Joint Distribution and Transitions of Pain and Activity in Critically Ill Patients

• URL: http://arxiv.org/abs/2004.09134v1
• Date: Mon, 20 Apr 2020 08:56:13 GMT
• Title: Joint Distribution and Transitions of Pain and Activity in Critically Ill Patients
• Authors: Florenc Demrozi, Graziano Pravadelli, Patrick J Tighe, Azra Bihorac and Parisa Rashidi
• Abstract summary: Pain and physical function are both essential indices of recovery in critically ill patients in the Intensive Care Units (ICU) We collected activity intensity data from 57 ICU patients, using the Actigraph GT3X device. Our results show the joint distribution and state transition of joint activity and pain states in critically ill patients.
• Score: 3.64161514437772
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Pain and physical function are both essential indices of recovery in critically ill patients in the Intensive Care Units (ICU). Simultaneous monitoring of pain intensity and patient activity can be important for determining which analgesic interventions can optimize mobility and function, while minimizing opioid harm. Nonetheless, so far, our knowledge of the relation between pain and activity has been limited to manual and sporadic activity assessments. In recent years, wearable devices equipped with 3-axis accelerometers have been used in many domains to provide a continuous and automated measure of mobility and physical activity. In this study, we collected activity intensity data from 57 ICU patients, using the Actigraph GT3X device. We also collected relevant clinical information, including nurse assessments of pain intensity, recorded every 1-4 hours. Our results show the joint distribution and state transition of joint activity and pain states in critically ill patients.

Related papers

• Deep Reinforcement Learning Empowered Activity-Aware Dynamic Health Monitoring Systems [69.41229290253605]
  Existing monitoring approaches were designed on the premise that medical devices track several health metrics concurrently. This means that they report all relevant health values within that scope, which can result in excess resource use and the gathering of extraneous data. We propose Dynamic Activity-Aware Health Monitoring strategy (DActAHM) for striking a balance between optimal monitoring performance and cost efficiency.
  arXiv  Detail & Related papers  (2024-01-19T16:26:35Z)
• Detecting Visual Cues in the Intensive Care Unit and Association with Patient Clinical Status [0.9867627975175174]
  Existing patient assessments in the ICU are mostly sporadic and administered manually. We developed a new "masked loss computation" technique that addresses the data imbalance problem. We performed AU inference on 634,054 frames to evaluate the association between facial AUs and clinically important patient conditions.
  arXiv  Detail & Related papers  (2023-11-01T15:07:03Z)
• Pain level and pain-related behaviour classification using GRU-based sparsely-connected RNNs [61.080598804629375]
  People with chronic pain unconsciously adapt specific body movements to protect themselves from injury or additional pain. Because there is no dedicated benchmark database to analyse this correlation, we considered one of the specific circumstances that potentially influence a person's biometrics during daily activities. We proposed a sparsely-connected recurrent neural networks (s-RNNs) ensemble with the gated recurrent unit (GRU) that incorporates multiple autoencoders. We conducted several experiments which indicate that the proposed method outperforms the state-of-the-art approaches in classifying both pain level and pain-related behaviour.
  arXiv  Detail & Related papers  (2022-12-20T12:56:28Z)
• Intelligent Sight and Sound: A Chronic Cancer Pain Dataset [74.77784420691937]
  This paper introduces the first chronic cancer pain dataset, collected as part of the Intelligent Sight and Sound (ISS) clinical trial. The data collected to date consists of 29 patients, 509 smartphone videos, 189,999 frames, and self-reported affective and activity pain scores. Using static images and multi-modal data to predict self-reported pain levels, early models show significant gaps between current methods available to predict pain.
  arXiv  Detail & Related papers  (2022-04-07T22:14:37Z)
• Estimation of Clinical Workload and Patient Activity using Deep Learning and Optical Flow [0.9774845227603628]
  We propose a novel method to estimate patient motion and observe clinical workload using a similar technical setup. Patient motion estimation was used to approximate patient agitation and sedation, while worker motion was used as a surrogate for caregiver workload.
  arXiv  Detail & Related papers  (2022-02-09T22:19:01Z)
• External Attention Assisted Multi-Phase Splenic Vascular Injury Segmentation with Limited Data [72.99534552950138]
  The spleen is one of the most commonly injured solid organs in blunt abdominal trauma. accurate segmentation of splenic vascular injury is challenging for the following reasons.
  arXiv  Detail & Related papers  (2022-01-04T02:35:56Z)
• Designing A Clinically Applicable Deep Recurrent Model to Identify Neuropsychiatric Symptoms in People Living with Dementia Using In-Home Monitoring Data [52.40058724040671]
  Agitation is one of the neuropsychiatric symptoms with high prevalence in dementia. Detecting agitation episodes can assist in providing People Living with Dementia (PLWD) with early and timely interventions. This preliminary study presents a supervised learning model to analyse the risk of agitation in PLWD using in-home monitoring data.
  arXiv  Detail & Related papers  (2021-10-19T11:45:01Z)
• Posture Recognition in the Critical Care Settings using Wearable Devices [4.396860522241306]
  Low physical activity levels in the intensive care units (ICU) have been linked to adverse clinical outcomes. We examined the feasibility of posture recognition in an ICU population using data from wearable sensors.
  arXiv  Detail & Related papers  (2021-10-04T12:11:36Z)
• Continuous Decoding of Daily-Life Hand Movements from Forearm Muscle Activity for Enhanced Myoelectric Control of Hand Prostheses [78.120734120667]
  We introduce a novel method, based on a long short-term memory (LSTM) network, to continuously map forearm EMG activity onto hand kinematics. Ours is the first reported work on the prediction of hand kinematics that uses this challenging dataset. Our results suggest that the presented method is suitable for the generation of control signals for the independent and proportional actuation of the multiple DOFs of state-of-the-art hand prostheses.
  arXiv  Detail & Related papers  (2021-04-29T00:11:32Z)
• Facial Action Unit Detection on ICU Data for Pain Assessment [1.8352113484137622]
  Current day pain assessment methods rely on patient self-report or by an observer like the Intensive Care Unit (ICU) nurses. In this study, we show the need for automated pain assessment system which is trained on real-world ICU data for clinically acceptable pain assessment system.
  arXiv  Detail & Related papers  (2020-04-24T17:12:56Z)
• Pain and Physical Activity Association in Critically Ill Patients [2.6249027950824506]
  Critical care patients experience varying levels of pain during their stay in the intensive care unit, often requiring analgesics and sedation. Such medications exacerbate the already sedentary physical activity profiles of critical care patients, contributing to delayed recovery. We examined the relationship between nurse assessed pain scores and physical activity as measured using a wearable accelerometer device.
  arXiv  Detail & Related papers  (2020-04-21T09:29:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.