Psychiatric Scale Guided Risky Post Screening for Early Detection of Depression
Depression is a prominent health challenge to the world, and early risk detection (ERD) of depression from online posts can be a promising technique for combating the threat. Early depression detection faces the challenge of efficiently tackling streaming data, balancing the tradeoff between timeliness, accuracy and explainability. To tackle these challenges, we propose a psychiatric scale guided risky post screening method that can capture risky posts related to the dimensions defined in clinical depression scales, and providing interpretable diagnostic basis. A Hierarchical Attentional Network equipped with BERT (HAN-BERT) is proposed to further advance explainable predictions. For ERD, we propose an online algorithm based on an evolving queue of risky posts that can significantly reduce the number of model inferences to boost efficiency. Experiments show that our method outperforms the competitive feature-based and neural models under conventional depression detection settings, and achieves simultaneous improvement in both efficacy and efficiency for ERD.
Code (1)
Tasks
Depression DetectionDiagnosticMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
When Symptoms Are Not Enough: Evidence-Weighting Patterns in Large Language Model Psychiatric Screening
As demand for mental health care outpaces clinician-delivered assessment, scalable screening tools are increasingly needed. Large language models (LLMs) may identify psychiatric risk from patient narratives, but their re…
An evidence-guided reinforcement learning method to improve psychiatric reasoning in small language models
Privacy and computational constraints limit the use of large language models in psychiatry, while adapting small language models (SLMs) often requires substantial data and expert annotation. We developed ClinMPO, an evid…
Reinforcement LearningAutomatic Detection and Prediction of Psychiatric Hospitalizations From Social Media Posts
We address the problem of predicting psychiatric hospitalizations using linguistic features drawn from social media posts. We formulate this novel task and develop an approach to automatically extract time spans of self-…
From Medical Records to Diagnostic Dialogues: A Clinical-Grounded Approach and Dataset for Psychiatric Comorbidity
Psychiatric comorbidity is clinically significant yet challenging due to the complexity of multiple co-occurring disorders. To address this, we develop a novel approach integrating synthetic patient electronic medical re…
Dialogue GenerationMentalHospital: A Virtual Environment for Evaluating Psychiatric Clinical Encounters
Large language models (LLMs) have shown strong performance on isolated psychiatric tasks, including dialogue, diagnosis, and treatment planning, yet existing benchmarks rarely simulate complete psychiatric clinical encou…