AIMC Topic: Regression Analysis

Clear Filters Showing 151 to 160 of 439 articles

Implementation of artificial intelligence in medicine: Status analysis and development suggestions.

Artificial intelligence in medicine
The general public's attitudes, demands, and expectations regarding medical AI could provide guidance for the future development of medical AI to satisfy the increasing needs of doctors and patients. The objective of this study is to investigate publ...

Pulse Wave Velocity and Machine Learning to Predict Cardiovascular Outcomes in Prediabetic and Diabetic Populations.

Journal of medical systems
Few studies have addressed the predictive value of arterial stiffness determined by pulse wave velocity (PWV) in a high-risk population with no prevalent cardiovascular disease and with obesity, hypertension, hyperglycemia, and preserved kidney funct...

Afann: bias adjustment for alignment-free sequence comparison based on sequencing data using neural network regression.

Genome biology
Alignment-free methods, more time and memory efficient than alignment-based methods, have been widely used for comparing genome sequences or raw sequencing samples without assembly. However, in this study, we show that alignment-free dissimilarity ca...

Implementing machine learning in bipolar diagnosis in China.

Translational psychiatry
Bipolar disorder (BPD) is often confused with major depression, and current diagnostic questionnaires are subjective and time intensive. The aim of this study was to develop a new Bipolar Diagnosis Checklist in Chinese (BDCC) by using machine learnin...

Prospective prediction of suicide attempts in community adolescents and young adults, using regression methods and machine learning.

Journal of affective disorders
BACKGROUND: The use of machine learning (ML) algorithms to study suicidality has recently been recommended. Our aim was to explore whether ML approaches have the potential to improve the prediction of suicide attempt (SA) risk. Using the epidemiologi...

Integrative Deep Learning for Identifying Differentially Expressed (DE) Biomarkers.

Computational and mathematical methods in medicine
As a large amount of genetic data are accumulated, an effective analytical method and a significant interpretation are required. Recently, various methods of machine learning have emerged to process genetic data. In addition, machine learning analysi...

A distributed multitask multimodal approach for the prediction of Alzheimer's disease in a longitudinal study.

NeuroImage
Predicting the progression of Alzheimer's Disease (AD) has been held back for decades due to the lack of sufficient longitudinal data required for the development of novel machine learning algorithms. This study proposes a novel machine learning algo...

Impact of age at onset on the phenomenology of depression in treatment-seeking adults in the STAR*D trial.

Journal of affective disorders
BACKGROUND: - Adolescence is characterized by biological, emotional, and behavioral changes. The onset of depression during this vulnerable time may confer specific risks. This study examined whether symptoms of depression were associated with age at...

Predicting degradation rate of genipin cross-linked gelatin scaffolds with machine learning.

Materials science & engineering. C, Materials for biological applications
Genipin can improve weak mechanical properties and control high degradation rate of gelatin, as a cross-linker of gelatin which is widely used in tissue engineering. In this study, genipin cross-linked gelatin biodegradable porous scaffolds with diff...