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norskBlar i forfatter "Nytrø, Øystein"
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Ability of clinical data to predict readmission in Child and Adolescent Mental Health Services
(Journal article; Tidsskriftartikkel; Peer reviewed, 2024-10-18)This study addresses the challenge of predicting readmissions in Child and Adolescent Mental Health Services (CAMHS) by analyzing the predictability of readmissions over short, medium, and long term periods. Using health records spanning 35 years, which included 22,643 patients and 30,938 episodes of care, we focused on the episode of care as a central unit, defined as a referral-discharge cycle ... -
Attitudes of Mental Health Service Users Toward Storage and Use of Electronic Health Records
(Journal article; Tidsskriftartikkel; Peer reviewed, 2022-09-01)Objective: Electronic health records (EHRs) are used for both clinical practice and research. Because mental health service users’ views are underrepresented in perspectives on EHR use, the authors examined service users’ awareness, attitudes, and opinions about EHR data storage and sharing.<p> <p>Methods: A mixed-methods, cross-sectional design was used to examine attitudes of 253 Norwegian ... -
Challenges in Interpreting Norwegian Child and Adolescent Mental Health Records
(Journal article; Tidsskriftartikkel; Peer reviewed, 2024)The Electronic Health Record system BUPdata served Norwegian Child and Adolescent Mental Health Services (CAMHS) for over 35 years and is still an important source of information for understanding clinical practice. Secondary usage of clinical data enables learning and service quality improvement. We present some insights from explorative data analysis for interpreting the records of patients referred ... -
Clinical Decision Support Systems for Child Neuropsychiatric Disorders: The Time Has Come?
(Journal article; Tidsskriftartikkel; Peer reviewed, 2017-04-10)Great advances in molecular biology, genetics and imaging serve to enhance the desire to develop multi-level and multi-scale models for "personalized medicine" but they remain very challenging for high prevalence, high impact childhood onset neuropsychiatric disorders. We currently have the capacity to develop innovative, effective and efficient clinical decision support models, while also creating ... -
In-hospital Mortality, Readmission, and Prolonged Length of Stay Risk Prediction Leveraging Historical Electronic Patient Records
(Journal article; Tidsskriftartikkel; Peer reviewed, 2024-09-14)Objective - This study aimed to investigate the predictive capabilities of historical patient records to predict patient adverse outcomes such as mortality, readmission, and prolonged length of stay (PLOS).<p> <p>Methods - Leveraging a de-identified dataset from a tertiary care university hospital, we developed an eXplainable Artificial Intelligence (XAI) framework combining tree-based and ... -
Inhospital Mortality, Readmission, and Prolonged Length of Stay Risk Prediction Leveraging Historical Electronic Health Records
(Journal article; Tidsskriftartikkel; Peer reviewed, 2024-09-14)Objective: This study aimed to investigate the predictive capabilities of historical patient records to predict patient adverse outcomes such as mortality, readmission, and prolonged length of stay (PLOS).<p> <p>Methods: Leveraging a de-identified dataset from a tertiary care university hospital, we developed an eXplainable Artificial Intelligence (XAI) framework combining tree-based and traditional ... -
Leveraging explainable artificial intelligence for early prediction of bloodstream infections using historical electronic health records
(Journal article; Tidsskriftartikkel; Peer reviewed, 2024-11-14)Bloodstream infections (BSIs) are a severe public health threat due to their rapid progression into critical conditions like sepsis. This study presents a novel eXplainable Artificial Intelligence (XAI) framework to predict BSIs using historical electronic health records (EHRs). Leveraging a dataset from St. Olavs Hospital in Trondheim, Norway, encompassing 35,591 patients, the framework integrates ... -
Method for Designing Semantic Annotation of Sepsis Signs in Clinical Text
(Chapter; Bokkapittel, 2023)Annotated clinical text corpora are essential for machine learning studies that model and predict care processes and disease progression. However, few studies describe the necessary experimental design of the annotation guideline and annotation phases. This makes replication, reuse, and adoption challenging. Using clinical questions about sepsis, we designed a semantic annotation guideline to ... -
Patients and family attitudes about clinical and research sharing of electronic clinical data
(Journal article; Tidsskriftartikkel; Peer reviewed, 2024-07-06)Purpose: To access the attitudes of service users about the sharing of health records for research and to foster collaboration between municipal health services and the specialist health services in Norway.<p> <p>Methods: Members (n≈2000) of the Norwegian mental health service users’ organizations (SUO’s), ADHD Norway, the Autism Association and the Tourette Association, representing Central ... -
Predicting in-hospital death from derived EHR trajectory features
(Chapter; Bokkapittel, 2023)Medical histories of patients can provide insight into the immediate future of a patient. While most studies propose to predict survival from vital signs and hospital tests within one episode of care, we carry out selective feature engineering from longitudinal historical medical records in this study to develop a dataset with derived features. We then train multiple machine learning models for the ... -
A review of information sources and analysis methods for data driven decision aids in child and adolescent mental health services
(Journal article; Tidsskriftartikkel; Peer reviewed, 2024-05-13)Objective: Clinical data analysis relies on effective methods and appropriate data. Recognizing distinctive clinical services and service functions may lead to improved decision-making. Our first objective is to categorize analytical methods, data sources, and algorithms used in current research on information analysis and decision support in child and adolescent mental health services (CAMHS). ... -
Success factors of an early EHR system for child and adolescent mental health: Lessons learned for future practice data-driven decision aids
(Chapter; Bokkapittel, 2022)This paper recounts the successful BUPdata, a discontinued electronic health record (EHR) system for Child and Adolescent Mental Health Services (CAMHS) in Norway. It was developed and owned by the national association for CAMHS and fulfilled needs for collaborative care, practice insight, and service management. It aimed to unify the requirements of government, administration, clinicians, ... -
Testing an individualized digital decision assist system for the diagnosis and management of mental and behavior disorders in children and adolescents
(Journal article; Tidsskriftartikkel; Peer reviewed, 2020-09-17)<i>Background</i> - Nearly half of all mental health disorders develop prior to the age of 15. Early assessments, diagnosis, and treatment are critical to shortening single episodes of care, reducing possible comorbidity and long-term disability. In Norway, approximately 20% of all children and adolescents are experiencing mental health problems. To address this, health officials in Norway have ... -
Usability of the IDDEAS prototype in child and adolescent mental health services: A qualitative study for clinical decision support system development
(Journal article; Tidsskriftartikkel; Peer reviewed, 2023-02-23)Introduction: Child and adolescent mental health services (CAMHS) clinical decision support system (CDSS) provides clinicians with real-time support as they assess and treat patients. CDSS can integrate diverse clinical data for identifying child and adolescent mental health needs earlier and more comprehensively. Individualized Digital Decision Assist System (IDDEAS) has the potential to improve ... -
Visualizing Patient Trajectories and Disorder Co-occurrences in Child and Adolescent Mental Health
(Chapter; Bokkapittel, 2024-12)Understanding patient trajectories and identifying patterns in episodes of care is critical for effective healthcare decision-making. We present a patient timeline visualization using clustered episodes of care derived from over 35 years of Child and Adolescent Mental Health Services (CAMHS) data. Patients were categorized into 12 groups based on three features: age group (preschoolers, middle ...
