Pain Forecasting using Self-supervised Learning and Patient Phenotyping: An attempt to prevent Opioid Addiction

Sickle Cell Disease (SCD) is a chronic genetic disorder characterized by recurrent acute painful episodes. Opioids are often used to manage these painful episodes; the extent of their use in managing pain in this disorder is an issue of debate. The risk of addiction and side effects of these opioid treatments can often lead to more pain episodes in the future. Hence, it is crucial to forecast future patient pain trajectories to help patients manage their SCD to improve their quality of life without compromising their treatment. It is challenging to obtain many pain records to design forecasting models since it is mainly recorded by patients' self-report. Therefore, it is expensive and painful (due to the need for patient compliance) to solve pain forecasting problems in a purely supervised manner. In light of this challenge, we propose to solve the pain forecasting problem using self-supervised learning methods. Also, clustering such time-series data is crucial for patient phenotyping, anticipating patients' prognoses by identifying "similar" patients, and designing treatment guidelines tailored to homogeneous patient subgroups. Hence, we propose a self-supervised learning approach for clustering time-series data, where each cluster comprises patients who share similar future pain profiles. Experiments on five years of real-world datasets show that our models achieve superior performance over state-of-the-art benchmarks and identify meaningful clusters that can be translated into actionable information for clinical decision-making.Comment: 8 page

Hybrid statistical and mechanistic mathematical model guides mobile health intervention for chronic pain

Nearly a quarter of visits to the Emergency Department are for conditions that could have been managed via outpatient treatment; improvements that allow patients to quickly recognize and receive appropriate treatment are crucial. The growing popularity of mobile technology creates new opportunities for real-time adaptive medical intervention, and the simultaneous growth of big data sources allows for preparation of personalized recommendations. Here we focus on the reduction of chronic suffering in the sickle cell disease community. Sickle cell disease is a chronic blood disorder in which pain is the most frequent complication. There currently is no standard algorithm or analytical method for real-time adaptive treatment recommendations for pain. Furthermore, current state-of-the-art methods have difficulty in handling continuous-time decision optimization using big data. Facing these challenges, in this study we aim to develop new mathematical tools for incorporating mobile technology into personalized treatment plans for pain. We present a new hybrid model for the dynamics of subjective pain that consists of a dynamical systems approach using differential equations to predict future pain levels, as well as a statistical approach tying system parameters to patient data (both personal characteristics and medication response history). Pilot testing of our approach suggests that it has significant potential to predict pain dynamics given patients' reported pain levels and medication usages. With more abundant data, our hybrid approach should allow physicians to make personalized, data driven recommendations for treating chronic pain.Comment: 13 pages, 15 figures, 5 table

Understanding Sleep in Pediatric Patients with Sickle Cell Disease Admitted for Vaso-Occlusive Pain Crisis through Objective Data

Sickle cell disease (SCD) is an inherited red cell disorder that leads to sickling of red blood cells, anemia and vaso-occlusion. The most common reason for hospitalization and morbidity in children is pain due to vaso-occlusive crisis (VOC). Importantly, poor sleep quality can lead to increased pain the subsequent day and nocturnal pain leads to reduced deep sleep, both which can then modify pain sensitivity. Studies using sleep diaries have shown this cyclical relationship between sleep and pain. Frequent occurrences of restless sleep are therefore believed to contribute to an increased severity and intensity of pain episodes. There is very little data, however, looking at objective data such as vital signs to define sleep in patients with SCD admitted for VOC. We aimed to make comparisons between sleep hours and daytime hours for pain scores, patient controlled analgesia (PCA) usage and vital sign data in effort to better define and understand sleep in SCD

Sickle cell disease complications: Prevalence and resource utilization

Objectives: This study evaluated the prevalence rate of vaso-occlusive crisis (VOC) episodes, rates of uncomplicated and complicated VOC episodes, and the primary reasons for emergency room (ER) visits and inpatient admissions for sickle cell disease (SCD) patients. Methods: The Medicaid Analytic extracts database was used to identify adult SCD patients using claims from 01JUL2009-31DEC2012. The date of the first observed SCD claim was designated as the index date. Patients were required to have continuous medical and pharmacy benefits for .6 months baseline and .12 months follow-up period. Patient demographics, baseline clinical characteristics, the rate of uncomplicated and complicated VOC (VOC with concomitant SCD complications) episodes, and reasons for ER visits and inpatient stays were analyzed descriptively. Results: A total of 8,521 patients were included in the analysis, with a median age of 30 years. The average follow-up period was 2.7 years. The rate of VOC episodes anytime in the follow-up was 3.31 in person-years. During the first-year follow-up period, an average of 2.79 VOC episodes were identified per SCD patients, with 1.06 VOC episodes treated in inpatient setting and 0.90 VOC episodes in ER without admission. A total of 76,154 VOC episodes were identified during the entire follow-up period for the overall SCD patients. Most of the VOC episodes (70.3% [n = 53,523]) were uncomplicated episodes, and 29.7% were complicated episodes. Using primary diagnosis claims only, the most frequent complications during the VOC episode were infectious diseases (25.9%), fever (21.8%), and pulmonary disorders (16.2%). Among ER and hospitalizations related to VOC or SCD complication, ~85.0% had VOCs as the primary reason for admission; 15.0% had SCD complications as the primary reason. Conclusion: In summary, SCD and its related comorbidities and complications result in high acute health care utilization. In addition, VOC remains the primary reason for SCD patients’ ER visits and inpatient admissions

