AIMC Topic: Sweden
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Detecting outliers in case-control cohorts for improving deep learning networks on Schizophrenia prediction.
Journal of integrative bioinformatics
Jul 15, 2024
This study delves into the intricate genetic and clinical aspects of Schizophrenia, a complex mental disorder with uncertain etiology. Deep Learning (DL) holds promise for analyzing large genomic datasets to uncover new risk factors. However, based o...
Prediction of mortality among severely injured trauma patients A comparison between TRISS and machine learning-based predictive models.
Injury
Jun 21, 2024
BACKGROUND: Given the huge impact of trauma on hospital systems around the world, several attempts have been made to develop predictive models for the outcomes of trauma victims. The most used, and in many studies most accurate predictive model, is t...
End-to-end pseudonymization of fine-tuned clinical BERT models : Privacy preservation with maintained data utility.
BMC medical informatics and decision making
Jun 12, 2024
Many state-of-the-art results in natural language processing (NLP) rely on large pre-trained language models (PLMs). These models consist of large amounts of parameters that are tuned using vast amounts of training data. These factors cause the model...
Deep learning-based risk stratification of preoperative breast biopsies using digital whole slide images.
Breast cancer research : BCR
Jun 3, 2024
BACKGROUND: Nottingham histological grade (NHG) is a well established prognostic factor in breast cancer histopathology but has a high inter-assessor variability with many tumours being classified as intermediate grade, NHG2. Here, we evaluate if Dee...
Prognostic subgroups of chronic pain patients using latent variable mixture modeling within a supervised machine learning framework.
Scientific reports
May 31, 2024
The present study combined a supervised machine learning framework with an unsupervised method, finite mixture modeling, to identify prognostically meaningful subgroups of diverse chronic pain patients undergoing interdisciplinary treatment. Question...
Effectiveness and Cost-effectiveness of Artificial Intelligence-assisted Pathology for Prostate Cancer Diagnosis in Sweden: A Microsimulation Study.
European urology oncology
May 23, 2024
BACKGROUND AND OBJECTIVE: Image-based artificial intelligence (AI) methods have shown high accuracy in prostate cancer (PCa) detection. Their impact on patient outcomes and cost effectiveness in comparison to human pathologists remains unknown. Our a...
Developing machine learning models to predict multi-class functional outcomes and death three months after stroke in Sweden.
PloS one
May 13, 2024
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
Feb 17, 2024
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
Dec 7, 2023
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
Nov 28, 2023
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...
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