The primary practice of healthcare artificial intelligence (AI) starts with model development, often using state-of-the-art AI, retrospectively evaluated using metrics lifted from the AI literature like AUROC and DICE score. However, good performance on these metrics may not translate to improved clinical outcomes. Instead, we argue for a better development pipeline constructed by working backward from the end goal of positively impacting clinically relevant outcomes using AI, leading to considerations of causality in model development and validation, and subsequently a better development pipeline. Healthcare AI should be "actionable," and the change in actions induced by AI should improve outcomes. Quantifying the effect of changes in actions on outcomes is causal inference. The development, evaluation, and validation of healthcare AI should therefore account for the causal effect of intervening with the AI on clinically relevant outcomes. Using a causal lens, we make recommendations for key stakeholders at various stages of the healthcare AI pipeline. Our recommendations aim to increase the positive impact of AI on clinical outcomes.

Delayed cerebral ischemia (DCI) is a complication seen in patients with subarachnoid hemorrhage stroke. It is a major predictor of poor outcomes and is detected late. Machine learning models are shown to be useful for early detection, however training such models suffers from small sample sizes due to rarity of the condition. Here we propose a Federated Learning approach to train a DCI classifier across three institutions to overcome challenges of sharing data across hospitals. We developed a framework for federated feature selection and built a federated ensemble classifier. We compared the performance of FL model to that obtained by training separate models at each site. FL significantly improved performance at only two sites. We found that this was due to feature distribution differences across sites. FL improves performance in sites with similar feature distributions, however, FL can worsen performance in sites with heterogeneous distributions. The results highlight both the benefit of FL and the need to assess dataset distribution similarity before conducting FL.

Summary In recent years, software development environments have changed from being driven by professional developers to being driven by technical end users. One of the key issues in end‐user‐driven software development environments is how to guarantee the quality of the software artifacts. Measuring the quality of developed software requires the correct specification of a quality range the software is expected to meet. However, technical end users are non‐professionals in engineering techniques for software requirements, and training a developer to be an expert in requirements engineering in a short period of time is difficult. This paper proposes a new software development life cycle based on reutilizing previously evaluated software requirements assets from completed projects. End‐User Requirements Engineering with Collaborative Animation, a tool that was developed to support the proposed software development life cycle, is described and demonstrated by application to three projects. The results from real cases are used as the basis for a discussion on the efficiency enhancement of requirements work, an increased rate of reusing legacy software requirements assets, and an improvement in the technical end user's individual competency level using the End‐User Requirements Engineering with Collaborative Animation. Copyright © 2017 John Wiley & Sons, Ltd.

Completeness in software requirements specification is one of the key factors for successful software development. For specifying software requirements, scenario-based approach is broadly used, comprising a basic flow regarding the successful use of the system, and alternative flows describing abnormal or less frequent interactions of the system. However, alternative flows tend to be frequently missed in many cases, because of the relative lower significance rather than the basic flow, which eventually have an influence on achieving the completeness of software requirements. In order to address the issue, we propose an approach for automatically recommending alternative flows from a basic flow by extracting the essential use case patterns based on the occurrence patterns of the agents and measuring the verb similarity between the main verbs of each scenario. In order to validate our approach, we apply it to three industrial case studies, and show comprehensiveness of the suggested alternative flows and synergic effectiveness for inexperienced developers.

Impressive advancements in recent smart devices suggests that the direction of software engineering's future is in development of System of Systems (SoS).Among many concepts that emerged from SoS, we focus on IT Ecosystem -a type of SoS that evolves itself in response to unplanned environment changes.Maintaining autonomy of its participant systems while preserving controllability over entire ecosystem involves various challenges that we need to solve.In this paper, we propose a middleware framework for supporting global adaptation of IT Ecosystem which guides how to determine the optimal adaptation strategy for configuring available systems to satisfy local constraints while achieving global goals.To support the selection of optimal set of participant systems, we have applied genetic algorithm.The effectiveness of our approach is evaluated through analyzing the results of a simulated unmanned forest management IT Ecosystem running the proposed framework while undergoing various environmental changes.

Security protocols are notoriously difficult to get right, and most go through several iterations before their hidden security vulnerabilities, which are hard to detect, are triggered. To help protocol designers and developers efficiently find non-trivial bugs, we introduce SYMCONF, a practical conformance testing tool that generates high-coverage test input packets using a conformance test suite and symbolic execution. Our approach can be viewed as the combination of conformance testing and symbolic execution: 1) it first selects symbolic inputs from an existing conformance test suite; 2) it then symbolically executes a network protocol implementation with the symbolic inputs; and 3) it finally generates high-coverage test input packets using a conformance test suite. We demonstrate the feasibility of this methodology by applying SYMCONF to the generation of a stream of high quality test input packets for multiple implementations of two network protocols, the Kerberos Telnet protocol and Dynamic Host Configuration Protocol (DHCP), and discovering non-trivial security bugs in the protocols.

Increased adoption of electronic health records has lead to a large amount of available patient data. However, these data are often difficult to access and visualize, leading to chokepoints in clinical practice. Wandering Data is a web application designed to deliver useful, intuitive visualizations of patient data, with a focus on efficient communication of information through uniform delivery and thoughtful design. In this paper we describe the system's development, structure, and functionality.

Medical cyber-physical systems (MCPS) are life-critical, context-aware, networked systems of medical devices. These systems are increasingly used in hospitals to provide high-quality continuous care for patients. The need to design complex MCPS that are both safe and effective has presented numerous challenges, including achieving high assurance in system software, intoperability, context-aware intelligence, autonomy, security and privacy, and device certifiability. In this paper, we discuss these challenges in developing MCPS, some of our work in addressing them, and several open research issues.