Phrama Clinical Trials

This section covers a very unique dataset that tags clinical trials with their predicted outcome success.

Data is updated weekly on Fridays as is made available from regulatory filers

Tutorials are the best documentation — Clinical Trials Tutorial

Input Datasets

Regulatory Filings; Biochemical Data

Models Used

Deep Learning Encoders; Langauge Models

Model Outputs

Success prediction; Expected duration

Description

We predict the success of a clinical trial, its duration, and the expected economic impact, including potential market reactions, using state-of-the-art machine learning models. Our solution also provides detailed metadata about each trial that allowed us to predict regulatory phase success and/or approval rate, empowering users to anticipate outcomes with greater accuracy.

Achieving an impressive 87% ROC-AUC—the highest among commercially available solutions—clients can rely on our predictions to make informed decisions. With an average of 1,052 new clinical trials launched each week, our platform lets you screen and focus on the most promising opportunities.

Data Access

Prediction Data:

import sovai as sov
df_clinical = sov.data("trials/predict", full_history=True)

Description Data

import sovai as sov
df_clinical = sov.data("trials/describe", full_history=True)

Accessing Specific Tickers

You can also retrieve data for specific tickers. For example:

import sovai as sov
df_pfizer = sov.data("trials/predict", tickers=["PFE"]) 

Data Dictionary

Column Name	Description
ticker	Stock ticker symbol of the company
date	Date the complaint was received
company	Name of the company the complaint is against
bloomberg_share_id	Bloomberg Global Share Class Level Identifier
culpability_score	Score indicating the company's culpability in the complaint
complaint_score	Score based on the severity of the complaint
grievance_score	Score based on the grievance level of the complaint
total_risk_rating	Overall risk rating combining culpability, complaint, and grievance scores
product	Financial product related to the complaint
sub_product	Specific sub-category of the financial product
issue	Main issue of the complaint
sub_issue	Specific sub-category of the issue
consumer_complaint_narrative	Narrative description of the complaint provided by the consumer
company_public_response	Public response provided by the company
state	State where the complaint was filed
zip_code	ZIP code of the consumer
tags	Any tags associated with the complaint (e.g., "Servicemember")
consumer_consent_provided	Indicates if the consumer provided consent for sharing details
submitted_via	Channel through which the complaint was submitted
date_sent_to_company	Date the complaint was sent to the company
company_response_to_consumer	Type of response provided by the company to the consumer
timely_response	Indicates if the company responded in a timely manner
consumer_disputed	Indicates if the consumer disputed the company's response
selected_name	Name used for company matching
similarity	Similarity score for company name matching

Use Cases

1. Risk Assessment: Evaluate the risk profile of financial institutions based on complaint data.

2. Consumer Sentiment Analysis: Analyze consumer sentiment towards different financial products and companies.

3. Regulatory Compliance: Monitor compliance issues and identify potential regulatory risks.

4. Product Performance Evaluation: Assess the performance and issues related to specific financial products.

5. Competitive Analysis: Compare complaint profiles across different financial institutions.

6. Geographic Trend Analysis: Identify regional trends in financial complaints.

7. Customer Service Improvement: Identify areas for improvement in customer service based on complaint types and resolutions.

8. ESG Research: Incorporate complaint data into Environmental, Social, and Governance (ESG) assessments.

9. Fraud Detection: Identify patterns that might indicate fraudulent activities.

10. Policy Impact Assessment: Evaluate the impact of policy changes on consumer complaints over time.

The resulting dataset provides a comprehensive view of consumer complaints in the financial sector, enabling detailed analysis of company performance, consumer issues, and regulatory compliance.