ProKnow: Process Knowledge for Safety Constrained and Explainable Question Generation for Mental Health Diagnostic Assistance

• URL: http://arxiv.org/abs/2305.08010v2
• Date: Thu, 1 Jun 2023 18:33:33 GMT
• Title: ProKnow: Process Knowledge for Safety Constrained and Explainable Question Generation for Mental Health Diagnostic Assistance
• Authors: Kaushik Roy, Manas Gaur, Misagh Soltani, Vipula Rawte, Ashwin Kalyan, Amit Sheth
• Abstract summary: Current Virtual Mental Health Assistants (VMHAs) provide counseling and suggestive care. They refrain from patient diagnostic assistance because they lack training in safety-constrained and specialized clinical process knowledge. We define Proknow as an ordered set of information that maps to evidence-based guidelines or categories of conceptual understanding to experts in a domain. We develop a method for natural language question generation (NLG) that collects diagnostic information from the patient interactively.
• Score: 11.716131800914445
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Current Virtual Mental Health Assistants (VMHAs) provide counseling and suggestive care. They refrain from patient diagnostic assistance because they lack training in safety-constrained and specialized clinical process knowledge. In this work, we define Proknow as an ordered set of information that maps to evidence-based guidelines or categories of conceptual understanding to experts in a domain. We also introduce a new dataset of diagnostic conversations guided by safety constraints and Proknow that healthcare professionals use. We develop a method for natural language question generation (NLG) that collects diagnostic information from the patient interactively. We demonstrate the limitations of using state-of-the-art large-scale language models (LMs) on this dataset. Our algorithm models the process knowledge through explicitly modeling safety, knowledge capture, and explainability. LMs augmented with ProKnow guided method generated 89% safer questions in the depression and anxiety domain. The Explainability of the generated question is assessed by computing similarity with concepts in depression and anxiety knowledge bases. Overall, irrespective of the type of LMs augmented with our ProKnow, we achieved an average 82% improvement over simple pre-trained LMs on safety, explainability, and process-guided question generation. We qualitatively and quantitatively evaluate the efficacy of the proposed ProKnow-guided methods by introducing three new evaluation metrics for safety, explainability, and process knowledge adherence.

Related papers

• Which Client is Reliable?: A Reliable and Personalized Prompt-based Federated Learning for Medical Image Question Answering [51.26412822853409]
  We present a novel personalized federated learning (pFL) method for medical visual question answering (VQA) models. Our method introduces learnable prompts into a Transformer architecture to efficiently train it on diverse medical datasets without massive computational costs.
  arXiv  Detail & Related papers  (2024-10-23T00:31:17Z)
• SouLLMate: An Application Enhancing Diverse Mental Health Support with Adaptive LLMs, Prompt Engineering, and RAG Techniques [9.146311285410631]
  Mental health issues significantly impact individuals' daily lives, yet many do not receive the help they need even with available online resources. This study aims to provide diverse, accessible, stigma-free, personalized, and real-time mental health support through cutting-edge AI technologies.
  arXiv  Detail & Related papers  (2024-10-17T22:04:32Z)
• A Cross Attention Approach to Diagnostic Explainability using Clinical Practice Guidelines for Depression [13.000907040545583]
  We develop a method to enhance attention in popular transformer models and generate clinician-understandable explanations for classification. Inspired by how clinicians rely on their expertise when interacting with patients, we leverage relevant clinical knowledge to model patient inputs. We develop such a system in the context of Mental Health (MH) using clinical practice guidelines (CPG) for diagnosing depression.
  arXiv  Detail & Related papers  (2023-11-23T08:42:18Z)
• Self-Knowledge Guided Retrieval Augmentation for Large Language Models [59.771098292611846]
  Large language models (LLMs) have shown superior performance without task-specific fine-tuning. Retrieval-based methods can offer non-parametric world knowledge and improve the performance on tasks such as question answering. Self-Knowledge guided Retrieval augmentation (SKR) is a simple yet effective method which can let LLMs refer to the questions they have previously encountered.
  arXiv  Detail & Related papers  (2023-10-08T04:22:33Z)
• Distinguish Before Answer: Generating Contrastive Explanation as Knowledge for Commonsense Question Answering [61.53454387743701]
  We propose CPACE, a concept-centric Prompt-bAsed Contrastive Explanation Generation model. CPACE converts obtained symbolic knowledge into a contrastive explanation for better distinguishing the differences among given candidates. We conduct a series of experiments on three widely-used question-answering datasets: CSQA, QASC, and OBQA.
  arXiv  Detail & Related papers  (2023-05-14T12:12:24Z)
• Directive Explanations for Monitoring the Risk of Diabetes Onset: Introducing Directive Data-Centric Explanations and Combinations to Support What-If Explorations [1.7109770736915972]
  This paper presents an explanation dashboard that predicts the risk of diabetes onset. It explains those predictions with data-centric, feature-importance, and example-based explanations. We conducted a study with 11 healthcare experts and a mixed-methods study with 45 healthcare experts and 51 diabetic patients.
  arXiv  Detail & Related papers  (2023-02-21T13:40:16Z)
• Informing clinical assessment by contextualizing post-hoc explanations of risk prediction models in type-2 diabetes [50.8044927215346]
  We consider a comorbidity risk prediction scenario and focus on contexts regarding the patients clinical state. We employ several state-of-the-art LLMs to present contexts around risk prediction model inferences and evaluate their acceptability. Our paper is one of the first end-to-end analyses identifying the feasibility and benefits of contextual explanations in a real-world clinical use case.
  arXiv  Detail & Related papers  (2023-02-11T18:07:11Z)
• A Unified End-to-End Retriever-Reader Framework for Knowledge-based VQA [67.75989848202343]
  This paper presents a unified end-to-end retriever-reader framework towards knowledge-based VQA. We shed light on the multi-modal implicit knowledge from vision-language pre-training models to mine its potential in knowledge reasoning. Our scheme is able to not only provide guidance for knowledge retrieval, but also drop these instances potentially error-prone towards question answering.
  arXiv  Detail & Related papers  (2022-06-30T02:35:04Z)
• Explainable Deep Learning in Healthcare: A Methodological Survey from an Attribution View [36.025217954247125]
  We introduce the methods for interpretability in depth and comprehensively as a methodological reference for future researchers or clinical practitioners. We discuss how these methods have been adapted and applied to healthcare problems and how they can help physicians better understand these data-driven technologies.
  arXiv  Detail & Related papers  (2021-12-05T17:12:53Z)
• MET: Multimodal Perception of Engagement for Telehealth [52.54282887530756]
  We present MET, a learning-based algorithm for perceiving a human's level of engagement from videos. We release a new dataset, MEDICA, for mental health patient engagement detection.
  arXiv  Detail & Related papers  (2020-11-17T15:18:38Z)

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.