AIMC Topic: Early Diagnosis

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Unbiased estimation of biomarker panel performance when combining training and testing data in a group sequential design.

Biometrics
Motivated by an ongoing study to develop a screening test able to identify patients with undiagnosed Sjögren's Syndrome in a symptomatic population, we propose methodology to combine multiple biomarkers and evaluate their performance in a two-stage g...

Rapid identification of slow healing wounds.

Wound repair and regeneration : official publication of the Wound Healing Society [and] the European Tissue Repair Society
Chronic nonhealing wounds have a prevalence of 2% in the United States, and cost an estimated $50 billion annually. Accurate stratification of wounds for risk of slow healing may help guide treatment and referral decisions. We have applied modern mac...

Analyzing depression tendency of web posts using an event-driven depression tendency warning model.

Artificial intelligence in medicine
OBJECTIVE: The Internet has become a platform to express individual moods/feelings of daily life, where authors share their thoughts in web blogs, micro-blogs, forums, bulletin board systems or other media. In this work, we investigate text-mining te...

Unsupervised learning based feature extraction for differential diagnosis of neurodegenerative diseases: A case study on early-stage diagnosis of Parkinson disease.

Journal of neuroscience methods
BACKGROUND: The development of MRI based methods could prove extremely valuable for identification of reliable biomarkers to aid diagnosis of neurodegenerative diseases (NDs). A great deal of current research has been aimed at identification biomarke...

A novel method for early diagnosis of Alzheimer's disease based on pseudo Zernike moment from structural MRI.

Neuroscience
Alzheimer's disease (AD) is a progressive neurodegenerative disorder and the most common type of dementia among older people. The number of patients with AD will grow rapidly each year and AD is the fifth leading cause of death for those aged 65 and ...

Policy forum. Data, privacy, and the greater good.

Science (New York, N.Y.)
Large-scale aggregate analyses of anonymized data can yield valuable results and insights that address public health challenges and provide new avenues for scientific discovery. These methods can extend our knowledge and provide new tools for enhanci...

A Robust Deep Model for Improved Classification of AD/MCI Patients.

IEEE journal of biomedical and health informatics
Accurate classification of Alzheimer's disease (AD) and its prodromal stage, mild cognitive impairment (MCI), plays a critical role in possibly preventing progression of memory impairment and improving quality of life for AD patients. Among many rese...

Discriminative analysis of Parkinson's disease based on whole-brain functional connectivity.

PloS one
Recently, there has been an increasing emphasis on applications of pattern recognition and neuroimaging techniques in the effective and accurate diagnosis of psychiatric or neurological disorders. In the present study, we investigated the whole-brain...

Bridging a translational gap: using machine learning to improve the prediction of PTSD.

BMC psychiatry
BACKGROUND: Predicting Posttraumatic Stress Disorder (PTSD) is a pre-requisite for targeted prevention. Current research has identified group-level risk-indicators, many of which (e.g., head trauma, receiving opiates) concern but a subset of survivor...

Development and external validation of a machine learning model for cardiac valve calcification early screening in dialysis patients: a multicenter study.

Renal failure
BACKGROUND: Cardiac valve calcification (CVC) is common in dialysis patients and associated with increased cardiovascular risk. However, early screening has been limited by cost concerns. This study aimed to develop and validate a machine learning mo...