AIMC Topic: Sweden
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Reconfiguration of uncertainty: Introducing AI for prediction of mortality at the emergency department.
Social science & medicine (1982)
Sep 6, 2024
The promise behind many advanced digital technologies in healthcare is to provide novel and accurate information, aiding medical experts to navigate and, ultimately, decrease uncertainty in their clinical work. However, sociological studies have star...
Prediction of neurologic outcome after out-of-hospital cardiac arrest: An interpretable approach with machine learning.
Resuscitation
Aug 12, 2024
UNLABELLED: Out-of-hospital cardiac arrest (OHCA) is a critical condition with low survival rates. In patients with a return of spontaneous circulation, brain injury is a leading cause of death. In this study, we propose an interpretable machine lear...
Healthcare leaders' experiences of implementing artificial intelligence for medical history-taking and triage in Swedish primary care: an interview study.
BMC primary care
Jul 24, 2024
BACKGROUND: Artificial intelligence (AI) holds significant promise for enhancing the efficiency and safety of medical history-taking and triage within primary care. However, there remains a dearth of knowledge concerning the practical implementation ...
ICURE: Intensive care unit (ICU) risk evaluation for 30-day mortality. Developing and evaluating a multivariable machine learning prediction model for patients admitted to the general ICU in Sweden.
Acta anaesthesiologica Scandinavica
Jul 21, 2024
BACKGROUND: A prediction model that estimates mortality at admission to the intensive care unit (ICU) is of potential benefit to both patients and society. Logistic regression models like Simplified Acute Physiology Score 3 (SAPS 3) and APACHE are th...
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...
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