AIMC Topic: Humans

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Predicting angiographic coronary artery disease using machine learning and high-frequency QRS.

BMC medical informatics and decision making
AIM: Exercise stress ECG is a common diagnostic test for stable coronary artery disease, but its sensitivity and specificity need to be further improved. In this paper, we construct a machine learning model for the prediction of angiographic coronary...

CT-based deep learning radiomics biomarker for programmed cell death ligand 1 expression in non-small cell lung cancer.

BMC medical imaging
BACKGROUND: Programmed cell death ligand 1 (PD-L1), as a reliable predictive biomarker, plays an important role in guiding immunotherapy of lung cancer. To investigate the value of CT-based deep learning radiomics signature to predict PD-L1 expressio...

scHiCyclePred: a deep learning framework for predicting cell cycle phases from single-cell Hi-C data using multi-scale interaction information.

Communications biology
The emergence of single-cell Hi-C (scHi-C) technology has provided unprecedented opportunities for investigating the intricate relationship between cell cycle phases and the three-dimensional (3D) structure of chromatin. However, accurately predictin...

Hybrid deep learning models for the screening of Diabetic Macular Edema in optical coherence tomography volumes.

Scientific reports
Several studies published so far used highly selective image datasets from unclear sources to train computer vision models and that may lead to overestimated results, while those studies conducted in real-life remain scarce. To avoid image selection ...

Clinical feasibility of deep learning based synthetic contrast enhanced abdominal CT in patients undergoing non enhanced CT scans.

Scientific reports
Our objective was to develop and evaluate the clinical feasibility of deep-learning-based synthetic contrast-enhanced computed tomography (DL-SynCCT) in patients designated for nonenhanced CT (NECT). We proposed a weakly supervised learning with the ...

SHAP based predictive modeling for 1 year all-cause readmission risk in elderly heart failure patients: feature selection and model interpretation.

Scientific reports
Heart failure (HF) is a significant global public health concern with a high readmission rate, posing a serious threat to the health of the elderly population. While several studies have used machine learning (ML) to develop all-cause readmission ris...

Accuracy and transportability of machine learning models for adolescent suicide prediction with longitudinal clinical records.

Translational psychiatry
Machine Learning models trained from real-world data have demonstrated promise in predicting suicide attempts in adolescents. However, their transportability, namely the performance of a model trained on one dataset and applied to different data, is ...

Making Co-Design More Responsible: Case Study on the Development of an AI-Based Decision Support System in Dementia Care.

JMIR human factors
BACKGROUND: Emerging technologies such as artificial intelligence (AI) require an early-stage assessment of potential societal and ethical implications to increase their acceptability, desirability, and sustainability. This paper explores and compare...

Would robots really bother with a bloody uprising?

Science robotics
In the amusing 1982 novel , robots punish their human overlords by raising prices on longevity drugs and organ transplants.

Advancements in prognostic markers and predictive models for intracerebral hemorrhage: from serum biomarkers to artificial intelligence models.

Neurosurgical review
Intracerebral hemorrhage (ICH) is a severe form of stroke with high morbidity and mortality, accounting for 10-15% of all strokes globally. Recent advancements in prognostic biomarkers and predictive models have shown promise in enhancing the predict...