Automated Diagnosis of Clinic Workflows

Outpatient clinics often run behind schedule due to patients who arrive late or appointments that run longer than expected. We sought to develop a generalizable method that would allow healthcare providers to diagnose problems in workflow that disrupt the schedule on any given provider clinic day. We use a constraint optimization problem to identify the least number of appointment modifications that make the rest of the schedule run on-time. We apply this method to an outpatient clinic at Vanderbilt. For patient seen in this clinic between March 27, 2017 and April 21, 2017, long cycle times tended to affect the overall schedule more than late patients. Results from this workflow diagnosis method could be used to inform interventions to help clinics run smoothly, thus decreasing patient wait times and increasing provider utilization

DxNAT - Deep Neural Networks for Explaining Non-Recurring Traffic Congestion

Non-recurring traffic congestion is caused by temporary disruptions, such as accidents, sports games, adverse weather, etc. We use data related to real-time traffic speed, jam factors (a traffic congestion indicator), and events collected over a year from Nashville, TN to train a multi-layered deep neural network. The traffic dataset contains over 900 million data records. The network is thereafter used to classify the real-time data and identify anomalous operations. Compared with traditional approaches of using statistical or machine learning techniques, our model reaches an accuracy of 98.73 percent when identifying traffic congestion caused by football games. Our approach first encodes the traffic across a region as a scaled image. After that the image data from different timestamps is fused with event- and time-related data. Then a crossover operator is used as a data augmentation method to generate training datasets with more balanced classes. Finally, we use the receiver operating characteristic (ROC) analysis to tune the sensitivity of the classifier. We present the analysis of the training time and the inference time separately

A Family of Domain-Specific Languages for Integrated Modular Avionics

UID/CEC/04516/2019 TUBITAK/ 0008/2014 2018/2019(Proc. DAAD 441.00)In the domain of avionics, we can find intricate software product lines constrained by both aircraft’s hardware and conformance to strict standards. Existing general-purpose languages are complicated, as they do not hide unnecessary low level-details. This situation potentially leads to a lengthy process in the specification phase and the loss of control over the quality of the specification itself and possibly resulting in the generation of inconsistent products. In Software development for avionics systems, the pressure of time-to-market is high. Additionally, the long time taken for systems certification of this sort of critical system pushes for the development of solutions that support specifications correct by construction. With that kind of solutions, we can release the burden of the software developer by positively constraining the configuration of the products. In this paper, we put into practice an in-house solution that implements the concept of Product Lines of Domain Specific Languages (DSLs). The solution allows generating dedicated DSLs for each sub-family/configuration in Modular avionics departing from the model of a given aircraft.authorsversionpublishe

Automated Reasoning for Multi-step Feature Model Configuration Problems

The increasing complexity and cost of software-intensive systems has led developers to seek ways of increasing software reusability. One software reuse approach is to develop a Software Product-line (SPL), which is a reconfigurable software architecture that can be reused across projects. Creating configurations of the SPL that meets arbitrary requirements is hard. Existing research has focused on techniques that produce a configuration of the SPL in a single step. This paper provides three contributions to the study of multi-step configuration for SPLs. First, we present a formal model of multi-step SPL configuration and map this model to constraint satisfaction problems (CSPs). Second, we show how solutions to these CSP configuration problem CSPs can be derived automatically with a constraint solver. Third, we present empirical results demonstrating that our CSP-based technique can solve multi-step configuration problems involving hundreds of features in seconds.Comisión Interministerial de Ciencia y Tecnología TIN2006-00472Junta de Andalucía TIC-253

NL2CMD: An Updated Workflow for Natural Language to Bash Commands Translation

Translating natural language into Bash Commands is an emerging research field that has gained attention in recent years. Most efforts have focused on producing more accurate translation models. To the best of our knowledge, only two datasets are available, with one based on the other. Both datasets involve scraping through known data sources (through platforms like stack overflow, crowdsourcing, etc.) and hiring experts to validate and correct either the English text or Bash Commands. This paper provides two contributions to research on synthesizing Bash Commands from scratch. First, we describe a state-of-the-art translation model used to generate Bash Commands from the corresponding English text. Second, we introduce a new NL2CMD dataset that is automatically generated, involves minimal human intervention, and is over six times larger than prior datasets. Since the generation pipeline does not rely on existing Bash Commands, the distribution and types of commands can be custom adjusted. Our empirical results show how the scale and diversity of our dataset can offer unique opportunities for semantic parsing researchers

FHIRChain: Applying Blockchain to Securely and Scalably Share Clinical Data

Secure and scalable data sharing is essential for collaborative clinical decision making. Conventional clinical data efforts are often siloed, however, which creates barriers to efficient information exchange and impedes effective treatment decision made for patients. This paper provides four contributions to the study of applying blockchain technology to clinical data sharing in the context of technical requirements defined in the "Shared Nationwide Interoperability Roadmap" from the Office of the National Coordinator for Health Information Technology (ONC). First, we analyze the ONC requirements and their implications for blockchain-based systems. Second, we present FHIRChain, which is a blockchain-based architecture designed to meet ONC requirements by encapsulating the HL7 Fast Healthcare Interoperability Resources (FHIR) standard for shared clinical data. Third, we demonstrate a FHIRChain-based decentralized app using digital health identities to authenticate participants in a case study of collaborative decision making for remote cancer care. Fourth, we highlight key lessons learned from our case study