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
1---2name: psytar-query3description: 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.4---5
6# PsyTAR Query Skill
7
8891 patient reviews → 6 009 annotated sentences → extracted ADR / WD / SSI /
9DI entities → mapped to 918 UMLS + 755 SNOMED CT concepts.
10
11## Entity Detection & Routing
12
13| Input Pattern | Detected As | Targets |
14|---|---|---|
15| `C0917801` | UMLS CUI | `*_Mapped` sheets only (UMLS1/UMLS2 cols) |
16| `Zoloft` / `sertraline` | Drug name | `drug_id` or `drug` columns (alias-aware) |
17| `nausea`, `insomnia` … | Free text | substring across all cell values |
18
19Generic ↔ brand aliases: sertraline↔Zoloft, escitalopram↔Lexapro,
20duloxetine↔Cymbalta, venlafaxine↔Effexor.
21
22## API
23
24| Function | Input | Returns |
25|---|---|---|
26| `search(entity, sheet?, label?)` | single string | `{sheet: [row_dict]}` |
27| `search_batch(entities, sheet?, label?)` | list of strings | `{entity: {sheet: [row_dict]}}` |
28| `summarize(results, entity)` | result dict + label | compact text |
29| `to_json(results)` | result dict | `list[dict]` (flat, adds `_sheet`) |
30| `describe()` | — | dataset overview text |
31
32### Parameters
33
34- **`sheet`** — restrict to one sheet (fuzzy-matched: `"ADR"` → `ADR_Identified`).
35- **`label`** — when on `Sentence_Labeling`, keep only rows where the named
36 label column (ADR / WD / EF / INF / SSI / DI) equals 1.
37
38## Sheet Schema
39
40| Sheet | Description | Key Columns |
41|---|---|---|
42| `Sample` | Original posts | drug_id, rating, indication, side-effect, comment, gender, age, duration |
43| `Sentence_Labeling` | 6 009 sentences, binary labels | drug_id, sentence_index, sentences, ADR, WD, EF, INF, SSI, DI, Findings, others, rating, category |
44| `ADR_Identified` | Extracted ADR mentions | drug_id, sentence_index, sentences, ADR1 … ADRn |
45| `WD_Identified` | Extracted WD mentions | drug_id, sentence_index, sentences, WD1 … WDn |
46| `SSI_Identified` | Extracted SSI mentions | drug_id, sentence_index, sentences, SSI1 … SSIn |
47| `DI_Identified` | Extracted DI mentions | drug_id, sentence_index, sentences, DI1 … DIn |
48| `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 |
49| `WD_Mapped` | WD → UMLS/SNOMED | (same structure as ADR_Mapped) |
50| `SSI_Mapped` | SSI → UMLS/SNOMED | (same structure) |
51| `DI_Mapped` | DI → UMLS/SNOMED | (same structure) |
52
53### Mapped-sheet qualifier columns
54
55`mild`, `moderate`, `severe` — severity descriptors;
56`persistent`, `not-persistent` — duration; `body-site` — anatomical site;
57`entity_type` — Cognitive / Physiological / Psychological / Functional.
58
59## Usage
60
61```python
62from importlib.machinery import SourceFileLoader
63m = SourceFileLoader("psytar", "36_PSYTAR.py").load_module()
64
65# overview
66print(m.describe())
67
68# drug → ADR mappings
69res = m.search("Zoloft", sheet="ADR_Mapped")
70print(m.summarize(res, "Zoloft"))
71
72# generic name works too
73res = m.search("sertraline", sheet="ADR_Mapped")
74
75# symptom in one Identified sheet
76res = m.search("nausea", sheet="ADR_Identified")
77
78# symptom across all sheets
79res = m.search("insomnia")
80
81# UMLS CUI (auto-scoped to Mapped sheets)
82res = m.search("C0917801")
83
84# withdrawal sentences for Effexor
85res = m.search("Effexor", sheet="Sentence_Labeling", label="WD")
86
87# batch
88batch = m.search_batch(["Lexapro", "insomnia", "C0917801"])
89
90# JSON for pipeline
91flat = m.to_json(m.search("Cymbalta"))
92```
93
94## Data Source
95
96- **Corpus**: PsyTAR v1.0 — CC BY 4.0
97- **File**: `PsyTAR_dataset.xlsx` — set via `DATA_PATH` or env `PSYTAR_XLSX`
98- **Paper**: Zolnoori et al., *Data in Brief* 24, 103838 (2019).
99 https://doi.org/10.1016/j.dib.2019.103838
100- **Stats**: 891 reviews, 6 009 sentences, 4 813 ADR + 590 WD + 1 219 SSI
101 + 792 DI mentions, 918 UMLS / 755 SNOMED concepts
gabrielmoreira