AIMC Topic: Disease Progression
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Individualized prediction of depressive disorder in the elderly: A multitask deep learning approach.
International journal of medical informatics
Sep 23, 2019
INTRODUCTION: Depressive disorder is one of the major public health problems among the elderly. An effective depression risk prediction model can provide insights on the disease progression and potentially inform timely targeted interventions. Theref...
Using path signatures to predict a diagnosis of Alzheimer's disease.
PloS one
Sep 19, 2019
The path signature is a means of feature generation that can encode nonlinear interactions in data in addition to the usual linear terms. It provides interpretable features and its output is a fixed length vector irrespective of the number of input p...
CT Texture Analysis and Machine Learning Improve Post-ablation Prognostication in Patients with Adrenal Metastases: A Proof of Concept.
Cardiovascular and interventional radiology
Sep 5, 2019
INTRODUCTION: To assess the performance of pre-ablation computed tomography texture features of adrenal metastases to predict post-treatment local progression and survival in patients who underwent ablation using machine learning as a prediction tool...
A large cohort study identifying a novel prognosis prediction model for lung adenocarcinoma through machine learning strategies.
BMC cancer
Sep 5, 2019
BACKGROUND: Predicting lung adenocarcinoma (LUAD) risk is crucial in determining further treatment strategies. Molecular biomarkers may improve risk stratification for LUAD.
Male
Middle Aged
Disease Progression
Transcriptome
Lung Neoplasms
Female
Aged
Survival Rate
Humans
Biomarkers, Tumor
Machine Learning
Proportional Hazards Models
Gene Expression Regulation, Neoplastic
Prognosis
Multivariate Analysis
Follow-Up Studies
Kaplan-Meier Estimate
Databases, Genetic
Genes, Neoplasm
Disease-Free Survival
Adenocarcinoma of Lung
Predicting PET-derived demyelination from multimodal MRI using sketcher-refiner adversarial training for multiple sclerosis.
Medical image analysis
Aug 24, 2019
Multiple sclerosis (MS) is the most common demyelinating disease. In MS, demyelination occurs in the white matter of the brain and in the spinal cord. It is thus essential to measure the tissue myelin content to understand the physiopathology of MS, ...
Radiomics features on non-contrast computed tomography predict early enlargement of spontaneous intracerebral hemorrhage.
Clinical neurology and neurosurgery
Aug 15, 2019
OBJECTIVE: To explore the value of radiomics features on non-contrast computed tomography (NCCT) in predicting early enlargement of spontaneous intracerebral hemorrhage (SICH).
Artificial intelligence predicts the progression of diabetic kidney disease using big data machine learning.
Scientific reports
Aug 14, 2019
Artificial intelligence (AI) is expected to support clinical judgement in medicine. We constructed a new predictive model for diabetic kidney diseases (DKD) using AI, processing natural language and longitudinal data with big data machine learning, b...
Learning to Quantify Emphysema Extent: What Labels Do We Need?
IEEE journal of biomedical and health informatics
Aug 1, 2019
Accurate assessment of pulmonary emphysema is crucial to assess disease severity and subtype, to monitor disease progression, and to predict lung cancer risk. However, visual assessment is time-consuming and subject to substantial inter-rater variabi...
Variation in the Thickness of Knee Cartilage. The Use of a Novel Machine Learning Algorithm for Cartilage Segmentation of Magnetic Resonance Images.
The Journal of arthroplasty
Jul 24, 2019
BACKGROUND: The variation in articular cartilage thickness (ACT) in healthy knees is difficult to quantify and therefore poorly documented. Our aims are to (1) define how machine learning (ML) algorithms can automate the segmentation and measurement ...
Response to repeat echoendoscopic celiac plexus neurolysis in pancreatic cancer patients: A machine learning approach.
Pancreatology : official journal of the International Association of Pancreatology (IAP) ... [et al.]
Jul 24, 2019
BACKGROUND: /Objectives: Efficacy of repeat echoendoscopic celiac plexus neurolysis is still unclear. Aim of the study was to assess the efficacy of repeat celiac plexus neurolysis and to build an artificial neural network model able to predict pain ...
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