AIMC Topic: Survival Analysis

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Deep learning for survival outcomes.

Statistics in medicine
Deep learning is a class of machine learning algorithms that are popular for building risk prediction models. When observations are censored, the outcomes are only partially observed and standard deep learning algorithms cannot be directly applied. W...

Transfer learning with convolutional neural networks for cancer survival prediction using gene-expression data.

PloS one
Precision medicine in oncology aims at obtaining data from heterogeneous sources to have a precise estimation of a given patient's state and prognosis. With the purpose of advancing to personalized medicine framework, accurate diagnoses allow prescri...

Machine learning and radiomic phenotyping of lower grade gliomas: improving survival prediction.

European radiology
BACKGROUND AND PURPOSE: Recent studies have highlighted the importance of isocitrate dehydrogenase (IDH) mutational status in stratifying biologically distinct subgroups of gliomas. This study aimed to evaluate whether MRI-based radiomic features cou...

Artificial intelligence as the next step towards precision pathology.

Journal of internal medicine
Pathology is the cornerstone of cancer care. The need for accuracy in histopathologic diagnosis of cancer is increasing as personalized cancer therapy requires accurate biomarker assessment. The appearance of digital image analysis holds promise to i...

DeepSurvNet: deep survival convolutional network for brain cancer survival rate classification based on histopathological images.

Medical & biological engineering & computing
Histopathological whole slide images of haematoxylin and eosin (H&E)-stained biopsies contain valuable information with relation to cancer disease and its clinical outcomes. Still, there are no highly accurate automated methods to correlate histolopa...

Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone.

BMC medical informatics and decision making
BACKGROUND: Cardiovascular diseases kill approximately 17 million people globally every year, and they mainly exhibit as myocardial infarctions and heart failures. Heart failure (HF) occurs when the heart cannot pump enough blood to meet the needs of...

Machine learning for the detection of early immunological markers as predictors of multi-organ dysfunction.

Scientific data
The immune response to major trauma has been analysed mainly within post-hospital admission settings where the inflammatory response is already underway and the early drivers of clinical outcome cannot be readily determined. Thus, there is a need to ...

Unsupervised Machine Learning of Topics Documented by Nurses about Hospitalized Patients Prior to a Rapid-Response Event.

Applied clinical informatics
BACKGROUND: In the hospital setting, it is crucial to identify patients at risk for deterioration before it fully develops, so providers can respond rapidly to reverse the deterioration. Rapid response (RR) activation criteria include a subjective co...

Machine learning and bioinformatics models to identify gene expression patterns of ovarian cancer associated with disease progression and mortality.

Journal of biomedical informatics
Ovarian cancer (OC) is a common cause of cancer death among women worldwide, so there is a pressing need to identify factors influencing OC mortality. Much OC patient clinical data is publicly accessible via the Broad Institute Cancer Genome Atlas (T...

Development and verification of prediction models for preventing cardiovascular diseases.

PloS one
OBJECTIVES: Cardiovascular disease (CVD) is one of the major causes of death worldwide. For improved accuracy of CVD prediction, risk classification was performed using national time-series health examination data. The data offers an opportunity to a...