curl -X GET "https://api.omophub.com/v1/search/trending?time_period=7d&trend_type=search_volume&include_statistics=true&page_size=10" \
-H "Authorization: Bearer YOUR_API_KEY" \
-H "Content-Type: application/json"
import { OMOPHub } from '@omophub/omophub-node';
// Not yet exposed as a dedicated SDK method - use the typed low-level helper.
const client = new OMOPHub();
const { data: trendingData } = await client.get<{ data: unknown[] }>('/search/trending', {
query: {
time_period: '7d',
trend_type: 'search_volume',
include_statistics: true,
page_size: 10,
},
});
console.log('Trending searches:', trendingData?.data);
import requests
url = "https://api.omophub.com/v1/search/trending"
params = {
"time_period": "7d",
"trend_type": "search_volume",
"include_statistics": True,
"page_size": 10
}
headers = {
"Authorization": "Bearer YOUR_API_KEY",
"Content-Type": "application/json"
}
response = requests.get(url, params=params, headers=headers)
trending_data = response.json()
print(f"Found {len(trending_data['data']['data'])} trending items")
for item in trending_data['data']['data']:
print(f"- {item['query']}: {item['trend_score']} score, {item['growth_rate']}% growth")
{
"success": true,
"data": {
"data": [
{
"query": "covid-19 symptoms",
"concept_id": "840539006",
"concept_name": "Disease caused by severe acute respiratory syndrome coronavirus 2",
"vocabulary_id": "SNOMED",
"domain_id": "Condition",
"trend_score": 95.8,
"search_volume": 15420,
"growth_rate": 234.7,
"trend_direction": "rising",
"statistics": {
"peak_volume": 2180,
"average_volume": 1850.0,
"consistency_score": 0.82,
"seasonal_factor": 1.15,
"first_seen": "2024-12-15T08:00:00Z",
"velocity": 12.3
},
"related_concepts": [
{
"concept_id": "49727002",
"concept_name": "Cough",
"correlation_score": 0.78,
"co_occurrence_rate": 0.45
},
{
"concept_id": "386661006",
"concept_name": "Fever",
"correlation_score": 0.72,
"co_occurrence_rate": 0.41
}
]
},
{
"query": "diabetes management",
"concept_id": "73211009",
"concept_name": "Diabetes mellitus",
"vocabulary_id": "SNOMED",
"domain_id": "Condition",
"trend_score": 87.3,
"search_volume": 8920,
"growth_rate": 45.2,
"trend_direction": "rising",
"statistics": {
"peak_volume": 1420,
"average_volume": 1274.3,
"consistency_score": 0.91,
"seasonal_factor": 1.03,
"first_seen": "2024-12-10T14:30:00Z",
"velocity": 8.7
}
},
{
"query": "hypertension treatment",
"concept_id": "38341003",
"concept_name": "Hypertensive disorder, systemic arterial",
"vocabulary_id": "SNOMED",
"domain_id": "Condition",
"trend_score": 79.1,
"search_volume": 6750,
"growth_rate": 28.9,
"trend_direction": "stable"
}
],
"meta": {
"pagination": {
"page": 1,
"page_size": 10,
"total_items": 156,
"total_pages": 16,
"has_next": true,
"has_previous": false
},
"analysis_period": {
"start_date": "2024-12-15T00:00:00Z",
"end_date": "2024-12-22T00:00:00Z",
"period_type": "7d"
},
"data_freshness": "2024-12-22T12:00:00Z",
"request_id": "req_search_trending_7d_analytics_001",
"timestamp": "2024-12-22T10:30:00Z",
"vocab_release": "2025.2"
}
}
}
Search Trending
Retrieve trending and popular OMOP concept search queries based on platform-wide usage patterns and analytics data to surface relevant terms.
curl -X GET "https://api.omophub.com/v1/search/trending?time_period=7d&trend_type=search_volume&include_statistics=true&page_size=10" \
-H "Authorization: Bearer YOUR_API_KEY" \
-H "Content-Type: application/json"
import { OMOPHub } from '@omophub/omophub-node';
// Not yet exposed as a dedicated SDK method - use the typed low-level helper.
