AIMC Topic: Humans

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Artificial intelligence for prediction of response to cancer immunotherapy.

Seminars in cancer biology
Artificial intelligence (AI) indicates the application of machines to imitate intelligent behaviors for solving complex tasks with minimal human intervention, including machine learning and deep learning. The use of AI in medicine improves health-car...

DeepIDC: A Prediction Framework of Injectable Drug Combination Based on Heterogeneous Information and Deep Learning.

Clinical pharmacokinetics
BACKGROUND AND OBJECTIVE: In clinical practice, injectable drug combination (IDC) usually provides good therapeutic effects for patients. Numerous clinical studies have directly indicated that inappropriate IDC generates adverse drug events (ADEs). T...

Monitoring Amyloidogenesis with a 3D Deep-Learning-Guided Biolaser Imaging Array.

Nano letters
Amyloidogenesis is a critical hallmark for many neurodegenerative diseases and drug screening; however, identifying intermediate states of protein aggregates at an earlier stage remains challenging. Herein, we developed a peptide-encapsulated droplet...

Skeleton-Based Human Pose Recognition Using Channel State Information: A Survey.

Sensors (Basel, Switzerland)
With the increasing demand for human-computer interaction and health monitoring, human behavior recognition with device-free patterns has attracted extensive attention. The fluctuations of the Wi-Fi signal caused by human actions in a Wi-Fi coverage ...

Sensor Fusion for Social Navigation on a Mobile Robot Based on Fast Marching Square and Gaussian Mixture Model.

Sensors (Basel, Switzerland)
Mobile robot navigation has been studied for a long time, and it is nowadays widely used in multiple applications. However, it is traditionally focused on two-dimensional geometric characteristics of the environments. There are situations in which ro...

Harnessing interpretable machine learning for holistic inverse design of origami.

Scientific reports
This work harnesses interpretable machine learning methods to address the challenging inverse design problem of origami-inspired systems. We established a work flow based on decision tree-random forest method to fit origami databases, containing both...

The Health Gym: synthetic health-related datasets for the development of reinforcement learning algorithms.

Scientific data
In recent years, the machine learning research community has benefited tremendously from the availability of openly accessible benchmark datasets. Clinical data are usually not openly available due to their confidential nature. This has hampered the ...

Neuro-cognitive assessment of intentional control methods for a soft elbow exosuit using error-related potentials.

Journal of neuroengineering and rehabilitation
Soft exosuits offer promise to support users in everyday workload tasks by providing assistance. However, acceptance of such systems remains low due to the difficulty of control compared with rigid mechatronic systems. Recently, there has been progre...

A deep learning approach for automated diagnosis of pulmonary embolism on computed tomographic pulmonary angiography.

BMC medical imaging
BACKGROUND: Computed tomographic pulmonary angiography (CTPA) is the diagnostic standard for confirming pulmonary embolism (PE). Since PE is a life-threatening condition, early diagnosis and treatment are critical to avoid PE-associated morbidity and...

Shuffle-ResNet: Deep learning for predicting LGG IDH1 mutation from multicenter anatomical MRI sequences.

Biomedical physics & engineering express
The world health organization recommended to incorporate gene information such as isocitrate dehydrogenase 1 (IDH1) mutation status to improve prognosis, diagnosis, and treatment of the central nervous system tumors. We proposed our Shuffle Residual ...