AIMC Topic: Machine Learning

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From promise to practice: towards the realisation of AI-informed mental health care.

The Lancet. Digital health
In this Series paper, we explore the promises and challenges of artificial intelligence (AI)-based precision medicine tools in mental health care from clinical, ethical, and regulatory perspectives. The real-world implementation of these tools is inc...

Functional Output Regression for Machine Learning in Materials Science.

Journal of chemical information and modeling
In recent years, there has been a rapid growth in the use of machine learning in material science. Conventionally, a trained predictive model describes a scalar output variable, such as thermodynamic, electronic, or mechanical properties, as a functi...

Decomposing predictability to identify dominant causal drivers in complex ecosystems.

Proceedings of the National Academy of Sciences of the United States of America
Ecosystems are complex systems of various physical, biological, and chemical processes. Since ecosystem dynamics are composed of a mixture of different levels of stochasticity and nonlinearity, handling these data is a challenge for existing methods ...

Machine Learning Approach to Predict the Performance of a Stratified Thermal Energy Storage Tank at a District Cooling Plant Using Sensor Data.

Sensors (Basel, Switzerland)
In the energy management of district cooling plants, the thermal energy storage tank is critical. As a result, it is essential to keep track of TES results. The performance of the TES has been measured using a variety of methodologies, both numerical...

Learning Assurance Analysis for Further Certification Process of Machine Learning Techniques: Case-Study Air Traffic Conflict Detection Predictor.

Sensors (Basel, Switzerland)
Designing and developing artificial intelligence (AI)-based systems that can be trusted justifiably is one of the main issues aviation must face in the coming years. European Union Aviation Safety Agency (EASA) has developed a user guide that could b...

Clustering of trauma patients based on longitudinal data and the application of machine learning to predict recovery.

Scientific reports
Predicting recovery after trauma is important to provide patients a perspective on their estimated future health, to engage in shared decision making and target interventions to relevant patient groups. In the present study, several unsupervised tech...

Using Artificial Intelligence for Predicting the Duration of Emergency Evacuation During Hospital Fire.

Disaster medicine and public health preparedness
OBJECTIVE: A danger threatening hospitals is fire. The most important action following a fire is to urgently evacuate the hospital during the shortest time possible. The aim of this study was to predict the duration of emergency evacuation following ...

Prediction of disorders with significant coronary lesions using machine learning in patients admitted with chest symptom.

PloS one
BACKGROUND: The early prediction of significant coronary artery lesion, including coronary vasospasm, have yet to be studied. It is essential to discern the disorders with significant coronary lesions (SCDs) requiring coronary angiography from mimick...

DeepCPPred: A Deep Learning Framework for the Discrimination of Cell-Penetrating Peptides and Their Uptake Efficiencies.

IEEE/ACM transactions on computational biology and bioinformatics
Cell-penetrating peptides (CPPs) are special peptides capable of carrying a variety of bioactive molecules, such as genetic materials, short interfering RNAs and nanoparticles, into cells. Recently, research on CPP has gained substantial interest fro...

Estimating tree-based dynamic treatment regimes using observational data with restricted treatment sequences.

Biometrics
A dynamic treatment regime (DTR) is a sequence of decision rules that provide guidance on how to treat individuals based on their static and time-varying status. Existing observational data are often used to generate hypotheses about effective DTRs. ...