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加速抑郁症干预:使用MCDM-MOORA技术识别关键心理因素以尽早开始治疗。

Accelerating depression intervention: identifying critical psychological factors using MCDM-MOORA technique for early therapy initiation.

作者信息

Majumder Pratham, Pal Arkita, Dorai D Ramya, Gopinathan B, Mallik Saurav, Ahmad Naim, Badawy Ahmed Said, Changalasetty Suresh Babu

机构信息

Jain (Deemed-to-Be) University, Jain Global Campus, Bangalore, India.

Department of Computer Science & Engineering, University of Calcutta, Kolkata, India.

出版信息

Ann Gen Psychiatry. 2024 Oct 9;23(1):35. doi: 10.1186/s12991-024-00518-w.

Abstract

BACKGROUND

A thorough psychosocial assessment is time-consuming, often requiring multiple sessions to uncover the psychological factors contributing to mental illness, such as depression. The duration varies depending on the severity of the patient's condition and how effectively the psychotherapist can establish rapport. However, prolonged assessment periods pose a significant risk of patient deterioration.

METHODS

The comprehensive psychosocial intervention, led by the Multi-Criteria Decision-Making (MCDM) approach utilizing the Multi-Objective Optimization by Ratio Analysis (MOORA) method, played a pivotal role in identifying the key psychological factors contributing to the depression of the client among the 21 factors specified by BDI-II analysis.

RESULTS

The integration of the MOORA strategy compared to traditional psychotherapy on 254 samples demonstrates a Jaccard similarity coefficient of 0.8, with a minimum error margin of 7% (vulnerability index = 0.57), indicating a significant agreement between the two approaches, both converging towards a similar solution. For patients with extreme depression, the number of sessions reduced from 18 ± 2 to 11 ± 2, showing a 33-35% reduction (χ = 6.94, p = 0.008). Severe depression patients experienced a reduction from 14 ± 2 to 8 ± 1 sessions i.e., 34-39% reduction (χ = 8.32, p = 0.004). Moderate depression patients saw sessions drop from 9 ± 1 to 5 ± 1, i.e., 37-43% reduction (χ = 0.29, p = 0.001). The accuracy for detecting dominant psychological factors improved to 82.88% for extreme, 86.74% for severe, and 90.34% for moderate depression, respectively.

CONCLUSION

The implementation of MOORA facilitated the identification and prioritization of key psychosocial intervention strategies, making the process significantly faster compared to traditional methods. This acceleration greatly enhanced the precision and efficacy of the work. Additionally, critical vulnerable factors were identified through ordered statistics and correlation analysis [Pearson (r) = 0.8929 and Spearman's rank (ρ) = 0.7551] on the Beck Depression Inventory-II model. These findings were supported by other MCDM schemes such as EDAS and TOPSIS, demonstrating high stability and robustness in dynamic decision-making environments, maintaining consistency across scenarios adapted by different psychotherapists. Overall, the combined application of MCDM (MOORA) and targeted psychological interventions yielded substantial positive outcomes in enhancing the well-being of individuals with psychological illnesses, such as depression, cognitive, affective, and somatic syndromes.

摘要

背景

全面的心理社会评估耗时较长,通常需要多次问诊才能发现导致精神疾病(如抑郁症)的心理因素。评估时长因患者病情严重程度以及心理治疗师建立融洽关系的效果而异。然而,较长的评估期会带来患者病情恶化的重大风险。

方法

以多标准决策(MCDM)方法为引领、运用比率分析多目标优化(MOORA)方法的综合心理社会干预,在贝克抑郁量表第二版(BDI-II)分析所确定的21个因素中,对识别导致来访者抑郁的关键心理因素起到了关键作用。

结果

与传统心理治疗相比,对254个样本采用MOORA策略的整合显示,杰卡德相似系数为0.8,最小误差幅度为7%(脆弱性指数 = 0.57),表明两种方法之间存在显著一致性,都趋向于类似的解决方案。对于重度抑郁症患者,问诊次数从18±2次减少到11±2次,减少了33 - 35%(χ = 6.94,p = 0.008)。中度抑郁症患者的问诊次数从9±1次减少到5±1次,即减少了37 - 43%(χ = 8.32,p = 0.004)。对于极端抑郁症患者,检测主要心理因素的准确率分别提高到82.88%,重度抑郁症患者为86.74%,中度抑郁症患者为90.34%。

结论

MOORA的实施有助于识别关键心理社会干预策略并确定其优先级,与传统方法相比,该过程显著加快。这种加速极大地提高了工作的精度和效率。此外,通过对贝克抑郁量表第二版模型进行有序统计和相关分析[皮尔逊(r)= 0.8929,斯皮尔曼等级(ρ)= 0.7551],确定了关键脆弱因素。这些发现得到了诸如加权评估法(EDAS)和理想解排序法(TOPSIS)等其他多标准决策方案的支持,在动态决策环境中显示出高稳定性和稳健性,在不同心理治疗师采用的不同场景中保持一致性。总体而言,多标准决策(MOORA)与针对性心理干预的联合应用在改善患有心理疾病(如抑郁症、认知、情感和躯体综合征)个体的幸福感方面产生了显著的积极成果。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f284/11465568/993cced543c5/12991_2024_518_Figa_HTML.jpg

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