Annotation of pain and anesthesia events for surgery-related processes and outcomes extraction
Pain and anesthesia information are crucial elements to identifying surgery-related processes and outcomes. However pain is not consistently recorded in the electronic medical record. Even when recorded, the rich complex granularity of the pain experience may be lost. Similarly, anesthesia information is recorded using local electronic collection systems; though the accuracy and completeness of the information is unknown. We propose an annotation schema to capture pain, pain management, and anesthesia event information.
Code (0)
등록된 구현이 없습니다.
Tasks
ManagementSimilar Papers 제목 키워드 기반
Analysis of Intra-Operative Physiological Responses Through Complex Higher-Order SVD for Long-Term Post-Operative Pain Prediction
Long-term pain conditions after surgery and patients' responses to pain relief medications are not yet fully understood. While recent studies developed an index for nociception level of patients under general anesthesia,…
Automatic Ultrasound Image Segmentation of Supraclavicular Nerve Using Dilated U-Net Deep Learning Architecture
Automated object recognition in medical images can facilitate medical diagnosis and treatment. In this paper, we automatically segmented supraclavicular nerves in ultrasound images to assist in injecting peripheral nerve…
Image SegmentationMedical DiagnosisObject RecognitionPosition+1Reduced neural activity during volatile anesthesia compared to TIVA: evidence from a novel EEG signal processing analysis
Post-operative cognitive decline is a well-known phenomenon and of crucial importance especially in the elderly. General anesthesia can be accomplished by inhalation-based (volatile) or total intravenous anesthesia (TIVA…
EEGElectroencephalogram (EEG)Medical device surveillance with electronic health records
Post-market medical device surveillance is a challenge facing manufacturers, regulatory agencies, and health care providers. Electronic health records are valuable sources of real world evidence to assess device safety a…
Reading ComprehensionMulti-Agent Deep Reinforcement Learning for Multiple Anesthetics Collaborative Control
Automated control of personalized multiple anesthetics in clinical Total Intravenous Anesthesia (TIVA) is crucial yet challenging. Current systems, including target-controlled infusion (TCI) and closed-loop systems, eith…
Deep Reinforcement Learningreinforcement-learningReinforcement Learning