AIMC Topic: Machine Learning

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Smartphone digital phenotyping in mental health disorders: A review of raw sensors utilized, machine learning processing pipelines, and derived behavioral features.

Psychiatry research
With increased access to digital technology, there has been a surge in the use of and interest in digital phenotyping as a tool to calculate various features from raw smart device data. However, the increased usage of digital phenotyping has created ...

Data independent acquisition proteomics and machine learning reveals that proteins associated with immunity are potential molecular markers for early diagnosis of autism.

Clinica chimica acta; international journal of clinical chemistry
BACKGROUND: Early diagnosis of autism is critical to its treatment, but so far, there is no clear molecular marker for early diagnosis in children.

Machine learning models based on a national-scale cohort accurately identify patients at high risk of deep vein thrombosis following primary total hip arthroplasty.

Orthopaedics & traumatology, surgery & research : OTSR
BACKGROUND: The occurrence of deep venous thrombosis (DVT) following total hip arthroplasty (THA) poses a substantial risk of morbidity and mortality, highlighting the need for preoperative risk stratification and prophylaxis initiatives. However, th...

Advancing hierarchical neural networks with scale-aware pyramidal feature learning for medical image dense prediction.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Hierarchical neural networks are pivotal in medical imaging for multi-scale representation, aiding in tasks such as object detection and segmentation. However, their effectiveness is often limited by the loss of intra-scale ...

Estimating Treatment Effect Heterogeneity in Psychiatry: A Review and Tutorial With Causal Forests.

International journal of methods in psychiatric research
BACKGROUND: Flexible machine learning tools are increasingly used to estimate heterogeneous treatment effects.

Learning Sequential Variation Information for Dynamic Facial Expression Recognition.

IEEE transactions on neural networks and learning systems
A multiscale sequence information fusion (MSSIF) method is presented for dynamic facial expression recognition (DFER) in video sequences. It exploits multiscale information by integrating features from individual frames, subsequences, and entire sequ...

IdenBAT: Disentangled representation learning for identity-preserved brain age transformation.

Artificial intelligence in medicine
Brain age transformation aims to convert reference brain images into synthesized images that accurately reflect the age-specific features of a target age group. The primary objective of this task is to modify only the age-related attributes of the re...

Combating Medical Label Noise through more precise partition-correction and progressive hard-enhanced learning.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Computer-aided diagnosis systems based on deep neural networks heavily rely on datasets with high-quality labels. However, manual annotation for lesion diagnosis relies on image features, often requiring professional experie...

Digenic variant interpretation with hypothesis-driven explainable AI.

NAR genomics and bioinformatics
The digenic inheritance hypothesis holds the potential to enhance diagnostic yield in rare diseases. Computational approaches capable of accurately interpreting and prioritizing digenic combinations of variants based on the proband's phenotypes and f...

Predicting hepatocellular carcinoma response to TACE: A machine learning study based on 2.5D CT imaging and deep features analysis.

European journal of radiology
OBJECTIVES: Prior to the commencement of treatment, it is essential to establish an objective method for accurately predicting the prognosis of patients with hepatocellular carcinoma (HCC) undergoing transarterial chemoembolization (TACE). In this st...