Interactive exploration of population scale pharmacoepidemiology datasets

Population-scale drug prescription data linked with adverse drug reaction (ADR) data supports the fitting of models large enough to detect drug use and ADR patterns that are not detectable using traditional methods on smaller datasets. However, detecting ADR patterns in large datasets requires tools for scalable data processing, machine learning for data analysis, and interactive visualization. To our knowledge no existing pharmacoepidemiology tool supports all three requirements. We have therefore created a tool for interactive exploration of patterns in prescription datasets with millions of samples. We use Spark to preprocess the data for machine learning and for analyses using SQL queries. We have implemented models in Keras and the scikit-learn framework. The model results are visualized and interpreted using live Python coding in Jupyter. We apply our tool to explore a 384 million prescription data set from the Norwegian Prescription Database combined with a 62 million prescriptions for elders that were hospitalized. We preprocess the data in two minutes, train models in seconds, and plot the results in milliseconds. Our results show the power of combining computational power, short computation times, and ease of use for analysis of population scale pharmacoepidemiology datasets. The code is open source and available at: https://github.com/uit-hdl/norpd_prescription_analyse

From Data to Actions in Intelligent Transportation Systems: A Prescription of Functional Requirements for Model Actionability

Advances in Data Science permeate every field of Transportation Science and Engineering, resulting in developments in the transportation sector that are data-driven. Nowadays, Intelligent Transportation Systems (ITS) could be arguably approached as a “story” intensively producing and consuming large amounts of data. A diversity of sensing devices densely spread over the infrastructure, vehicles or the travelers’ personal devices act as sources of data flows that are eventually fed into software running on automatic devices, actuators or control systems producing, in turn, complex information flows among users, traffic managers, data analysts, traffic modeling scientists, etc. These information flows provide enormous opportunities to improve model development and decision-making. This work aims to describe how data, coming from diverse ITS sources, can be used to learn and adapt data-driven models for efficiently operating ITS assets, systems and processes; in other words, for data-based models to fully become actionable. Grounded in this described data modeling pipeline for ITS, we define the characteristics, engineering requisites and challenges intrinsic to its three compounding stages, namely, data fusion, adaptive learning and model evaluation. We deliberately generalize model learning to be adaptive, since, in the core of our paper is the firm conviction that most learners will have to adapt to the ever-changing phenomenon scenario underlying the majority of ITS applications. Finally, we provide a prospect of current research lines within Data Science that can bring notable advances to data-based ITS modeling, which will eventually bridge the gap towards the practicality and actionability of such models.This work was supported in part by the Basque Government for its funding support through the EMAITEK program (3KIA, ref. KK-2020/00049). It has also received funding support from the Consolidated Research Group MATHMODE (IT1294-19) granted by the Department of Education of the Basque Government

A Synthetic Representation of Inter-Organizational Multi-Actor Collaborative Structures: A Pragmatic Look at U.S. E-Prescribing

A synthetic representation of the collaborative structure in U.S. e-prescribing offers explanations for unintended enactments (e.g., surrogate prescribers) of this healthcare information technology. The generation and transmission of a prescription requires collaboration between at least a prescriber, pharmacy, and patient. The interactions of these actors are modeled through one or more pairs of synthetic representations built using various theoretical lenses such as language-action models. The pluralist pragmatic basis for building a synthetic representation is interpretive synthesis used widely in healthcare. The paper describes how 240+ academic articles in various fields of healthcare are used to synthesize a model of existing (manual prescribing) and intended (e-prescribing) practices. Comparison of these two synthetic models identifies differences such as change in roles or new relationships. These differences can then be interpreted through a theoretical framework which ultimately leads to research propositions, informing design or future policy

Google matrix of business process management

Development of efficient business process models and determination of their characteristic properties are subject of intense interdisciplinary research. Here, we consider a business process model as a directed graph. Its nodes correspond to the units identified by the modeler and the link direction indicates the causal dependencies between units. It is of primary interest to obtain the stationary flow on such a directed graph, which corresponds to the steady-state of a firm during the business process. Following the ideas developed recently for the World Wide Web, we construct the Google matrix for our business process model and analyze its spectral properties. The importance of nodes is characterized by Page-Rank and recently proposed CheiRank and 2DRank, respectively. The results show that this two-dimensional ranking gives a significant information about the influence and communication properties of business model units. We argue that the Google matrix method, described here, provides a new efficient tool helping companies to make their decisions on how to evolve in the exceedingly dynamic global market.Comment: submitted to European Journal of Physics

Developing an inter-enterprise alignment maturity model: research challenges and solutions

