AIMC Topic: Prognosis

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Feature-weighted survival learning machine for COPD failure prediction.

Artificial intelligence in medicine
Chronic obstructive pulmonary disease (COPD) yields a high rate of failures such as hospital readmission and death in the United States, Canada and worldwide. COPD failure imposes a significant social and economic burden on society, and predicting su...

Weakly Supervised Deep Learning for Brain Disease Prognosis Using MRI and Incomplete Clinical Scores.

IEEE transactions on cybernetics
As a hot topic in brain disease prognosis, predicting clinical measures of subjects based on brain magnetic resonance imaging (MRI) data helps to assess the stage of pathology and predict future development of the disease. Due to incomplete clinical ...

Automated tumour budding quantification by machine learning augments TNM staging in muscle-invasive bladder cancer prognosis.

Scientific reports
Tumour budding has been described as an independent prognostic feature in several tumour types. We report for the first time the relationship between tumour budding and survival evaluated in patients with muscle invasive bladder cancer. A machine lea...

Machine Learning for the Prediction of Cervical Spondylotic Myelopathy: A Post Hoc Pilot Study of 28 Participants.

World neurosurgery
BACKGROUND: Cervical spondylotic myelopathy (CSM) severity and presence of symptoms are often difficult to predict based simply on clinical imaging alone. Similarly, improved machine learning techniques provide new tools with immense clinical potenti...

Can We Accurately Identify Peritoneal Metastases Based on Their Appearance? An Assessment of the Current Practice of Intraoperative Gastrointestinal Cancer Staging.

Annals of surgical oncology
BACKGROUND: Peritoneal lesions are common findings during operative abdominal cancer staging. The decision to perform biopsy is made subjectively by the surgeon, a practice the authors hypothesized to be imprecise. This study aimed to describe optica...

Identifying progressive CKD from healthy population using Bayesian network and artificial intelligence: A worksite-based cohort study.

Scientific reports
Identifying progressive early chronic kidney disease (CKD) patients at a health checkup is a good opportunity to improve their prognosis. However, it is difficult to identify them using common health tests. This worksite-based cohort study for 7 year...

Predicting factors for survival of breast cancer patients using machine learning techniques.

BMC medical informatics and decision making
BACKGROUND: Breast cancer is one of the most common diseases in women worldwide. Many studies have been conducted to predict the survival indicators, however most of these analyses were predominantly performed using basic statistical methods. As an a...

LUADpp: an effective prediction model on prognosis of lung adenocarcinomas based on somatic mutational features.

BMC cancer
BACKGROUND: Lung adenocarcinoma is the most common type of lung cancers. Whole-genome sequencing studies disclosed the genomic landscape of lung adenocarcinomas. however, it remains unclear if the genetic alternations could guide prognosis prediction...

Group Lasso Regularized Deep Learning for Cancer Prognosis from Multi-Omics and Clinical Features.

Genes
Accurate prognosis of patients with cancer is important for the stratification of patients, the optimization of treatment strategies, and the design of clinical trials. Both clinical features and molecular data can be used for this purpose, for insta...

Predicting Ewing Sarcoma Treatment Outcome Using Infrared Spectroscopy and Machine Learning.

Molecules (Basel, Switzerland)
BACKGROUND: Improved outcome prediction is vital for the delivery of risk-adjusted, appropriate and effective care to paediatric patients with Ewing sarcoma-the second most common paediatric malignant bone tumour. Fourier transform infrared (FTIR) sp...