Papers by Mina Valizadeh

5 papers
Modeling Dialogue in Conversational Cognitive Health Screening Interviews (2020.lrec-1)

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Challenge: Dementia is one of the most pressing healthcare concerns as median age rises . a conversational agent capable of conducting cognitive health screening interviews could be an inexpensive, flexible, low-stress alternative .
Approach: They propose an annotation schema for assigning dialogue act labels to utterances in patient-interviewer conversations collected as part of a clinically-validated cognitive health screening task.
Outcome: The proposed system is characterized by high inter-annotator agreement and is able to perform clinically-validated cognitive health screening tasks.
The AI Doctor Is In: A Survey of Task-Oriented Dialogue Systems for Healthcare Applications (2022.acl-long)

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Challenge: Task-oriented dialogue systems have been surveyed in the medical community from a non-technical perspective, but a systematic review from . a rigorous computational perspective has to date remained noticeably absent.
Approach: They analyze 4070 papers on task-oriented dialogue systems for healthcare applications and identify gaps in their analysis.
Outcome: The proposed system-level implementation details remain limited or underspecified, slowing the pace of innovation in this area.
CareCorpus: A Corpus of Real-World Solution-Focused Caregiver Strategies for Personalized Pediatric Rehabilitation Service Design (2024.lrec-main)

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Challenge: Pediatric rehabilitation services focus on functional skills and participation, defined as attendance and involvement in home, school, and community activities.
Approach: They propose to use a dataset of 780 real-world strategies written by caregivers to sort caregiver strategies for use in designing pediatric rehabilitation care plans.
Outcome: The proposed model can be used to sort caregiver strategies for use in designing pediatric rehabilitation care plans.
Identifying Medical Self-Disclosure in Online Communities (2021.naacl-main)

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Challenge: a new dataset of health-related posts from online social platforms is available for analysis . medical self-disclosure may be useful for early detection and treatment of medical issues .
Approach: They propose to analyze medical self-disclosure in online health conversations . they release a dataset of health-related posts from online social platforms with high inter-annotator agreement .
Outcome: The proposed model achieves an accuracy of 81.02% and sets a strong performance benchmark.
What Clued the AI Doctor In? On the Influence of Data Source and Quality for Transformer-Based Medical Self-Disclosure Detection (2023.eacl-main)

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Challenge: Recognizing medical self-disclosure is important in many healthcare contexts, but it has been under-explored by the NLP community.
Approach: They analyze a social media-based task to expand existing medical self-disclosure corpus and compare Transformer-based models to determine their merits.
Outcome: The proposed dataset outperforms the state-of-the-art dataset for this task by 16.73%.

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