Improving Pain Assessment Using Vital Signs and Pain Medication for Patients With Sickle Cell Disease: Retrospective Study

Background: Sickle cell disease (SCD) is the most common inherited blood disorder affecting millions of people worldwide. Most patients with SCD experience repeated, unpredictable episodes of severe pain. These pain episodes are the leading cause of emergency department visits among patients with SCD and may last for several weeks. Arguably, the most challenging aspect of treating pain episodes in SCD is assessing and interpreting a patient\u27s pain intensity level. Objective: This study aims to learn deep feature representations of subjective pain trajectories using objective physiological signals collected from electronic health records. Methods: This study used electronic health record data collected from 496 Duke University Medical Center participants over 5 consecutive years. Each record contained measures for 6 vital signs and the patient\u27s self-reported pain score, with an ordinal range from 0 (no pain) to 10 (severe and unbearable pain). We also extracted 3 features related to medication: medication type, medication status (given or applied, or missed or removed or due), and total medication dosage (mg/mL). We used variational autoencoders for representation learning and designed machine learning classification algorithms to build pain prediction models. We evaluated our results using an accuracy and confusion matrix and visualized the qualitative data representations. Results: We designed a classification model using raw data and deep representational learning to predict subjective pain scores with average accuracies of 82.8%, 70.6%, 49.3%, and 47.4% for 2-point, 4-point, 6-point, and 11-point pain ratings, respectively. We observed that random forest classification models trained on deep represented features outperformed models trained on unrepresented data for all pain rating scales. We observed that at varying Likert scales, our models performed better when provided with medication data along with vital signs data. We visualized the data representations to understand the underlying latent representations, indicating neighboring representations for similar pain scores with a higher resolution of pain ratings. Conclusions: Our results demonstrate that medication information (the type of medication, total medication dosage, and whether the medication was given or missed) can significantly improve subjective pain prediction modeling compared with modeling with only vital signs. This study shows promise in data-driven estimated pain scores that will help clinicians with additional information about the patient\u27s condition, in addition to the patient\u27s self-reported pain scores

Clustering of Pain Dynamics in Sickle Cell Disease from Sparse, Uneven Samples

Irregularly sampled time series data are common in a variety of fields. Many typical methods for drawing insight from data fail in this case. Here we attempt to generalize methods for clustering trajectories to irregularly and sparsely sampled data. We first construct synthetic data sets, then propose and assess four methods of data alignment to allow for application of spectral clustering. We also repeat the same process for real data drawn from medical records of patients with sickle cell disease -- patients whose subjective experiences of pain were tracked for several months via a mobile app. We find that different methods for aligning irregularly sampled sparse data sets can lead to different optimal numbers of clusters, even for synthetic data with known properties. For the case of sickle cell disease, we find that three clusters is a reasonable choice, and these appear to correspond to (1) a low pain group with occasionally acute pain, (2) a group which experiences moderate mean pain that fluctuates often from low to high, and (3) a group that experiences persistent high levels of pain. Our results may help physicians and patients better understand and manage patients\u27 pain levels over time, and we expect that the methods we develop will apply to a wide range of other data sources in medicine and beyond

Can Subjective Pain Be Inferred From Objective Physiological Data? Evidence From Patients With Sickle Cell Disease

