WAVES - The Lucile Packard Children's Hospital Pediatric Physiological Waveforms Dataset.

WAVES is a large, single-center dataset comprising 9 years of high-frequency physiological waveform data from patients in intensive and acute care units at a large academic, pediatric medical center. The data comprise approximately 10.6 million hours of 1 to 20 concurrent waveforms over approximately 50,364 distinct patient encounters. The data have been de-identified, cleaned, and organized to facilitate research. Initial analyses demonstrate the potential of the data for clinical applications such as non-invasive blood pressure monitoring and methodological applications such as waveform-agnostic data imputation. WAVES is the largest pediatric-focused and second largest physiological waveform dataset available for research.

Association of Common Pediatric Surgeries With New Onset Chronic Pain in Patients 0-21 Years of Age in the United States.

Chronic pain (CP) is a major public health issue. While new onset CP is known to occur frequently after some pediatric surgeries, its incidence after the most common pediatric surgeries is unknown. This retrospective cohort study used insurance claims data from 2002 to 2017 for patients 0 to 21 years of age. The primary outcome was CP 90 to 365 days after each of the 20 most frequent surgeries in 5 age categories (identified using CP ICD codes). Multivariable logistic regression identified surgeries and risk factors associated with CP after surgery. A total of 424,590 surgical patients aged 0 to 21 were included, 22,361 of whom developed CP in the 90 to 365 days after surgery. The incidences of CP after surgery were: 1.1% in age group 0 to 1 years; 3.0% in 2 to 5 years; 5.6% in 6 to 11 years; 10.1% in 12 to 18 years; 9.9% in 19 to 21 years. Some surgeries and patient variables were associated with CP. Approximately 1 in 10 adolescents who underwent the most common surgeries developed CP, as did a striking percentage of children in other age groups. Given the long-term consequences of CP, resources should be allocated toward identification of high-risk pediatric patients and strategies to prevent CP after surgery. PERSPECTIVE: This study identifies the incidences of and risk factors for chronic pain after common surgeries in patients 0 to 21 years of age. Our findings suggest that resources should be allocated toward the identification of high-risk pediatric patients and strategies to prevent CP after surgery.

Wireless Body Area Network Control Policies for Energy-Efficient Health Monitoring.

Wireless body area networks (WBANs) have strong potential in the field of health monitoring. However, the energy consumption required for accurate monitoring determines the time between battery charges of the wearable sensors, which is a key performance factor (and can be critical in the case of implantable devices). In this paper, we study the inherent trade-off between the power consumption of the sensors and the probability of misclassifying a patient's health state. We formulate this trade-off as a dynamic problem, in which at each step, we can choose to activate a subset of sensors that provide noisy measurements of the patient's health state. We assume that the (unknown) health state follows a Markov chain, so our problem is formulated as a partially observable Markov decision problem (POMDP). We show that all the past measurements can be summarized as a belief state on the true health state of the patient, which allows tackling the POMDP problem as an MDP on the belief state. Then, we empirically study the performance of a greedy one-step look-ahead policy compared to the optimal policy obtained by solving the dynamic program. For that purpose, we use an open-source Continuous Glucose Monitoring (CGM) dataset of 232 patients over six months and extract the transition matrix and sensor accuracies from the data. We find that the greedy policy saves ≈50% of the energy costs while reducing the misclassification costs by less than 2% compared to the most accurate policy possible that always activates all sensors. Our sensitivity analysis reveals that the greedy policy remains nearly optimal across different cost parameters and a varying number of sensors. The results also have practical importance, because while the optimal policy is too complicated, a greedy one-step look-ahead policy can be easily implemented in WBAN systems.

Prediction of Prolonged Opioid Use After Surgery in Adolescents: Insights From Machine Learning.

