AIMC Topic: Denmark
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Suitability of machine learning models for prediction of clinically defined Stage III/IV periodontitis from questionnaires and demographic data in Danish cohorts.
Journal of clinical periodontology
Sep 10, 2023
AIM: To evaluate if, and to what extent, machine learning models can capture clinically defined Stage III/IV periodontitis from self-report questionnaires and demographic data.
Machine learning models of healthcare expenditures predicting mortality: A cohort study of spousal bereaved Danish individuals.
PloS one
Aug 7, 2023
BACKGROUND: The ability to accurately predict survival in older adults is crucial as it guides clinical decision making. The added value of using health care usage for predicting mortality remains unexplored. The aim of this study was to investigate ...
Robot-assisted radical cystectomy with intracorporeal urinary diversion: a Danish 11-year series.
BJU international
Jul 3, 2023
OBJECTIVES: To evaluate the oncological and perioperative outcomes from a large, single-centre, robot-assisted radical cystectomy (RARC) cohort performed with intracorporeal urinary diversion (ICUD).
Higher depression risks in medium- than in high-density urban form across Denmark.
Science advances
May 24, 2023
Urban areas are associated with higher depression risks than rural areas. However, less is known about how different types of urban environments relate to depression risk. Here, we use satellite imagery and machine learning to quantify three-dimensio...
Clinically explainable machine learning models for early identification of patients at risk of hospital-acquired urinary tract infection.
The Journal of hospital infection
Mar 31, 2023
BACKGROUND: Machine learning (ML) models for early identification of patients at risk of hospital-acquired urinary tract infection (HA-UTI) may enable timely and targeted preventive and therapeutic strategies. However, clinicians are often challenged...
Using Explainable Artificial Intelligence to Predict Potentially Preventable Hospitalizations: A Population-Based Cohort Study in Denmark.
Medical care
Feb 3, 2023
BACKGROUND: The increasing aging population and limited health care resources have placed new demands on the healthcare sector. Reducing the number of hospitalizations has become a political priority in many countries, and special focus has been dire...
Learning curves in robot-assisted minimally invasive liver surgery at a high-volume center in Denmark: Report of the first 100 patients and review of literature.
Scandinavian journal of surgery : SJS : official organ for the Finnish Surgical Society and the Scandinavian Surgical Society
Jan 31, 2023
BACKGROUND AND OBJECTIVE: Minimally invasive liver surgery is evolving worldwide, and robot-assisted liver surgery (RLS) can deliver obvious benefits for patients. However, so far no large case series have documented the learning curve for RLS.
Prediction of risk of acquiring urinary tract infection during hospital stay based on machine-learning: A retrospective cohort study.
PloS one
Mar 31, 2021
BACKGROUND: Healthcare associated infections (HAI) are a major burden for the healthcare system and associated with prolonged hospital stay, increased morbidity, mortality and costs. Healthcare associated urinary tract infections (HA-UTI) accounts fo...
Effect of Machine Learning on Dispatcher Recognition of Out-of-Hospital Cardiac Arrest During Calls to Emergency Medical Services: A Randomized Clinical Trial.
JAMA network open
Jan 4, 2021
IMPORTANCE: Emergency medical dispatchers fail to identify approximately 25% of cases of out-of-hospital cardiac arrest (OHCA), resulting in lost opportunities to save lives by initiating cardiopulmonary resuscitation.
Cohort profile: CROSS-TRACKS: a population-based open cohort across healthcare sectors in Denmark.
BMJ open
Oct 29, 2020
PURPOSE: This paper describes the open cohort CROSS-TRACKS, which comprises population-based data from primary care, secondary care and national registries to study patient pathways and transitions across sectors while adjusting for sociodemographic ...
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