Search Trending

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"
    }
  }
}

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

vocabulary_ids

string

Comma-separated list of vocabulary IDs to filter trending searches
Example: SNOMED,ICD10CM,LOINC

domain_ids

string

Comma-separated list of domain IDs to focus trending analysis
Example: Condition,Drug,Procedure

time_period

string

default:"7d"

Time period for trending analysis
Options: 1d, 7d, 30d, 90d

trend_type

string

default:"search_volume"

Type of trending metric to analyze
Options: search_volume, new_concepts, rising_queries, seasonal_patterns

include_statistics

boolean

default:"false"

Include detailed trending statistics and growth metrics

Include related trending concepts and co-occurrence patterns

page_size

integer

default:"20"

Number of trending items to return (max 100)

page

integer

default:"1"

Page number for pagination (1-based)

Response

data

array

Array of trending search items with analytics data

Show Trending Item Object

query

string

The trending search query or concept term

concept_id

string

Associated concept ID if trending item is a specific concept (returned as string to prevent precision loss)

concept_name

string

Standard concept name if applicable

vocabulary_id

string

Vocabulary containing the trending concept

domain_id

string

Domain classification of the trending concept

trend_score

number

Normalized trending score (0-100)

search_volume

integer

Number of searches in the specified time period

growth_rate

number

Percentage growth rate compared to previous period

trend_direction

string

Direction of the trend
Values: rising, declining, stable, new

statistics

object

Detailed trending statistics (when include_statistics=true)

Show Statistics Object

peak_volume

integer

Highest search volume in the period

average_volume

number

Average daily search volume

consistency_score

number

Trend consistency score (0-1)

seasonal_factor

number

Seasonal adjustment factor

first_seen

string

ISO timestamp when trend was first detected

velocity

number

Rate of change in search volume

Related trending concepts (when include_related=true)

Show Related Concept Object

concept_id

string

Related concept identifier (returned as string to prevent precision loss)

concept_name

string

Related concept name

correlation_score

number

Correlation strength with main trending item (0-1)

co_occurrence_rate

number

Rate of co-occurrence in searches

geographic_data

object

Geographic distribution of trend (when available)

Show Geographic Data Object

top_regions

array

Top geographic regions for this trend

regional_scores

object

Regional trending scores

meta

object

Response metadata and pagination information

Show Metadata Object

pagination

object

page

integer

Current page number

page_size

integer

Items per page

total_items

integer

Total trending items available

total_pages

integer

Total number of pages

has_next

boolean

Whether next page exists

has_previous

boolean

Whether previous page exists

analysis_period

object

Time period used for trend analysis

start_date

string

Analysis start date (ISO format)

end_date

string

Analysis end date (ISO format)

period_type

string

Type of period analyzed

data_freshness

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

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' },
});

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

Search Suggest Get Concept

API Reference

Search

Concepts

Concept Hierarchy

Mappings

FHIR Resolver

Relationships

Domains & Classifications

Vocabularies

Query Parameters

Response

Usage Examples

Domain-Specific Trends

Rising Query Detection

Vocabulary-Specific Analysis

Seasonal Pattern Analysis

Notes

​Query Parameters

​Response

​Usage Examples

​Basic Trending Analysis

​Domain-Specific Trends

​Rising Query Detection

​Vocabulary-Specific Analysis

​Seasonal Pattern Analysis

​Related Endpoints

​Notes

Query Parameters

Response

Usage Examples

Basic Trending Analysis

Domain-Specific Trends

Rising Query Detection

Vocabulary-Specific Analysis

Seasonal Pattern Analysis

Related Endpoints

Notes