A Systematic Review of NLP for Dementia- Tasks, Datasets and Opportunities
<|reference_start|>A Systematic Review of NLP for Dementia- Tasks, Datasets and Opportunities: The close link between cognitive decline and language has fostered long-standing collaboration between the NLP and medical communities in dementia research. To examine this, we reviewed over 200 papers applying NLP to dementia related efforts, drawing from medical, technological, and NLP-focused literature. We identify key research areas, including dementia detection, linguistic biomarker extraction, caregiver support, and patient assistance, showing that half of all papers focus solely on dementia detection using clinical data. However, many directions remain unexplored: artificially degraded language models, synthetic data, digital twins, and more. We highlight gaps and opportunities around trust, scientific rigor, applicability, and cross-community collaboration, and showcase the diverse datasets encountered throughout our review: recorded, written, structured, spontaneous, synthetic, clinical, social media based, and more. This review aims to inspire more creative approaches to dementia research within the medical and NLP communities.<|reference_end|>
@article{peled-cohen2024a,
    title={A Systematic Review of NLP for Dementia- Tasks, Datasets and
  Opportunities},
    author={Lotem Peled-Cohen, Roi Reichart},
    journal={arXiv preprint arXiv:2409.19737},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.19737},
    primaryClass={cs.CL}
}