AIMC Topic: Humans

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TW-YOLO: An Innovative Blood Cell Detection Model Based on Multi-Scale Feature Fusion.

Sensors (Basel, Switzerland)
As deep learning technology has progressed, automated medical image analysis is becoming ever more crucial in clinical diagnosis. However, due to the diversity and complexity of blood cell images, traditional models still exhibit deficiencies in bloo...

Generisch-Net: A Generic Deep Model for Analyzing Human Motion with Wearable Sensors in the Internet of Health Things.

Sensors (Basel, Switzerland)
The Internet of Health Things (IoHT) is a broader version of the Internet of Things. The main goal is to intervene autonomously from geographically diverse regions and provide low-cost preventative or active healthcare treatments. Smart wearable IMUs...

PROTA: A Robust Tool for Protamine Prediction Using a Hybrid Approach of Machine Learning and Deep Learning.

International journal of molecular sciences
Protamines play a critical role in DNA compaction and stabilization in sperm cells, significantly influencing male fertility and various biotechnological applications. Traditionally, identifying these proteins is a challenging and time-consuming proc...

Prevention of adverse HIV treatment outcomes: machine learning to enable proactive support of people at risk of HIV care disengagement in Tanzania.

BMJ open
OBJECTIVES: This study aimed to develop a machine learning (ML) model to predict disengagement from HIV care, high viral load or death among people living with HIV (PLHIV) with the goal of enabling proactive support interventions in Tanzania. The alg...

Automated System to Capture Patient Symptoms From Multitype Japanese Clinical Texts: Retrospective Study.

JMIR medical informatics
BACKGROUND: Natural language processing (NLP) techniques can be used to analyze large amounts of electronic health record texts, which encompasses various types of patient information such as quality of life, effectiveness of treatments, and adverse ...

A comprehensive comparison of machine learning models for ICH prognostication: Retrospective review of 1501 intra-cerebral hemorrhage patients from the Qatar stroke database.

Neurosurgical review
Multiple prognostic scores have been developed to predict morbidity and mortality in patients with spontaneous intracerebral hemorrhage(sICH). Since the advent of machine learning(ML), different ML models have also been developed for sICH prognostica...

Acute cholecystitis diagnosis in the emergency department: an artificial intelligence-based approach.

Langenbeck's archives of surgery
OBJECTIVES: This study aimed to assess the diagnostic performance of a support vector machine (SVM) algorithm for acute cholecystitis and evaluate its effectiveness in accurately diagnosing this condition.

Development and validation of a machine-learning model for predicting postoperative pneumonia in aneurysmal subarachnoid hemorrhage.

Neurosurgical review
Pneumonia is a common postoperative complication in patients with aneurysmal subarachnoid hemorrhage (aSAH), which is associated with poor prognosis and increased mortality. The aim of this study was to develop a predictive model for postoperative pn...

"Artificial intelligence-driven infection risk prediction in ventriculoperitoneal shunting: a novel approach for normal pressure hydrocephalus treatment".

Neurosurgical review
Idiopathic normal pressure hydrocephalus (iNPH) affects approximately 1.5% of the population, with a higher prevalence in men than women. Ventriculoperitoneal shunting (VPS) is the standard treatment for iNPH, but it poses a notable risk of infection...