BMC medical informatics and decision making
Nov 21, 2019
BACKGROUND: Neuropsychological tests (NPTs) are important tools for informing diagnoses of cognitive impairment (CI). However, interpreting NPTs requires specialists and is thus time-consuming. To streamline the application of NPTs in clinical settin...
BACKGROUND: Atrial fibrillation (AF) is the most common sustained heart arrhythmia. However, as many cases are asymptomatic, a large proportion of patients remain undiagnosed until serious complications arise. Efficient, cost-effective detection of t...
There is significant interest in the development and application of deep neural networks (DNNs) to neuroimaging data. A growing literature suggests that DNNs outperform their classical counterparts in a variety of neuroimaging applications, yet there...
Previous structural and functional neuroimaging studies have implicated distributed brain regions and networks in depression. However, there are no robust imaging biomarkers that are specific to depression, which may be due to clinical heterogeneity ...
OBJECTIVES: Current models for patient risk prediction rely on practitioner expertise and domain knowledge. This study presents a deep learning model-a type of machine learning that does not require human inputs-to analyze complex clinical and financ...
BACKGROUND: Prediction of future lung function will enable the identification of individuals at high risk of developing COPD, but the trajectory of lung function decline varies greatly among individuals. This study involved the development and valida...
The cellular and synaptic architecture of the rodent hippocampus has been described in thousands of peer-reviewed publications. However, no human- or machine-readable public catalog of synaptic electrophysiology data exists for this or any other neur...
Racial disparities in the utilization of epilepsy surgery are well documented, but it is unknown whether a natural language processing (NLP) algorithm trained on physician notes would produce biased recommendations for epilepsy presurgical evaluation...
BACKGROUND: We aimed to demonstrate that supervised machine learning (ML) models can better predict postoperative complications after total shoulder arthroplasty (TSA) than comorbidity indices.
White matter hyperintensities (WMH) or white matter lesions exhibit high variability in their characteristics both at population- and subject-level, making their detection a challenging task. Population-level factors such as age, vascular risk factor...