain burden, fatigue, sleep disruption, mobility limitations, and social connectedness, are predominantly documented in unstructured clinical narratives, patient-reported outcomes, and patient-generated text. Recent advances in clinical Natural Language Processing (NLP) and large language models (LLMs) offer new opportunities to systematically extract, model, and monitor these signals, enabling earlier detection of deterioration and more patient-centered clinical decision-making.

Despite growing methodological progress, significant challenges limit the reliable and scalable use of NLP for QoL assessment. QoL signals are not single, well-defined clinical entities; they are often implicitly expressed and require contextual interpretation of severity, duration, and temporal change rather than simple presence or absence. Furthermore, there is a lack of standardized conceptual models, annotation guidelines, evaluation metrics, and governance frameworks for QoL-focused NLP systems, hindering reproducibility, comparability, and clinical trust. These gaps impede translation into real-world workflows, even though poor QoL is strongly associated with adverse outcomes such as functional decline, mental health crises, hospitalization, and mortality. Addressing these challenges requires coordinated, multidisciplinary efforts to establish shared benchmarks, trustworthy modeling practices, and implementation-ready frameworks for integrating NLP-derived QoL indicators into routine clinical care.

CNLP4QoL 2026 convenes multidisciplinary experts to develop robust, explainable, and clinically actionable NLP frameworks for measuring and integrating these signals into real-world care. By moving beyond disease-centric paradigms, this workshop adopts a whole-person perspective on QoL, encompassing wellness, function, psychosocial well-being, and lived experience.

Call for Papers

We invite researchers, clinicians, informaticians, data scientists, and innovators to submit original, unpublished research and work-in-progress papers.

Thematic Scope & Challenges

Significant challenges currently limit the scalable use of NLP for QoL assessment. QoL signals are not single, well-defined clinical entities; they are often implicitly expressed and require contextual interpretation of severity, duration, and temporal change rather than simple presence/absence detection. Furthermore, the field lacks standardized conceptual models and governance frameworks.

Topics of Interest

We encourage submissions covering, but not limited to:

• Extraction, interpretation, and prediction of QoL indicators from text.
• Modeling and leveraging QoL-related information from unstructured documents.
• Reasoning and trustworthiness in language models for whole-person care.
• Standardization of annotation frameworks for QoL concept extraction.
• Annotation strategies and protocols for subjective QoL constructs.
• NLP architectures for capturing symptoms, behaviors, and psychosocial factors
• Concept modeling and associated technologies for QoL indicators.
• Synthetic text generation for psychosocial health indicators.
• NLP for analyzing patient portals, messages, conversations, and remote monitoring data.
• Evaluation metrics for QoL concept extraction and narrative quality.
• Explainable and reliable clinical NLP systems for patient-centered QoL.
• Real-world deployment studies and clinical workflow integration of NLP models for QoL.
• Resources for QoL: datasets, ontologies, knowledge graphs and benchmarks.

Submission Guidelines

All submitted papers and abstracts will undergo a single-blind peer-review process.

• Regular Papers (8–10 pages) will describe mature ideas, where a substantial amount of implementation, experimentation, or data collection and analysis has been completed.
• Short Papers (4–6 pages) will describe innovative ideas, where preliminary implementation and validation work have been conducted.
• Position Papers (4–6 pages) will articulate emerging ideas, critical viewpoints, or unmet needs in clinical NLP for Quality of Life.
• Abstracts (2 pages) will describe your vision, work in progress and preliminary results.

Templates: Please follow the IEEE conference templates.Submission site: TBD

Important Dates

Program Schedule (Tentative)

Organizing Committee

Workshop Organizers

• Muskan Garg - Mayo Clinic, USA (Google Scholar · LinkedIn)
• Humayera Islam - University of Chicago, USA (Google Scholar · LinkedIn)
• Pushkala Jayaraman - Icahn School of Medicine at Mount Sinai, USA (Google Scholar · LinkedIn)
• Sunyang Fu - University of Texas, USA (Google Scholar · LinkedIn)
• Sunghwan Sohn - Mayo Clinic, USA (Google Scholar · LinkedIn)

Program Committee

• Abeed Sarker (Emory University)
• Adam Jatowt (University of Innsbruck)
• Aly-Fasly, Saghir A. (Mayo Clinic)
• Aman Chadha (Apple Inc., USA)
• Amitava Das (NIT, Goa)
• Dezhi Wu (University of South Carolina)
• Elina Guralnik (GMU)
• Esther Lázaro Pérez (Universidad Internacional de Valencia)
• Eunji Jeon (Mayo Clinic)
• Kerstin Denecke (Berner Fachhochschule, Switzerland)
• Kushal Chawla (Capital One, USA)
• Ruixue Lian (Amazon, USA)
• Shaina Raza (University of Toronto)
• Shebuti Rayana (SUNY at Old Westbury)
• Tianlin Zhang (The University of Manchester)
• Ugur Kursuncu (Georgia State University, USA)
• Usha Lokala (University of South Carolina)
• Xingyi Liu (Mayo Clinic)
• Ziming Liu (University of Oklahoma)
• Zehan (Leo) Li (DeepKin AI, USA)

Venue & Contact

IEEE ICHI 2026 will be held in Minneapolis, MN, USA.Please visit the ICHI 2026 website for registration, travel grants, and accommodation details.

Contact: For questions regarding the workshop, submission eligibility, or sponsorship, please email: cnlp4qol@gmail.com