const client = new OMOPHub();
const { data: trendingData } = await client.get<{ data: unknown[] }>('/search/trending', {
query: {
time_period: '7d',
trend_type: 'search_volume',
include_statistics: true,
page_size: 10,
},
});
console.log('Trending searches:', trendingData?.data);
import requests
url = "https://api.omophub.com/v1/search/trending"
params = {
"time_period": "7d",
"trend_type": "search_volume",
"include_statistics": True,
"page_size": 10
}
headers = {
"Authorization": "Bearer YOUR_API_KEY",
"Content-Type": "application/json"
}
response = requests.get(url, params=params, headers=headers)
trending_data = response.json()
print(f"Found {len(trending_data['data']['data'])} trending items")
for item in trending_data['data']['data']:
print(f"- {item['query']}: {item['trend_score']} score, {item['growth_rate']}% growth")
{
"success": true,
"data": {
"data": [
{
"query": "covid-19 symptoms",
"concept_id": "840539006",
"concept_name": "Disease caused by severe acute respiratory syndrome coronavirus 2",
"vocabulary_id": "SNOMED",
"domain_id": "Condition",
"trend_score": 95.8,
"search_volume": 15420,
"growth_rate": 234.7,
"trend_direction": "rising",
"statistics": {
"peak_volume": 2180,
"average_volume": 1850.0,
"consistency_score": 0.82,
"seasonal_factor": 1.15,
"first_seen": "2024-12-15T08:00:00Z",
"velocity": 12.3
},
"related_concepts": [
{
"concept_id": "49727002",
"concept_name": "Cough",
"correlation_score": 0.78,
"co_occurrence_rate": 0.45
},
{
"concept_id": "386661006",
"concept_name": "Fever",
"correlation_score": 0.72,
"co_occurrence_rate": 0.41
}
]
},
{
"query": "diabetes management",
"concept_id": "73211009",
"concept_name": "Diabetes mellitus",
"vocabulary_id": "SNOMED",
"domain_id": "Condition",
"trend_score": 87.3,
"search_volume": 8920,
"growth_rate": 45.2,
"trend_direction": "rising",
"statistics": {
"peak_volume": 1420,
"average_volume": 1274.3,
"consistency_score": 0.91,
"seasonal_factor": 1.03,
"first_seen": "2024-12-10T14:30:00Z",
"velocity": 8.7
}
},
{
"query": "hypertension treatment",
"concept_id": "38341003",
"concept_name": "Hypertensive disorder, systemic arterial",
"vocabulary_id": "SNOMED",
"domain_id": "Condition",
"trend_score": 79.1,
"search_volume": 6750,
"growth_rate": 28.9,
"trend_direction": "stable"
}
],
"meta": {
"pagination": {
"page": 1,
"page_size": 10,
"total_items": 156,
"total_pages": 16,
"has_next": true,
"has_previous": false
},
"analysis_period": {
"start_date": "2024-12-15T00:00:00Z",
"end_date": "2024-12-22T00:00:00Z",
"period_type": "7d"
},
"data_freshness": "2024-12-22T12:00:00Z",
"request_id": "req_search_trending_7d_analytics_001",
"timestamp": "2024-12-22T10:30:00Z",
"vocab_release": "2025.2"
}
}
}
This endpoint provides insights into trending medical concepts and popular search queries, helping users discover relevant terminology based on community usage patterns and temporal trends.
Query Parameters
string
Comma-separated list of vocabulary IDs to filter trending searches
Example:
Example:
SNOMED,ICD10CM,LOINCstring
Comma-separated list of domain IDs to focus trending analysis
Example:
Example:
Condition,Drug,Procedurestring
default:"7d"
Time period for trending analysis
Options:
Options:
1d, 7d, 30d, 90dstring
default:"search_volume"
Type of trending metric to analyze
Options:
Options:
search_volume, new_concepts, rising_queries, seasonal_patternsboolean
default:"false"
Include detailed trending statistics and growth metrics
boolean
default:"false"
Include related trending concepts and co-occurrence patterns
integer
default:"20"
Number of trending items to return (max 100)
integer
default:"1"
Page number for pagination (1-based)
Response
array
Array of trending search items with analytics data
Show Trending Item Object
Show Trending Item Object
string
The trending search query or concept term
string
Associated concept ID if trending item is a specific concept (returned as string to prevent precision loss)
string
Standard concept name if applicable
string
Vocabulary containing the trending concept
string
Domain classification of the trending concept
number
Normalized trending score (0-100)
integer
Number of searches in the specified time period
number
Percentage growth rate compared to previous period
string
Direction of the trend
Values:
Values:
rising, declining, stable, newobject
Detailed trending statistics (when include_statistics=true)
array
object
Response metadata and pagination information
Show Metadata Object
Show Metadata Object
object
object
string
Timestamp of last trending data update
curl -X GET "https://api.omophub.com/v1/search/trending?time_period=7d&trend_type=search_volume&include_statistics=true&page_size=10" \
-H "Authorization: Bearer YOUR_API_KEY" \
-H "Content-Type: application/json"
import { OMOPHub } from '@omophub/omophub-node';
// Not yet exposed as a dedicated SDK method - use the typed low-level helper.
