Data Clustering Study With Artificial Intelligence and Phenotyping of Patients Who Presented With Acute Pulmonary Embolism
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 发起方
- 入组人数
- 2,350
- 试验地点
- 1
- 主要终点
- Primary: Identify homogeneous groups of patients based on their medical characteristics at diagnosis, and then compare their evolution at 6 months.
研究概览
简要总结
The aim will be to identify clinically relevant phenotypes in patients with acute pulmonary embolism. Hierarchical clustering methods combined with unsupervised learning (machine learning) will be used to obtain groups of patients who are homogeneous at diagnosis. Evaluating their prognosis at 6 months (recurrence or chronic thromboembolic pulmonary hypertension), account the first 3 months of anticoagulant treatment, would provide an aid to medical decision-making.
This research will include a retrospective and a prospective parts. The retrospective part will include patients who have been admitted to CHITS for acute pulmonary embolism since 2019. For the prospective part, it is planned to include patients with same characteristics over the years 2024 and 2025. More than 2,500 patients are expected to be included.
This research will have no impact on current patient care. Data from consultations and various examinations carried out as part of care will be collected for six months post-diagnosis in order to meet the research objectives.
详细描述
Context :
Artificial Intelligence : clustering and unsupervised learning:
Artificial Intelligence (AI) is a field that combines computer science with data sets, with the aim of enabling a machine to imitate the cognitive abilities of human being. Machine learning (ML) and its sub-domain deep learning, which uses layers of neurons, are two major sub-domains of AI. The difference lies in training of each algorithm. Supervised learning, which involves training a model on known input and output data to predict future outputs, and unsupervised learning involves the discovery of hidden patterns and intrinsic underlying structures in the input data.
The aim of clustering methods is to group a set of individuals into homogeneous classes. Non-hierarchical methods can be used to classify massive data but require to fixe in advance the number of classes. Hierarchical methods, which are more time-consuming to compute, consist of a series of nested partitions represented by a clustering tree. The optimal number of classes can be determined a posteriori by reading the tree. In presence of a large number of individuals, it is common to combine non-hierarchical and hierarchical techniques. When classes are not clearly known in advance, clustering methods are use with unsupervised learning (ML) [1]. Datasets are generally divided into three disjoint datasets: training data, used to train the chosen algorithm(s); validation data, used to check performance of result; and test data, used only at the end of the process.
Venous thromboembolic disease:
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Other
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Age ≥ 18 years;
- •Patient with acute pulmonary embolism in CHITS (hospitalised or not).
排除标准
- •Sub-segmental pulmonary embolisms ;
- •Patient opposition.
研究组 & 干预措施
Patient with acute pulmonary embolism
Patient with acute pulmonary embolism in Centre Hospitalier Intercommunal Toulon La Seyne sur Mer, hospitalised or not since 2019
干预措施: Hierarchical clustering methods (Other)
结局指标
主要结局
Primary: Identify homogeneous groups of patients based on their medical characteristics at diagnosis, and then compare their evolution at 6 months.
时间窗: 6 months
Hierarchical clustering methods will be used to form homogeneous groups of patients based on their data at diagnosis: presence or absence of symptoms, clinical and biological data, and presence or absence of favouring factors. Patient evolution at 6 months can fall into categories: stable, aggravation or progress, which are determined by events such as recurrence, hemorrhage, functional sequelae or death.
次要结局
- Secondary: Determine factors predictive of 6-month progression within the first three months of treatment.(3 months)