AIMC Topic: Humans

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Comparison of machine learning algorithms for the identification of acute exacerbations in chronic obstructive pulmonary disease.

Computer methods and programs in biomedicine
OBJECTIVES: Identifying acute exacerbations in chronic obstructive pulmonary disease (AECOPDs) is of utmost importance for reducing the associated mortality and financial burden. In this research, the authors aimed to develop identification models fo...

Multi-Institutional Validation of Deep Learning for Pretreatment Identification of Extranodal Extension in Head and Neck Squamous Cell Carcinoma.

Journal of clinical oncology : official journal of the American Society of Clinical Oncology
PURPOSE: Extranodal extension (ENE) is a well-established poor prognosticator and an indication for adjuvant treatment escalation in patients with head and neck squamous cell carcinoma (HNSCC). Identification of ENE on pretreatment imaging represents...

Decoding and mapping task states of the human brain via deep learning.

Human brain mapping
Support vector machine (SVM)-based multivariate pattern analysis (MVPA) has delivered promising performance in decoding specific task states based on functional magnetic resonance imaging (fMRI) of the human brain. Conventionally, the SVM-MVPA requir...

Story Arcs in Serious Illness: Natural Language Processing features of Palliative Care Conversations.

Patient education and counseling
OBJECTIVE: Serious illness conversations are complex clinical narratives that remain poorly understood. Natural Language Processing (NLP) offers new approaches for identifying hidden patterns within the lexicon of stories that may reveal insights abo...

Deep learning segmentation of orbital fat to calibrate conventional MRI for longitudinal studies.

NeuroImage
In conventional non-quantitative magnetic resonance imaging, image contrast is consistent within images, but absolute intensity can vary arbitrarily between scans. For quantitative analysis of intensity data, images are typically normalized to a cons...

Highly precise risk prediction model for new-onset hypertension using artificial intelligence techniques.

Journal of clinical hypertension (Greenwich, Conn.)
Hypertension is a significant public health issue. The ability to predict the risk of developing hypertension could contribute to disease prevention strategies. This study used machine learning techniques to develop and validate a new risk prediction...

Artificial intelligence approaches to predicting and detecting cognitive decline in older adults: A conceptual review.

Psychiatry research
Preserving cognition and mental capacity is critical to aging with autonomy. Early detection of pathological cognitive decline facilitates the greatest impact of restorative or preventative treatments. Artificial Intelligence (AI) in healthcare is th...

Identifying bladder rupture following traumatic pelvic fracture: A machine learning approach.

Injury
INTRODUCTION: Bladder rupture following blunt pelvic trauma is rare though can have significant sequelae. We sought to determine whether machine learning could help predict the presence of bladder injury using certain factors at the time of presentat...

Computational modeling of the monoaminergic neurotransmitter and male neuroendocrine systems in an analysis of therapeutic neuroadaptation to chronic antidepressant.

European neuropsychopharmacology : the journal of the European College of Neuropsychopharmacology
Second-line depression treatment involves augmentation with one (rarely two) additional drugs, of chronic administration of a selective serotonin reuptake inhibitor (SSRI), which is the first-line depression treatment. Unfortunately, many depressed p...