CfsSubsetEval (26)-balanced-final.

BackgroundThe prevalence of violence in acute psychiatric wards is a critical concern. According to a meta-analysis investigating violence in psychiatric inpatient units, researchers estimated that approximately 17% of inpatients commit one or more acts of violence during their stay. Inpatient violence negatively affects health-care providers and patients and may contribute to high staff turnover. Therefore, predicting which psychiatric inpatients will commit violence is of considerable clinical significance.ObjectiveThe present study aimed to estimate the violence rate for psychiatric inpatients and establish a predictive model for violence in psychiatric inpatients.MethodsWe collected the structured and unstructured data from Chinese nursing electronic medical records (EMRs) for the violence prediction. The data was obtained from the psychiatry department of a regional hospital in southern Taiwan, covering the period between January 2008 and December 2018. Several text mining and machine learning techniques were employed to analyze the data.ResultsThe results demonstrated that the rate of violence in psychiatric inpatients is 19.7%. The patients with violence in psychiatric wards were generally younger, had a more violent history, and were more likely to be unmarried. Furthermore, our study supported the feasibility of predicting aggressive incidents in psychiatric wards by using nursing EMRs and the proposed method can be incorporated into routine clinical practice to enable early prediction of inpatient violence.ConclusionsOur findings may provide clinicians with a new basis for judgment of the risk of violence in psychiatric wards.</div

Structured with SBERT (787)-balanced-final.

BackgroundThe prevalence of violence in acute psychiatric wards is a critical concern. According to a meta-analysis investigating violence in psychiatric inpatient units, researchers estimated that approximately 17% of inpatients commit one or more acts of violence during their stay. Inpatient violence negatively affects health-care providers and patients and may contribute to high staff turnover. Therefore, predicting which psychiatric inpatients will commit violence is of considerable clinical significance.ObjectiveThe present study aimed to estimate the violence rate for psychiatric inpatients and establish a predictive model for violence in psychiatric inpatients.MethodsWe collected the structured and unstructured data from Chinese nursing electronic medical records (EMRs) for the violence prediction. The data was obtained from the psychiatry department of a regional hospital in southern Taiwan, covering the period between January 2008 and December 2018. Several text mining and machine learning techniques were employed to analyze the data.ResultsThe results demonstrated that the rate of violence in psychiatric inpatients is 19.7%. The patients with violence in psychiatric wards were generally younger, had a more violent history, and were more likely to be unmarried. Furthermore, our study supported the feasibility of predicting aggressive incidents in psychiatric wards by using nursing EMRs and the proposed method can be incorporated into routine clinical practice to enable early prediction of inpatient violence.ConclusionsOur findings may provide clinicians with a new basis for judgment of the risk of violence in psychiatric wards.</div

Prediction model performance assessment using 10-fold cross-validation.

Prediction model performance assessment using 10-fold cross-validation.</p

The text preprocessing steps.

BackgroundThe prevalence of violence in acute psychiatric wards is a critical concern. According to a meta-analysis investigating violence in psychiatric inpatient units, researchers estimated that approximately 17% of inpatients commit one or more acts of violence during their stay. Inpatient violence negatively affects health-care providers and patients and may contribute to high staff turnover. Therefore, predicting which psychiatric inpatients will commit violence is of considerable clinical significance.ObjectiveThe present study aimed to estimate the violence rate for psychiatric inpatients and establish a predictive model for violence in psychiatric inpatients.MethodsWe collected the structured and unstructured data from Chinese nursing electronic medical records (EMRs) for the violence prediction. The data was obtained from the psychiatry department of a regional hospital in southern Taiwan, covering the period between January 2008 and December 2018. Several text mining and machine learning techniques were employed to analyze the data.ResultsThe results demonstrated that the rate of violence in psychiatric inpatients is 19.7%. The patients with violence in psychiatric wards were generally younger, had a more violent history, and were more likely to be unmarried. Furthermore, our study supported the feasibility of predicting aggressive incidents in psychiatric wards by using nursing EMRs and the proposed method can be incorporated into routine clinical practice to enable early prediction of inpatient violence.ConclusionsOur findings may provide clinicians with a new basis for judgment of the risk of violence in psychiatric wards.</div

Characteristics of patients in violent and nonviolent groups.

Characteristics of patients in violent and nonviolent groups.</p

Structured with TF-IDF (648)-final.