Business-IT alignment is pervasive today, as organizations strive to achieve competitive advantage. Like in other areas, e.g., software development, maintenance and IT services, there are maturity models to assess such alignment. Those models, however, do not specifically address the aspects needed for achieving alignment between business and IT in inter-enterprise settings. In this paper, we present the challenges we face in the development of an inter-enterprise alignment maturity model, as well as the current solutions to counter these problems

Toward a Dependability Case Language and Workflow for a Radiation Therapy System

We present a near-future research agenda for bringing a suite of modern programming-languages verification tools - specifically interactive theorem proving, solver-aided languages, and formally defined domain-specific languages - to the development of a specific safety-critical system, a radiotherapy medical device. We sketch how we believe recent programming-languages research advances can merge with existing best practices for safety-critical systems to increase system assurance and developer productivity. We motivate hypotheses central to our agenda: That we should start with a single specific system and that we need to integrate a variety of complementary verification and synthesis tools into system development

A Secure Intelligent Decision Support System for Prescribing Health Medications

The process of electronic approach to writing and sending medical prescription promises to improve patient safety, healthoutcomes, maintaining patients’ privacy, promoting clinician acceptance and prescription security when compared with thecustomary paper method. Traditionally, medical prescriptions are typically handwritten or printed on paper and handdeliveredto pharmacists. Paper-based medical prescriptions are generating major concerns as the incidences of prescriptionerrors have been increasing and causing minor to serious problems to patients, including deaths. In this paper, intelligent eprescriptionmodel that comprises a knowledge base of drug details and an inference engine that can help in decision makingwhen writing a prescription was developed. The research implements the e-prescription model with multifactorauthentication techniques which comprises password and biometric technology. Microsoft Visual Studio 2008, using C#programming language, and Microsoft SQL Server 2005 database were employed in developing the system’s front end andback end respectively. This work implements a knowledge base to the e-prescription system which has added intelligence forvalidating doctor’s prescription and also added security feature to the e-prescription system..Key words: e-Prescription, biometrics prescription, secured prescription, intelligent systems. DSS

Development and validation of sex-specific hip fracture prediction models using electronic health records: a retrospective, population-based cohort study

Background: Hip fracture is associated with immobility, morbidity, mortality, and high medical cost. Due to limited availability of dual-energy X-ray absorptiometry (DXA), hip fracture prediction models without using bone mineral density (BMD) data are essential. We aimed to develop and validate 10-year sex-specific hip fracture prediction models using electronic health records (EHR) without BMD. Methods: In this retrospective, population-based cohort study, anonymized medical records were retrieved from the Clinical Data Analysis and Reporting System for public healthcare service users in Hong Kong aged ≥60 years as of 31 December 2005. A total of 161,051 individuals (91,926 female; 69,125 male) with complete follow-up from 1 January 2006 till the study end date on 31 December 2015 were included in the derivation cohort. The sex-stratified derivation cohort was randomly divided into 80% training and 20% internal testing datasets. An independent validation cohort comprised 3046 community-dwelling participants aged ≥60 years as of 31 December 2005 from the Hong Kong Osteoporosis Study, a prospective cohort which recruited participants between 1995 and 2010. With 395 potential predictors (age, diagnosis, and drug prescription records from EHR), 10-year sex-specific hip fracture prediction models were developed using stepwise selection by logistic regression (LR) and four machine learning (ML) algorithms (gradient boosting machine, random forest, eXtreme gradient boosting, and single-layer neural networks) in the training cohort. Model performance was evaluated in both internal and independent validation cohorts. Findings: In female, the LR model had the highest AUC (0.815; 95% Confidence Interval [CI]: 0.805–0.825) and adequate calibration in internal validation. Reclassification metrics showed the LR model had better discrimination and classification performance than the ML algorithms. Similar performance was attained by the LR model in independent validation, with high AUC (0.841; 95% CI: 0.807–0.87) comparable to other ML algorithms. In internal validation for male, LR model had high AUC (0.818; 95% CI: 0.801–0.834) and it outperformed all ML models as indicated by reclassification metrics, with adequate calibration. In independent validation, the LR model had high AUC (0.898; 95% CI: 0.857–0.939) comparable to ML algorithms. Reclassification metrics demonstrated that LR model had the best discrimination performance. Interpretation: Even without using BMD data, the 10-year hip fracture prediction models developed by conventional LR had better discrimination performance than the models developed by ML algorithms. Upon further validation in independent cohorts, the LR models could be integrated into the routine clinical workflow, aiding the identification of people at high risk for DXA scan. Funding: Health and Medical Research Fund, Health Bureau, Hong Kong SAR Government (reference: 17181381)