AIMC Topic: Machine Learning

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Decoding attentional states for neurofeedback: Mindfulness vs. wandering thoughts.

NeuroImage
Neurofeedback requires a direct translation of neuronal brain activity to sensory input given to the user or subject. However, decoding certain states, e.g., mindfulness or wandering thoughts, from ongoing brain activity remains an unresolved problem...

Prediction of bioavailability and toxicity of complex chemical mixtures through machine learning models.

Chemosphere
Empirical data from a 6-month mesocosms experiment were used to assess the ability and performance of two machine learning (ML) models, including artificial neural network (NN) and random forest (RF), to predict temporal bioavailability changes of co...

Characterization of expressed human meibum using hyperspectral stimulated Raman scattering microscopy.

The ocular surface
PURPOSE: This study examined whether hyperspectral stimulated Raman scattering (hsSRS) microscopy can detect differences in meibum lipid to protein composition of normal and evaporative dry eye subjects with meibomian gland dysfunction.

Automated seizure prediction.

Epilepsy & behavior : E&B
In the past two decades, significant advances have been made on automated electroencephalogram (EEG)-based diagnosis of epilepsy and seizure detection. A number of innovative algorithms have been introduced that can aid in epilepsy diagnosis with a h...

Radiomics features on non-contrast-enhanced CT scan can precisely classify AVM-related hematomas from other spontaneous intraparenchymal hematoma types.

European radiology
OBJECTIVE: To investigate the classification ability of quantitative radiomics features extracted on non-contrast-enhanced CT (NECT) image for discrimination of AVM-related hematomas from those caused by other etiologies.

The hepatotoxic potential of protein kinase inhibitors predicted with Random Forest and Artificial Neural Networks.

Toxicology letters
Protein kinases (PKs) play a role in many pivotal aspects of cellular function. Dysregulation and mutations of protein kinases are involved in the development of different diseases, which might be treated by inhibition of the corresponding kinase. Pr...

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma.

Journal of visualized experiments : JoVE
Intra-arterial therapies are the standard of care for patients with hepatocellular carcinoma who cannot undergo surgical resection. The objective of this study was to develop a method to predict response to intra-arterial treatment prior to intervent...

Predicting Adherence to Internet-Delivered Psychotherapy for Symptoms of Depression and Anxiety After Myocardial Infarction: Machine Learning Insights From the U-CARE Heart Randomized Controlled Trial.

Journal of medical Internet research
BACKGROUND: Low adherence to recommended treatments is a multifactorial problem for patients in rehabilitation after myocardial infarction (MI). In a nationwide trial of internet-delivered cognitive behavior therapy (iCBT) for the high-risk subgroup ...

Explainable machine-learning predictions for the prevention of hypoxaemia during surgery.

Nature biomedical engineering
Although anaesthesiologists strive to avoid hypoxemia during surgery, reliably predicting future intraoperative hypoxemia is not currently possible. Here, we report the development and testing of a machine-learning-based system that, in real time dur...

Using fMRI and machine learning to predict symptom improvement following cognitive behavioural therapy for psychosis.

NeuroImage. Clinical
Cognitive behavioural therapy for psychosis (CBTp) involves helping patients to understand and reframe threatening appraisals of their psychotic experiences to reduce distress and increase functioning. Whilst CBTp is effective for many, it is not eff...