Using machine learning to study the effect of medication adherence in Opioid Use Disorder

Background: Opioid Use Disorder (OUD) and opioid overdose (OD) impose huge social and economic burdens on society and health care systems. Research suggests that Medication for Opioid Use Disorder (MOUD) is effective in the treatment of OUD. We use machine learning to investigate the association between patient’s adherence to prescribed MOUD along with other risk factors in patients diagnosed with OUD and potential OD following the treatment. Methods: We used longitudinal Medicaid claims for two selected US states to subset a total of 26,685 patients with OUD diagnosis and appropriate Medicaid coverage between 2015 and 2018. We considered patient age, sex, region level socio-economic data, past comorbidities, MOUD prescription type and other selected prescribed medications along with the Proportion of Days Covered (PDC) as a proxy for adherence to MOUD as predictive variables for our model, and overdose events as the dependent variable. We applied four different machine learning classifiers and compared their performance, focusing on the importance and effect of PDC as a variable. We also calculated results based on risk stratification, where our models separate high risk individuals from low risk, to assess usefulness in clinical decision-making. Results: Among the selected classifiers, the XGBoost classifier has the highest AUC (0.77) closely followed by the Logistic Regression (LR). The LR has the best stratification result: patients in the top 10% of risk scores account for 35.37% of overdose events over the next 12 month observation period. PDC score calculated over the treatment window is one of the most important features, with better PDC lowering risk of OD, as expected. In terms of risk stratification results, of the 35.37% of overdose events that the predictive model could detect within the top 10% of risk scores, 72.3% of these cases were non-adherent in terms of their medication (PDC <0.8). Targeting the top 10% outcome of the predictive model could decrease the total number of OD events by 10.4%. Conclusions: The best performing models allow identification of, and focus on, those at high risk of opioid overdose. With MOUD being included for the first time as a factor of interest, and being identified as a significant factor, outreach activities related to MOUD can be targeted at those at highest risk

Approaches for semantic interoperability between domain ontologies

Domain ontologies and knowledge-based systems have become very important in the agent and semantic web communities. As their use has increased, providing means of resolving semantic differences has also become very important. In this paper we survey the approaches that have been proposed for providing interoperability among domain ontologies. We also discuss some key issues that still need to be addressed if we were to move from semi to fully automated approaches to provide consensus among heterogeneous ontologies.10 page(s

A New subtree-transfer approach to syntax-based reordering for statistical machine translation

In this paper we address the problem of translating between languages with word order disparity. The idea of augmenting statistical machine translation (SMT) by using a syntax-based reordering step prior to translation, proposed in recent years, has been quite successful in improving translation quality. We present a new technique for extracting syntax-based reordering rules, which are derived through a syntactically augmented alignment of source and target texts. The parallel corpus with reordered source side is then passed to an N-gram-based machine translation system and the obtained results are contrasted with a monotone system performance. In experiments, we show significant improvement for the Chinese-to-English translation task.8 page(s

Coupling hierarchical word reordering and decoding in phrase-based statistical machine translation

Abstract In this paper, we start with the existing idea of taking reordering rules automatically derived from syntactic representations, and applying them in a preprocessing step before translation to make the source sentence structurally more like the target; and we propose a new approach to hierarchically extracting these rules. We evaluate this, combined with a lattice-based decoding, and show improvements over stateof-the-art distortion models

Estabilidad de la adenosina deaminasa en diferentes medios de transporte del líquido pleural

La determinaci&oacute;n de los niveles de la adenosinadeaminasa en el l&iacute;quido pleural es sensible y espec&iacute;ficapara la tuberculosis pleural. La adenosina deaminasa enel l&iacute;quido pleural disminuye con el tiempo a temperatura ambiente. El objetivo de este estudio es demostrar si existediferencia en los valores de la adenosina deaminasa enl&iacute;quidos pleurales en cuatro medios diferentes de transporte(hielo, citrato de sodio, heparina y ninguna sustanciaqu&iacute;mica a&ntilde;adida). Se determinaron los niveles de laenzima en ochenta y ocho (88) muestras de l&iacute;quido pleuralprocedentes de 22 pacientes con derrames pleurales nodiagnosticados. Se demostr&oacute; la concordancia diagn&oacute;sticaentre los diferentes medios de transporte. No se demostr&oacute; diferencia significativa entre los niveles de la adenosinadeaminasa en cada uno de los diferentes medios detransporte hasta dos (2) horas posterior a su recolecci&oacute;n.Se recomienda enviar las muestras de l&iacute;quido pleuralcon el conservativo adecuado o con &aacute;cido etilen diaminotetrac&eacute;tico de rutina en nuestro pa&iacute;s.Palabras clave: Adenosina deaminasa; Transporte; Derrame pleural: L&iacute;quido pleural.SUMMARYThe determination of the levels of adenosine deaminase inpleural fluid is sensitive and specific for pleural tuberculosis.Adenosine deaminase in pleural fluid decreases over timeat room temperature. The objective of this study is todemonstrate if there is difference on the average valuesof adenosine deaminase in pleural fluids in four differentmeans of transport (ice, sodium citrate, heparin and noadded chemical substance). The levels of the enzyme ineighty-eight (88) pleural fluid samples from 22 patientswith undiagnosed pleural effusions were determined. Wedemonstrated diagnostic concordance between the differentmodes of transport. No significant difference is betweenthe levels of adenosine deaminase in each of the differentmeans of transport up to two (2) hours after collection. Itis recommended to send by routine in our country samplesof pleural fluid with the right conservative or Acid etilendiamino tetracetic.Key words: Adenosine deaminase. Transportation.Pleural effusion. Pleural fluid