Measuring Pain in Sickle Cell Disease using Clinical Text

• URL: http://arxiv.org/abs/2008.11081v1
• Date: Wed, 5 Aug 2020 23:39:57 GMT
• Title: Measuring Pain in Sickle Cell Disease using Clinical Text
• Authors: Amanuel Alambo, Ryan Andrew, Sid Gollarahalli, Jacqueline Vaughn, Tanvi Banerjee, Krishnaprasad Thirunarayan, Daniel Abrams, Nirmish Shah
• Abstract summary: Sickle Cell Disease (SCD) is a hereditary disorder of red blood cells in humans. acute pain is known to be the primary symptom of SCD. We propose a binary classification model to predict pain relevance of clinical notes and a multiclass classification model to predict pain level.
• Score: 4.1053421899056435
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Sickle Cell Disease (SCD) is a hereditary disorder of red blood cells in humans. Complications such as pain, stroke, and organ failure occur in SCD as malformed, sickled red blood cells passing through small blood vessels get trapped. Particularly, acute pain is known to be the primary symptom of SCD. The insidious and subjective nature of SCD pain leads to challenges in pain assessment among Medical Practitioners (MPs). Thus, accurate identification of markers of pain in patients with SCD is crucial for pain management. Classifying clinical notes of patients with SCD based on their pain level enables MPs to give appropriate treatment. We propose a binary classification model to predict pain relevance of clinical notes and a multiclass classification model to predict pain level. While our four binary machine learning (ML) classifiers are comparable in their performance, Decision Trees had the best performance for the multiclass classification task achieving 0.70 in F-measure. Our results show the potential clinical text analysis and machine learning offer to pain management in sickle cell patients.

Related papers

• 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)
• RoS-KD: A Robust Stochastic Knowledge Distillation Approach for Noisy Medical Imaging [67.02500668641831]
  Deep learning models trained on noisy datasets are sensitive to the noise type and lead to less generalization on unseen samples. We propose a Robust Knowledge Distillation (RoS-KD) framework which mimics the notion of learning a topic from multiple sources to ensure deterrence in learning noisy information. RoS-KD learns a smooth, well-informed, and robust student manifold by distilling knowledge from multiple teachers trained on overlapping subsets of training data.
  arXiv  Detail & Related papers  (2022-10-15T22:32:20Z)
• 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)
• In-Line Image Transformations for Imbalanced, Multiclass Computer Vision Classification of Lung Chest X-Rays [91.3755431537592]
  This study aims to leverage a body of literature in order to apply image transformations that would serve to balance the lack of COVID-19 LCXR data. Deep learning techniques such as convolutional neural networks (CNNs) are able to select features that distinguish between healthy and disease states. This study utilizes a simple CNN architecture for high-performance multiclass LCXR classification at 94 percent accuracy.
  arXiv  Detail & Related papers  (2021-04-06T02:01:43Z)
• Modeling Disease Progression in Mild Cognitive Impairment and Alzheimer's Disease with Digital Twins [0.0]
  Digital Twins are simulated clinical records that share baseline data with actual subjects. We show how Digital Twins simultaneously capture the progression of a number of key endpoints in clinical trials across a broad spectrum of disease severity.
  arXiv  Detail & Related papers  (2020-12-24T22:29:47Z)
• Pain Assessment based on fNIRS using Bidirectional LSTMs [1.9654272166607836]
  We propose the use of functional near-infrared spectroscopy (fNIRS) and deep learning for the assessment of human pain. The aim of this study is to explore the use deep learning to automatically learn features from fNIRS raw data to reduce the level of subjectivity and domain knowledge required in the design of hand-crafted features.
  arXiv  Detail & Related papers  (2020-12-24T12:55:39Z)
• Pain Intensity Assessment in Sickle Cell Disease patients using Vital Signs during Hospital Visits [0.0]
  Pain in sickle cell disease (SCD) is often associated with increased morbidity, mortality, and high healthcare costs. Medical providers struggle to manage patients based on subjective pain reports correctly. Recent studies have shown that objective physiological measures can predict subjective self-reported pain scores.
  arXiv  Detail & Related papers  (2020-11-24T15:25:29Z)
• Identification of Ischemic Heart Disease by using machine learning technique based on parameters measuring Heart Rate Variability [50.591267188664666]
  In this study, 18 non-invasive features (age, gender, left ventricular ejection fraction and 15 obtained from HRV) of 243 subjects were used to train and validate a series of several ANN. The best result was obtained using 7 input parameters and 7 hidden nodes with an accuracy of 98.9% and 82% for the training and validation dataset.
  arXiv  Detail & Related papers  (2020-10-29T19:14:41Z)
• Multiple Sclerosis Lesion Activity Segmentation with Attention-Guided Two-Path CNNs [49.32653090178743]
  convolutional neural networks (CNNs) are studied for lesion activity segmentation from two time points. CNNs are designed and evaluated that combine the information from two points in different ways. It is demonstrated that deep learning-based methods outperform classic approaches.
  arXiv  Detail & Related papers  (2020-08-05T08:49:20Z)
• Generating Digital Twins with Multiple Sclerosis Using Probabilistic Neural Networks [0.0]
  Digital twins are simulated subjects having the same baseline data as actual subjects. We show that digital twins generated by the model are statistically indistinguishable from their actual subject counterparts along a number of measures.
  arXiv  Detail & Related papers  (2020-02-04T02:57:08Z)

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.