BACKGROUND: Long-term opioid use has negative health care consequences. Patients who undergo surgery are at risk for prolonged opioid use after surgery (POUS). While risk factors have been previously identified, no methods currently exist to determine higher-risk patients. We assessed the ability of a variety of machine-learning algorithms to predict adolescents at risk of POUS and to identify factors associated with this risk. METHODS: A retrospective cohort study was conducted using a national insurance claims database of adolescents aged 12-21 years who underwent 1 of 1297 surgeries, with general anesthesia, from January 1, 2011 to December 30, 2017. Logistic regression with an L2 penalty and with a logistic regression with an L1 lasso (Lasso) penalty, random forests, gradient boosting machines, and extreme gradient boosted models were trained using patient and provider characteristics to predict POUS (≥1 opioid prescription fill within 90-180 days after surgery) risk. Predictive capabilities were assessed using the area under the receiver-operating characteristic curve (AUC)/C-statistic, mean average precision (MAP); individual decision thresholds were compared using sensitivity, specificity, Youden Index, F1 score, and number needed to evaluate. The variables most strongly associated with POUS risk were identified using permutation importance. RESULTS: Of 186,493 eligible patient surgical visits, 8410 (4.51%) had POUS. The top-performing algorithm achieved an overall AUC of 0.711 (95% confidence interval [CI], 0.699-0.723) and significantly higher AUCs for certain surgeries (eg, 0.823 for spinal fusion surgery and 0.812 for dental surgery). The variables with the strongest association with POUS were the days' supply of opioids and oral morphine milligram equivalents of opioids in the year before surgery. CONCLUSIONS: Machine-learning models to predict POUS risk among adolescents show modest to strong results for different surgeries and reveal variables associated with higher risk. These results may inform health care system-specific identification of patients at higher risk for POUS and drive development of preventative measures.

Incidence of and Factors Associated With Prolonged and Persistent Postoperative Opioid Use in Children 0-18 Years of Age.

BACKGROUND: Long-term opioid use has negative health care consequences. Opioid-naïve adults are at risk for prolonged and persistent opioid use after surgery. While these outcomes have been examined in some adolescent and teenage populations, little is known about the risk of prolonged and persistent postoperative opioid use after common surgeries compared to children who do not undergo surgery and factors associated with these issues among pediatric surgical patients of all ages. METHODS: Using a national administrative claims database, we identified 175,878 surgical visits by opioid-naïve children aged ≤18 years who underwent ≥1 of the 20 most common surgeries from each of 4 age groups between December 31, 2002, and December 30, 2017, and who filled a perioperative opioid prescription 30 days before to 14 days after surgery. Prolonged opioid use after surgery (filling ≥1 opioid prescription 90-180 days after surgery) was compared to a reference sample of 1,354,909 nonsurgical patients randomly assigned a false "surgery" date. Multivariable logistic regression models were used to estimate the association of surgical procedures and 22 other variables of interest with prolonged opioid use and persistent postoperative opioid use (filling ≥60 days' supply of opioids 90-365 days after surgery) for each age group. RESULTS: Prolonged opioid use after surgery occurred in 0.77%, 0.76%, 1.00%, and 3.80% of surgical patients ages 0-<2, 2-<6, 6-<12, and 12-18, respectively. It was significantly more common in surgical patients than in nonsurgical patients (ages 0-<2: odds ratio [OR] = 4.6 [95% confidence interval (CI), 3.7-5.6]; ages 2-<6: OR = 2.5 [95% CI, 2.1-2.8]; ages 6-<12: OR = 2.1 [95% CI, 1.9-2.4]; and ages 12-18: OR = 1.8 [95% CI, 1.7-1.9]). In the multivariable models for ages 0-<12 years, few surgical procedures and none of the other variables of interest were associated with prolonged opioid use. In the models for ages 12-18 years, 10 surgical procedures and 5 other variables of interest were associated with prolonged opioid use. Persistent postoperative opioid use occurred in <0.1% of patients in all age groups. CONCLUSIONS: Some patient characteristics and surgeries are positively and negatively associated with prolonged opioid use in opioid-naïve children of all ages, but persistent opioid use is rare. Specific pediatric subpopulations (eg, older patients with a history of mood/personality disorder or chronic pain) may be at markedly higher risk.

A fuzzy reinforcement learning approach to power control in wireless transmitters.

We address the issue of power-controlled shared channel access in wireless networks supporting packetized data traffic. We formulate this problem using the dynamic programming framework and present a new distributed fuzzy reinforcement learning algorithm (ACFRL-2) capable of adequately solving a class of problems to which the power control problem belongs. Our experimental results show that the algorithm converges almost deterministically to a neighborhood of optimal parameter values, as opposed to a very noisy stochastic convergence of earlier algorithms. The main tradeoff facing a transmitter is to balance its current power level with future backlog in the presence of stochastically changing interference. Simulation experiments demonstrate that the ACFRL-2 algorithm achieves significant performance gains over the standard power control approach used in CDMA2000. Such a large improvement is explained by the fact that ACFRL-2 allows transmitters to learn implicit coordination policies, which back off under stressful channel conditions as opposed to engaging in escalating "power wars."