A Cross Attention Approach to Enhance Diagnostic Explainability for Depression using Clinical Practice Guidelines

Temel Kavramlar

A cross-attention based language model, PSAT, that incorporates clinical knowledge from depression-related questionnaires like PHQ-9 to provide user-level explainable predictions for depression detection.

Özet

The paper presents a method called PSAT (PHQ-9-infused croSs ATtention) that aims to enhance the explainability of depression detection models by incorporating external clinical knowledge.
Key highlights:

PSAT leverages relevant clinical knowledge, such as the Patient Health Questionnaire (PHQ-9), to model patient inputs and provide meaningful explanations for classification.
PSAT maps the user's input text to clinically relevant phrases using a constructed PHQ-9 ontology, and then computes attention weights based on the cross-attention between the input and the PHQ-9 concepts.
This allows PSAT to highlight phrases that are aligned with the PHQ-9 questions, making the model's predictions more understandable to mental health professionals (MHPs).
Evaluation on two datasets, CLEF e-RISK and PRIMATE, shows that PSAT outperforms baseline models in terms of performance metrics like Matthews Correlation Coefficient (MCC) and a new metric called Average Knowledge Capture (AKC) that measures the alignment between the model's attention and the PHQ-9 ontology.
PSAT also demonstrates its ability to provide MHP-level explainability, where the attention visualization matches the ground truth annotations based on the PHQ-9 questionnaire.
The transferability of PSAT is tested on a suicide risk assessment dataset, showing its potential for application in other mental health domains.

İstatistikler

"How often have you been bothered by little interest or pleasure in doing things?"
"How often are you bothered by feeling down, depressed, or hopeless?"
"How often have you been bothered by trouble falling or staying asleep, or sleeping too much?"
"How often have you been bothered by feeling tired or having little energy?"
"How often have you been bothered by poor appetite or overeating?"
"How often have you been bothered by feeling bad about yourself - that you are a failure or have let yourself or your family down?"
"How often have you been bothered by trouble concentrating while reading newspaper or watching television"
"How often have you been bothered by moving or speaking so slowly that other people could have noticed? Or the opposite — being so fidgety or restless a lot more than usual ?"
"How often have you been bothered by thoughts that you would be better off dead or of hurting yourself in some way ?"

Alıntılar

"For the past several weeks, I have no to little interest to write my life any better than it is at the moment."
"feeling really low, can't make myself leave the bed, crying out of the blue, serious issues"

Önemli Bilgiler Şuradan Elde Edildi

A Cross Attention Approach to Diagnostic Explainability using Clinical Practice Guidelines for Depression

by Sumit Dalal,... : arxiv.org 04-30-2024

https://arxiv.org/pdf/2311.13852.pdf

Daha Derin Sorular

How can the proposed approach be extended to incorporate feedback from mental health professionals to further improve the model's explainability and clinical relevance?

Incorporating feedback from mental health professionals can significantly enhance the explainability and clinical relevance of the PSAT model. One approach to achieve this is through a feedback loop mechanism where mental health professionals can review the model's predictions and explanations. Based on their expertise, they can provide feedback on the accuracy and relevance of the model's outputs. This feedback can be used to fine-tune the model, improving its performance in detecting and assessing mental health conditions.
Additionally, mental health professionals can contribute to the development of the model by providing insights into the clinical guidelines, diagnostic criteria, and relevant terminology used in mental health assessments. By involving professionals in the training and validation process, the model can better align with real-world clinical practices and terminology, making it more clinically relevant.
Furthermore, creating a collaborative platform where mental health professionals can interact with the model, ask questions, and provide feedback can facilitate continuous learning and improvement. This interactive approach can help the model adapt to new information, updates in clinical guidelines, and evolving practices in mental health diagnosis and treatment.

How can the PSAT model be adapted to handle other mental health conditions beyond depression, such as anxiety or post-traumatic stress disorder?

To adapt the PSAT model to handle other mental health conditions beyond depression, such as anxiety or post-traumatic stress disorder (PTSD), several steps can be taken:

Dataset Expansion: Acquire and curate datasets specific to anxiety and PTSD that contain user posts related to these conditions. These datasets should be annotated with relevant diagnostic criteria and guidelines similar to PHQ-9 for depression.

Ontology Development: Create specific ontologies for anxiety and PTSD, similar to the PHQ-9 ontology used in the PSAT model. These ontologies should capture the key concepts, symptoms, and diagnostic criteria for each mental health condition.

Model Training: Fine-tune the PSAT model on the new datasets related to anxiety and PTSD, incorporating the specific ontologies developed for these conditions. This process will involve adjusting the cross-attention mechanism to align with the unique features of anxiety and PTSD.

Evaluation and Validation: Evaluate the performance of the adapted PSAT model on datasets related to anxiety and PTSD, measuring metrics such as accuracy, precision, recall, and explainability. Validate the model's predictions with mental health professionals to ensure clinical relevance and accuracy.

By following these steps, the PSAT model can be effectively adapted to handle a broader range of mental health conditions beyond depression, providing valuable insights and explanations for anxiety and PTSD assessments.

What are the potential challenges and limitations in scaling the knowledge-infusion approach used in PSAT to larger language models and more diverse clinical datasets?

Scaling the knowledge-infusion approach used in PSAT to larger language models and more diverse clinical datasets may face several challenges and limitations:

Data Complexity: Larger language models require extensive computational resources and data for training, which can be challenging to manage, especially with diverse clinical datasets that may vary in size and complexity.

Knowledge Integration: Integrating diverse clinical knowledge sources into the model may lead to information overload and potential conflicts between different sources. Ensuring the consistency and accuracy of the integrated knowledge poses a significant challenge.

Model Interpretability: As models scale up, interpretability becomes more complex. Ensuring that the knowledge-infusion approach maintains explainability and transparency in larger models can be challenging.

Generalization: Scaling the model to handle a wide range of mental health conditions may impact its ability to generalize across different conditions. Ensuring that the model remains effective and accurate for various disorders is crucial.

Ethical Considerations: Handling sensitive mental health data at scale requires robust privacy and ethical considerations to protect user information and ensure responsible use of the model.

Addressing these challenges will be essential in successfully scaling the knowledge-infusion approach used in PSAT to larger language models and diverse clinical datasets while maintaining effectiveness, accuracy, and ethical standards.