HarmoniCA
Collection
Harmonizing Clinical Assessments: enabling integration of symptom measures in large multi-site studies. • 6 items • Updated
How to use julia-pfarr/HarmoniCA_sleep with sentence-transformers:
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("julia-pfarr/HarmoniCA_sleep")
sentences = [
"The weather is lovely today.",
"It's so sunny outside!",
"He drove to the stadium."
]
embeddings = model.encode(sentences)
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]Part of the HarmoniCA collection for harmonising psychiatric questionnaire items across studies. This model assigns items from sleep questionnaires to one of four theoretically motivated symptom dimensions.
| ID | Label | Description |
|---|---|---|
| 1 | Daytime Sleepiness & Alertness | excessive sleepiness during the day, difficulty staying awake, drowsiness, reduced vigilance |
| 2 | Nocturnal Sleep Disturbances | difficulty falling asleep, waking during the night, insomnia, poor sleep continuity or quality |
| 3 | REM Sleep Behavior & Dreaming | acting out dreams, vivid or disturbing dreams, movements or talking during sleep |
| 4 | Sleep-Disordered Breathing | snoring, sleep apnea, pauses in breathing, breathing difficulties at night |
Items rated as not belonging to the sleep construct are assigned dimension -1 (Does not fit).
Evaluated on 14 held-out items from unseen questionnaires against expert labels.
| Cohen's κ | Accuracy |
|---|---|
| 0.468 | 57.1% |
Note: The test set is very small (n=14).
Cross-validation (leave-one-questionnaire-out, 17 folds): mean accuracy 93.4% ± 12.1%.
in preparation
Base model
princeton-nlp/sup-simcse-roberta-large