AIMC Topic: Humans

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Development of Human iPSC-Derived Functional Neuronal Networks on Laser-Fabricated 3D Scaffolds.

ACS applied materials & interfaces
Neural progenitor cells generated from human induced pluripotent stem cells (hiPSCs) are the forefront of ″brain-on-chip″ investigations. Viable and functional hiPSC-derived neuronal networks are shaping powerful models for evaluating the normal and...

Machine Learning Algorithms for Activity-Intensity Recognition Using Accelerometer Data.

Sensors (Basel, Switzerland)
In pervasive healthcare monitoring, activity recognition is critical information for adequate management of the patient. Despite the great number of studies on this topic, a contextually relevant parameter that has received less attention is intensit...

Automated diagnostic support system with deep learning algorithms for distinction of Philadelphia chromosome-negative myeloproliferative neoplasms using peripheral blood specimen.

Scientific reports
Philadelphia chromosome-negative myeloproliferative neoplasms (Ph-negative MPNs) such as polycythemia vera (PV), essential thrombocythemia (ET), and primary myelofibrosis are characterized by abnormal proliferation of mature bone marrow cell lineages...

Machine learning prediction models for prognosis of critically ill patients after open-heart surgery.

Scientific reports
We aimed to build up multiple machine learning models to predict 30-days mortality, and 3 complications including septic shock, thrombocytopenia, and liver dysfunction after open-heart surgery. Patients who underwent coronary artery bypass surgery, a...

Using blood data for the differential diagnosis and prognosis of motor neuron diseases: a new dataset for machine learning applications.

Scientific reports
Early differential diagnosis of several motor neuron diseases (MNDs) is extremely challenging due to the high number of overlapped symptoms. The routine clinical practice is based on clinical history and examination, usually accompanied by electrophy...

Forecasting influenza activity using machine-learned mobility map.

Nature communications
Human mobility is a primary driver of infectious disease spread. However, existing data is limited in availability, coverage, granularity, and timeliness. Data-driven forecasts of disease dynamics are crucial for decision-making by health officials a...

Deep learning-augmented radiotherapy visualization with a cylindrical radioluminescence system.

Physics in medicine and biology
This study aims to demonstrate a low-cost camera-based radioluminescence imaging system (CRIS) for high-quality beam visualization that encourages accurate pre-treatment verifications on radiation delivery in external beam radiotherapy. To ameliorate...

Analysis of the nonperfused volume ratio of adenomyosis from MRI images based on fewshot learning.

Physics in medicine and biology
The nonperfused volume (NPV) ratio is the key to the success of high intensity focused ultrasound (HIFU) ablation treatment of adenomyosis. However, there are no qualitative interpretation standards for predicting the NPV ratio of adenomyosis using m...

Association Between Coffee Intake and Incident Heart Failure Risk: A Machine Learning Analysis of the FHS, the ARIC Study, and the CHS.

Circulation. Heart failure
BACKGROUND: Coronary heart disease, heart failure (HF), and stroke are complex diseases with multiple phenotypes. While many risk factors for these diseases are well known, investigation of as-yet unidentified risk factors may improve risk assessment...

A Correlation Analysis between SNPs and ROIs of Alzheimer's Disease Based on Deep Learning.

BioMed research international
. At present, the research methods for image genetics of Alzheimer's disease based on machine learning are mainly divided into three steps: the first step is to preprocess the original image and gene information into digital signals that are easy to ...