AIMC Topic: Algorithms

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Preprocessing to Address Bias in Healthcare Data.

Studies in health technology and informatics
Multimorbidity, having a diagnosis of two or more chronic conditions, increases as people age. It is a predictor used in clinical decision-making, but underdiagnosis in underserved populations produces bias in the data that support algorithms used in...

Robust Random Forest-Based All-Relevant Feature Ranks for Trustworthy AI.

Studies in health technology and informatics
Feature selection is a fundamental challenge in machine learning. For instance in bioinformatics, it is essential when one wishes to detect biomarkers. Tree-based methods are predominantly used for this purpose. In this paper, we study the stability ...

Explainable Artificial Intelligence in Ambulatory Digital Dementia Screenings.

Studies in health technology and informatics
Recently, digital apps have entered the market to enable the early diagnosis of dementia by offering digital dementia screenings. Some of these apps use Machine Learning (ML) to predict cognitive impairment. The aim of this work is to find explanatio...

Supporting AI-Explainability by Analyzing Feature Subsets in a Machine Learning Model.

Studies in health technology and informatics
Machine learning algorithms become increasingly prevalent in the field of medicine, as they offer the ability to recognize patterns in complex medical data. Especially in this sensitive area, the active usage of a mostly black box is a controversial ...

Using Machine Learning and Deep Learning Methods to Predict the Complexity of Breast Cancer Cases.

Studies in health technology and informatics
In many countries, the management of cancer patients must be discussed in multidisciplinary tumor boards (MTBs). These meetings have been introduced to provide a collaborative and multidisciplinary approach to cancer care. However, the benefits of MT...

MISeval: A Metric Library for Medical Image Segmentation Evaluation.

Studies in health technology and informatics
Correct performance assessment is crucial for evaluating modern artificial intelligence algorithms in medicine like deep-learning based medical image segmentation models. However, there is no universal metric library in Python for standardized and re...

DNA-binding protein prediction based on deep transfer learning.

Mathematical biosciences and engineering : MBE
The study of DNA binding proteins (DBPs) is of great importance in the biomedical field and plays a key role in this field. At present, many researchers are working on the prediction and detection of DBPs. Traditional DBP prediction mainly uses machi...

Survival prediction model for right-censored data based on improved composite quantile regression neural network.

Mathematical biosciences and engineering : MBE
With the development of the field of survival analysis, statistical inference of right-censored data is of great importance for the study of medical diagnosis. In this study, a right-censored data survival prediction model based on an improved compos...

Towards the Application of Machine Learning in Emergency Informatics.

Studies in health technology and informatics
Emergency care is one of the cornerstone parts of the world health organization's action plan. Rapid response and immediate care are considered in agile emergency care. Artificial intelligence (AI) and informatics have been applied to fulfill these r...

Effective method for detecting error causes from incoherent biological ontologies.

Mathematical biosciences and engineering : MBE
Computing the minimal axiom sets (MinAs) for an unsatisfiable class is an important task in incoherent ontology debugging. Ddebugging ontologies based on patterns (DOBP) is a pattern-based debugging method that uses a set of heuristic strategies base...