AIMC Topic: Sweden
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Predicting spatiotemporally-resolved mean air temperature over Sweden from satellite data using an ensemble model.
Environmental research
Aug 28, 2021
Mapping of air temperature (Ta) at high spatiotemporal resolution is critical to reducing exposure assessment errors in epidemiological studies on the health effects of air temperature. In this study, we applied a three-stage ensemble model to estima...
Artificial intelligence and the medical physics profession - A Swedish perspective.
Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
Jul 22, 2021
BACKGROUND: There is a continuous and dynamic discussion on artificial intelligence (AI) in present-day society. AI is expected to impact on healthcare processes and could contribute to a more sustainable use of resources allocated to healthcare in t...
Developing a short-term prediction model for asthma exacerbations from Swedish primary care patients' data using machine learning - Based on the ARCTIC study.
Respiratory medicine
May 26, 2021
OBJECTIVE: The ability to predict impending asthma exacerbations may allow better utilization of healthcare resources, prevention of hospitalization and improve patient outcomes. We aimed to develop models using machine learning to predict risk of ex...
[Machine learning and suicide prevention: is an algorithm the solution?].
Nederlands tijdschrift voor geneeskunde
May 12, 2021
Suicide is inherently difficult to predict. Epidemiological research identified many general risk factors such as a depression, but these predictors have limited predictive power. Machine learning offers a set of tools that can combine hundreds of pr...
Predicting women with depressive symptoms postpartum with machine learning methods.
Scientific reports
Apr 12, 2021
Postpartum depression (PPD) is a detrimental health condition that affects 12% of new mothers. Despite negative effects on mothers' and children's health, many women do not receive adequate care. Preventive interventions are cost-efficient among high...
Predicting suicide attempt or suicide death following a visit to psychiatric specialty care: A machine learning study using Swedish national registry data.
PLoS medicine
Nov 6, 2020
BACKGROUND: Suicide is a major public health concern globally. Accurately predicting suicidal behavior remains challenging. This study aimed to use machine learning approaches to examine the potential of the Swedish national registry data for predict...
Predicting vaginal birth after previous cesarean: Using machine-learning models and a population-based cohort in Sweden.
Acta obstetricia et gynecologica Scandinavica
Oct 31, 2020
INTRODUCTION: Predicting a woman's probability of vaginal birth after cesarean could facilitate the antenatal decision-making process. Having a previous vaginal birth strongly predicts vaginal birth after cesarean. Delivery outcome in women with only...
Ankle fracture classification using deep learning: automating detailed AO Foundation/Orthopedic Trauma Association (AO/OTA) 2018 malleolar fracture identification reaches a high degree of correct classification.
Acta orthopaedica
Oct 26, 2020
Background and purpose - Classification of ankle fractures is crucial for guiding treatment but advanced classifications such as the AO Foundation/Orthopedic Trauma Association (AO/OTA) are often too complex for human observers to learn and use. We h...
Key insights in the AIDA community policy on sharing of clinical imaging data for research in Sweden.
Scientific data
Oct 6, 2020
Development of world-class artificial intelligence (AI) for medical imaging requires access to massive amounts of training data from clinical sources, but effective data sharing is often hindered by uncertainty regarding data protection. We describe ...
A machine-learning algorithm for neonatal seizure recognition: a multicentre, randomised, controlled trial.
The Lancet. Child & adolescent health
Aug 27, 2020
BACKGROUND: Despite the availability of continuous conventional electroencephalography (cEEG), accurate diagnosis of neonatal seizures is challenging in clinical practice. Algorithms for decision support in the recognition of neonatal seizures could ...
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