Improving risk prediction for diseases using big data

Data and Information Integration for Risk Prediction in the Era of Big Data

['FUNDING_R01'] · UNIVERSITY OF PENNSYLVANIA · NIH-10912740

This study is working on new ways to better predict disease risks using large amounts of data, so that patients can get more accurate assessments and personalized prevention plans.

Quick facts

Phase['FUNDING_R01']
Study typeNih_funding
SexAll
SponsorUNIVERSITY OF PENNSYLVANIA (nih funded)
Locations1 site (PHILADELPHIA, UNITED STATES)
Trial IDNIH-10912740 on ClinicalTrials.gov

What this research studies

This research aims to enhance the accuracy of risk predictions for diseases by developing new statistical methods that utilize big data. It addresses challenges such as the lack of independent validation data and differences between study and target populations. By leveraging external data and established predictors, the researchers will create innovative models that improve the evaluation of candidate risk factors. Patients may benefit from more precise risk assessments that can lead to tailored prevention strategies.

Who could benefit from this research

Good fit: Ideal candidates for this research are individuals at risk for diseases such as breast cancer or gestational diabetes who could benefit from improved risk prediction models.

Not a fit: Patients with conditions that are not addressed by the risk prediction models developed in this research may not receive any benefit.

Why it matters

Potential benefit: If successful, this research could provide patients with more accurate risk assessments for diseases, leading to personalized prevention and treatment strategies.

How similar studies have performed: Other research has shown success in using big data and statistical methods for risk prediction, indicating that this approach has potential for meaningful advancements.

Where this research is happening

PHILADELPHIA, UNITED STATES

Researchers

About this research

  1. This is an active NIH-funded research project — typically early-stage science, not a clinical trial accepting patient enrollment.
  2. Some NIH-funded labs run parallel clinical studies or seek volunteers for related work. To check, contact the principal investigator or institution listed above.
  3. For full project details, budget, and progress reports, visit the official NIH RePORTER page below.

View on NIH RePORTER →

Conditions: Breast Cancer Risk Factor, Cancers

Last reviewed 2026-05-15 by the Find a Trial editorial team. Information on this page is for educational purposes and is not medical advice. Always consult qualified healthcare professionals about clinical trial participation.