Annex Publishers

Artificial Intelligence and Machine Learning (ML) are increasingly being used in the Neurocritical Care and healthcare in general. The ML model algorithms have many existing and potential uses in triage, diagnosis, clinical decision support, mon itoring, and prevention of clinical syndromes. Combining and appropriately analyzing the vast number of neurocritical care data parameters, ranging from clinical (including electronic medical record), laboratory, imaging, multimodal monitoring, and many others is beyond human capability. ML algorithms can help the providers and patients in analyzing these data pa rameters to address certain defined problems. Machine learning does have limitations in several aspects (technical, medi co-legal, financial, clinical, ethical, social, etc.), which can prevent realization of its full potential. Addressing these pitfalls with appropriate solutions in a timely manner is important to get the maximum benefit out of this valuable technological ad vancement

Objective: This study was explored the impact of Nihavent theme on decreasing of adaptation difficulty in patients with Alzheimer’s disease. Materials and methods: The study was conducted with a total of 30 patients, 15 patients in the intervention group and 15 patients in the control group. Before the application, The Descriptive Characteristics Data Form and Assessment Scale of Adaptation Difficulty for the Elderly were administered to both groups. The patients in intervention group had music ses sions for 12 weeks. Patients in the control group received standard care but did not participate in the specific intervention. One week after the music session completed, the Assessment Scale of Adaptation Difficulty for the Elderly was re-adminis tered to both groups.

This study examined the effectiveness of Solution-Focused Therapy (SFT) in reducing tobacco smoking dependency be haviour among incarcerated individuals within correctional settings in Oyo State. Anchored on social cognitive theory, which highlights the role of self-efficacy in behaviour change, the research was designed to address the pressing public health concern of tobacco smoking among the inmates.

Aim: The aim of this study was to generate linear anthropometric data for the design of Negroid anatomical model for fu ture standardization. Method: The research design was cross-sectional and non-experimental design. A total number of three hundred (300) sub jects were recruited between the ages of 18 to 37 years with a BMI range of 18.50 to < 30.00 for this study. Taro Yamane’s formula was used to determine the minimum sample size. BMI and linear body anthropometric measurements were taken using a stadiometer, calibrated flexible meter tape, meter rule, and weighing scale. Data were analyzed using SPSS for dis crete statistics and t-tests of significance

Artificial Intelligence and Machine Learning (ML) are increasingly being used in the Neurocritical Care and healthcare in general. The ML model algorithms have many existing and potential uses in triage, diagnosis, clinical decision support, mon itoring, and prevention of clinical syndromes. Combining and appropriately analyzing the vast number of neurocritical care data parameters, ranging from clinical (including electronic medical record), laboratory, imaging, multimodal monitoring, and many others is beyond human capability. ML algorithms can help the providers and patients in analyzing these data pa rameters to address certain defined problems. Machine learning does have limitations in several aspects (technical, medi co-legal, financial, clinical, ethical, social, etc.), which can prevent realization of its full potential. Addressing these pitfalls with appropriate solutions in a timely manner is important to get the maximum benefit out of this valuable technological ad vancement

Objective: This study was explored the impact of Nihavent theme on decreasing of adaptation difficulty in patients with Alzheimer’s disease. Materials and methods: The study was conducted with a total of 30 patients, 15 patients in the intervention group and 15 patients in the control group. Before the application, The Descriptive Characteristics Data Form and Assessment Scale of Adaptation Difficulty for the Elderly were administered to both groups. The patients in intervention group had music ses sions for 12 weeks. Patients in the control group received standard care but did not participate in the specific intervention. One week after the music session completed, the Assessment Scale of Adaptation Difficulty for the Elderly was re-adminis tered to both groups.

This study examined the effectiveness of Solution-Focused Therapy (SFT) in reducing tobacco smoking dependency be haviour among incarcerated individuals within correctional settings in Oyo State. Anchored on social cognitive theory, which highlights the role of self-efficacy in behaviour change, the research was designed to address the pressing public health concern of tobacco smoking among the inmates.

Aim: The aim of this study was to generate linear anthropometric data for the design of Negroid anatomical model for fu ture standardization. Method: The research design was cross-sectional and non-experimental design. A total number of three hundred (300) sub jects were recruited between the ages of 18 to 37 years with a BMI range of 18.50 to < 30.00 for this study. Taro Yamane’s formula was used to determine the minimum sample size. BMI and linear body anthropometric measurements were taken using a stadiometer, calibrated flexible meter tape, meter rule, and weighing scale. Data were analyzed using SPSS for dis crete statistics and t-tests of significance