Review The Video Case: Suicide Assessment Of Client With Ini

Review The Video Casesuicide Assessment Of Client With Initially Subt

Review The Video Casesuicide Assessment Of Client With Initially Subt Review the video case : Suicide assessment of Client with initially Subtle Warning Signs of Suicide Complete a SOAP Note as if you were the psychotherapist in the video. Then write a one page summary that highlights the warning signs of suicidality in the patient and why you chose the treatment plan you choose in your SOAP Note. SOAP Template: Patient Name: XXX MRN: XXX Date of Service: Start Time: 10:00 End Time: 10:54 Billing Code(s): 90213, 90836 (be sure you include strictly psychotherapy codes or both E&M and add on psychotherapy codes if prescribing provider visit) Accompanied by: Brother CC: follow-up appt. for counseling after discharge from inpatient psychiatric unit 2 days ago HPI: 1 week from inpatient care to current partial inpatient care daily individual psychotherapy session and extended daily group sessions S- Patient states that he generally has been doing well with depressive and anxiety symptoms improved but he still feels down at times. He states he is sleeping better, achieving 7-8 hours of restful sleep each night. He states he feels the medication is helping somewhat and without any noticeable side-effects. Crisis Issues: He states he has no suicide plan and has not thought about suicide since the recent attempt. He states has no access to prescription medications, other than the fluoxetine. He believes the classes he participated in while inpatient have helped him with coping mechanisms. Reviewed Allergies: NKA Current Medications: Fluoxetine 10mg daily ROS: no complaints O- Vitals: T 98.4, P 82, R 16, BP 122/78 PE: (not always required and performed, especially in psychotherapy only visits) Heart- RRR, no murmurs, no gallops Lungs- CTA bilaterally Skin- no lesions or rashes Labs: CBC, lytes, and TSH all within normal limits Results of any Psychiatric Clinical Tests: BAI=34 MSE: Gary Davis, a 36-year-old white male, was disheveled and unkempt on presentation to the outpatient office. He was wearing dirty khaki pants, an unbuttoned golf shirt, and white shoes and appeared slightly younger than his stated age. During the interview, he was attentive and calm. He was impatient, but polite in his interactions with this examiner. Mr. Davis reported that today was the best day of his life, because he had decided he was going to be better and start his own company. His affect was labile, but appropriate to the content of his speech (i.e., he became tearful when reporting he had “bogeyed number 15 in golf yesterday). His speech was loud, pressured at times then he would quickly gain composure to a more neutral tone. He exhibited loosening of associations and flight of ideas; he intermittently and unpredictably shifted the topic of conversation from golf, to the mating habits of geese, to the likelihood of extraterrestrial life. Mr. Davis described grandiose delusions regarding his sexual and athletic performance. He reported no auditory hallucinations. He was oriented to time and place. He denied suicidal and homicidal ideation. He refused to participate in intellectual- or memory-related portions of the examination. Reliability, judgment, and insight were impaired. A - with (ICD-10 code) Differential Diagnoses: 1. choose 3 differential diagnoses 2. 3. Definitive Diagnosis: Major Depressive Disorder, recurrent, without psychotic features F33.4 Generalized Anxiety Disorder F41.1 P- Continue Fluoxetine increasing dose to 20mg. Continue outpatient counseling: partial inpatient program continued with individual and group sessions Non-pharmacological Tx: Psychotherapy Modality used: CBT Pharmacological Tx: (be specific and give detailed Rx information) Education: discussed smoking cessation Reviewed medication side effects and adherence importance Follow-up: in one week or earlier if any depressive symptoms worsen. Referrals: none at this time Grading Rubic: Assignment Criteria Level III Level II Level I Not Present Criteria 1 Level III Max Points Points: 8 Level II Max Points Points: 6.4 Level I Max Points Points: 4. Points Subjective Information 1. Complete and concise summary of pertinent information. 1. Well organized; partial but accurate summary of pertinent information (>80%). 1. Poorly organized and/or limited summary of pertinent information (50%-80%); information other than “S” provided. 1. Does not meet the criteria Assignment Criteria Level III Level II Level I Not Present Criteria 2 Level III Max Points Points: 8 Level II Max Points Points: 6.4 Level I Max Points Points: 4. Points Objective Information 1. Complete and concise summary of pertinent information. 1. Partial but accurate summary of pertinent information (>80%). 1. Poorly organized and/or limited summary of pertinent information (50%-80%); information other than “O” provided. 1. Does not meet the criteria Assignment Criteria Level III Level II Level I Not Present Criteria 3 Level III Max Points Points: 8 Level II Max Points Points: 6.4 Level I Max Points Points: 4. Points Assessment: Problem Identification and Prioritization 1. Complete problem list generated and rationally prioritized; no extraneous information or issues listed. 1. Most problems are identified and rationally prioritized, including the “main” problem for the case (>80%). 1. Some problems are identified (50%-80%); incomplete or inappropriate problem prioritization; includes nonexistent problems or extraneous information included. 1. Does not meet the criteria Criteria 4 Level III Max Points Points: 8 Level II Max Points Points: 6.4 Level I Max Points Points: 4. Points Assessment: Assessment of Current Psychiatric & Medical Condition(s) or Drug Therapy-related Problem 1. An optimal and thorough assessment is present for each problem 1. An assessment is present for each problem listed but not optimal 1. Assessment is present for 50-80% of problems 1. Does not meet the criteria Assignment Criteria Level III Level II Level I Not Present Criteria 5 Level III Max Points Points: 6 Level II Max Points Points: 4.8 Level I Max Points Points: 3. Points Assessment: Treatment Goals 1. Appropriate and relevant therapeutic goals for each identified problem. 1. Appropriate therapeutic goals for most identified problems (>80%). 1. Appropriate therapeutic goals for a few identified problems (50%-80%). 1. Less than 50% of problems have appropriate therapeutic goals. Assignment Criteria Level III Level II Level I Not Present Criteria 6 Level III Max Points Points: 6 Level II Max Points Points: 4.8 Level I Max Points Points: 3. Points Plan: Treatment Plan 1. Specific, appropriate and justified recommendations (including drug name, strength, route, frequency, and duration of therapy) for each identified problem are included. 1. Includes most of the requirements for each identified problem (>80%). 1. Incomplete and/or inappropriate for a few identified problems (50%-80%); information other than “P” provided. 1. Less than 50% of problems have an appropriate and complete treatment plan. Criteria 7 Level III Max Points Points: 6 Level II Max Points Points: 4.8 Level I Max Points Points: 3. Points Plan: Counseling, Referral, Monitoring & Follow-up 1.

