AIMC Topic: Prognosis

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Prognostic accuracy of MALDI-TOF mass spectrometric analysis of plasma in COVID-19.

Life science alliance
SARS-CoV-2 infection poses a global health crisis. In parallel with the ongoing world effort to identify therapeutic solutions, there is a critical need for improvement in the prognosis of COVID-19. Here, we report plasma proteome fingerprinting that...

Predicting lethal courses in critically ill COVID-19 patients using a machine learning model trained on patients with non-COVID-19 viral pneumonia.

Scientific reports
In a pandemic with a novel disease, disease-specific prognosis models are available only with a delay. To bridge the critical early phase, models built for similar diseases might be applied. To test the accuracy of such a knowledge transfer, we inves...

Machine learning-based glucose prediction with use of continuous glucose and physical activity monitoring data: The Maastricht Study.

PloS one
BACKGROUND: Closed-loop insulin delivery systems, which integrate continuous glucose monitoring (CGM) and algorithms that continuously guide insulin dosing, have been shown to improve glycaemic control. The ability to predict future glucose values ca...

Tissue outcome prediction in hyperacute ischemic stroke: Comparison of machine learning models.

Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism
Machine Learning (ML) has been proposed for tissue fate prediction after acute ischemic stroke (AIS), with the aim to help treatment decision and patient management. We compared three different ML models to the clinical method based on diffusion-perf...

Use of Multiprognostic Index Domain Scores, Clinical Data, and Machine Learning to Improve 12-Month Mortality Risk Prediction in Older Hospitalized Patients: Prospective Cohort Study.

Journal of medical Internet research
BACKGROUND: The Multidimensional Prognostic Index (MPI) is an aggregate, comprehensive, geriatric assessment scoring system derived from eight domains that predict adverse outcomes, including 12-month mortality. However, the prediction accuracy of us...

Remaining Useful Life Prognostics of Bearings Based on a Novel Spatial Graph-Temporal Convolution Network.

Sensors (Basel, Switzerland)
As key equipment in modern industry, it is important to diagnose and predict the health status of bearings. Data-driven methods for remaining useful life (RUL) prognostics have achieved excellent performance in recent years compared to traditional me...

Prediction of venous thromboembolism with machine learning techniques in young-middle-aged inpatients.

Scientific reports
Accumulating studies appear to suggest that the risk factors for venous thromboembolism (VTE) among young-middle-aged inpatients are different from those among elderly people. Therefore, the current prediction models for VTE are not applicable to you...

Multivariable mortality risk prediction using machine learning for COVID-19 patients at admission (AICOVID).

Scientific reports
In Coronavirus disease 2019 (COVID-19), early identification of patients with a high risk of mortality can significantly improve triage, bed allocation, timely management, and possibly, outcome. The study objective is to develop and validate individu...

Deep learning for diagnosis and survival prediction in soft tissue sarcoma.

Annals of oncology : official journal of the European Society for Medical Oncology
BACKGROUND: Clinical management of soft tissue sarcoma (STS) is particularly challenging. Here, we used digital pathology and deep learning (DL) for diagnosis and prognosis prediction of STS.