Home-Based Telemedicine for Automatic Pain Assessment in Cancer Patients: Dataset Creation and Development of Machine Learning Algorithms
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 发起方
- 入组人数
- 40
- 试验地点
- 2
- 主要终点
- To develop a machine learning algorithm useful for predicting the level of pain in cancer patients. A database containing clinical data and pain features will be obtained.
研究概览
简要总结
In cancer patients, the integration between anticancer therapies and palliative care is of fundamental importance. In this context, telemedicine can improve the quality of life (QoL) of chronic patients through self-management and remote monitoring solutions. This approach can favor the effectiveness of the treatment and therapeutic adherence. Of note, telemedicine can also be applied to the management of cancer pain. In the advanced stages of cancer disease, pain is one of the most obvious and most disabling symptoms. Consequently, proper pain management has a significant impact on the QoL, the ability to withstand treatment, and the recovery of patients. On the other hand, given the complexity of cancer pain, the main obstacle to its proper management is the lack of adequate measurement methods. Although in recent years a great deal of effort has been made in the direction of automatic pain assessment, both concerning the creation of datasets and the development of classification algorithms, the literature is lacking regarding the automatic measurement of pain in the setting of cancer patients. Observation by experienced clinical staff and self-assessment by patients could be useful for obtaining the ground truth and, in turn, for training automatic pain recognition systems.
详细描述
For the entire duration of the study, patients will remain under the care of the Early Palliative Care and Simultaneous Care Outpatient team of the Istituto Nazionale Tumori, Fondazione Pascale, at home. Pain and other symptoms will be managed according to the good clinical practice and patients will receive assistance in agreement to the routine medical care.
The following devices will be used:
- Software
- Instrumentation
- Clinical Assessment Tools: European Organisation for Research and Treatment of Cancer Quality-of-life Questionnaire Core 30 (EORTC QLQ-C30), Daily Pain Diary, 0-10 numeric rating scale (NRS).
The project will be divided into three main Work Packages (WPs), dedicated respectively to the creation of the IT infrastructure to support acquisitions (WP1), the patient data collection campaign (WP2), and the development of machine learning algorithms for automatic pain recognition (WP3). The application of the devices and verification of correct functioning will be carried out at the patient's home by the IT staff involved in the study.
WP1 - The system consists of three main components: the server, with the attached database, the application for mobile devices, also responsible for managing data acquisition from physiological signal acquisition devices, and the desktop application, used by the clinical staff to monitor the progress of data collection.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Prospective
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
- •Patients aged > 18 years
- •Home care patients diagnosed with advanced cancer disease and life expectancy ≤ 1 year
- •Patients receiving treatment for cancer pain
- •Patients who have given their consent
排除标准
- •Patients aged < 18 years
- •Willingness to sign the informed consent form (unable to read or write)
- •Cognitive deficit (e.g. Alzheimer disease or senile dementia)
结局指标
主要结局
To develop a machine learning algorithm useful for predicting the level of pain in cancer patients. A database containing clinical data and pain features will be obtained.
时间窗: Whenever the patient has a worsening of his/her pain, up to 2 weeks
Pain features: A daily Pain Diary will be used. Name and amount of drug used and time it was taken.
次要结局
- Patients' quality of life assessed by the EORTC QLQ-C30 questionnaire.(At the beginning and at the end of the observation, up to 2 weeks)