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Hospitals, Psychiatric

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Long Term Effect on Professionals' Knowledge, Practice and Attitudes towards User Involvement Four Years after Implementing an Organisational Development Plan: A Controlled Study.

PloS one
BACKGROUND: Health service organisations are increasingly implementing user involvement initiatives according to requirements from governments, such as user representation in administrational boards, better information to users, and more involvement ...

Predicting hospital-acquired pneumonia among schizophrenic patients: a machine learning approach.

BMC medical informatics and decision making
BACKGROUND: Medications are frequently used for treating schizophrenia, however, anti-psychotic drug use is known to lead to cases of pneumonia. The purpose of our study is to build a model for predicting hospital-acquired pneumonia among schizophren...

Machine Learning Approach to Inpatient Violence Risk Assessment Using Routinely Collected Clinical Notes in Electronic Health Records.

JAMA network open
IMPORTANCE: Inpatient violence remains a significant problem despite existing risk assessment methods. The lack of robustness and the high degree of effort needed to use current methods might be mitigated by using routinely registered clinical notes.

Symptomatology differences of major depression in psychiatric versus general hospitals: A machine learning approach.

Journal of affective disorders
BACKGROUND: Symptomatology differences of major depressive disorder (MDD) in psychiatric and general hospitals in China leads to possible misdiagnosis. Looking at the symptomatology of first-visit patients with MDD in different mental health services...

Personalized prognostic prediction of treatment outcome for depressed patients in a naturalistic psychiatric hospital setting: A comparison of machine learning approaches.

Journal of consulting and clinical psychology
OBJECTIVE: Research on predictors of treatment outcome in depression has largely derived from randomized clinical trials involving strict standardization of treatments, stringent patient exclusion criteria, and careful selection and supervision of st...

Predicting patient outcomes in psychiatric hospitals with routine data: a machine learning approach.

BMC medical informatics and decision making
BACKGROUND: A common problem in machine learning applications is availability of data at the point of decision making. The aim of the present study was to use routine data readily available at admission to predict aspects relevant to the organization...

Implementation of a Clinical, Patient-Level Dashboard at a Mental Health Hospital: Lessons Learned from Two Pilot Clinics.

Studies in health technology and informatics
The Centre for Addiction and Mental Health has implemented mechanisms to standardize routine data collection with the vision of a Learning Health System. To improve clinical decision-making and patient outcomes, a clinical dashboard was implemented t...

Exploring correlates of high psychiatric inpatient utilization in Switzerland: a descriptive and machine learning analysis.

BMC psychiatry
BACKGROUND: This study investigated socio-demographic, psychiatric, and psychological characteristics of patients with high versus low utilization of psychiatric inpatient services. Our objective was to better understand the utilization pattern and t...

Time series forecasting of bed occupancy in mental health facilities in India using machine learning.

Scientific reports
Machine learning models are vital for forecasting and optimizing healthcare parameters, especially in the context of rising mental health issues in India and globally. With increasing demand for mental health services, effective resource management, ...