AIMC Topic: Multivariate Analysis

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Prediction models for high risk of suicide in Korean adolescents using machine learning techniques.

PloS one
OBJECTIVE: Suicide in adolescents is a major problem worldwide and previous history of suicide ideation and attempt represents the strongest predictors of future suicidal behavior. The aim of this study was to develop prediction model to identify Kor...

Predicting cochlear dead regions in patients with hearing loss through a machine learning-based approach: A preliminary study.

PloS one
We propose a machine learning (ML)-based model for predicting cochlear dead regions (DRs) in patients with hearing loss of various etiologies. Five hundred and fifty-five ears from 380 patients (3,770 test samples) diagnosed with sensorineural hearin...

Prediction of the five-day biochemical oxygen demand and chemical oxygen demand in natural streams using machine learning methods.

Environmental monitoring and assessment
Rivers, as the most prominent component of water resources, have a key role to play in increasing the life expectancy of living creatures. The essential characteristics of water pollutants can be described by water quality indices (WQIs). Hence, a fe...

A comparison of machine learning algorithms and covariate balance measures for propensity score matching and weighting.

Biometrical journal. Biometrische Zeitschrift
Propensity score matching (PSM) and propensity score weighting (PSW) are popular tools to estimate causal effects in observational studies. We address two open issues: how to estimate propensity scores and assess covariate balance. Using simulations,...

Multivariate LSTM-FCNs for time series classification.

Neural networks : the official journal of the International Neural Network Society
Over the past decade, multivariate time series classification has received great attention. We propose transforming the existing univariate time series classification models, the Long Short Term Memory Fully Convolutional Network (LSTM-FCN) and Atten...

High-Sensitivity Determination of Nutrient Elements in by Laser-induced Breakdown Spectroscopy and Chemometric Methods.

Molecules (Basel, Switzerland)
High-accuracy and fast detection of nutritive elements in traditional Chinese medicine (PN) is beneficial for providing useful assessment of the healthy alimentation and pharmaceutical value of PN herbs. Laser-induced breakdown spectroscopy (LIBS) w...

Deep learning and manual assessment show that the absolute mitotic count does not contain prognostic information in triple negative breast cancer.

Cellular oncology (Dordrecht, Netherlands)
PURPOSE: The prognostic value of mitotic count for invasive breast cancer is firmly established. As yet, however, limited studies have been aimed at assessing mitotic counts as a prognostic factor for triple negative breast cancers (TNBC). Here, we a...

Main factors influencing recovery in MERS Co-V patients using machine learning.

Journal of infection and public health
BACKGROUND: Middle East Respiratory Syndrome (MERS) is a major infectious disease which has affected the Middle Eastern countries, especially the Kingdom of Saudi Arabia (KSA) since 2012. The high mortality rate associated with this disease has been ...

Performing Multi-Target Regression via a Parameter Sharing-Based Deep Network.

International journal of neural systems
Multi-target regression (MTR) comprises the prediction of multiple continuous target variables from a common set of input variables. There are two major challenges when addressing the MTR problem: the exploration of the inter-target dependencies and ...

Comparison of long-term outcomes of laparoscopic and robot-assisted laparoscopic partial nephrectomy.

The Kaohsiung journal of medical sciences
In this study, we compared the long-term oncological and functional outcomes of laparoscopic partial nephrectomy (LPN) and robot-assisted laparoscopic partial nephrectomy (RAPN) performed in the treatment of renal tumors. The data of 142 patients (RA...