Article An interpretable machine learning model for individualized ...

This study demonstrates retrospectively that using a machine learning model for selecting starting FSH can achieve optimal laboratory outcomes.

AN INTERPRETABLE MACHINE LEARNING MODEL FOR ...

AN INTERPRETABLE MACHINE LEARNING MODEL FOR INDIVIDUALIZED PROTOCOL SELECTION AND GONADOTROPIN DOSE SELECTION DURING OVARIAN STIMULATION ... Previous articleNext ...

AN INTERPRETABLE MACHINE LEARNING MODEL FOR ...

OBJECTIVE: To develop an interpretable machine learning model for individualized gonadotropin dose selection during controlled ovarian stimulation. MATERIALS ...

PD36-01 INTERPRETABLE MACHINE LEARNING MODELS FOR ...

PD36-01 INTERPRETABLE MACHINE LEARNING MODELS FOR PERSONALIZED PROGNOSTICATION IN INTERMEDIATE-RISK RADICAL PROSTATECTOMY PATIENTS. Crossmark ...

Development and validation of an interpretable machine learning ...

We used the SHAP force to predict individual and overall MCI in the high-risk group of stroke in the logistic regression model, and the results ...

Abstract 15033: Development of an Interpretable Machine Learning ...

Abstract 15033: Development of an Interpretable Machine Learning Model for Predicting Individual Response to Antihypertensive Treatments.

Developing an interpretable machine learning model for predicting ...

Research article. Developing an interpretable ... This analysis provides an individual and a population-based perspective model explanation.

An interpretable machine learning model based on a quick pre ...

A high-performing interpretable model is proposed to predict the risk of deterioration in coronavirus disease 2019 (COVID-19) patients.

Interpretable machine learning-based individual analysis of acute ...

The temporal changes in individual predictive reasoning in AKI prediction models represented the key features contributing to each AKI prediction and clustered ...

On the importance of interpretable machine learning predictions to ...

... Interpretable and Explainable Machine Learning Models in Oncology View all 6 articles ... individual predictions is to create an interpretable model (such ...

Definitions, methods, and applications in interpretable machine ...

Machine-learning models have demonstrated great success in learning complex patterns that enable them to make predictions about unobserved data.

Interpretable Machine Learning for Discovery: Statistical Challenges ...

... interpretable machine learning, or machine learning models and techniques that yield human-understandable insights. In this article, we ...

Interpretable Deep-Learning Approaches for Osteoporosis Risk ...

... individualized explanation. Objective: The aim of this study was to develop an interpretable deep-learning (DL) model for osteoporosis risk ...

Enhancing heart failure treatment decisions: interpretable machine ...

Machine learning models with weights transferred from Personalized LTR generally performed better than those with weights from Standard LTR ...

Interpretable Machine Learning for Personalized Medical ... - MDPI

This paper proposes an approach of deep learning with a local interpretable model–agnostic explanations (LIME)-based interpretable recommendation system to ...

Interpretable Machine Learning - Christoph Molnar

... individual predictions with Shapley values and LIME. In addition, the book ... machine learning models interpretable. You can buy the PDF and e-book ...

How Interpretable Machine Learning Can Benefit Process ...

For readers interested in a more detailed technical understanding of the IML algorithms and methods discussed in this paper, we recommend ...

Interpretable machine learning models for predicting in-hospital and ...

Standard risk stratification tools can be used to customize personalized clinical interventions based on individual patient-specific predictions ...

Interpretability in Machine Learning. An Overview - Train in Data's Blog

Some machine learning models are interpretable by design ... model behavior, local interpretability zooms in on individual predictions.

Interpretability Methods in Machine Learning: A Brief Survey

This article summarizes several of the most common of these, including their relative advantages and disadvantages. Interpretable ML models and “Black Boxes”.