Guardado en:
| Autores principales: | , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2511.18199 |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866917099826315264 |
|---|---|
| author | Hang, Genesis Chen, Annie Neveux, Hope Nock, Matthew K. Yacoby, Yaniv |
| author_facet | Hang, Genesis Chen, Annie Neveux, Hope Nock, Matthew K. Yacoby, Yaniv |
| contents | Ecological Momentary Assessment provides real-time data on suicidal thoughts and behaviors, but predicting suicide attempts remains challenging due to their rarity and patient heterogeneity. We show that single models fit to all patients perform poorly, while individualized models improve performance but still overfit to patients with limited data. To address this, we introduce Latent Similarity Gaussian Processes (LSGPs) to capture patient heterogeneity, enabling those with little data to leverage similar patients' trends. Preliminary results show promise: even without kernel-design, we outperform all but one baseline while offering a new understanding of patient similarity. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_18199 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Improving Forecasts of Suicide Attempts for Patients with Little Data Hang, Genesis Chen, Annie Neveux, Hope Nock, Matthew K. Yacoby, Yaniv Machine Learning Ecological Momentary Assessment provides real-time data on suicidal thoughts and behaviors, but predicting suicide attempts remains challenging due to their rarity and patient heterogeneity. We show that single models fit to all patients perform poorly, while individualized models improve performance but still overfit to patients with limited data. To address this, we introduce Latent Similarity Gaussian Processes (LSGPs) to capture patient heterogeneity, enabling those with little data to leverage similar patients' trends. Preliminary results show promise: even without kernel-design, we outperform all but one baseline while offering a new understanding of patient similarity. |
| title | Improving Forecasts of Suicide Attempts for Patients with Little Data |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2511.18199 |