Sickle cell disease (SCD) is an inherited red blood cell disorder that can cause a multitude of complications throughout a patient's life. Pain is the most common complication and a significant cause of morbidity. Since pain is a highly subjective experience, both medical providers and patients express difficulty in determining ideal treatment and management strategies for pain. Therefore, the development of objective pain assessment and pain forecasting methods is critical to pain management in SCD. On the other hand, the rapidly increasing use of mobile health (mHealth) technology and wearable devices gives the ability to build a remote health intervention system for SCD. Hence, the objective of this study is to leverage machine learning techniques, mHealth, and wearable devices together to improve pain management in SCD in both clinical and remote environments. First, we developed an objective pain assessment model based on clean physiological measurements collected from Electronic Health Records (EHRs). Specifically, we used six objective physiological measures in EHRs as features to estimate pain scores based on an 11-point pain rating scale and other pain rating scales. Overall, our preliminary machine learning models show that subjective pain scores can be predicted with objective physiological signals with promising results. Second, we designed a regression-based pain assessment model using noisy physiological and body movement data obtained from wearable devices, patient-reported pain scores from our self-developed mobile app, and nursing-obtained pain scores. The performance of the proposed model is comparable to the model learned with EHRs. We also compared the performance of the regression model and the classification model on the pain intensity estimation problem. Third, we further implemented an ensemble feature selection method to select the most robust and stable features in pain estimation to better understand pain. With robust feature selection and stacked generalization of different regression models, we were able to obtain a more compact and generalizable pain assessment model. Finally, we applied the self-supervised learning method to build a pain forecasting system with limited pain value labels. Our system outperformed the model trained in a purely supervised manner. Such a pain forecasting system would permit timely and adequate pain relief medication usage and other pain treatment plans.

"Sickle cell disease (SCD) is an inherited blood disorder resulting in defective hemoglobin and is the most prevalent genetic condition in the United States. The most common complaint associated with the disease is pain and is the main reason why SCD patients seek care in emergency departments. Nevertheless, most healthcare providers have negative attitudes towards SCD patients and often stigmatize them as drug seekers and inadequately manage their pain. The purpose of this evidence-based project was improving the current pain management practices by improving providers' understanding and attitudes towards management of pain in SCD patients. The design for this project was pretest - posttest involving the provision of an educational intervention to improve pain management practices in SCD patients. The knowledge of the providers was determined using the Knowledge Sickle Cell Disease questionnaire, and their attitudes were measured using the General Perceptions about Sickle Cell Patient Scale, which is a validated scale for assessing providers' attitudes towards patients living with SCD. Following the implementation of the intervention, the knowledge scores of the participants improved by 44%. Scores on negative attitudes decreased by 36%, while positive attitudes increased by 23%. A total of 68 registered nurses, nurse practitioners, physicians, and physician assistants took part in the project. Other healthcare settings can develop similar projects to improve providers' attitudes and knowledge regarding evidence-based pain management practices in SCD. Keywords: Sickle cell disease, pain undertreatment, evidence-based practice, knowledge, attitudes, health care providers, vaso-occlusive crises " -- Abstract

Sickle Cell Pain is a panoramic, in-depth exploration of every scientific, human, and social dimension of this cruel disease. This comprehensive, definitive work is unique in that it is the only book devoted to sickle cell pain, as opposed to general aspects of the disease. The 752-page book links sickle cell pain to basic, clinical, and translational research, addressing various aspects of sickle pain from molecular biology to the psychosocial aspects of the disease. Supplemented with patient narratives, case studies, and visual art, Sickle Cell Pain’s scientific rigor extends through its discussion of analgesic pharmacology, including abuse-deterrent formulations. The book also addresses in great detail inequities in access to care, stereotyping and stigmatization of patients, the implications of rapidly evolving models of care, and recent legislation and litigation and their consequences.

Sickle Cell Disease (SCD) is a hereditary disorder in red blood cells that can lead to excruciating pain episodes. SCD causes the normal red blood cells to distort its shape and turn into sickle shape. The distorted shape makes the hemoglobin inflexible and stick to the walls of the vessels thereby obstructing the free flow of blood and eventually making the tissues suffer from lack of oxygen. The lack of oxygen causes serious problems including Acute Chest Syndrome (ACS), stroke, infection, organ damage, and over the lifetime an SCD can harm a persons spleen, brain, kidneys, eyes, bones. Sickling of RBC can be triggered by a number of conditions such as dehydration, acidity, low levels of oxygen, stress, and change in temperature. There is no specific medication for pain crisis and the signs and symptoms varies from person to person, making it difficult to provide a common treatment for SCD and understanding the disease. It is believed that 90,000 to 100,000 American are affected by SCD. Myriad number of studies have been working on gaining better understanding of the disease and predict pain crisis and pain level. These studies help people to mitigate or prevent pain crisis by taking precautions. However, no study has used clinical notes to predict pain score and pain sentiment. Clinical notes provide patient specific information including procedures and medication; and can therefore help in predicting accurate scores.Our study focuses on four research problems namely patient informative, pain informactive, pain sentiment and pain scores using SCD data. Notes are taken for a patient during hospitalization but only few provide beneficial information, therefore patient informative and pain informative helps healthcare professionals to scan through the notes that can pro- vide valuable information from all the clinical notes maintained. Pain sentiment and pain score predict the change in pain and pain level for a particular note. Our study experimented with two feature sets, firstly features obtained from cTAKES, a Natural Language Processing (NLP) and secondly features obtained from text using NLP techniques. Four supervised machine learning models namely Logistic Regression, Random Forest, Support Vector Machines, and Multinomial Naive Bayes are built on these different sets of features. From the results, it can be noted that cTAKES features are performing well for SCD problem for all the four research problems with F1 score ranging from 0.40 to 0.86. This indicates that there is promise for using NLP techniques in clinical notes as a means to better understand pain in SCD patients.

This 2-volume set constitutes the post-conference proceedings of the 4th EAI International Conference on Cognitive Computing and Cyber Physical Systems, IC4S 2023, Bhimavaram, Andhra Pradesh, India, during August 4-6, 2023. The theme of IC4S 2023 was: cognitive approaches with machine learning and advanced communications. The 70 full papers were carefully reviewed and selected from 165 submissions. The papers are clustered in thematical issues as follows: machine learning and its applications; cyber security and signal processing; image processing; smart power systems; smart city eco-system and communications.

This 8-volumes set constitutes the refereed of the 25th International Conference on Pattern Recognition Workshops, ICPR 2020, held virtually in Milan, Italy and rescheduled to January 10 - 11, 2021 due to Covid-19 pandemic. The 416 full papers presented in these 8 volumes were carefully reviewed and selected from about 700 submissions. The 46 workshops cover a wide range of areas including machine learning, pattern analysis, healthcare, human behavior, environment, surveillance, forensics and biometrics, robotics and egovision, cultural heritage and document analysis, retrieval, and women at ICPR2020.