AIMC Topic: Sweden

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Developing machine learning models to predict multi-class functional outcomes and death three months after stroke in Sweden.

PloS one
Globally, stroke is the third-leading cause of mortality and disability combined, and one of the costliest diseases in society. More accurate predictions of stroke outcomes can guide healthcare organizations in allocating appropriate resources to imp...

Prediction of pregnancy-related complications in women undergoing assisted reproduction, using machine learning methods.

Fertility and sterility
OBJECTIVE: To use machine learning methods to develop prediction models of pregnancy complications in women who conceived with assisted reproductive techniques (ART).

Machine learning-driven development of a disease risk score for COVID-19 hospitalization and mortality: a Swedish and Norwegian register-based study.

Frontiers in public health
AIMS: To develop a disease risk score for COVID-19-related hospitalization and mortality in Sweden and externally validate it in Norway.

Wavelet scattering networks in deep learning for discovering protein markers in a cohort of Swedish rectal cancer patients.

Cancer medicine
BACKGROUND: Cancer biomarkers play a pivotal role in the diagnosis, prognosis, and treatment response prediction of the disease. In this study, we analyzed the expression levels of RhoB and DNp73 proteins in rectal cancer, as captured in immunohistoc...

Indirect Sensing of Subclinical Intramammary Infections in Dairy Herds with a Milking Robot.

Sensors (Basel, Switzerland)
This study determined the impact of subclinical intramammary infections (IMIs), such as the major and minor udder pathogens (MaPs and MiPs), on the somatic cell count (SCC) in cow milk and investigated the possibilities of indirect sensing of the udd...

On Scene Injury Severity Prediction (OSISP) model for trauma developed using the Swedish Trauma Registry.

BMC medical informatics and decision making
BACKGROUND: Providing optimal care for trauma, the leading cause of death for young adults, remains a challenge e.g., due to field triage limitations in assessing a patient's condition and deciding on transport destination. Data-driven On Scene Injur...

Technological trends in Swedish medical libraries.

Health information and libraries journal
Medical libraries in Sweden are digitised to a large extent, technically advanced and developing rapidly. This paper investigates technological trends among Swedish medical libraries in the near and distant future and their application within differe...

Predicting childhood and adolescent attention-deficit/hyperactivity disorder onset: a nationwide deep learning approach.

Molecular psychiatry
Attention-deficit/hyperactivity disorder (ADHD) is a heterogeneous disorder with a high degree of psychiatric and physical comorbidity, which complicates its diagnosis in childhood and adolescence. We analyzed registry data from 238,696 persons born ...

[Pharmacological knowledge bases in Sweden: successes and future in a time of information overflow].

Lakartidningen
Skewed information about medicines in social media influence the healthcare-patient contact. Healthcare staff need situation adapted evidence that can be linked to patient data. For 20 years Sweden has provided praised Pharmacological Knowledge Bases...

Challenges to implementing artificial intelligence in healthcare: a qualitative interview study with healthcare leaders in Sweden.

BMC health services research
BACKGROUND: Artificial intelligence (AI) for healthcare presents potential solutions to some of the challenges faced by health systems around the world. However, it is well established in implementation and innovation research that novel technologies...