# Mitigating Hematologic Adverse Events in Patients with Myeloid Malignancies: A Novel Causal Artificial Intelligence Approach

> **NIH NIH R01** · HARVARD MEDICAL SCHOOL · 2024 · $813,042

## Abstract

PROJECT SUMMARY/ABSTRACT
 Thrombosis and bleeding are the major causes of morbidity and mortality among
survivors of myeloid malignancies. Previous studies proposed clinical and laboratory
variables associated with these life-threatening comorbidities, such as older age.
However, these crude predictors do not capture the heterogeneity of patients’
pathology, genomics, laboratory, and clinical profiles to arrive at accurate and
personalized risk assessment. In addition, conventional machine learning prediction
models cannot elucidate the biological mechanisms underpinning the observed
correlations, and association-based prediction models do not provide reliable treatment
suggestions. The long-term goal of this project is to reduce the hemorrhagic and
thrombotic disease burden among patients with myeloid malignancies by providing
personalized risk predictions that enable treatment optimization. The central hypothesis
is that causal machine learning methods will empower a reliable analytical platform for
hematologic comorbidity risk prediction and mitigation. In this study, we will employ
large and diverse clinical datasets from multiple clinical centers to develop integrative
causal machine learning models. Specifically, we will (i) integrate laboratory, pathology,
and clinical data to identify the key predictors of thrombosis for patients with myeloid
malignancies, (ii) quantify the risk of bleeding by machine learning and causal inference
methods, and (iii) personalize treatment protocols to minimize risks of bleeding and
thrombosis using causal machine learning. We will validate our data-driven models
through rigorous external validation. Our methods are innovative because they depart
from the status quo by linking advanced causal inference methods with machine
learning algorithms. Our proposed studies are significant because they will create a
reliable data-driven framework for multi-modality data fusion and enable personalized
treatment strategies to mitigate hematologic comorbidities. This novel causal machine
learning platform will guide individualized clinical decisions to reduce hematologic
adverse events among patients with myeloid malignancies. Our approaches address
the National Heart, Lung, and Blood Institute’s Compelling Question 5.CQ.10 on
reducing cardiovascular morbidity and mortality in cancer survivors.

## Key facts

- **NIH application ID:** 10940122
- **Project number:** 1R01HL174679-01
- **Recipient organization:** HARVARD MEDICAL SCHOOL
- **Principal Investigator:** Kun-Hsing Yu
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $813,042
- **Award type:** 1
- **Project period:** 2024-07-01 → 2027-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10940122

## Citation

> US National Institutes of Health, RePORTER application 10940122, Mitigating Hematologic Adverse Events in Patients with Myeloid Malignancies: A Novel Causal Artificial Intelligence Approach (1R01HL174679-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10940122. Licensed CC0.

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