AIMC Topic: Algorithms

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Validation of a machine learning algorithm for identifying infants at risk of hypoxic ischaemic encephalopathy in a large unseen data set.

Archives of disease in childhood. Fetal and neonatal edition
OBJECTIVE: To validate a hypoxic ischaemic encephalopathy (HIE) prediction algorithm to identify infants at risk of HIE immediately after birth using readily available clinical data.

Predicting Powder Blend Flowability from Individual Constituent Properties Using Machine Learning.

Pharmaceutical research
PURPOSE: Predicting powder blend flowability is necessary for pharmaceutical manufacturing but challenging and resource-intensive. The purpose was to develop machine learning (ML) models to help predict flowability across multiple flow categories, id...

Early prediction of sepsis associated encephalopathy in elderly ICU patients using machine learning models: a retrospective study based on the MIMIC-IV database.

Frontiers in cellular and infection microbiology
BACKGROUND: Sepsis associated encephalopathy (SAE) is prevalent among elderly patients in the ICU and significantly affects patient prognosis. Due to the symptom similarity with other neurological disorders and the absence of specific biomarkers, ear...

Improved performance of fNIRS-BCI by stacking of deep learning-derived frequency domain features.

PloS one
The functional near-infrared spectroscopy-based brain-computer interface (fNIRS-BCI) systems recognize patterns in brain signals and generate control commands, thereby enabling individuals with motor disabilities to regain autonomy. In this study han...

TriLex: A fusion approach for unsupervised sentiment analysis of short texts.

PloS one
In recent years, online customer reviews and social media platforms have significantly impacted individuals' daily lives. Despite the generally short nature of textual content on these platforms, they convey a wide range of user sentiments. However, ...

Machine Learning-Based Prediction of Unplanned Readmission Due to Major Adverse Cardiac Events Among Hospitalized Patients with Blood Cancers.

Cancer control : journal of the Moffitt Cancer Center
BackgroundHospitalized patients with blood cancer face an elevated risk for cardiovascular diseases caused by cardiotoxic cancer therapies, which can lead to cardiovascular-related unplanned readmissions.ObjectiveWe aimed to develop a machine learnin...

Influence Enhanced Sparse Coordination Graphs for Multi-Agent Reinforcement Learning.

Neural networks : the official journal of the International Neural Network Society
In contemporary Multi-Agent Reinforcement Learning (MARL), effectively enhancing the expressive capacity of value functions has been a persistent research focus. Many studies have employed value decomposition methods; however, due to the neglect of i...

Enhancing Accuracy of Operative Reports with Automated Artificial Intelligence Analysis of Surgical Video.

Journal of the American College of Surgeons
BACKGROUND: The creation of operative reports is a tedious documentation task that increases administrative burden, which is a potential driver of burnout. Additionally, operative reports are inherently subjective and may contain inaccuracies and inc...

Topology optimization of random memristors for input-aware dynamic SNN.

Science advances
Machine learning has advanced unprecedentedly, exemplified by GPT-4 and SORA. However, they cannot parallel human brains in efficiency and adaptability due to differences in signal representation, optimization, runtime reconfigurability, and hardware...

A multimodal MRI-based machine learning framework for classifying cognitive impairment in cerebral small vessel disease.

Scientific reports
The heterogeneity of cerebral small vessel disease (CSVD) with mild cognitive impairment (MCI) presents a challenge for diagnosis and classification. This study aims to propose a multimodal magnetic resonance imaging (MRI)-based machine learning fram...