PDF(Pubmed)
Abstract：
BACKGROUND: Psoriasis vulgaris (PsV) and psoriatic arthritis (PsA) are complex, multifactorial diseases significantly impacting health and quality of life. Predicting treatment response and disease progression is crucial for optimizing therapeutic interventions, yet challenging. Automated machine learning (AutoML) technology shows promise for rapidly creating accurate predictive models based on patient features and treatment data.
OBJECTIVE: This study aims to develop highly accurate machine learning (ML) models using AutoML to address key clinical questions for PsV and PsA patients, including predicting therapy changes, identifying reasons for therapy changes, and factors influencing skin lesion progression or an abnormal Bath Ankylosing Spondylitis Disease Activity Index (BASDAI) score.
METHODS: Clinical study data from 309 PsV and PsA patients were extensively prepared and analyzed using AutoML to build and select the most accurate predictive models for each variable of interest.
RESULTS: Therapy change at 24 weeks follow-up was modeled using the extreme gradient boosted trees classifier with early stopping (area under the receiver operating characteristic curve [AUC] of 0.9078 and logarithmic loss [LogLoss] of 0.3955 for the holdout partition). Key influencing factors included the initial systemic therapeutic agent, the Classification Criteria for Psoriatic Arthritis score at baseline, and changes in quality of life. An average blender incorporating three models (gradient boosted trees classifier, ExtraTrees classifier, and Eureqa generalized additive model classifier) with an AUC of 0.8750 and LogLoss of 0.4603 was used to predict therapy changes for 2 hypothetical patients, highlighting the significance of these factors. Treatments such as methotrexate or specific biologicals showed a lower propensity for change. An average blender of a random forest classifier, an extreme gradient boosted trees classifier, and a Eureqa classifier (AUC of 0.9241 and LogLoss of 0.4498) was used to estimate PASI (Psoriasis Area and Severity Index) change after 24 weeks. Primary predictors included the initial PASI score, change in pruritus levels, and change in therapy. A lower initial PASI score and consistently low pruritus were associated with better outcomes. BASDAI classification at onset was analyzed using an average blender of a Eureqa generalized additive model classifier, an extreme gradient boosted trees classifier with early stopping, and a dropout additive regression trees classifier with an AUC of 0.8274 and LogLoss of 0.5037. Influential factors included initial pain, disease activity, and Hospital Anxiety and Depression Scale scores for depression and anxiety. Increased pain, disease activity, and psychological distress generally led to higher BASDAI scores.
CONCLUSIONS: The practical implications of these models for clinical decision-making in PsV and PsA can guide early investigation and treatment, contributing to improved patient outcomes.
OBJECTIVE: This study aims to develop highly accurate machine learning (ML) models using AutoML to address key clinical questions for PsV and PsA patients, including predicting therapy changes, identifying reasons for therapy changes, and factors influencing skin lesion progression or an abnormal Bath Ankylosing Spondylitis Disease Activity Index (BASDAI) score.
METHODS: Clinical study data from 309 PsV and PsA patients were extensively prepared and analyzed using AutoML to build and select the most accurate predictive models for each variable of interest.
RESULTS: Therapy change at 24 weeks follow-up was modeled using the extreme gradient boosted trees classifier with early stopping (area under the receiver operating characteristic curve [AUC] of 0.9078 and logarithmic loss [LogLoss] of 0.3955 for the holdout partition). Key influencing factors included the initial systemic therapeutic agent, the Classification Criteria for Psoriatic Arthritis score at baseline, and changes in quality of life. An average blender incorporating three models (gradient boosted trees classifier, ExtraTrees classifier, and Eureqa generalized additive model classifier) with an AUC of 0.8750 and LogLoss of 0.4603 was used to predict therapy changes for 2 hypothetical patients, highlighting the significance of these factors. Treatments such as methotrexate or specific biologicals showed a lower propensity for change. An average blender of a random forest classifier, an extreme gradient boosted trees classifier, and a Eureqa classifier (AUC of 0.9241 and LogLoss of 0.4498) was used to estimate PASI (Psoriasis Area and Severity Index) change after 24 weeks. Primary predictors included the initial PASI score, change in pruritus levels, and change in therapy. A lower initial PASI score and consistently low pruritus were associated with better outcomes. BASDAI classification at onset was analyzed using an average blender of a Eureqa generalized additive model classifier, an extreme gradient boosted trees classifier with early stopping, and a dropout additive regression trees classifier with an AUC of 0.8274 and LogLoss of 0.5037. Influential factors included initial pain, disease activity, and Hospital Anxiety and Depression Scale scores for depression and anxiety. Increased pain, disease activity, and psychological distress generally led to higher BASDAI scores.
