Extracting Patterns in Medical Claims Data for Predicting Opioid Overdose

The goal of this project is to develop an efficient methodology for extracting features from time-dependent variables in transaction data. Transaction data is collected at varying time intervals making feature extraction more difficult. Unsupervised representational learning techniques are investigated, and the results compared with those from other feature engineering techniques. A successful methodology provides features that improve the accuracy of any machine learning technique. This methodology is then applied to insurance claims data in order to find features to predict whether a patient is at risk of overdosing on opioids. This data covers prescription, inpatient, and outpatient transactions. Features created are input to recurrent neural networks with long short-term memory cells. Hyperparameters are found through Bayesian optimization. Validation data features are reduced using weights from the best model and compared against those found using unsupervised learning techniques in other classifiers

Recent Changes in Drug Abuse Scenario: The Novel Psychoactive Substances (NPS) Phenomenon

copyright 2019 by the authors. Articles in this book are Open Access and distributed under the Creative Commons Attribution (CC BY) license, which allows users to download, copy and build upon published articles, as long as the author and publisher are properly credited, which ensures maximum dissemination and a wider impact of our publications. The book as a whole is distributed by MDPI under the terms and conditions of the Creative Commons license CC BY-NC-ND.Final Published versio

Empirical Support for the HCR‐20: A Critical Analysis of the Violence Literature

Summary: The purpose of this project was to conduct a comprehensive search of the empirical literature published in peer-reviewed journals between 1997 and 2005 to identify studies that presented support for variables included on the HCR-20. This report includes a separate section for each of the measure’s 20 items, with one exception. Empirical support for Items C5 (Unresponsive to Treatment) and R4 (Noncompliance with Remediation Attempts) are presented together under a single heading because studies that were relevant to one item also tended to apply to the other item. Moreover, these studies could not be differentiated on the basis of having a present (clinical) or future (risk management) focus. Under each section, the most significant and methodologically sound studies identified in the search are summarized; abstracts of additional studies of relevance to the item are reproduced (with separate headings for studies that presented data on violent or non-vonviolent outcomes). Each section also lists narrative/qualitative literature reviews relevant to the item, as well as studies that offer “contradictory” empirical evidence. A table is presented at the beginning of the report that indicates whether, for each reference, a summary is provided, the abstract only is reprinted, or if it is a narrative review. Bookmark links are provided for each section of this report

Exploring digital biomarkers of illness activity in mood episodes:Hypotheses generating and model development study

Background: Depressive and manic episodes within bipolar disorder (BD) and major depressive disorder (MDD) involve altered mood, sleep, and activity, alongside physiological alterations wearables can capture. Objective: Firstly, we explored whether physiological wearable data could predict (aim 1) the severity of an acute affective episode at the intra-individual level and (aim 2) the polarity of an acute affective episode and euthymia among different individuals. Secondarily, we explored which physiological data were related to prior predictions, generalization across patients, and associations between affective symptoms and physiological data.Methods: We conducted a prospective exploratory observational study including patients with BD and MDD on acute affective episodes (manic, depressed, and mixed) whose physiological data were recorded using a research-grade wearable (Empatica E4) across 3 consecutive time points (acute, response, and remission of episode). Euthymic patients and healthy controls were recorded during a single session (approximately 48 h). Manic and depressive symptoms were assessed using standardized psychometric scales. Physiological wearable data included the following channels: acceleration (ACC), skin temperature, blood volume pulse, heart rate (HR), and electrodermal activity (EDA). Invalid physiological data were removed using a rule-based filter, and channels were time aligned at 1-second time units and segmented at window lengths of 32 seconds, as best-performing parameters. We developed deep learning predictive models, assessed the channels’ individual contribution using permutation feature importance analysis, and computed physiological data to psychometric scales’ items normalized mutual information (NMI). We present a novel, fully automated method for the preprocessing and analysis of physiological data from a research-grade wearable device, including a viable supervised learning pipeline for time-series analyses.Results: Overall, 35 sessions (1512 hours) from 12 patients (manic, depressed, mixed, and euthymic) and 7 healthy controls (mean age 39.7, SD 12.6 years; 6/19, 32% female) were analyzed. The severity of mood episodes was predicted with moderate (62%-85%) accuracies (aim 1), and their polarity with moderate (70%) accuracy (aim 2). The most relevant features for the former tasks were ACC, EDA, and HR. There was a fair agreement in feature importance across classification tasks (Kendall W=0.383). Generalization of the former models on unseen patients was of overall low accuracy, except for the intra-individual models. ACC was associated with “increased motor activity” (NMI>0.55), “insomnia” (NMI=0.6), and “motor inhibition” (NMI=0.75). EDA was associated with “aggressive behavior” (NMI=1.0) and “psychic anxiety” (NMI=0.52).Conclusions: Physiological data from wearables show potential to identify mood episodes and specific symptoms of mania and depression quantitatively, both in BD and MDD. Motor activity and stress-related physiological data (EDA and HR) stand out as potential digital biomarkers for predicting mania and depression, respectively. These findings represent a promising pathway toward personalized psychiatry, in which physiological wearable data could allow the early identification and intervention of mood episodes

Linking social media, medical literature, and clinical notes using deep learning.