const client = new OMOPHub();
const { data: trendingData } = await client.get<{ data: unknown[] }>('/search/trending', {
query: {
time_period: '7d',
trend_type: 'search_volume',
include_statistics: true,
page_size: 10,
},
});
console.log('Trending searches:', trendingData?.data);
import requests
url = "https://api.omophub.com/v1/search/trending"
params = {
"time_period": "7d",
"trend_type": "search_volume",
"include_statistics": True,
"page_size": 10
}
headers = {
"Authorization": "Bearer YOUR_API_KEY",
"Content-Type": "application/json"
}
response = requests.get(url, params=params, headers=headers)
trending_data = response.json()
print(f"Found {len(trending_data['data']['data'])} trending items")
for item in trending_data['data']['data']:
print(f"- {item['query']}: {item['trend_score']} score, {item['growth_rate']}% growth")
{
"success": true,
"data": {
"data": [
{
"query": "covid-19 symptoms",
"concept_id": "840539006",
"concept_name": "Disease caused by severe acute respiratory syndrome coronavirus 2",
"vocabulary_id": "SNOMED",
"domain_id": "Condition",
"trend_score": 95.8,
"search_volume": 15420,
"growth_rate": 234.7,
"trend_direction": "rising",
"statistics": {
"peak_volume": 2180,
"average_volume": 1850.0,
"consistency_score": 0.82,
"seasonal_factor": 1.15,
"first_seen": "2024-12-15T08:00:00Z",
"velocity": 12.3
},
"related_concepts": [
{
"concept_id": "49727002",
"concept_name": "Cough",
"correlation_score": 0.78,
"co_occurrence_rate": 0.45
},
{
"concept_id": "386661006",
"concept_name": "Fever",
"correlation_score": 0.72,
"co_occurrence_rate": 0.41
}
]
},
{
"query": "diabetes management",
"concept_id": "73211009",
"concept_name": "Diabetes mellitus",
"vocabulary_id": "SNOMED",
"domain_id": "Condition",
"trend_score": 87.3,
"search_volume": 8920,
"growth_rate": 45.2,
"trend_direction": "rising",
"statistics": {
"peak_volume": 1420,
"average_volume": 1274.3,
"consistency_score": 0.91,
"seasonal_factor": 1.03,
"first_seen": "2024-12-10T14:30:00Z",
"velocity": 8.7
}
},
{
"query": "hypertension treatment",
"concept_id": "38341003",
"concept_name": "Hypertensive disorder, systemic arterial",
"vocabulary_id": "SNOMED",
"domain_id": "Condition",
"trend_score": 79.1,
"search_volume": 6750,
"growth_rate": 28.9,
"trend_direction": "stable"
}
],
"meta": {
"pagination": {
"page": 1,
"page_size": 10,
"total_items": 156,
"total_pages": 16,
"has_next": true,
"has_previous": false
},
"analysis_period": {
"start_date": "2024-12-15T00:00:00Z",
"end_date": "2024-12-22T00:00:00Z",
"period_type": "7d"
},
"data_freshness": "2024-12-22T12:00:00Z",
"request_id": "req_search_trending_7d_analytics_001",
"timestamp": "2024-12-22T10:30:00Z",
"vocab_release": "2025.2"
}
}
}
Usage Examples
Basic Trending Analysis
Get the most popular trending searches in the past week:TypeScript
const { data: trending } = await client.get('/search/trending', {
query: { time_period: '7d', page_size: 20 },
});
Domain-Specific Trends
Find trending searches within specific medical domains:TypeScript
const { data: conditionTrends } = await client.get('/search/trending', {
query: { domain_ids: 'Condition,Drug', time_period: '30d' },
});
Rising Query Detection
Identify rapidly growing search queries:TypeScript
const { data: risingQueries } = await client.get('/search/trending', {
query: { trend_type: 'rising_queries', include_statistics: true },
});
Vocabulary-Specific Analysis
Analyze trends within specific vocabularies:TypeScript
const { data: snomedTrends } = await client.get('/search/trending', {
query: { vocabulary_ids: 'SNOMED', time_period: '90d', include_related: true },
});
Seasonal Pattern Analysis
Detect seasonal trending patterns:TypeScript
const { data: seasonalTrends } = await client.get('/search/trending', {
query: { trend_type: 'seasonal_patterns', time_period: '90d' },
});
Related Endpoints
- Search Concepts - Primary concept search functionality
- Search Autocomplete - Real-time search suggestions
- Search Facets - Search result faceting and filtering
- Search Suggest - Intelligent search suggestions
- Get Vocabulary Statistics - Overall vocabulary usage stats
Notes
- Trending data is updated every 4 hours with the latest search analytics
- Trend scores are normalized across all vocabularies and time periods
- Geographic data may not be available for all trending items due to privacy considerations
- Rising queries are identified using proprietary algorithms that account for baseline search volume
- Seasonal patterns require at least 90 days of historical data for accurate detection
- Some trending data may be filtered to exclude potentially sensitive health information
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