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Survival Analysis

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Reconstructing cancer drug response networks using multitask learning.

BMC systems biology
BACKGROUND: Translating in vitro results to clinical tests is a major challenge in systems biology. Here we present a new Multi-Task learning framework which integrates thousands of cell line expression experiments to reconstruct drug specific respon...

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...

Feature selection through validation and un-censoring of endovascular repair survival data for predicting the risk of re-intervention.

BMC medical informatics and decision making
BACKGROUND: Feature selection (FS) process is essential in the medical area as it reduces the effort and time needed for physicians to measure unnecessary features. Choosing useful variables is a difficult task with the presence of censoring which is...

Predicting two-year survival versus non-survival after first myocardial infarction using machine learning and Swedish national register data.

BMC medical informatics and decision making
BACKGROUND: Machine learning algorithms hold potential for improved prediction of all-cause mortality in cardiovascular patients, yet have not previously been developed with high-quality population data. This study compared four popular machine learn...

Modeling time-to-event (survival) data using classification tree analysis.

Journal of evaluation in clinical practice
RATIONALE, AIMS, AND OBJECTIVES: Time to the occurrence of an event is often studied in health research. Survival analysis differs from other designs in that follow-up times for individuals who do not experience the event by the end of the study (cal...

DCE-MRI prediction of survival time for patients with glioblastoma multiforme: using an adaptive neuro-fuzzy-based model and nested model selection technique.

NMR in biomedicine
This pilot study investigates the construction of an Adaptive Neuro-Fuzzy Inference System (ANFIS) for the prediction of the survival time of patients with glioblastoma multiforme (GBM). ANFIS is trained by the pharmacokinetic (PK) parameters estimat...

Sensitive detection of rare disease-associated cell subsets via representation learning.

Nature communications
Rare cell populations play a pivotal role in the initiation and progression of diseases such as cancer. However, the identification of such subpopulations remains a difficult task. This work describes CellCnn, a representation learning approach to de...

Positive surgical margin in robot-assisted radical prostatectomy: correlation with pathology findings and risk of biochemical recurrence.

Minerva urologica e nefrologica = The Italian journal of urology and nephrology
BACKGROUND: To analyze the correlation of surgical margin status with other findings on final pathology and risk of biochemical recurrence (BCR) in patients undergoing robot-assisted radical prostatectomy (RALP).

Robot-assisted intersphincteric resection facilitates an efficient sphincter-saving in patients with low rectal cancer.

International journal of colorectal disease
PURPOSE: Few investigations of robot-assisted intersphincteric resection (ISR) are presently available to support this procedure as a safe and efficient procedure. We aimed to evaluate the utility of robot-assisted ISR by comparison between ISR and a...