CONCLUSIONS: The practical implications of these models for clinical decision-making in PsV and PsA can guide early investigation and treatment, contributing to improved patient outcomes.
摘要：
背景：寻常型银屑病（PsV）和银屑病关节炎（PsA）是相互交织的多因素疾病，对健康和生活质量有重大影响，由于慢性和治疗复杂性，这可能会使人衰弱。预测这些条件下的治疗反应和疾病进展具有挑战性，但对于优化治疗干预措施至关重要。自动化机器学习(AutoML)的先进技术为基于患者特征和治疗数据快速构建高度准确的预测模型提供了巨大的前景。
目的：该研究旨在使用AutoML开发高度准确的ML模型，以解决PsV和PsA患者的关键临床问题。包括预测治疗变化和确定治疗变化的原因，影响皮肤病变进展的因素或与异常BASDAI评分相关的因素。
方法：在对309例PsV和PsA患者的临床研究数据进行广泛的数据集准备后，我们创建了一个二级数据集，并最终使用AutoML进行分析，以构建各种预测模型，并为每个感兴趣的变量选择最准确的模型。
结果：“24周随访时的治疗变化”使用极限梯度增强树分类器和早期停止模型（保留分区的AUC为0.9078，LogLoss为0.3955）进行建模，以深入了解影响治疗变化的因素。例如最初的全身性治疗剂，基线时在CASPAR分类标准中获得的分数，和生活质量的变化。3种型号的AVG混合器(梯度增强树分类器，ExtraTrees分类器,AUC为0.8750和LogLoss为0.4603的Eureqa广义加性模型分类器）用于预测两名假设患者的治疗变化，以突出此类影响因素的重要性。值得注意的是,MTX或特定生物制剂等治疗显示出较低的变化倾向。另一个随机森林分类器的AVG混合器，然后使用eXtreme梯度增强树分类器和Eureqa分类器（AUC为0.9241，LogLoss为0.4498）来估计“24周后PASI变化”，主要预测因子是初始PASI评分，瘙痒的改变和治疗的改变。较低的初始PASI分数，持续低瘙痒与更好的结局相关.最后,使用Eureqa广义加性模型分类器的AVGBlender分析了“基线BASDAI分类”，具有早期停止和缺失加性回归的极限梯度增强树分类器具有0.8274的AUC和0.5037的Logloss。影响BASDAI评分的因素包括初始疼痛，抑郁和焦虑的疾病活动和HADS评分。疼痛加剧,疾病活动和心理困扰通常可能导致更高的BASDAI评分。
结论：这些模型对PsV和PsA临床决策的实际意义有可能指导早期研究和治疗。有助于改善患者预后。
背景：
目的：该研究旨在使用AutoML开发高度准确的ML模型，以解决PsV和PsA患者的关键临床问题。包括预测治疗变化和确定治疗变化的原因，影响皮肤病变进展的因素或与异常BASDAI评分相关的因素。
方法：在对309例PsV和PsA患者的临床研究数据进行广泛的数据集准备后，我们创建了一个二级数据集，并最终使用AutoML进行分析，以构建各种预测模型，并为每个感兴趣的变量选择最准确的模型。
结果：“24周随访时的治疗变化”使用极限梯度增强树分类器和早期停止模型（保留分区的AUC为0.9078，LogLoss为0.3955）进行建模，以深入了解影响治疗变化的因素。例如最初的全身性治疗剂，基线时在CASPAR分类标准中获得的分数，和生活质量的变化。3种型号的AVG混合器(梯度增强树分类器，ExtraTrees分类器,AUC为0.8750和LogLoss为0.4603的Eureqa广义加性模型分类器）用于预测两名假设患者的治疗变化，以突出此类影响因素的重要性。值得注意的是,MTX或特定生物制剂等治疗显示出较低的变化倾向。另一个随机森林分类器的AVG混合器，然后使用eXtreme梯度增强树分类器和Eureqa分类器（AUC为0.9241，LogLoss为0.4498）来估计“24周后PASI变化”，主要预测因子是初始PASI评分，瘙痒的改变和治疗的改变。较低的初始PASI分数，持续低瘙痒与更好的结局相关.最后,使用Eureqa广义加性模型分类器的AVGBlender分析了“基线BASDAI分类”，具有早期停止和缺失加性回归的极限梯度增强树分类器具有0.8274的AUC和0.5037的Logloss。影响BASDAI评分的因素包括初始疼痛，抑郁和焦虑的疾病活动和HADS评分。疼痛加剧,疾病活动和心理困扰通常可能导致更高的BASDAI评分。
结论：这些模型对PsV和PsA临床决策的实际意义有可能指导早期研究和治疗。有助于改善患者预后。
背景：
