AIMC Topic: Area Under Curve

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i4mC-EL: Identifying DNA N4-Methylcytosine Sites in the Mouse Genome Using Ensemble Learning.

BioMed research international
As one of important epigenetic modifications, DNA N4-methylcytosine (4mC) plays a crucial role in controlling gene replication, expression, cell cycle, DNA replication, and differentiation. The accurate identification of 4mC sites is necessary to und...

Prediction of Major Complications and Readmission After Lumbar Spinal Fusion: A Machine Learning-Driven Approach.

World neurosurgery
BACKGROUND: Given the significant cost and morbidity of patients undergoing lumbar fusion, accurate preoperative risk-stratification would be of great utility. We aim to develop a machine learning model for prediction of major complications and readm...

LSTMCNNsucc: A Bidirectional LSTM and CNN-Based Deep Learning Method for Predicting Lysine Succinylation Sites.

BioMed research international
Lysine succinylation is a typical protein post-translational modification and plays a crucial role of regulation in the cellular process. Identifying succinylation sites is fundamental to explore its functions. Although many computational methods wer...

Toward understanding COVID-19 pneumonia: a deep-learning-based approach for severity analysis and monitoring the disease.

Scientific reports
We report a new approach using artificial intelligence (AI) to study and classify the severity of COVID-19 using 1208 chest X-rays (CXRs) of 396 COVID-19 patients obtained through the course of the disease at Emory Healthcare affiliated hospitals (At...

Clinical risk prediction models and informative cluster size: Assessing the performance of a suicide risk prediction algorithm.

Biometrical journal. Biometrische Zeitschrift
Clinical visit data are clustered within people, which complicates prediction modeling. Cluster size is often informative because people receiving more care are less healthy and at higher risk of poor outcomes. We used data from seven health systems ...

COVID-19 diagnosis by routine blood tests using machine learning.

Scientific reports
Physicians taking care of patients with COVID-19 have described different changes in routine blood parameters. However, these changes hinder them from performing COVID-19 diagnoses. We constructed a machine learning model for COVID-19 diagnosis that ...

End-to-end deep learning for recognition of ploidy status using time-lapse videos.

Journal of assisted reproduction and genetics
PURPOSE: Our retrospective study is to investigate an end-to-end deep learning model in identifying ploidy status through raw time-lapse video.

Eliminating Indefiniteness of Clinical Spectrum for Better Screening COVID-19.

IEEE journal of biomedical and health informatics
The coronavirus disease 2019 (COVID-19) has swept all over the world. Due to the limited detection facilities, especially in developing countries, a large number of suspected cases can only receive common clinical diagnosis rather than more effective...

Development and validation of MRI-based deep learning models for prediction of microsatellite instability in rectal cancer.

Cancer medicine
BACKGROUND: Microsatellite instability (MSI) predetermines responses to adjuvant 5-fluorouracil and immunotherapy in rectal cancer and serves as a prognostic biomarker for clinical outcomes. Our objective was to develop and validate a deep learning m...

ai-corona: Radiologist-assistant deep learning framework for COVID-19 diagnosis in chest CT scans.

PloS one
The development of medical assisting tools based on artificial intelligence advances is essential in the global fight against COVID-19 outbreak and the future of medical systems. In this study, we introduce ai-corona, a radiologist-assistant deep lea...