A5092384577- Nosocomial Infections in ICU
- Machine Learning in Healthcare
- Management of metastatic bone disease
- Emergency and Acute Care Studies
- Digital Mental Health Interventions
- Cardiac, Anesthesia and Surgical Outcomes
- Eating Disorders and Behaviors
- Hip and Femur Fractures
- Atrial Fibrillation Management and Outcomes
- Artificial Intelligence in Healthcare and Education
- Head and Neck Cancer Studies
- Antiplatelet Therapy and Cardiovascular Diseases
- Blood disorders and treatments
- Sepsis Diagnosis and Treatment
- Trauma Management and Diagnosis
- Bone health and osteoporosis research
- Hyperglycemia and glycemic control in critically ill and hospitalized patients
- Topic Modeling
- Emotion and Mood Recognition
- Advanced Radiotherapy Techniques
- Lung Cancer Diagnosis and Treatment
Odense University Hospital
2023-2025
Objective: To evaluate the validity of diagnosis codes for Major Osteoporotic Fracture (MOF) in Danish National Patient Registry (NPR) and secondly to whether fracture was incident/acute using register-based definitions including date criteria procedural codes.Methods: We identified a random sample 2400 records with code MOF NPR dates year 2018.Diagnoses were coded 10th revision International Classification Diseases (ICD-10).The included 2375 unique patients from Region Southern...
Major bleeding is a severe complication in critically ill medical patients, resulting significant morbidity, mortality, and healthcare costs. This study aims to assess the incidence risk factors for major hospitalised patients using Natural Language Processing (NLP) model. We conducted retrospective, cross-sectional observational electronic health records of adult admitted through Emergency Department at Odense University Hospital from January 2017 December 2022. during admission was...
Abstract Objectives This study evaluated if medical doctors could identify more hemorrhage events during chart review in a clinical setting when assisted by an artificial intelligence (AI) model and doctors' perception of using the AI model. Methods To develop model, sentences from 900 electronic health records were labeled as positive or negative for categorized into one 12 anatomical locations. The was on test cohort consisting 566 admissions. Using eye-tracking technology, we investigated...
Abstract Purpose This study addresses the lack of information about bleeding incidences, location and risk factors in admitted children. The primary objective this was to determine incidence children hospital. Methods In an observational cohort study, first admittance 13,842 (< 18years) Odense University Hospital from 2015–2020 analyzed. Bleeding episodes anatomical were identified electronic health record (EHR) text using a combination artificial intelligence manual validation....
This study addresses the lack of information about bleeding incidences, location and risk factors in children admitted to hospital. The primary objective this was determine incidence Methods : In a retrospective observational cohort study, first admittance 13,842 (<18 years old) Odense University Hospital from 2015-2020 analyzed. Bleeding episodes anatomical were identified electronic health record (EHR) text using combination artificial intelligence manual validation. determined...