AIMC Topic: Algorithms

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Using Optimal Survival Tree Model for AF Event-Free Survival Time Prediction.

Studies in health technology and informatics
This study presents a methodology to acquire, integrate, and analyze clinical data based on an innovative application of the Optimal Survival Tree (OST) algorithm. It has been tested on a clinical dataset of 4114 patients with a follow-up of 59.0 ± 1...

Fine-Tuning an Existing Large Language Model with Knowledge from the Medical Expert System Hepaxpert.

Studies in health technology and informatics
The analysis and individual interpretation of hepatitis serology test results is a complex task in laboratory medicine, requiring either experienced physicians or specialized expert systems. This study explores fine-tuning a large language model (LLM...

A Medical Decision Support System for Automatic Treatment Plan Generation Using Machine Learning Algorithms.

Studies in health technology and informatics
Due to demographic change, health economics is increasingly focused on the quality of life in advanced age and the associated cost aspects. Dementia is one of the key issues in this area and its efficient treatment will become increasingly relevant i...

Understanding Stain Separation Improves Cross-Scanner Adenocarcinoma Segmentation with Joint Multi-Task Learning.

Studies in health technology and informatics
Digital pathology has made significant advances in tumor diagnosis and segmentation; however, image variability resulting from tissue preparation and digitization - referred to as domain shift - remains a significant challenge. Variations caused by h...

Evaluating machine learning algorithms for predicting HIV status among young Thai men who have sex with men.

BMJ health & care informatics
OBJECTIVE: This study aimed to develop machine learning (ML) models to predict HIV status and assessed the factors associated with HIV infection among young men who have sex with men (MSM) under the Universal Health Coverage (UHC) programme in Thaila...

Feasibility study of automatic radiotherapy treatment planning for cervical cancer using a large language model.

Radiation oncology (London, England)
BACKGROUND: Radiotherapy treatment planning traditionally involves complex and time-consuming processes, often relying on trial-and-error methods. The emergence of artificial intelligence, particularly Large Language Models (LLMs), surpassing human c...

Comparison of methods for tuning machine learning model hyper-parameters: with application to predicting high-need high-cost health care users.

BMC medical research methodology
BACKGROUND: Supervised machine learning is increasingly being used to estimate clinical predictive models. Several supervised machine learning models involve hyper-parameters, whose values must be judiciously specified to ensure adequate predictive p...

Artificial intelligence for severity triage based on conversations in an emergency department in Korea.

Scientific reports
In the fast-paced emergency departments, where crises unfold unpredictably, the systematic prioritization of critical patients based on a severity classification is vital for swift and effective treatment. This study aimed to enhance the quality of e...

Machine learning approach for differentiating iron deficiency anemia and thalassemia using random forest and gradient boosting algorithms.

Scientific reports
Formulas based on red blood cell indices have been used to differentiate between iron deficiency anemia (IDA) and thalassemia (Thal). However, they exhibit varying efficiencies. In this study, we aimed to develop a tool for discriminating between IDA...

Research on agricultural disease recognition methods based on very large Kernel convolutional network-RepLKNet.

Scientific reports
Agricultural diseases pose significant challenges to plant production. With the rapid advancement of deep learning, the accuracy and efficiency of plant disease identification have substantially improved. However, conventional convolutional neural ne...