Iterated cross validation method for prediction of survival in diffuse large B-cell lymphoma for small size dataset.

Efforts have been made to improve the risk stratification model for patients with diffuse large B-cell lymphoma (DLBCL). This study aimed to evaluate the disease prognosis using machine learning models with iterated cross validation (CV) method. A total of 122 patients with pathologically confirmed DLBCL and receiving rituximab-containing chemotherapy were enrolled.

A Semi-Supervised Machine Learning Approach in Predicting High-Risk Pregnancies in the Philippines.

Early risk tagging is crucial in maternal health, especially because it threatens both the mother and the long-term development of the baby. By tagging high-risk pregnancies, mothers would be given extra care before, during, and after pregnancies, thus reducing the risk of complications. In the Philippines, where the fertility rate is high, especially among the youth, awareness of risks can significantly contribute to the overall outcome of the pregnancy and, to an extent, the Maternal mortality rate.

Development of a Machine Learning Based Web Application for Early Diagnosis of COVID-19 Based on Symptoms.

Detecting the presence of a disease requires laboratory tests, testing kits, and devices; however, these were not always available on hand. This study proposes a new approach in disease detection using machine learning algorithms by analyzing symptoms experienced by a person without requiring laboratory tests. Six supervised machine learning algorithms such as J48 decision tree, random forest, support vector machine, k-nearest neighbors, naïve Bayes algorithms, and artificial neural networks were applied in the "COVID-19 Symptoms and Presence Dataset" from Kaggle.

Risk Prediction of Barrett's Esophagus in a Taiwanese Health Examination Center Based on Regression Models.

Determining the target population for the screening of Barrett's esophagus (BE), a precancerous condition of esophageal adenocarcinoma, remains a challenge in Asia. The aim of our study was to develop risk prediction models for BE using logistic regression (LR) and artificial neural network (ANN) methods. Their predictive performances were compared.

Emergency department disposition prediction using a deep neural network with integrated clinical narratives and structured data.

Background: Emergency department (ED) overcrowding has been a serious issue and demands effective clinical decision-making of patient disposition. In previous studies, emergency clinical narratives provide a rich context for clinical decisions. We aimed to develop the disposition prediction model using deep learning modeling strategy with the heterogeneous data, including the physicians' narratives.

Early short-term prediction of emergency department length of stay using natural language processing for low-acuity outpatients.

Background: Low-acuity outpatients constitute the majority of emergency department (ED) patients, and these patients often experience an unpredictable length of stay (LOS). Effective LOS prediction might improve the quality of ED care and reduce ED crowding.

Objective: The objective of this study was to explore the potential of natural language processing (NLP) of the first ED physicians' clinical notes and to evaluate NLP-based short-term prediction models based on mixed-type clinical data.

Application of genetic algorithm to hexagon-based motion estimation.

With the improvement of science and technology, the development of the network, and the exploitation of the HDTV, the demands of audio and video become more and more important. Depending on the video coding technology would be the solution for achieving these requirements. Motion estimation, which removes the redundancy in video frames, plays an important role in the video coding.

Study on Huber fractal image compression.

In this paper, a new similarity measure for fractal image compression (FIC) is introduced. In the proposed Huber fractal image compression (HFIC), the linear Huber regression technique from robust statistics is embedded into the encoding procedure of the fractal image compression. When the original image is corrupted by noises, we argue that the fractal image compression scheme should be insensitive to those noises presented in the corrupted image.