psytar-query

name	psytar-query
description	Query the PsyTAR psychiatric adverse-reaction corpus. Use when the user asks about patient-reported ADRs, withdrawal symptoms, drug indications, or effectiveness for Zoloft, Lexapro, Cymbalta, or Effexor XR. Accepts drug names (brand or generic), symptom terms, or UMLS CUIs.

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