BackgroundThe prevalence of violence in acute psychiatric wards is a critical concern. According to a meta-analysis investigating violence in psychiatric inpatient units, researchers estimated that approximately 17% of inpatients commit one or more acts of violence during their stay. Inpatient violence negatively affects health-care providers and patients and may contribute to high staff turnover. Therefore, predicting which psychiatric inpatients will commit violence is of considerable clinical significance.ObjectiveThe present study aimed to estimate the violence rate for psychiatric inpatients and establish a predictive model for violence in psychiatric inpatients.MethodsWe collected the structured and unstructured data from Chinese nursing electronic medical records (EMRs) for the violence prediction. The data was obtained from the psychiatry department of a regional hospital in southern Taiwan, covering the period between January 2008 and December 2018. Several text mining and machine learning techniques were employed to analyze the data.ResultsThe results demonstrated that the rate of violence in psychiatric inpatients is 19.7%. The patients with violence in psychiatric wards were generally younger, had a more violent history, and were more likely to be unmarried. Furthermore, our study supported the feasibility of predicting aggressive incidents in psychiatric wards by using nursing EMRs and the proposed method can be incorporated into routine clinical practice to enable early prediction of inpatient violence.ConclusionsOur findings may provide clinicians with a new basis for judgment of the risk of violence in psychiatric wards.</div

Confusion matrix.

BackgroundThe prevalence of violence in acute psychiatric wards is a critical concern. According to a meta-analysis investigating violence in psychiatric inpatient units, researchers estimated that approximately 17% of inpatients commit one or more acts of violence during their stay. Inpatient violence negatively affects health-care providers and patients and may contribute to high staff turnover. Therefore, predicting which psychiatric inpatients will commit violence is of considerable clinical significance.ObjectiveThe present study aimed to estimate the violence rate for psychiatric inpatients and establish a predictive model for violence in psychiatric inpatients.MethodsWe collected the structured and unstructured data from Chinese nursing electronic medical records (EMRs) for the violence prediction. The data was obtained from the psychiatry department of a regional hospital in southern Taiwan, covering the period between January 2008 and December 2018. Several text mining and machine learning techniques were employed to analyze the data.ResultsThe results demonstrated that the rate of violence in psychiatric inpatients is 19.7%. The patients with violence in psychiatric wards were generally younger, had a more violent history, and were more likely to be unmarried. Furthermore, our study supported the feasibility of predicting aggressive incidents in psychiatric wards by using nursing EMRs and the proposed method can be incorporated into routine clinical practice to enable early prediction of inpatient violence.ConclusionsOur findings may provide clinicians with a new basis for judgment of the risk of violence in psychiatric wards.</div

Prediction model performance assessment using 10-fold cross-validation.

Prediction model performance assessment using 10-fold cross-validation.</p

Comparative analysis of prevalence and risk factors of violence in psychiatric inpatients: A review of recent studies (2019–2022).

Comparative analysis of prevalence and risk factors of violence in psychiatric inpatients: A review of recent studies (2019–2022).</p

One-Step Preparation of Silver Hexagonal Microsheets as Electrically Conductive Adhesive Fillers for Printed Electronics

A facile one-step solution-phase chemical reduction method has been developed to synthesize Ag microsheets at room temperature. The morphology of Ag sheets is a regular hexagon more than 1 μm in size and about 200 nm in thickness. The hexagonal Ag microsheets possess a smoother and straighter surface compared with that of the commercial Ag micrometer-sized flakes prepared by ball milling for electrically conductive adhesives (ECAs). The function of the reagents and the formation mechanism of Ag hexagonal microsheets are also investigated. For the polyvinylpyrrolidone (PVP) and citrate facet-selective capping, the Ag atoms freshly reduced by N₂H₄ would orientationally grow alone on the {111} facet of Ag seeds, with the synergistically selective etching of irregular and small Ag particles by H₂O_2, to form Ag hexagonal microsheets. The hexagonal Ag microsheet-filled epoxy adhesives, as electrically conductive materials, can be easily printed on various substrates such as polyethylene terephthalate (PET), epoxy, glass, and flexible papers. The hexagonal Ag microsheet filled ECAs demonstrate lower bulk resistivity (approximately 8 × 10^–5 Ω cm) than that of the traditional Ag micrometer-sized-flake-filled ECAs with the same Ag content of 80 wt % (approximately 1.2 × 10^–4 Ω cm)