ML Predicts Dual Mortality Risk for HCC Liver Transplant Candidates (11,647 Cases)

Using a dataset of 11,647 hepatocellular carcinoma patients, a French research team combined ensemble learning, SHAP explainability, UMAP dimensionality reduction and K‑medoids clustering to build an interpretable model that outperforms traditional scores in predicting three‑month wait‑list mortality and defines seven clinically distinct risk sub‑groups.

Hepatocellular CarcinomaK-MedoidsLiver Transplantation

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ML Predicts Dual Mortality Risk for HCC Liver Transplant Candidates (11,647 Cases)

Data Party THU

Sep 14, 2025 · Artificial Intelligence

How Machine Learning Predicts Genetic Variant Penetrance Across Populations

Researchers at Mount Sinai Icahn School of Medicine used gradient‑boosting trees on over one million electronic health records to build machine‑learning models for ten hereditary diseases, quantifying the penetrance of genetic variants and demonstrating how probabilistic risk scores can improve clinical interpretation and patient management.

Risk Predictionclinical geneticselectronic health records

0 likes · 8 min read

How Machine Learning Predicts Genetic Variant Penetrance Across Populations

G7 EasyFlow Tech Circle

Sep 15, 2021 · Artificial Intelligence

Can Predictive Models Uncover Causal Effects? A Truck Risk Case Study

Using a road freight accident prediction example, the article warns that interpreting predictive model explanations as causal effects can be misleading, explains when such models may answer causal questions, demonstrates SHAP analysis on an XGBoost model, and recommends causal inference tools like ecoml for reliable effect estimation.

Risk PredictionSHAPXGBoost

0 likes · 10 min read

Can Predictive Models Uncover Causal Effects? A Truck Risk Case Study

Qunar Tech Salon

Dec 10, 2020 · Operations

Improving International Hotel After‑Sales Service: Metrics, Optimization Strategies, and Risk Prediction with LightGBM

The article analyzes the after‑sales process of international hotel bookings, defines key metrics such as defect rate and SPO, describes operational improvements, and presents a LightGBM‑based risk‑prediction model to reduce on‑site defects and enhance overall service efficiency.

Hotel IndustryLightGBMOperations

0 likes · 14 min read

Improving International Hotel After‑Sales Service: Metrics, Optimization Strategies, and Risk Prediction with LightGBM