AI Medical Compendium

Explore the latest research on artificial intelligence and machine learning in medicine.

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Ensemble Approach on Deep and Handcrafted Features for Neonatal Bowel Sound Detection.

IEEE journal of biomedical and health informatics
For the care of neonatal infants, abdominal auscultation is considered a safe, convenient, and inexpensive method to monitor bowel conditions. With the help of early automated detection of bowel dysfunction, neonatologists could create a diagnosis pl...

Synthetic Patient Data Generation and Evaluation in Disease Prediction Using Small and Imbalanced Datasets.

IEEE journal of biomedical and health informatics
The increasing prevalence of chronic non-communicable diseases makes it a priority to develop tools for enhancing their management. On this matter, Artificial Intelligence algorithms have proven to be successful in early diagnosis, prediction and ana...

MaskSleepNet: A Cross-Modality Adaptation Neural Network for Heterogeneous Signals Processing in Sleep Staging.

IEEE journal of biomedical and health informatics
Deep learning methods have become an important tool for automatic sleep staging in recent years. However, most of the existing deep learning-based approaches are sharply constrained by the input modalities, where any insertion, substitution, and dele...

Advancing Stuttering Detection via Data Augmentation, Class-Balanced Loss and Multi-Contextual Deep Learning.

IEEE journal of biomedical and health informatics
Stuttering is a neuro-developmental speech impairment characterized by uncontrolled utterances (interjections) and core behaviors (blocks, repetitions, and prolongations), and is caused by the failure of speech sensorimotors. Due to its complex natur...

TaughtNet: Learning Multi-Task Biomedical Named Entity Recognition From Single-Task Teachers.

IEEE journal of biomedical and health informatics
In Biomedical Named Entity Recognition (BioNER), the use of current cutting-edge deep learning-based methods, such as deep bidirectional transformers (e.g. BERT, GPT-3), can be substantially hampered by the absence of publicly accessible annotated da...

Deep Learning With Convolutional Neural Networks for Motor Brain-Computer Interfaces Based on Stereo-Electroencephalography (SEEG).

IEEE journal of biomedical and health informatics
OBJECTIVE: Deep learning based on convolutional neural networks (CNN) has achieved success in brain-computer interfaces (BCIs) using scalp electroencephalography (EEG). However, the interpretation of the so-called 'black box' method and its applicati...

LightSeizureNet: A Lightweight Deep Learning Model for Real-Time Epileptic Seizure Detection.

IEEE journal of biomedical and health informatics
The monitoring of epilepsy patients in non-hospital environment is highly desirable, where ultra-low power wearable seizure detection devices are essential in such a system. The state-of-the-art epileptic seizure detection algorithms targeting such d...

Interpretability and Optimisation of Convolutional Neural Networks Based on Sinc-Convolution.

IEEE journal of biomedical and health informatics
Interpretability often seeks domain-specific facts, which is understandable to human, from deep-learning (DL) or other machine-learning (ML) models of black-box nature. This is particularly important to establish transparency in ML model's inner-work...

Trustworthy Deep Neural Network for Inferring Anticancer Synergistic Combinations.

IEEE journal of biomedical and health informatics
The lack of a gold standard synergy quantification method for chemotherapeutic drug combinations warrants the consideration of different synergy metrics to develop efficient predictive models. Furthermore, neglecting combination sensitivity may lead ...

Securing Multimedia Using a Deep Learning Based Chaotic Logistic Map.

IEEE journal of biomedical and health informatics
Telemedicine and online consultations with doctors has become very popular during the pandemic and involves the transmission of medical data through the internet. Thus this raises concern about the security of the medical data of the patient as the r...