AIMC Topic: Prognosis

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Risk prediction model for in-hospital mortality in women with ST-elevation myocardial infarction: A machine learning approach.

Heart & lung : the journal of critical care
BACKGROUND: Studies had shown that mortality due to ST-elevation myocardial infarction (STEMI) is higher in women compared with men. The purpose of this study is to develop and validate prediction models for all-cause in-hospital mortality in women a...

Deep Learning-Based Multi-Omics Integration Robustly Predicts Survival in Liver Cancer.

Clinical cancer research : an official journal of the American Association for Cancer Research
Identifying robust survival subgroups of hepatocellular carcinoma (HCC) will significantly improve patient care. Currently, endeavor of integrating multi-omics data to explicitly predict HCC survival from multiple patient cohorts is lacking. To fill ...

Machine Learning and Neurosurgical Outcome Prediction: A Systematic Review.

World neurosurgery
OBJECTIVE: Accurate measurement of surgical outcomes is highly desirable to optimize surgical decision-making. An important element of surgical decision making is identification of the patient cohort that will benefit from surgery before the interven...

Unsupervised deep learning reveals prognostically relevant subtypes of glioblastoma.

BMC bioinformatics
BACKGROUND: One approach to improving the personalized treatment of cancer is to understand the cellular signaling transduction pathways that cause cancer at the level of the individual patient. In this study, we used unsupervised deep learning to le...

Homecare Robots to Improve Health and Well-Being in Mild Cognitive Impairment and Early Stage Dementia: Results From a Scoping Study.

Journal of the American Medical Directors Association
OBJECTIVES: This scoping study is the first step of a multiphase, international project aimed at designing a homecare robot that can provide functional support, track physical and psychological well-being, and deliver therapeutic intervention specifi...

Single Nucleotide Polymorphism relevance learning with Random Forests for Type 2 diabetes risk prediction.

Artificial intelligence in medicine
OBJECTIVE: The use of artificial intelligence techniques to find out which Single Nucleotide Polymorphisms (SNPs) promote the development of a disease is one of the features of medical research, as such techniques may potentially aid early diagnosis ...

Using artificial neural networks to select the parameters for the prognostic of mild cognitive impairment and dementia in elderly individuals.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVES: A huge number of solutions based on computational systems have been recently developed for the classification of cognitive abnormalities in older people, so that individuals at high risk of developing neurodegenerative dise...

Survivability prediction of colon cancer patients using neural networks.

Health informatics journal
We utilize deep neural networks to develop prediction models for patient survival and conditional survival of colon cancer. Our models are trained and validated on data obtained from the Surveillance, Epidemiology, and End Results Program. We provide...

Predicting clinical outcomes from large scale cancer genomic profiles with deep survival models.

Scientific reports
Translating the vast data generated by genomic platforms into accurate predictions of clinical outcomes is a fundamental challenge in genomic medicine. Many prediction methods face limitations in learning from the high-dimensional profiles generated ...