Journal of Medical Internet Research

Explainable Machine Learning Model for Predicting Persistent Sepsis-Associated Acute Kidney Injury: Development and Validation Study

Conclusions: The interpretable GBM model was shown to successfully and accurately predict the occurrence of persistent SA-AKI, demonstrating good predictive ability in both internal and external ...
GitHub

Guided Easy Data Augmentation (GEDA)

Stage 1 - Label-Aware Language Modeling: We utilize Conditional BERT (CBERT) to fine-tune a language model that incorporates class label information, ensuring that synthetic substitutions remain label ...
IEEE

Learning Corruption-Invariant Components and Cross-Modal Correspondence for Unsupervised Visible-Infrared Person Re-Identification

Abstract: Unsupervised Visible-Infrared Person Re-Identification (US-VI-ReID) has great potential prospects because it does not require label information. However, corrupted pedestrian images ...
GitHub

Master’s Thesis in Data Science

Full thesis PDF Statistical validation of the DALY NCD/CD ratio K-Means and DTW clustering models Socio-demographic analysis and COVID-19 first impact results data/IHME-GBD_2021_DATA-4e9c07f2-1.csv ...
The Lancet

Development and validation of an explainable machine learning model for predicting postoperative pulmonary complications after lung cancer surgery: a machine learning study

bSchool of Nursing, Hunan University of Medicine, Huaihua, Hunan Province, China cSchool of Nursing, Zunyi Medical University, Zunyi, Guizhou Province, China dThoracic Surgery Department, Affiliated ...
IEEE

Unsupervised Multi-Target Cross-Service Log Anomaly Detection

Abstract: Log analysis, especially log anomaly detection, can help debug systems and analyze root causes to provide reliable services. Deep learning is a promising technology for log anomaly detection ...