AIMC Topic: Prognosis

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A Deep-Learning-Based Health Indicator Constructor Using Kullback-Leibler Divergence for Predicting the Remaining Useful Life of Concrete Structures.

Sensors (Basel, Switzerland)
This paper proposes a new technique for the construction of a concrete-beam health indicator based on the Kullback-Leibler divergence (KLD) and deep learning. Health indicator (HI) construction is a vital part of remaining useful lifetime (RUL) appro...

Optimizing acute stroke outcome prediction models: Comparison of generalized regression neural networks and logistic regressions.

PloS one
BACKGROUND: Generalized regression neural network (GRNN) and logistic regression (LR) are extensively used in the medical field; however, the better model for predicting stroke outcome has not been established. The primary goal of this study was to c...

Machine learning model prediction of 6-month functional outcome in elderly patients with intracerebral hemorrhage.

Neurosurgical review
Spontaneous intracerebral hemorrhage (ICH) has an increasing incidence and a worse outcome in elderly patients. The ability to predict the functional outcome in these patients can be helpful in supporting treatment decisions and establishing prognost...

Deep learning and the future of the Model for End-Stage Liver Disease-sodium score.

Liver transplantation : official publication of the American Association for the Study of Liver Diseases and the International Liver Transplantation Society

A novel deep learning prognostic system improves survival predictions for stage III non-small cell lung cancer.

Cancer medicine
BACKGROUND: Accurate prognostic prediction plays a crucial role in the clinical setting. However, the TNM staging system fails to provide satisfactory individual survival prediction for stage III non-small cell lung cancer (NSCLC). The performance of...

Spatial analysis of tumor-infiltrating lymphocytes in histological sections using deep learning techniques predicts survival in colorectal carcinoma.

The journal of pathology. Clinical research
This study aimed to explore the prognostic impact of spatial distribution of tumor-infiltrating lymphocytes (TILs) quantified by deep learning (DL) approaches based on digitalized whole-slide images stained with hematoxylin and eosin in patients with...

Outcome Prediction in Patients with Severe Traumatic Brain Injury Using Deep Learning from Head CT Scans.

Radiology
Background After severe traumatic brain injury (sTBI), physicians use long-term prognostication to guide acute clinical care yet struggle to predict outcomes in comatose patients. Purpose To develop and evaluate a prognostic model combining deep lear...

Not just "big" data: Importance of sample size, measurement error, and uninformative predictors for developing prognostic models for digital interventions.

Behaviour research and therapy
There is strong interest in developing a more efficient mental health care system. Digital interventions and predictive models of treatment prognosis will likely play an important role in this endeavor. This article reviews the application of popular...

Interpretability Analysis of One-Year Mortality Prediction for Stroke Patients Based on Deep Neural Network.

IEEE journal of biomedical and health informatics
Clinically, physicians collect the benchmark medical data to establish archives for a stroke patient and then add the follow up data regularly. It has great significance on prognosis prediction for stroke patients. In this paper, we present an interp...