AIMC Topic: Disease Progression

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Mixed effect machine learning: A framework for predicting longitudinal change in hemoglobin A1c.

Journal of biomedical informatics Sep 4, 2018
Accurate and reliable prediction of clinical progression over time has the potential to improve the outcomes of chronic disease. The classical approach to analyzing longitudinal data is to use (generalized) linear mixed-effect models (GLMM). However,...
Adult Disease Progression Diabetes Mellitus, Type 2 Longitudinal Studies Glycated Hemoglobin Humans Machine Learning Algorithms
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Identifying people at risk of developing type 2 diabetes: A comparison of predictive analytics techniques and predictor variables.

International journal of medical informatics Aug 28, 2018
BACKGROUND: The present study aims to identify the patients at risk of type 2 diabetes (T2D). There is a body of literature that uses machine learning classification algorithms to predict development of T2D among patients. The current study compares ...
Humans Logistic Models Mass Screening Support Vector Machine Predictive Value of Tests Risk Factors Male Middle Aged Disease Progression Diabetes Mellitus, Type 2 Neural Networks, Computer Machine Learning Algorithms Female
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Artificial neural networks help to identify disease subsets and to predict lymphoma in primary Sjögren's syndrome.

Clinical and experimental rheumatology Aug 14, 2018
OBJECTIVES: Primary Sjögren's syndrome (pSS) is a complex chronic systemic disorder, for which specific and effective therapeutic interventions are still lacking. In this era of precision medicine, there is a clear need for a better definition of dis...
Disease Progression Severity of Illness Index Diagnosis, Computer-Assisted Lymphoma Diagnosis, Differential Predictive Value of Tests Principal Component Analysis Autoantibodies Data Mining Serologic Tests Reproducibility of Results Risk Factors Aged Male Middle Aged Phenotype Biomarkers Sjogren's Syndrome Prognosis Humans Female Neural Networks, Computer Decision Support Techniques
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Deep learning-based detection and classification of geographic atrophy using a deep convolutional neural network classifier.

Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie Aug 8, 2018
PURPOSE: To automatically detect and classify geographic atrophy (GA) in fundus autofluorescence (FAF) images using a deep learning algorithm.
Disease Progression Diagnosis, Computer-Assisted Tomography, Optical Coherence Fundus Oculi Humans Neural Networks, Computer Healthy Volunteers Aged Fluorescein Angiography Machine Learning Sensitivity and Specificity Geographic Atrophy Algorithms Male Female Middle Aged Reproducibility of Results
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Discovering and identifying New York heart association classification from electronic health records.

BMC medical informatics and decision making Jul 23, 2018
BACKGROUND: Cardiac Resynchronization Therapy (CRT) is an established pacing therapy for heart failure patients. The New York Heart Association (NYHA) class is often used as a measure of a patient's response to CRT. Identifying NYHA class for heart f...
Cardiac Resynchronization Therapy New York Disease Progression Treatment Outcome Natural Language Processing Heart Failure Humans Machine Learning Electronic Health Records Female Aged Male Middle Aged
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A sequence-to-sequence model-based deep learning approach for recognizing activity of daily living for senior care.

Journal of biomedical informatics Jul 10, 2018
Ensuring the health and safety of independent-living senior citizens is a growing societal concern. Researchers have developed sensor based systems to monitor senior citizens' Activity of Daily Living (ADL), a set of daily activities that can indicat...
Humans Neural Networks, Computer Algorithms Monitoring, Ambulatory Pattern Recognition, Automated Assisted Living Facilities Health Services for the Aged Aged Disease Progression Deep Learning Data Mining Activities of Daily Living
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Machine learning models to predict the progression from early to late stages of papillary renal cell carcinoma.

Computers in biology and medicine Jun 28, 2018
Papillary Renal Cell Carcinoma (PRCC) is a heterogeneous disease with variations in disease progression and clinical outcomes. The advent of next generation sequencing techniques (NGS) has generated data from patients that can be analysed to develop ...
Predictive Value of Tests Models, Biological Male Kidney Neoplasms Humans Disease Progression Carcinoma, Renal Cell Transcriptome Machine Learning Sequence Analysis, RNA Female Gene Expression Regulation, Neoplastic
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Granulomatosis with polyangiitis in Northeastern Brazil: study of 25 cases and review of the literature.

Advances in rheumatology (London, England) Jun 28, 2018
BACKGROUND: Little has been published about the epidemiology of Granulomatosis with polyangiitis (GPA) in South America, especially in the intertropical zone, and no epidemiological data from Brazil are available. The purpose of the present study was...
Arthritis Gastrointestinal Diseases Granulomatosis with Polyangiitis Nervous System Diseases Disease Progression Lung Diseases Creatinine Skin Diseases Proteinuria Heart Diseases Delayed Diagnosis Eye Diseases Adrenal Cortex Hormones Humans Adult Retrospective Studies Cross-Sectional Studies Acute Kidney Injury Male Female Middle Aged Brazil
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Detection of Longitudinal Visual Field Progression in Glaucoma Using Machine Learning.

American journal of ophthalmology Jun 18, 2018
PURPOSE: Global indices of standard automated perimerty are insensitive to localized losses, while point-wise indices are sensitive but highly variable. Region-wise indices sit in between. This study introduces a machine learning-based index for glau...
Tonometry, Ocular Gonioscopy Follow-Up Studies Vision Disorders Visual Fields Visual Field Tests Glaucoma, Open-Angle Male Middle Aged Disease Progression Diagnosis, Computer-Assisted Humans Machine Learning Female Aged Cross-Sectional Studies Intraocular Pressure
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Distinguishing True Progression From Radionecrosis After Stereotactic Radiation Therapy for Brain Metastases With Machine Learning and Radiomics.

International journal of radiation oncology, biology, physics May 24, 2018
PURPOSE: Treatment effect or radiation necrosis after stereotactic radiosurgery (SRS) for brain metastases is a common phenomenon often indistinguishable from true progression. Radiomics is an emerging field that promises to improve on conventional i...
Humans Machine Learning Aged Aged, 80 and over Adult Radiosurgery Radiation Injuries Brain Neoplasms Middle Aged Disease Progression Magnetic Resonance Imaging Diagnosis, Differential
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