# Predictive Analytics in Hemodialysis: Enabling Precision Care for Patient with ESKD

> **NIH NIH R01** · DUKE UNIVERSITY · 2022 · $321,952

## Abstract

Machine learning (ML) based clinical prediction models (CPMs) have proliferated over the past
few years, becoming a central component of healthcare. These tools show great promise in
informing both providers and patients of impending health outcomes, ultimately allowing for
greater personalization of patient care. In our parent R01 we are developing a ML based mortality
prediction model for patients undergoing hemodialysis. The goal of this tool is to predict both short
and long term mortality risk in order to promote shared decision making between patients and
providers. Importantly, there are a number of ethical concerns inherent in ML based CPMs. These
include consideration of ethical performance (i.e., algorithmic bias), ethical usage (i.e., ensuring
outputs are properly interpreted) and ethical implementation (i.e., the tool is properly integrated
into the clinical workflow. For this current NOSI we propose to explore questions of ethical usage
of ML based CPMs. To do this, we will conduct focus group of patients & their caregivers,
providers and data scientists. We will address questions of trust in ML based CPMs,
understanding of risk and optimal risk communication, interaction with CPMs and AI/ML tools and
usage of CPMs to empower patients and promote shared decision-making. We will compare
responses across constituencies. We will use our findings to develop practice oriented guidances
that target both develops and users of ML based CPMs.

## Key facts

- **NIH application ID:** 10598693
- **Project number:** 3R01DK123062-03S1
- **Recipient organization:** DUKE UNIVERSITY
- **Principal Investigator:** Benjamin Alan Goldstein
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $321,952
- **Award type:** 3
- **Project period:** 2020-07-01 → 2024-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10598693

## Citation

> US National Institutes of Health, RePORTER application 10598693, Predictive Analytics in Hemodialysis: Enabling Precision Care for Patient with ESKD (3R01DK123062-03S1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10598693. Licensed CC0.

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