AIMC Topic: Algorithms

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Novel Tinnitus Diagnosis: Biology and Technology for Public Health Management.

Studies in health technology and informatics
Tinnitus, characterized by the perception of ringing or buzzing in the ears, significantly affects millions globally and negatively impacts their quality of life. Current management strategies vary in effectiveness, underscoring the need for precise,...

Practical Approach for Evaluating Machine Learning Anomaly Detection Algorithms for Epidemic Early Warning Systems.

Studies in health technology and informatics
Anomaly detection methods in time series data can play a pivotal role in epidemic surveillance Early Warning Systems (EWS). Statistical and rules-based methods have been traditionally employed in such systems, but are challenged by data dynamics and ...

Assessment of Machine Learning Algorithms to Predict Medical Specialty Choice.

Studies in health technology and informatics
Equitable distribution of physicians across specialties is a significant public health challenge. While previous studies primarily relied on classic statistics models to estimate factors affecting medical students' career choices, this study explores...

Machine Learning-Based Hospital Readmission Prediction: A Comparative Analysis of Speciality-Specific vs. All-Specialities Models.

Studies in health technology and informatics
Hospital readmissions are a major challenge for healthcare systems, leading to increased costs and adverse patient outcomes. Predicting which patients are at risk of readmission is critical for improving care and optimizing resource allocation. This ...

Open-Source Synthetic Data Generation of Clinical Routine Data.

Studies in health technology and informatics
Clinical routine data is a valuable resource for research and analysis in hospitals. Even though regulations allow access within a clinical system, decentralized research with up-to-date data remains a problem limited by privacy concerns. Synthetic d...

XGBOrdinal: An XGBoost Extension for Ordinal Data.

Studies in health technology and informatics
We propose XGBOrdinal, an extension of XGBoost designed for ordinal classification problems commonly found in fields like medicine, where outcomes are often represented as scores, scales, stages, or grades. The proposed approach builds on the theoret...

Comparing a Top-Down and a Bottom-Up Approach for Implementing AI in Radiology Practice.

Studies in health technology and informatics
This paper examines how two health regions in Norway adopted different strategies for implementing commercial AI algorithms to outline. One region employs a top-down, research-driven approach, while the other takes a bottom-up, innovation-focused app...

Beyond Model Performance: Information Needs for an Algorithmovigilance Sociotechnical System.

Studies in health technology and informatics
Proactive and ongoing monitoring of AI systems, or algorithmovigilance, is essential for mitigating patient safety risks from AI in healthcare. In this study, we describe the information needs for an AI monitoring and operations system and provide de...

A Semi-Automated Approach Based on Network Analysis to Suggest New Collaborations and Foster Multisite Clinical Trials.

Studies in health technology and informatics
Several research institutions nowadays collaboratively conduct many scientific projects. Within a national Italian initiative on robotic rehabilitation, this study aims to develop new collaborations that can support the project's missions. Bibliograp...

Predicting 1-Year Survival Using Machine Learning in Very Old Patients Before ICU Admission.

Studies in health technology and informatics
Discussions about the benefits of admitting very old individuals to intensive care unit (ICU) remain challenging. We hypothesized that data-driven algorithms could leverage extensive real-life data to provide more accurate long-term predictions. Our ...