Specific patient education points, monitoring parameters, follow-up plan and (where applicable) referral plan for each identified problem. 1. Patient education points, monitoring parameters, follow-up plan and referral plan (where applicable) for >80% of identified problems. 1. Patient education points, monitoring parameters, follow-up plan and referral plan (where applicable) for a few identified problems (50%-80%). 1. Less than 50% of problems include appropriate counseling, monitoring, referral and/or follow-up plan. Maximum Total Points Minimum Total Points 41 points minimum 31 points minimum 1 point minimum Enhancing Database Security through Machine Learning: Anomaly Detection and Response Prince Boateng Instructor: American Military University Class:ISSC/16/2024 More sophisticated security measures must be in place as the nature of cyber threats continues to change in favor of complexity, particularly for highly valuable database systems. Efforts to avert new or even complex hazards are hardly supported by basic precautionary measures. Given these conditions, this research article suggests utilizing machine learning (ML) to enhance security by means of anomaly identification and automated reactions to such anomalies within a database system. To detect patterns and indicators of a security breach, large amounts of data can be analyzed using models that incorporate machine learning. The discussion that follows offers many MLALGs in this regard. The benefits of real-time anomaly detection are accessed in order to evaluate real-time threat handling with minimal impact on the database. In 2020, Gupta and colleagues introduced taxonomy of machine learning models utilized in safe data analytics, highlighting their suitability and constraints for threat discovery and mitigation. The authors highlight the necessity of creating a suitable threat model that will show how cyber threats are always changing and taking on new forms, which is why they advocate for ML-based security solutions that are intelligent and adaptive. Similarly, when employing machine learning models, Xue et al. (2020) talked about security issues and potential solutions. The authors emphasize that evaluating the security of ML-models and countermeasures against hostile attacks is a crucial area of focus for their research. Every one of these examples highlights the potential and needs for machine learning (ML)-based techniques in database security that aim to increase efficiency and capability. The efficiency of ML-based IDS on imbalanced datasets is the subject of an article by Karatas et al. (2020), which is followed by this discussion. According to this paper, optimized machine learning models will aid in danger identification, lower false alarms, and preserve accessibility and dependability in database systems. High-level Outline i.d Issues in implementing security some databases Challenges associated with conventional approaches to security. Essential Machine Learning Concepts and Choices for Anomaly Detection Objectives and Significance of the Study ii. Literature Review A Brief Insight into Machine Learning in Cyber Security,Gupta et al. (2020) Security Threats and Risk Mitigation Algorithms in Machine Learning (Xue et al., 2020) The Effectiveness of IDS ML in the Creation and Training of Data Sets,Karatas et al., (2020) iii. Methodology Capture and pre-process end-user data Artificial and live database transaction data. Machine learning's farthest limit: coming out ahead Supervised, unsupervised, and semi-sup Training and Assessment Measures Accuracy, detection speed, false positive rate iv. Automated Response Strategies Design of Automated Response Mechanism access control, intrusion detection and alarm/notification Integration with anomaly detection models Evaluation of response effectiveness v. Performance Impact Analysis Securing database performance assessment by machine-learning-based security approaches Trade-offs between security and performance Examples or case studies of lived experiences vi. Discussion Implications of the Findings Compare to a regular protocol for security Challenges and limitations vi. Conclusion Summary of key achievements Future research avidness Concluding remarks References Gupta, R., Tanwar, S., Tyagi, S., & Kumar, N. (2020). Machine learning models for secure data analytics: A taxonomy and threat model. Computer Communications , 153 , . Karatas, G., Demir, O., &Sahingoz, O. K. (2020). Increasing the performance of machine learning-based IDSs on an imbalanced and up-to-date dataset. IEEE access , 8 , . Xue, M., Yuan, C., Wu, H., Zhang, Y., & Liu, W. (2020). Machine learning security: Threats, countermeasures, and evaluations. IEEE Access , 8 , .