AIMC Topic: Machine Learning

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From Proprietary Printed Forms to Standardized Digital Exchange Formats - Care Transition Records to FHIR as an Example.

Studies in health technology and informatics
INTRODUCTION: The transition from proprietary, paper-based care transition records (CTRs) to standardized digital formats like HL7 FHIR remains a significant challenge for healthcare institutions. Variability in document layouts, coupled with slow ad...

Factors Influencing Hearing Preservation in Cochlear Implant Patients: A Predictive Modelling Approach.

Studies in health technology and informatics
INTRODUCTION: Hearing loss, affecting over 19% of the global population, is a major disability worldwide, with its prevalence expected to increase due to demographic changes. Cochlear implants (CIs) provide a crucial treatment for severe to profound ...

Classifying patients with invasive fungal disease: towards a unified case definition?

The Journal of antimicrobial chemotherapy
Management of invasive fungal disease (IFD) is increasingly challenging due to recognition of novel at-risk groups, emergence of new fungal pathogens and antifungal drug resistance. Together with the availability of new diagnostic tests and treatment...

Machine Learning for Detecting Iron Deficiency through Comprehensive Blood Analysis.

Clinical chemistry
BACKGROUND: Iron deficiency (ID) is a prevalent global health issue with a major impact on well-being. Early detection of ID is crucial but challenging due to its nonspecific symptoms and the limitations of traditional diagnostic tests, which are imp...

Using Data Mining to Differentiate Dengue with Warning Signs from Severe Dengue: A Predictive Model from Oaxaca, Mexico.

The American journal of tropical medicine and hygiene
Dengue with warning signs (DWS) and severe dengue are significant public health concerns in tropical and subtropical regions globally. Accurate and timely differentiation between these clinical forms of dengue, although crucial, is often complex. In ...

SBC-SHAP: Increasing the Accessibility and Interpretability of Machine Learning Algorithms for Sepsis Prediction.

The journal of applied laboratory medicine
BACKGROUND: Sepsis is a life-threatening condition that is one of the major causes of death worldwide. Early detection of sepsis is required for fast initialization of an appropriate therapy. Complete blood count data containing information about whi...

RhDnostics: A Machine Learning-Based Predictive Algorithm Model for RhD-Negative and DEL Blood Group Screening.

The journal of applied laboratory medicine
BACKGROUND: The D-elution (DEL) phenotype is serologically mislabeled as Rh-negative because of the very low amount of D antigen on red blood cells. The adsorption-elution test and genotyping are recommended tests for confirmation. However, turnaroun...

Machine learning insights on the effectiveness of non-pharmaceutical interventions against COVID-19 in Nigeria.

International health
BACKGROUND: The lack of effective pharmacological measures during the early phase of the COVID-19 pandemic prompted the implementation of non-pharmaceutical interventions (NPIs) as initial mitigation strategies. The impact of these NPIs on COVID-19 i...

The sky's the limit: improving satellite imagery data literacy to address non-communicable diseases.

International health
Low-and middle-income countries experience 77% of the world's premature deaths caused by non-communicable diseases, and their underlying health determinant data are often scarce and inaccurate. Improving satellite imagery data literacy worldwide is a...

Artificial intelligence-enhanced biosurveillance for antimicrobial resistance in sub-Saharan Africa.

International health
Antimicrobial resistance (AMR) remains a critical global health threat, with significant impacts on individuals and healthcare systems, particularly in low-income countries. By 2019, AMR was responsible for >4.9 million fatalities globally, and proje...