Researchers analyze data, information, and knowledge through many sources, formats, and methods. The dominant data format includes text and images. In the healthcare industry, professionals generate a large quantity of unstructured data. The complexity of this data and the lack of computational power causes delays in analysis. However, with emerging deep learning algorithms and access to computational powers such as graphics processing unit (GPU) and tensor processing units (TPUs), processing text and images is becoming more accessible. Deep learning algorithms achieve remarkable results in natural language processing (NLP) and computer vision. In this study, we focus on NLP in the healthcare industry and collect data not only from electronic medical records (EMRs) but also medical literature and social media. We propose a framework for linking social media, medical literature, and EMRs clinical notes using deep learning algorithms. Connecting data sources requires defining a link between them, and our key is finding concepts in the medical text. The National Library of Medicine (NLM) introduces a Unified Medical Language System (UMLS) and we use this system as the foundation of our own system. We recognize social media’s dynamic nature and apply supervised and semi-supervised methodologies to generate concepts. Named entity recognition (NER) allows efficient extraction of information, or entities, from medical literature, and we extend the model to process the EMRs’ clinical notes via transfer learning. The results include an integrated, end-to-end, web-based system solution that unifies social media, literature, and clinical notes, and improves access to medical knowledge for the public and experts

Automation of Patient Trajectory Management: A deep-learning system for critical care outreach

The application of machine learning models to big data has become ubiquitous, however their successful translation into clinical practice is currently mostly limited to the field of imaging. Despite much interest and promise, there are many complex and interrelated barriers that exist in clinical settings, which must be addressed systematically in advance of wide-spread adoption of these technologies. There is limited evidence of comprehensive efforts to consider not only their raw performance metrics, but also their effective deployment, particularly in terms of the ways in which they are perceived, used and accepted by clinicians. The critical care outreach team at St Vincent’s Public Hospital want to automatically prioritise their workload by predicting in-patient deterioration risk, presented as a watch-list application. This work proposes that the proactive management of in-patients at risk of serious deterioration provides a comprehensive case-study in which to understand clinician readiness to adopt deep-learning technology due to the significant known limitations of existing manual processes. Herein is described the development of a proof of concept application uses as its input the subset of real-time clinical data available in the EMR. This data set has the noteworthy challenge of not including any electronically recorded vital signs data. Despite this, the system meets or exceeds similar benchmark models for predicting in-patient death and unplanned ICU admission, using a recurrent neural network architecture, extended with a novel data-augmentation strategy. This augmentation method has been re-implemented in the public MIMIC-III data set to confirm its generalisability. The method is notable for its applicability to discrete time-series data. Furthermore, it is rooted in knowledge of how data entry is performed within the clinical record and is therefore not restricted in applicability to a single clinical domain, instead having the potential for wide-ranging impact. The system was presented to likely end-users to understand their readiness to adopt it into their workflow, using the Technology Adoption Model. In addition to confirming feasibility of predicting risk from this limited data set, this study investigates clinician readiness to adopt artificial intelligence in the critical care setting. This is done with a two-pronged strategy, addressing technical and clinically-focused research questions in parallel

Applying Neural Network Models to Predict Recurrent Maltreatment in Child Welfare Cases with Static and Dynamic Risk Factors

Risk assessment in child welfare has a long tradition of being based on models that assume the likelihood of recurrent maltreatment is a linear function of its various predictors: Gambrill & Shlonsky, 2000). Despite repeated testing of many child, parent, family, maltreatment incident, and service delivery variables, no consistent set of findings have emerged to describe the set of risk and protective factors that best account for increases and decreases in the likelihood of recurrent maltreatment. Shifts in predictors\u27 statistical significance, strength, and direction of effects coupled with evidence of risk assessment models\u27 poor predictive accuracy have led to questions regarding the fit between assumptions of linearity and the true relationship between the likelihood of recurrent maltreatment and its predictors: Gambrill & Shlonsky, 2000, 2001; Knoke & Trocmé, 2005). Hence, this dissertation study uses a distinctly nonlinear approach to modeling the likelihood of recurrent maltreatment by employing a combination of random forest and neural network models to identify the predictors that best explain the risk of recurrent maltreatment. The risk of recurrent maltreatment was assessed for a cohort of children living in a large Midwestern metropolitan area who were first reported for maltreatment between January 1, 1993 and January 1, 2002. Administrative child welfare records for 6,747 children were merged with administrative records from income maintenance, mental health, special education, juvenile justice, and criminal justice systems in order to identify the effects that various public sector service system contacts have on the risk of recurrent maltreatment. Each child was followed for a period of at least seven years to identify the risk of recurrent maltreatment in relationship to a second report for maltreatment. Post-hoc analyses comparing the predictive validity of the neural network model and a binary logistic regression model with random intercepts shows that the neural network model was superior in its predictive validity with an area under the ROC curve of 0.7825 in comparison with an area under the ROC curve of 0.7552 for the logistic regression model. Additional post-hoc analyses provided empirical insight into the four prominent risk factors and four risk moderating service variables that best explain variation in the risk of recurrent maltreatment. Specifically, the number of income maintenance spells received, community-level poverty, the child\u27s age at the first maltreatment report, and the parent\u27s status as the perpetrator of the first maltreatment incident defined 21 risk-based groups where the average probability of recurrent maltreatment was dependent upon values for the four primary risk factors, and the risk of maltreatment was moderated by juvenile court involvement, special education eligibility, receipt of CPS family centered services, and the child\u27s receipt of a mental health/substance abuse service in the community. Findings are discussed within a Risk-Need-Responsivity theory of service delivery: Andrews & Bonta, 2006), which links the empiricism of risk assessment with the clinical implementation of a preventive service delivery plan through the identified modifiable risk factors that drive the likelihood of recurrent maltreatment