AIMC Topic: Logistic Models

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Machine learning for the prediction of bone metastasis in patients with newly diagnosed thyroid cancer.

Cancer medicine
OBJECTIVES: This study aimed to establish a machine learning prediction model that can be used to predict bone metastasis (BM) in patients with newly diagnosed thyroid cancer (TC).

Development of machine learning model algorithm for prediction of 5-year soft tissue myxoid liposarcoma survival.

Journal of surgical oncology
BACKGROUND: Predicting survival in myxoid liposarcoma (MLS) patients is very challenging given its propensity to metastasize and the controversial role of adjuvant therapy. The purpose of this study was to develop a machine-learning algorithm for the...

Semi-automated tracking of pain in critical care patients using artificial intelligence: a retrospective observational study.

Scientific reports
Monitoring the pain intensity in critically ill patients is crucial because intense pain can cause adverse events, including poor survival rates; however, continuous pain evaluation is difficult. Vital signs have traditionally been considered ineffec...

Prediction of Sepsis in COVID-19 Using Laboratory Indicators.

Frontiers in cellular and infection microbiology
BACKGROUND: The outbreak of coronavirus disease 2019 (COVID-19) has become a global public health concern. Many inpatients with COVID-19 have shown clinical symptoms related to sepsis, which will aggravate the deterioration of patients' condition. We...

Androgen Receptor Binding Category Prediction with Deep Neural Networks and Structure-, Ligand-, and Statistically Based Features.

Molecules (Basel, Switzerland)
Substances that can modify the androgen receptor pathway in humans and animals are entering the environment and food chain with the proven ability to disrupt hormonal systems and leading to toxicity and adverse effects on reproduction, brain developm...

Artificial neural network and logistic regression modelling to characterize COVID-19 infected patients in local areas of Iran.

Biomedical journal
BACKGROUND: COVID-19 is an infectious disease that started spreading globally at the end of 2019. Due to differences in patient characteristics and symptoms in different regions, in this research, a comparative study was performed on COVID-19 patient...

Prediction of Patient Management in COVID-19 Using Deep Learning-Based Fully Automated Extraction of Cardiothoracic CT Metrics and Laboratory Findings.

Korean journal of radiology
OBJECTIVE: To extract pulmonary and cardiovascular metrics from chest CTs of patients with coronavirus disease 2019 (COVID-19) using a fully automated deep learning-based approach and assess their potential to predict patient management.

Predicting the 9-year course of mood and anxiety disorders with automated machine learning: A comparison between auto-sklearn, naïve Bayes classifier, and traditional logistic regression.

Psychiatry research
BACKGROUND: Predicting the onset and course of mood and anxiety disorders is of clinical importance but remains difficult. We compared the predictive performances of traditional logistic regression, basic probabilistic machine learning (ML) methods, ...

Establishing Classifiers With Clinical Laboratory Indicators to Distinguish COVID-19 From Community-Acquired Pneumonia: Retrospective Cohort Study.

Journal of medical Internet research
BACKGROUND: The initial symptoms of patients with COVID-19 are very much like those of patients with community-acquired pneumonia (CAP); it is difficult to distinguish COVID-19 from CAP with clinical symptoms and imaging examination.