---
title: >-
  Performance of machine learning algorithms for hysterectomy risk prediction
  among women with endometriosis in the United States
description: >-
  Explore this publication on real-world evidence: Performance of machine
  learning algorithms for hysterectomy risk prediction among women with
  endometriosis in…
date: '2018-01-01'
category: Publications
tags:
  - ICPE
  - Approval & Commercialization
  - 'No'
  - Oral Presentation
  - Methods/Pharmacoepi
  - R&D
canonical_url: >-
  https://www.headwaterscience.com/resources/publications/icpe/performance-of-machine-learning-algorithms-for-hysterectomy/
source: Headwater Science
license: © 2026 Headwater Science. All rights reserved.
slug: performance-of-machine-learning-algorithms-for-hysterectomy
id: 52PToMBNeAUOddlecFZOpW
contentType: article
---

## Challenge

Hysterectomy risk prediction in endometriosis patients could enable more personalized clinical management, but the performance of machine learning algorithms for this prediction task in a large real-world endometriosis cohort had not been evaluated.

## Solution

Target RWE researchers evaluated the performance of multiple machine learning algorithms for hysterectomy risk prediction among women with endometriosis using real-world clinical data, comparing ML approaches against traditional logistic regression.

## Impact

Demonstrating that ML algorithms can predict hysterectomy risk in endometriosis provides evidence for precision medicine approaches in women's health, supporting pharma partners developing therapies who need patient risk stratification tools.

## Use Cases / Links

Machine learning hysterectomy risk prediction for endometriosis precision medicine and trial enrollment, Predictive analytics evaluation for women's health drug development patient stratification