Psytar

PsyTAR Query Skill

891 patient reviews → 6 009 annotated sentences → extracted ADR / WD / SSI / DI entities → mapped to 918 UMLS + 755 SNOMED CT concepts.

Entity Detection & Routing

Input Pattern	Detected As	Targets
C0917801	UMLS CUI	*_Mapped sheets only (UMLS1/UMLS2 cols)
Zoloft / sertraline	Drug name	drug_id or drug columns (alias-aware)
nausea, insomnia …	Free text	substring across all cell values

Generic ↔ brand aliases: sertraline↔Zoloft, escitalopram↔Lexapro, duloxetine↔Cymbalta, venlafaxine↔Effexor.

API

Function	Input	Returns
search(entity, sheet?, label?)	single string	{sheet: [row_dict]}
search_batch(entities, sheet?, label?)	list of strings	{entity: {sheet: [row_dict]}}
summarize(results, entity)	result dict + label	compact text
to_json(results)	result dict	list[dict] (flat, adds _sheet)
describe()	—	dataset overview text

Parameters

• sheet — restrict to one sheet (fuzzy-matched: "ADR" → ADR_Identified).
• label — when on Sentence_Labeling, keep only rows where the named label column (ADR / WD / EF / INF / SSI / DI) equals 1.

Sheet Schema

Sheet	Description	Key Columns
Sample	Original posts	drug_id, rating, indication, side-effect, comment, gender, age, duration
Sentence_Labeling	6 009 sentences, binary labels	drug_id, sentence_index, sentences, ADR, WD, EF, INF, SSI, DI, Findings, others, rating, category
ADR_Identified	Extracted ADR mentions	drug_id, sentence_index, sentences, ADR1 … ADRn
WD_Identified	Extracted WD mentions	drug_id, sentence_index, sentences, WD1 … WDn
SSI_Identified	Extracted SSI mentions	drug_id, sentence_index, sentences, SSI1 … SSIn
DI_Identified	Extracted DI mentions	drug_id, sentence_index, sentences, DI1 … DIn
ADR_Mapped	ADR → UMLS/SNOMED	drug_id, sentence_index, ADR/ADRs, UMLS1, UMLS2, SNOMED-CT, mild, moderate, severe, persistent, not-persistent, body-site, rating, drug, class, type, entity_type
WD_Mapped	WD → UMLS/SNOMED	(same structure as ADR_Mapped)
SSI_Mapped	SSI → UMLS/SNOMED	(same structure)
DI_Mapped	DI → UMLS/SNOMED	(same structure)

Mapped-sheet qualifier columns

mild, moderate, severe — severity descriptors; persistent, not-persistent — duration; body-site — anatomical site; entity_type — Cognitive / Physiological / Psychological / Functional.

Usage

from importlib.machinery import SourceFileLoader
m = SourceFileLoader("psytar", "36_PSYTAR.py").load_module()

# overview
print(m.describe())

# drug → ADR mappings
res = m.search("Zoloft", sheet="ADR_Mapped")
print(m.summarize(res, "Zoloft"))

# generic name works too
res = m.search("sertraline", sheet="ADR_Mapped")

# symptom in one Identified sheet
res = m.search("nausea", sheet="ADR_Identified")

# symptom across all sheets
res = m.search("insomnia")

# UMLS CUI (auto-scoped to Mapped sheets)
res = m.search("C0917801")

# withdrawal sentences for Effexor
res = m.search("Effexor", sheet="Sentence_Labeling", label="WD")

# batch
batch = m.search_batch(["Lexapro", "insomnia", "C0917801"])

# JSON for pipeline
flat = m.to_json(m.search("Cymbalta"))

Data Source

• Corpus: PsyTAR v1.0 — CC BY 4.0
• File: PsyTAR_dataset.xlsx — set via DATA_PATH or env PSYTAR_XLSX
• Paper: Zolnoori et al., Data in Brief 24, 103838 (2019). https://doi.org/10.1016/j.dib.2019.103838
• Stats: 891 reviews, 6 009 sentences, 4 813 ADR + 590 WD + 1 219 SSI
  • 792 DI mentions, 918 UMLS / 755 SNOMED concepts