Tag: Predictive modeling
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UB Pharmacy Professor Develops AI Model to Predict Hospitalization for At-Risk Cardiac Patients
UB Pharmacy Professor Develops AI Tool to Predict Hospitalizations in Cardiac Patients A pharmacist-turned-innovator at the University at Buffalo has created an artificial intelligence model designed to identify cardiac patients most at risk of hospitalization. The effort, led by a UB professor with a background in pharmacotherapy and healthcare data, aims to transform how clinicians…
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Fatigue Life Prediction of Vehicle Rubber Elastic Supports: A Physics-Based Approach
Introduction Rubber components play a pivotal role in automotive vibration isolation, absorbing shocks and dampening noise while maintaining ride comfort. Among these, rubber elastic supports (often called mountings or bushings) are exposed to prolonged cyclic loading, temperature variation, and environmental aging. Predicting their fatigue life is essential for reliability, maintenance planning, and safety. A physics-based…
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Video-Based Machine Learning for Predicting DBS Outcomes in Parkinson’s Disease
Overview Deep brain stimulation (DBS) represents a transformative option for carefully selected Parkinson’s disease (PD) patients, offering relief from motor symptoms when medication alone is insufficient. Recent advances in video-based machine learning (ML) seek to forecast DBS outcomes more accurately, enabling clinicians to tailor interventions, set realistic expectations, and optimize patient selection. Building on foundational…
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MIT’s Deep-Learning Model Predicts Fruit Fly Cell Behavior with 90% Accuracy
Overview: A New Window into Early Development Researchers at the Massachusetts Institute of Technology, led by associate professor Ming Guo, have unveiled a deep-learning model that can predict, minute by minute, how individual cells in a developing fruit fly embryo move, fold, divide, and rearrange. The breakthrough sits at the intersection of biology and artificial…
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Development and external validation of a machine learning-based model for predicting heart failure risk in Chinese adults with type 2 diabetes
Introduction Type 2 diabetes mellitus (T2DM) is a growing public health concern in China, affecting an estimated 11.2% of the adult population and acting as an independent risk factor for heart failure (HF). The close link between T2DM and HF has driven interest in data-driven approaches that can identify high-risk patients early. This article summarizes…
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Endoscopic Intervention in Renal Failure UGIB: Efficacy & Models
Introduction Upper gastrointestinal bleeding (UGIB) poses a significant risk in patients with renal insufficiency. Endoscopic intervention is a standard therapy aimed at stopping bleeding and reducing mortality. However, the effectiveness of endoscopy may vary according to kidney function, comorbidities, and the severity of bleeding. This article explores disease severity thresholds where endoscopic intervention may fail…
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Endoscopic Intervention in Renal Failure with Upper Gastrointestinal Bleeding: Efficacy and Predictive Modeling
Introduction Upper gastrointestinal bleeding (UGIB) represents a critical challenge in patients with renal insufficiency. Renal impairment complicates hemostasis, alters pharmacokinetics of medications, and can influence the success rates of endoscopic interventions. This article reviews the efficacy of endoscopy in this high‑risk population, identifies disease severity thresholds where intervention may fail to reduce mortality, and describes…
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Radiomics in NPC: AI Predicts Immunotherapy Response More Accurately Than Clinical Models
Unlocking Radiomics for Nasopharyngeal Carcinoma (NPC) Nasopharyngeal carcinoma (NPC) is a highly aggressive cancer often diagnosed at locally advanced stages. In recent years, immune checkpoint inhibitors (such as PD-1 blockers) have offered new hope, yet most patients derive only limited durable benefit. A multicenter study led by researchers from the First Affiliated Hospital of Jinan…