AIMC Topic: Humans

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DeepFlu: a deep learning approach for forecasting symptomatic influenza A infection based on pre-exposure gene expression.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Not everyone gets sick after an exposure to influenza A viruses (IAV). Although KLRD1 has been identified as a potential biomarker for influenza susceptibility, it remains unclear whether forecasting symptomatic flu infectio...

Phenotype Discovery and Geographic Disparities of Late-Stage Breast Cancer Diagnosis across U.S. Counties: A Machine Learning Approach.

Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology
BACKGROUND: Disparities in the stage at diagnosis for breast cancer have been independently associated with various contextual characteristics. Understanding which combinations of these characteristics indicate highest risk, and where they are locate...

Artificial intelligence strategy integrating morphologic and architectural biomarkers provides robust diagnostic accuracy for disease progression in chronic lymphocytic leukemia.

The Journal of pathology
Artificial intelligence-based tools designed to assist in the diagnosis of lymphoid neoplasms remain limited. The development of such tools can add value as a diagnostic aid in the evaluation of tissue samples involved by lymphoma. A common diagnosti...

A novel multi-branch architecture for state of the art robust detection of pathological phonocardiograms.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences
Heart auscultation is an inexpensive and fundamental technique to effectively diagnose cardiovascular disease. However, due to relatively high human error rates even when auscultation is performed by an experienced physician, and due to the not unive...

Advanced computation in cardiovascular physiology: new challenges and opportunities.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences
Recent developments in computational physiology have successfully exploited advanced signal processing and artificial intelligence tools for predicting or uncovering characteristic features of physiological and pathological states in humans. While th...

Echo state network models for nonlinear Granger causality.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences
While Granger causality (GC) has been often employed in network neuroscience, most GC applications are based on linear multivariate autoregressive (MVAR) models. However, real-life systems like biological networks exhibit notable nonlinear behaviour,...

A novel artificial intelligence based intensive care unit monitoring system: using physiological waveforms to identify sepsis.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences
A massive amount of multimodal data are continuously collected in the intensive care unit (ICU) along each patient stay, offering a great opportunity for the development of smart monitoring devices based on artificial intelligence (AI). The two main ...

De novo design of novel protease inhibitor candidates in the treatment of SARS-CoV-2 using deep learning, docking, and molecular dynamic simulations.

Computers in biology and medicine
The main protease of SARS-CoV-2 is a critical target for the design and development of antiviral drugs. 2.5 M compounds were used in this study to train an LSTM generative network via transfer learning in order to identify the four best candidates ca...

Variable selection with missing data in both covariates and outcomes: Imputation and machine learning.

Statistical methods in medical research
Variable selection in the presence of both missing covariates and outcomes is an important statistical research topic. Parametric regression are susceptible to misspecification, and as a result are sub-optimal for variable selection. Flexible machine...

Superelastic, Antifreezing, Antidrying, and Conductive Organohydrogels for Wearable Strain Sensors.

ACS applied materials & interfaces
Sensors based on conductive hydrogels have received extensive attention in various fields, such as artificial intelligence, electronic skin, and health monitoring. However, the poor resilience and fatigue resistance, icing, and water loss of traditio...