Using LLMs to Aid Annotation and Collection of Clinically-Enriched Data in Bipolar Disorder and Schizophrenia
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866914839114285056 |
|---|---|
| author | Aich, Ankit Quynh, Avery Osseyi, Pamela Pinkham, Amy Harvey, Philip Curtis, Brenda Depp, Colin Parde, Natalie |
| author_facet | Aich, Ankit Quynh, Avery Osseyi, Pamela Pinkham, Amy Harvey, Philip Curtis, Brenda Depp, Colin Parde, Natalie |
| contents | NLP in mental health has been primarily social media focused. Real world practitioners also have high case loads and often domain specific variables, of which modern LLMs lack context. We take a dataset made by recruiting 644 participants, including individuals diagnosed with Bipolar Disorder (BD), Schizophrenia (SZ), and Healthy Controls (HC). Participants undertook tasks derived from a standardized mental health instrument, and the resulting data were transcribed and annotated by experts across five clinical variables. This paper demonstrates the application of contemporary language models in sequence-to-sequence tasks to enhance mental health research. Specifically, we illustrate how these models can facilitate the deployment of mental health instruments, data collection, and data annotation with high accuracy and scalability. We show that small models are capable of annotation for domain-specific clinical variables, data collection for mental-health instruments, and perform better then commercial large models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_12687 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Using LLMs to Aid Annotation and Collection of Clinically-Enriched Data in Bipolar Disorder and Schizophrenia Aich, Ankit Quynh, Avery Osseyi, Pamela Pinkham, Amy Harvey, Philip Curtis, Brenda Depp, Colin Parde, Natalie Computation and Language NLP in mental health has been primarily social media focused. Real world practitioners also have high case loads and often domain specific variables, of which modern LLMs lack context. We take a dataset made by recruiting 644 participants, including individuals diagnosed with Bipolar Disorder (BD), Schizophrenia (SZ), and Healthy Controls (HC). Participants undertook tasks derived from a standardized mental health instrument, and the resulting data were transcribed and annotated by experts across five clinical variables. This paper demonstrates the application of contemporary language models in sequence-to-sequence tasks to enhance mental health research. Specifically, we illustrate how these models can facilitate the deployment of mental health instruments, data collection, and data annotation with high accuracy and scalability. We show that small models are capable of annotation for domain-specific clinical variables, data collection for mental-health instruments, and perform better then commercial large models. |
| title | Using LLMs to Aid Annotation and Collection of Clinically-Enriched Data in Bipolar Disorder and Schizophrenia |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2406.12687 |