# Real time risk prognostication via scalable hazard trees and forests

> **NIH NIH R01** · UNIVERSITY OF MIAMI SCHOOL OF MEDICINE · 2023 · $557,401

## Abstract

Project Summary/Abstract
Wearable sensing devices and Electronic Health Records (EHRs) are some examples of emerging
information technologies expected to generate huge volumes of data recording individual’s health data over
time. If properly utilized, these data provide a treasure trove of information for building real-time warning
systems for adverse outcomes and to construct individualized risk prediction. To model the dynamic
changes of covariate effects, time-varying survival models have emerged as a powerful approach. To deal with
the size and complexity of data, with potential interactions among large number of variables, and interactions
with time itself, we propose a state of the art machine learning approach using hazard trees and forests for
estimating flexible hazard models with time-dependent covariates. Scalable and user friendly open source
software implementing the methodology will be developed and made publicly available. The software will be
applied to a rich, multicenter study of heart failure patients listed for heart transplantation to develop a state of
the heart hazard risk prediction model.

## Key facts

- **NIH application ID:** 10655749
- **Project number:** 1R01HL164405-01A1
- **Recipient organization:** UNIVERSITY OF MIAMI SCHOOL OF MEDICINE
- **Principal Investigator:** Hemant Ishwaran
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $557,401
- **Award type:** 1
- **Project period:** 2023-04-01 → 2027-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10655749

## Citation

> US National Institutes of Health, RePORTER application 10655749, Real time risk prognostication via scalable hazard trees and forests (1R01HL164405-01A1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10655749. Licensed CC0.

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