Black-Box Medicine

Personalized medicine, where Big Data meets Big Health, has been hailed as the next leap forward in health care, but that leap raises tremendous challenges for our current policy landscape. This Article is the first to label the phenomenon of black-box medicine, a version of personalized medicine in which researchers use sophisticated algorithms to examine huge troves of health data, finding complex, implicit relationships and making individualized assessments for patients. This new form of medicine offers potentially immense benefits but faces major hurdles both in development and in application. Development requires high investment; firms must develop new datasets, models, and validations, which are all nonrivalrous information goods that require incentives for welfare-optimizing levels of development. However, current innovation policy lacks the necessary incentives and instead pushes firms in socially suboptimal directions. Black-box medicine also raises significant challenges with respect to privacy, regulation, and commercialization. This Article describes black-box medicine, explains its differences-in-kind from current forms of medicine, and briefly explores the landscape of policy challenges ahead

Sensitivity analysis of expensive black-box systems using metamodeling

Simulations are becoming ever more common as a tool for designing complex products. Sensitivity analysis techniques can be applied to these simulations to gain insight, or to reduce the complexity of the problem at hand. However, these simulators are often expensive to evaluate and sensitivity analysis typically requires a large amount of evaluations. Metamodeling has been successfully applied in the past to reduce the amount of required evaluations for design tasks such as optimization and design space exploration. In this paper, we propose a novel sensitivity analysis algorithm for variance and derivative based indices using sequential sampling and metamodeling. Several stopping criteria are proposed and investigated to keep the total number of evaluations minimal. The results show that both variance and derivative based techniques can be accurately computed with a minimal amount of evaluations using fast metamodels and FLOLA-Voronoi or density sequential sampling algorithms.Comment: proceedings of winter simulation conference 201

Regulating Black-Box Medicine

Data drive modern medicine. And our tools to analyze those data are growing ever more powerful. As health data are collected in greater and greater amounts, sophisticated algorithms based on those data can drive medical innovation, improve the process of care, and increase efficiency. Those algorithms, however, vary widely in quality. Some are accurate and powerful, while others may be riddled with errors or based on faulty science. When an opaque algorithm recommends an insulin dose to a diabetic patient, how do we know that dose is correct? Patients, providers, and insurers face substantial difficulties in identifying high-quality algorithms; they lack both expertise and proprietary information. How should we ensure that medical algorithms are safe and effective? Medical algorithms need regulatory oversight, but that oversight must be appropriately tailored. Unfortunately, the Food and Drug Administration (FDA) has suggested that it will regulate algorithms under its traditional framework, a relatively rigid system that is likely to stifle innovation and to block the development of more flexible, current algorithms. This Article draws upon ideas from the new governance movement to suggest a different path. FDA should pursue a more adaptive regulatory approach with requirements that developers disclose information underlying their algorithms. Disclosure would allow FDA oversight to be supplemented with evaluation by providers, hospitals, and insurers. This collaborative approach would supplement the agency’s review with ongoing real-world feedback from sophisticated market actors. Medical algorithms have tremendous potential, but ensuring that such potential is developed in high-quality ways demands a careful balancing between public and private oversight, and a role for FDA that mediates—but does not dominate—the rapidly developing industry

Inside the Black Box: What Makes a Bank Efficient?

A decade of econometric research has shown that X-efficiency dominates scale and scope as the drivers of inefficiency in the U.S. banking industry. However, this research falls short in explaining the causes of the high degree of X-efficiency in the industry. This paper summarizes a four-year research effort to understand the drivers of this inefficiency. Key findings from this research, based on the most comprehensive studies to date of management practices in the retail banking industry, give insight into the drivers of X-efficiency. The paper provides a comprehensive framework for the analysis of X-efficiency in financial services. This paper was presented at the Wharton Financial Institutions Center's conference onRetail Banking, Services, Efficiency, Technology Management, Human Resource Management

Expert System Shell Based On AIML And FAQ

In the 2016, Forbes Magazine put McDonald's as the best fast food chains. The main factor of McDonald's achievements is the customers convenience. Currently, the system that is running in McDonald's generally less effective and lack of its time efficiency. Based on this problem the author aims to develop an intelligent system based on AIML interpreter, this application called as McBot intelligent system which is web based mobile application by transforming it into an intelligent waiter. There are three knowledge base to build the McBot intelligent base, which are Annotated A.L.I.C.E. AIML (AAA) files, Domain Specific Conversation and Frequently Asked Questions (FAQ). To implement this research, qualitative approach is used in this research. The evaluation of the McBot’s intelligence system is done by using black-box testing which is conducted using fifty datasets compiled from past competition questions of Loebner Prize Scored. The results of this testing was McBot was able to scored 72 points out of 100 points. Based on the result most of the questions are properly and correctly answered with total of 32 questions, 8 questions are partially met by the response and 10 questions are not met at all by the response. The results have shown that McBot has great potential being a conversation bot that can interact with end users. Even though the intelligent in this study is limited to only an general knowledge base and promotion, more information about the product can be added in the future. The contribution of this project is to build the McDonald’s expert system shell to adapt generic AIML and FAQ

Automated black box detection of HTTP GET request-based access control vulnerabilities in web applications

Automated and reproducible security testing of web applications is getting more and more important, driven by short software development cycles and constraints with respect to time and budget. Some types of vulnerabilities can already be detected reasonably well by automated security scanners, e.g., SQL injection or cross-site scripting vulnerabilities. However, other types of vulnerabilities are much harder to uncover in an automated way. This includes access control vulnerabilities, which are highly relevant in practice as they can grant unauthorized users access to security-critical data or functions in web applications. In this paper, a practical solution to automatically detect access control vulnerabilities in the context of HTTP GET requests is presented. The solution is based on previously proposed ideas, which are extended with novel approaches to enable completely automated access control testing with minimal configuration effort that enables frequent and reproducible testing. An evaluation using four web applications based on different technologies demonstrates the general applicability of the solution and that it can automatically uncover most access control vulnerabilities while keeping the number of false positives relatively low

Opening the Black Box: Explaining the Process of Basing a Health Recommender System on the I-Change Behavioral Change Model

Recommender systems are gaining traction in healthcare because they can tailor recommendations based on users' feedback concerning their appreciation of previous health-related messages. However, recommender systems are often not grounded in behavioral change theories, which may further increase the effectiveness of their recommendations. This paper's objective is to describe principles for designing and developing a health recommender system grounded in the I-Change behavioral change model that shall be implemented through a mobile app for a smoking cessation support clinical trial. We built upon an existing smoking cessation health recommender system that delivered motivational messages through a mobile app. A group of experts assessed how the system may be improved to address the behavioral change determinants of the I-Change behavioral change model. The resulting system features a hybrid recommender algorithm for computer tailoring smoking cessation messages. A total of 331 different motivational messages were designed using 10 health communication methods. The algorithm was designed to match 58 message characteristics to each user pro le by following the principles of the I-Change model and maintaining the bene ts of the recommender system algorithms. The mobile app resulted in a streamlined version that aimed to improve the user experience, and this system's design bridges the gap between health recommender systems and the use of behavioral change theories. This article presents a novel approach integrating recommender system technology, health behavior technology, and computer-tailored technology. Future researchers will be able to build upon the principles applied in this case study.European Union's Horizon 2020 Research and Innovation Programme under Grant 68112