Chronic Pain and Language: A Topic Modelling Approach to Personal Pain
Descriptions
- URL: http://arxiv.org/abs/2109.00402v1
- Date: Wed, 1 Sep 2021 14:31:16 GMT
- Title: Chronic Pain and Language: A Topic Modelling Approach to Personal Pain
Descriptions
- Authors: Diogo A. P. Nunes, David Martins de Matos, Joana Ferreira Gomes, Fani
Neto
- Abstract summary: Chronic pain is recognized as a major health problem, with impacts not only at the economic, but also at the social, and individual levels.
Being a private and subjective experience, it is impossible to externally and impartially experience, describe, and interpret chronic pain as a purely noxious stimulus.
We propose and discuss a topic modelling approach to recognize patterns in verbal descriptions of chronic pain, and use these patterns to quantify and qualify experiences of pain.
- Score: 0.688204255655161
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Chronic pain is recognized as a major health problem, with impacts not only
at the economic, but also at the social, and individual levels. Being a private
and subjective experience, it is impossible to externally and impartially
experience, describe, and interpret chronic pain as a purely noxious stimulus
that would directly point to a causal agent and facilitate its mitigation,
contrary to acute pain, the assessment of which is usually straightforward.
Verbal communication is, thus, key to convey relevant information to health
professionals that would otherwise not be accessible to external entities,
namely, intrinsic qualities about the painful experience and the patient. We
propose and discuss a topic modelling approach to recognize patterns in verbal
descriptions of chronic pain, and use these patterns to quantify and qualify
experiences of pain. Our approaches allow for the extraction of novel insights
on chronic pain experiences from the obtained topic models and latent spaces.
We argue that our results are clinically relevant for the assessment and
management of chronic pain.
Related papers
- MentalArena: Self-play Training of Language Models for Diagnosis and Treatment of Mental Health Disorders [59.515827458631975]
Mental health disorders are one of the most serious diseases in the world.
Privacy concerns limit the accessibility of personalized treatment data.
MentalArena is a self-play framework to train language models.
arXiv Detail & Related papers (2024-10-09T13:06:40Z) - Towards Mitigating Hallucination in Large Language Models via
Self-Reflection [63.2543947174318]
Large language models (LLMs) have shown promise for generative and knowledge-intensive tasks including question-answering (QA) tasks.
This paper analyses the phenomenon of hallucination in medical generative QA systems using widely adopted LLMs and datasets.
arXiv Detail & Related papers (2023-10-10T03:05:44Z) - Pain Forecasting using Self-supervised Learning and Patient Phenotyping:
An attempt to prevent Opioid Addiction [0.3749861135832073]
It is crucial to forecast future patient pain trajectories to help patients manage their Sickle Cell Disease.
It is challenging to obtain many pain records to design forecasting models since it is mainly recorded by patients' self-report.
We propose a self-supervised learning approach for clustering time-series data, where each cluster comprises patients who share similar future pain profiles.
arXiv Detail & Related papers (2023-10-09T18:31:50Z) - GDPR Compliant Collection of Therapist-Patient-Dialogues [48.091760741427656]
We elaborate on the challenges we faced in starting our collection of therapist-patient dialogues in a psychiatry clinic under the General Data Privacy Regulation of the European Union.
We give an overview of each step in our procedure and point out the potential pitfalls to motivate further research in this field.
arXiv Detail & Related papers (2022-11-22T15:51:10Z) - Chronic pain patient narratives allow for the estimation of current pain
intensity [0.9459979060644313]
We show that language features from patient narratives indeed convey information relevant for pain intensity estimation.
Our results show that patients with mild pain focus more on the use of verbs, whilst moderate and severe pain patients focus on adverbs, and nouns and adjectives, respectively.
arXiv Detail & Related papers (2022-10-31T16:59:21Z) - PainPoints: A Framework for Language-based Detection of Chronic Pain and
Expert-Collaborative Text-Summarization [9.429043279361148]
PainPoints is a framework that detects the sub-type of pain and generates clinical notes via summarizing the patient interviews.
PainPoints makes use of large language models to perform sentence-level classification of the text obtained from interviews of FM and NP patients.
We generate summaries of these interviews via expert interventions by introducing a novel facet-based approach.
arXiv Detail & Related papers (2022-09-14T06:08:13Z) - Intelligent Sight and Sound: A Chronic Cancer Pain Dataset [74.77784420691937]
This paper introduces the first chronic cancer pain dataset, collected as part of the Intelligent Sight and Sound (ISS) clinical trial.
The data collected to date consists of 29 patients, 509 smartphone videos, 189,999 frames, and self-reported affective and activity pain scores.
Using static images and multi-modal data to predict self-reported pain levels, early models show significant gaps between current methods available to predict pain.
arXiv Detail & Related papers (2022-04-07T22:14:37Z) - Why Interpretable Causal Inference is Important for High-Stakes Decision
Making for Critically Ill Patients and How To Do It [80.24494623756839]
We present a framework for interpretable estimation of causal effects for critically ill patients.
We apply this framework to the effect of seizures and other potentially harmful electrical events in the brain on outcomes.
arXiv Detail & Related papers (2022-03-09T18:03:35Z) - Analysis of Chronic Pain Experiences Based on Online Reports: the RRCP
Dataset [0.688204255655161]
We present the Reddit Reports of Chronic Pain dataset, which comprises social media textual descriptions and discussion of various forms of chronic pain experiences.
For each pathology, we identify the main concerns emergent of its consequent experience of chronic pain, as represented by a subset of documents explicitly related to it.
We argue that our unsupervised semantic analysis of descriptions of chronic pain echoes clinical research on how different pathologies manifest in terms of the chronic pain experience.
arXiv Detail & Related papers (2021-08-23T14:53:03Z) - Non-contact Pain Recognition from Video Sequences with Remote
Physiological Measurements Prediction [53.03469655641418]
We present a novel multi-task learning framework which encodes both appearance changes and physiological cues in a non-contact manner for pain recognition.
We establish the state-of-the-art performance of non-contact pain recognition on publicly available pain databases.
arXiv Detail & Related papers (2021-05-18T20:47:45Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.