AIMC Topic: Prognosis

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Explainable artificial intelligence for prediction of refractory ulcerative colitis: analysis of a Japanese Nationwide Registry.

Annals of medicine
OBJECTIVE: Ulcerative colitis (UC) is a chronic inflammatory bowel disease for which remission is dependent on corticosteroid (CS) treatment. The diversity of disease pathophysiology necessitates optimal case-specific treatment selection. This study ...

A machine learning approach to risk-stratification of gastric cancer based on tumour-infiltrating immune cell profiles.

Annals of medicine
BACKGROUND: Gastric cancer (GC) is a highly heterogeneous disease, and the response of patients to clinical treatment varies substantially. There is no satisfactory strategy for predicting curative effects to date. We aimed to explore a new method fo...

Risk prediction for acute kidney disease and adverse outcomes in patients with chronic obstructive pulmonary disease: an interpretable machine learning approach.

Renal failure
BACKGROUND: Little is known about acute kidney injury (AKI) and acute kidney disease (AKD) in patients with chronic obstructive pulmonary disease (COPD) and COPD mortality based on the acute/subacute renal injury. This study develops machine learning...

Identification of early prognostic biomarkers in Severe Fever with Thrombocytopenia Syndrome using machine learning algorithms.

Annals of medicine
OBJECTIVE: We aimed at identifying acute phase biomarkers in Severe Fever with Thrombocytopenia Syndrome (SFTS), and to establish a model to predict mortality outcomes.

Machine learning-based integration develops a disulfidptosis-related lncRNA signature for improving outcomes in gastric cancer.

Artificial cells, nanomedicine, and biotechnology
Gastric cancer remains one of the deadliest cancers globally due to delayed detection and limited treatment options, underscoring the critical need for innovative prognostic methods. Disulfidptosis, a recently discovered programmed cell death trigger...

Identification and validation of susceptibility modules and hub genes in polyarticular juvenile idiopathic arthritis using WGCNA and machine learning.

Autoimmunity
BACKGROUND: Juvenile idiopathic arthritis (JIA), superseding juvenile rheumatoid arthritis (JRA), is a chronic autoimmune disease affecting children and characterized by various types of childhood arthritis. JIA manifests clinically with joint inflam...

Plasma lipid levels predict chemotherapy response and survival in acute myeloid leukemia.

Blood
Acute myeloid leukemia (AML) is characterized by a low 5-year survival rate. Despite having many clinical metrics to assess patient prognosis, there remain opportunities to improve risk stratification. We hypothesized that an underexplored resource t...

AI in Hypertensive Disorders of Pregnancy: Review.

American journal of hypertension
BACKGROUND: Hypertensive disorders of pregnancy (HDP) are a leading cause of maternal and fetal mortality worldwide. Early detection and risk stratification are critical for timely intervention to prevent severe complications such as eclampsia, strok...

Prognostic factors and prediction model for facial scar improvement in laser-treated patients: A machine learning-based retrospective cohort study.

Medicine
The face, being central and exposed, is highly susceptible to trauma and subsequent scar formation. Laser therapy is a common and effective treatment method for facial scars. However, treatment outcomes vary substantially. Consequently, we aimed to i...

Multimodal Approach Predicts Relapse upon Cessation of Immune Checkpoint Inhibitors in Advanced Melanoma.

Clinical cancer research : an official journal of the American Association for Cancer Research
PURPOSE: Treatment with immune checkpoint inhibitors (ICI) in advanced melanoma can result in durable responses, yet an algorithm to decide which patients can safely discontinue ICI is still lacking.