AIMC Topic: Prognosis

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Is Artificial Intelligence Better Than Human Clinicians in Predicting Patient Outcomes?

Journal of medical Internet research
In contrast with medical imaging diagnostics powered by artificial intelligence (AI), in which deep learning has led to breakthroughs in recent years, patient outcome prediction poses an inherently challenging problem because it focuses on events tha...

A novel image signature-based radiomics method to achieve precise diagnosis and prognostic stratification of gliomas.

Laboratory investigation; a journal of technical methods and pathology
Radiomics has potential advantages in the noninvasive histopathological and molecular diagnosis of gliomas. We aimed to develop a novel image signature (IS)-based radiomics model to achieve multilayered preoperative diagnosis and prognostic stratific...

The role of sacubitril/valsartan in the treatment of chronic heart failure with reduced ejection fraction in hypertensive patients with comorbidities: From clinical trials to real-world settings.

Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie
BACKGROUND: Sacubitril/valsartan, the first agent to be approved in a new class of drugs called angiotensin receptor neprilysin inhibitors (ARNIs), has been shown to reduce cardiovascular mortality and morbidity compared to enalapril in outpatient su...

Machine learning for RNA sequencing-based intrinsic subtyping of breast cancer.

Scientific reports
Stratification of breast cancer (BC) into molecular subtypes by multigene expression assays is of demonstrated clinical utility. In principle, global RNA-sequencing (RNA-seq) should enable reconstructing existing transcriptional classifications of BC...

A supervised machine learning-based methodology for analyzing dysregulation in splicing machinery: An application in cancer diagnosis.

Artificial intelligence in medicine
Deregulated splicing machinery components have shown to be associated with the development of several types of cancer and, therefore, the determination of such alterations can help the development of tumor-specific molecular targets for early prognos...

Predicting the progression of mild cognitive impairment to Alzheimer's disease by longitudinal magnetic resonance imaging-based dictionary learning.

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
OBJECTIVE: Efficient prediction of the progression of mild cognitive impairment (MCI) to Alzheimer's disease (AD) is important for the early intervention and management of AD. The aim of our study was to develop a longitudinal structural magnetic res...

Machine learning improves mortality risk prediction after cardiac surgery: Systematic review and meta-analysis.

The Journal of thoracic and cardiovascular surgery
BACKGROUND: Interest in the usefulness of machine learning (ML) methods for outcomes prediction has continued to increase in recent years. However, the advantage of advanced ML model over traditional logistic regression (LR) remains controversial. We...

Identifying sarcopenia in advanced non-small cell lung cancer patients using skeletal muscle CT radiomics and machine learning.

Thoracic cancer
BACKGROUND: Sarcopenia has been confirmed as a poor prognostic indicator of lung cancer. However, the lack of abdominal computed tomography (CT) hindered the application to assess the status of sarcopenia. The purpose of this study was to assess the ...

A fully automatic deep learning system for COVID-19 diagnostic and prognostic analysis.

The European respiratory journal
Coronavirus disease 2019 (COVID-19) has spread globally, and medical resources become insufficient in many regions. Fast diagnosis of COVID-19 and finding high-risk patients with worse prognosis for early prevention and medical resource optimisation ...