AIMC Topic: Prognosis

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Predictive Value of Machine Learning for the Risk of In-Hospital Death in Patients With Heart Failure: A Systematic Review and Meta-Analysis.

Clinical cardiology
BACKGROUND: The efficiency of machine learning (ML) based predictive models in predicting in-hospital mortality for heart failure (HF) patients is a topic of debate. In this context, this study's objective is to conduct a meta-analysis to compare and...

A Novel Effective Models for Identifying BRCA Patients and Optimizing Clinical Treatments.

Anti-cancer agents in medicinal chemistry
OBJECTIVE: This study aimed to develop an effective model that identifies high-risk breast cancer (BRCA) patients and optimizes clinical treatments.

PROGNOSTIC ACCURACY OF MACHINE LEARNING MODELS FOR IN-HOSPITAL MORTALITY AMONG CHILDREN WITH PHOENIX SEPSIS ADMITTED TO THE PEDIATRIC INTENSIVE CARE UNIT.

Shock (Augusta, Ga.)
Objective: The Phoenix sepsis criteria define sepsis in children with suspected or confirmed infection who have ≥2 in the Phoenix Sepsis Score. The adoption of the Phoenix sepsis criteria eliminated the Systemic Inflammatory Response Syndrome criteri...

Establishment and Validation of the Novel Necroptosis-related Genes for Predicting Stemness and Immunity of Hepatocellular Carcinoma Machine-learning Algorithm.

Combinatorial chemistry & high throughput screening
BACKGROUND: Necroptosis, a recently identified mechanism of programmed cell death, exerts significant influence on various aspects of cancer biology, including tumor cell proliferation, stemness, metastasis, and immunosuppression. However, the role o...

Machine learning-based infection diagnostic and prognostic models in post-acute care settings: a systematic review.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVES: This study aims to (1) review machine learning (ML)-based models for early infection diagnostic and prognosis prediction in post-acute care (PAC) settings, (2) identify key risk predictors influencing infection-related outcomes, and (3) e...

Identification of miR-20a as a Diagnostic and Prognostic Biomarker in Colorectal Cancer: MicroRNA Sequencing and Machine Learning Analysis.

MicroRNA (Shariqah, United Arab Emirates)
INTRODUCTION: The differential expression of miRNAs, a key regulator in many cell signaling pathways, has been studied in various malignancies and may have an important role in cancer progression, including colorectal cancer (CRC).

Machine Learning and Augmented Intelligence Enables Prognosis of Type 2 Diabetes Prior to Clinical Manifestation.

Current diabetes reviews
BACKGROUND: The global incidence of type 2 diabetes (T2D) persists at epidemic proportions. Early diagnosis and/or preventive efforts are critical to attenuate the multi-systemic clinical manifestation and consequent healthcare burden. Despite enormo...

Developing a Predictive Model for Metastatic Potential in Pancreatic Neuroendocrine Tumor.

The Journal of clinical endocrinology and metabolism
CONTEXT: Pancreatic neuroendocrine tumors (PNETs) exhibit a wide range of behavior from localized disease to aggressive metastasis. A comprehensive transcriptomic profile capable of differentiating between these phenotypes remains elusive.

Harnessing the Power of AI for Enhanced Diagnosis and Treatment of Hepatocellular Carcinoma.

The Turkish journal of gastroenterology : the official journal of Turkish Society of Gastroenterology
Since its advent, artificial intelligence (AI) has been continuously researched, and substantial progress has been made in many fields, such as the diagnosis and therapies for cancer. Due to the advantages of high efficiency, rapidity, and precision,...