Patients with sickle cell disease (SCD) experience lifelong struggles with both chronic and acute pain, often requiring medical interventMaion. Pain can be managed with medications, but dosages must balance the goal of pain mitigation against the risks of tolerance, addiction and other adverse effects. Setting appropriate dosages requires knowledge of a patient\u27s subjective pain, but collecting pain reports from patients can be difficult for clinicians and disruptive for patients, and is only possible when patients are awake and communicative. Here we investigate methods for estimating SCD patients\u27 pain levels indirectly using vital signs that are routinely collected and documented in medical records. Using machine learning, we develop both sequential and non-sequential probabilistic models that can be used to infer pain levels or changes in pain from sequences of these physiological measures. We demonstrate that these models outperform null models and that objective physiological data can be used to inform estimates for subjective pain. Author summary: Understanding subjective human pain remains a major challenge. If objective data could be used in place of reported pain levels, it could reduce patient burdens and enable the collection of much larger data sets that could deepen our understanding of causes of pain and allow for accurate forecasting and more effective pain management. Here we apply two machine learning approaches to data from patients with sickle cell disease, who often experience debilitating pain crises. Using vital sign data routinely collected in hospital settings including respiratory rate, heart rate, and blood pressure and amidst the real-world challenges of irregular timing, missing data, and inter-patient variation, we demonstrate that these models outperform baseline models in estimating subjective pain, distinguishing between typical and atypical pain levels, and detecting changes in pain. Once trained, these types of models could be used to improve pain estimates in real time in the absence of direct pain reports

Measuring Pain in Sickle Cell Disease using Clinical Text

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.Comment: The 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Societ

Medical Resource Use and Costs of Treating Sickle Cell-related Vaso-occlusive Crisis Episodes: A Retrospective Claims Study

Background: The study investigated the economic burden of vaso-occlusive crisis (VOC) among sickle cell disease (SCD) patients, through assessment of overall utilization and costs and costs per VOC episode (regarding the number of VOC episodes and health care setting, respectively). Methods: Using the Medicaid Analytic Extracts database, the first SCD-related diagnosis claim (index claim) between June 1, 2009–December 31, 2012 was identified among eligible adults. Patients were required to have continuous medical and pharmacy benefits for 6 months pre- and 12 months post-index. Discrete VOC claims identified within a 3-day gap were combined as a single VOC episode. Annual all-cause and SCD-related medical resources and costs were identified and stratified by number of VOC episodes during the 1-year follow-up period. Health care costs per VOC episode were also examined, stratified by care setting. Results: Enrollees included 8521 eligible patients with a mean age of 32.88 years (SD=12.21). Of these, 66.5% had a Charlson Comorbidity index (CCI) score of 0 (no comorbidities) and 67.3% were female. The average total medical costs were US$34 136 (median=US$12 691) annually, and SCD accounted for 60% of the total costs (mean=US$20 206, median=US$1204). Patients with \u3e3 episodes had the highest annual SCD-related costs (mean=US$58 950) across all settings. Health care resource utilization (HCRU) and costs increased substantially as the number of VOC episodes increased. This study was limited to observation of associations rather than causal inference, and by possible coding and identification discrepancies and the restricted generalizability of the population. Conclusions: VOC has a severe impact on medical resource use and costs among the adult SCD population. Further research among broader study populations is needed to facilitate the reduction of VOC episodes and thereby improve clinical and economic outcomes for SCD patients

Pain Intensity Assessment in Sickle Cell Disease patients using Vital Signs during Hospital Visits

Pain in sickle cell disease (SCD) is often associated with increased morbidity, mortality, and high healthcare costs. The standard method for predicting the absence, presence, and intensity of pain has long been self-report. However, medical providers struggle to manage patients based on subjective pain reports correctly and pain medications often lead to further difficulties in patient communication as they may cause sedation and sleepiness. Recent studies have shown that objective physiological measures can predict subjective self-reported pain scores for inpatient visits using machine learning (ML) techniques. In this study, we evaluate the generalizability of ML techniques to data collected from 50 patients over an extended period across three types of hospital visits (i.e., inpatient, outpatient and outpatient evaluation). We compare five classification algorithms for various pain intensity levels at both intra-individual (within each patient) and inter-individual (between patients) level. While all the tested classifiers perform much better than chance, a Decision Tree (DT) model performs best at predicting pain on an 11-point severity scale (from 0-10) with an accuracy of 0.728 at an inter-individual level and 0.653 at an intra-individual level. The accuracy of DT significantly improves to 0.941 on a 2-point rating scale (i.e., no/mild pain: 0-5, severe pain: 6-10) at an intra-individual level. Our experimental results demonstrate that ML techniques can provide an objective and quantitative evaluation of pain intensity levels for all three types of hospital visits.Comment: Accepted for presentation at the FIRST WORKSHOP ON COMPUTATIONAL & AFFECTIVE INTELLIGENCE IN HEALTHCARE APPLICATIONS (VULNERABLE POPULATIONS) In Conjunction with the International Conference on Pattern Recognition (ICPR) 202