Enhancing Drug Overdose Mortality Surveillance through Natural Language Processing and Machine Learning

Epidemiological surveillance is key to monitoring and assessing the health of populations. Drug overdose surveillance has become an increasingly important part of public health practice as overdose morbidity and mortality has increased due in large part to the opioid crisis. Monitoring drug overdose mortality relies on death certificate data, which has several limitations including timeliness and the coding structure used to identify specific substances that caused death. These limitations stem from the need to analyze the free-text cause-of-death sections of the death certificate that are completed by the medical certifier during death investigation. Other fields, including clinical sciences, have utilized natural language processing (NLP) methods to gain insight from free-text data, but thus far, adoption of NLP methods in epidemiological surveillance has been limited. Through a narrative review of NLP methods currently used in public health surveillance and the integration of two NLP tasks, classification and named entity recognition, this dissertation enhances the capabilities of public health practitioners and researchers to perform drug overdose mortality surveillance. This dissertation advances both surveillance science and public health practice by integrating methods from bioinformatics into the surveillance pipeline which provides more timely and increased quality overdose mortality surveillance, which is essential to guiding effective public health response to the continuing drug overdose epidemic

A Physics-Aware Dead Reckoning Technique for Entity State Updates in Distributed Interactive Applications

This paper proposes a novel entity state update technique for physics-rich environments in peer-to-peer Distributed Interactive Applications. The proposed technique consists of a dynamic authority scheme for shared objects and a physics-aware dead reckoning model with an adaptive error threshold. The former is employed to place a bound on the overall inconsistency present in shared objects, while the latter is implemented to minimise the instantaneous inconsistency during users’ interactions with shared objects. The performance of the proposed entity state update mechanism is validated using a simulated application

An Apple Extract Beverage Combined with Caffeine Can Improve Alertness, Mental Fatigue, and Information Processing Speed

The psychological effects of low-dose caffeine combined with polyphenols from apples have rarely been explored scientifically yet synergistic effects are plausible. A randomized, double-blind, placebo-controlled cross-over experiment was used to test the psychological effects of apple extract beverages combined with 10, 20, 37.5, and 75 mg caffeine. Comparisons were made to both a placebo drink that was artificially sweetened and colored to mimic the test beverages and a positive control drink with 75 mg caffeine but without apple extract. Compared to placebo, it was hypothesized that dose-dependent improvements in cognitive performance, mood, and motivation would be realized after consuming the beverage with apple extract containing added caffeine. Outcomes were assessed before, 60 to 110, and 125 to 175 min post-beverage. The positive control beverage resulted in more serial seven subtractions, greater motivation to perform cognitive tasks, and reduced feelings of fatigue (all p \u3c .005). The study found that psychological effects (i) were not observed for beverages containing apple extract and 10 or 20 mg caffeine, (ii) of the apple extract beverage containing 75 mg caffeine generally mimicked the effects of the positive control drink and significantly increased serial seven processing speed, and (iii) of the apple extract beverage containing 37.5 mg improved feelings of alertness and mental fatigue. In sum, effects of apple extract combined with caffeine were not dose-dependent; the apple extract beverage containing 75 mg caffeine improved information processing speed and the apple extract beverage with 37.5 mg caffeine improved feelings of alertness and mental fatigue

Coastal Forest Seawater Exposure Increases Stem Methane Concentration

Methane (CH4) exchange between trees and the atmosphere has recently emerged as an important, but poorly quantified process regulating global climate. The sources (soil and/or tree) and mechanisms driving the increase of CH4 in trees and degassing to the atmosphere are inadequately understood, particularly for coastal forests facing increased exposure to seawater. We investigated the eco‐physiological relationship between tree stem wood density, soil and stem oxygen saturation (an indicator of redox state), soil and stem CH4 concentrations, soil and stem carbon dioxide (CO2) concentrations, and soil salinity in five forests along the United States coastline. We aim to evaluate the mechanisms underlying greenhouse gas increase in trees and the influence of seawater exposure on stem CH4 accumulation. Seawater exposure corresponded with decreased tree survival and increased tree stem methane. Tree stem wood density was significantly correlated with increased stem CH4 in seawater exposed gymnosperms, indicating that dying gymnosperm trees may accumulate higher levels of CH4 in association with seawater flooding. Further, we found that significant differences in seawater exposed and unexposed gymnosperm tree populations are associated with increased soil and stem CH4 and CO2, indicating that seawater exposure significantly impacts soil and stem greenhouse gas abundance. Our results provide new insight into the potential mechanisms driving tree CH4 accumulation within gymnosperm coastal forests

Orbit Determination During Spacecraft Emergencies with Sparse Tracking Data - THEMIS and TDRS-3 Lessons Learned

This paper provides an overview of the lessons learned from the National Aeronautics and Space Administration (NASA) Goddard Space Flight Center s (GSFC) Flight Dynamics Facility s (FDF) support of the Time History of Events and Macroscale Interactions during Substorms (THEMIS) spacecraft emergency in February 2007, and the Tracking and Data Relay Satellite-3 (TDRS-3) spacecraft emergency in March 2006. A successful and timely recovery from both of these spacecraft emergencies depended on accurate knowledge of the orbit. Unfortunately, the combination of each spacecraft emergency with very little tracking data contributed to difficulties in estimating and predicting the orbit and delayed recovery efforts in both cases. In both the THEMIS and TDRS-3 spacecraft emergencies, numerous factors contributed to problems with obtaining nominal tracking data measurements. This paper details the various causative factors and challenges. This paper further enumerates lessons learned from FDF s recovery efforts involving the THEMIS and TDRS-3 spacecraft emergencies and scant tracking data, as well as recommendations for improvements and corrective actions. In addition, this paper describes the broad range of resources and complex navigation methods employed within the FDF for supporting critical navigation activities during all mission phases, including launch, early orbit, and on-orbit operations

The effect of time constraint on anticipation, decision making, and option generation in complex and dynamic environments

Researchers interested in performance in complex and dynamic situations have focused on how individuals predict their opponent(s) potential courses of action (i.e., during assessment) and generate potential options about how to respond (i.e., during intervention). When generating predictive options, previous research supports the use of cognitive mechanisms that are consistent with long-term working memory (LTWM) theory (Ericsson and Kintsch in Phychol Rev 102(2):211–245, 1995; Ward et al. in J Cogn Eng Decis Mak 7:231–254, 2013). However, when generating options about how to respond, the extant research supports the use of the take-the-first (TTF) heuristic (Johnson and Raab in Organ Behav Hum Decis Process 91:215–229, 2003). While these models provide possible explanations about how options are generated in situ, often under time pressure, few researchers have tested the claims of these models experimentally by explicitly manipulating time pressure. The current research investigates the effect of time constraint on option-generation behavior during the assessment and intervention phases of decision making by employing a modified version of an established option-generation task in soccer. The results provide additional support for the use of LTWM mechanisms during assessment across both time conditions. During the intervention phase, option-generation behavior appeared consistent with TTF, but only in the non-time-constrained condition. Counter to our expectations, the implementation of time constraint resulted in a shift toward the use of LTWM-type mechanisms during the intervention phase. Modifications to the cognitive-process level descriptions of decision making during intervention are proposed, and implications for training during both phases of decision making are discussed

Vaccine hesitancy: clarifying a theoretical framework for an ambiguous notion.

Today, according to many public health experts, public confidence in vaccines is waning. The term "vaccine hesitancy" (VH) is increasingly used to describe the spread of such vaccine reluctance. But VH is an ambiguous notion and its theoretical background appears uncertain. To clarify this concept, we first review the current definitions of VH in the public health literature and examine its most prominent characteristics. VH has been defined as a set of beliefs, attitudes, or behaviours, or some combination of them, shared by a large and heterogeneous portion of the population and including people who exhibit reluctant conformism (they may either decline a vaccine, delay it or accept it despite their doubts) and vaccine-specific behaviours. Secondly, we underline some of the ambiguities of this notion and argue that it is more a catchall category than a real concept. We also call into question the usefulness of understanding VH as an intermediate position along a continuum ranging from anti-vaccine to pro-vaccine attitudes, and we discuss its qualification as a belief, attitude or behaviour. Thirdly, we propose a theoretical framework, based on previous literature and taking into account some major structural features of contemporary societies, that considers VH as a kind of decision-making process that depends on people's level of commitment to healthism/risk culture and on their level of confidence in the health authorities and mainstream medicine

Enhancing Timeliness of Drug Overdose Mortality Surveillance: A Machine Learning Approach

BACKGROUND: Timely data is key to effective public health responses to epidemics. Drug overdose deaths are identified in surveillance systems through ICD-10 codes present on death certificates. ICD-10 coding takes time, but free-text information is available on death certificates prior to ICD-10 coding. The objective of this study was to develop a machine learning method to classify free-text death certificates as drug overdoses to provide faster drug overdose mortality surveillance. METHODS: Using 2017–2018 Kentucky death certificate data, free-text fields were tokenized and features were created from these tokens using natural language processing (NLP). Word, bigram, and trigram features were created as well as features indicating the part-of-speech of each word. These features were then used to train machine learning classifiers on 2017 data. The resulting models were tested on 2018 Kentucky data and compared to a simple rule-based classification approach. Documented code for this method is available for reuse and extensions: https://github.com/pjward5656/dcnlp. RESULTS: The top scoring machine learning model achieved 0.96 positive predictive value (PPV) and 0.98 sensitivity for an F-score of 0.97 in identification of fatal drug overdoses on test data. This machine learning model achieved significantly higher performance for sensitivity (p \u3c 0.001) than the rule-based approach. Additional feature engineering may improve the model’s prediction. This model can be deployed on death certificates as soon as the free-text is available, eliminating the time needed to code the death certificates. CONCLUSION: Machine learning using natural language processing is a relatively new approach in the context of surveillance of health conditions. This method presents an accessible application of machine learning that improves the timeliness of drug overdose mortality surveillance. As such, it can be employed to inform public health responses to the drug overdose epidemic in near-real time as opposed to several weeks following events