Machine learning risk prediction model for acute coronary syndrome and death from use of non-steroidal anti-inflammatory drugs in administrative data

Our aim was to investigate the usefulness of machine learning approaches on linked administrative health data at the population level in predicting older patients’ one-year risk of acute coronary syndrome and death following the use of non-steroidal anti-inflammatory drugs (NSAIDs). Patients from a Western Australian cardiovascular population who were supplied with NSAIDs between 1 Jan 2003 and 31 Dec 2004 were identified from Pharmaceutical Benefits Scheme data. Comorbidities from linked hospital admissions data and medication history were inputs. Admissions for acute coronary syndrome or death within one year from the first supply date were outputs. Machine learning classification methods were used to build models to predict ACS and death. Model performance was measured by the area under the receiver operating characteristic curve (AUC-ROC), sensitivity and specificity. There were 68,889 patients in the NSAIDs cohort with mean age 76 years and 54% were female. 1882 patients were admitted for acute coronary syndrome and 5405 patients died within one year after their first supply of NSAIDs. The multi-layer neural network, gradient boosting machine and support vector machine were applied to build various classification models. The gradient boosting machine achieved the best performance with an average AUC-ROC of 0.72 predicting ACS and 0.84 predicting death. Machine learning models applied to linked administrative data can potentially improve adverse outcome risk prediction. Further investigation of additional data and approaches are required to improve the performance for adverse outcome risk prediction

The Auditing Imperative for Automated Hiring

The high bar of proof to demonstrate either a disparate treatment or disparate impact cause of action under Title VII of the Civil Rights Act, coupled with the “black box” nature of many automated hiring systems, renders the detection and redress of bias in such algorithmic systems difficult. This Article, with contributions at the intersection of administrative law, employment & labor law, and law & technology, makes the central claim that the automation of hiring both facilitates and obfuscates employment discrimination. That phenomenon and the deployment of intellectual property law as a shield against the scrutiny of automated systems combine to form an insurmountable obstacle for disparate impact claimants.To ensure against the identified “bias in, bias out” phenomenon associated with automated decision-making, I argue that the employer’s affirmative duty of care as posited by other legal scholars creates “an auditing imperative” for algorithmic hiring systems. This auditing imperative mandates both internal and external audits of automated hiring systems, as well as record-keeping initiatives for job applications. Such audit requirements have precedent in other areas of law, as they are not dissimilar to the Occupational Safety and Health Administration (OSHA) audits in labor law or the Sarbanes-Oxley Act audit requirements in securities law.I also propose that employers that have subjected their automated hiring platforms to external audits could receive a certification mark, “the Fair Automated Hiring Mark,” which would serve to positively distinguish them in the labor market. Labor law mechanisms such as collective bargaining could be an effective approach to combating the bias in automated hiring by establishing criteria for the data deployed in automated employment decision-making and creating standards for the protection and portability of said data. The Article concludes by noting that automated hiring, which captures a vast array of applicant data, merits greater legal oversight given the potential for “algorithmic blackballing,” a phenomenon that could continue to thwart many applicants’ future job bids

Five Privacy Principles (from the GDPR) the United States Should Adopt To Advance Economic Justice

Algorithmic profiling technologies are impeding the economic security of low-income people in the United States. Based on their digital profiles, low- income people are targeted for predatory marketing campaigns and financial products. At the same time, algorithmic decision-making can result in their exclusion from mainstream employment, housing, financial, health care, and educational opportunities. Government agencies are turning to algorithms to apportion social services, yet these algorithms lack transparency, leaving thousands of people adrift without state support and not knowing why. Marginalized communities are also subject to disproportionately high levels of surveillance, including facial recognition technology and the use of predictive policing software.American privacy law is no bulwark against these profiling harms, instead placing the onus of protecting personal data on individuals while leaving government and businesses largely free to collect, analyze, share, and sell personal data. By contrast, in the European Union, the General Data Protection Regulation (GDPR) gives EU residents numerous, enforceable rights to control their personal data. Spurred in part by the GDPR, Congress is debating whether to adopt comprehensive privacy legislation in the United States. This article contends that the GDPR contains several provisions that have the potential to limit digital discrimination against the poor, while enhancing their economic stability and mobility. The GDPR provides the following: (1) the right to an explanation about automated decision-making; (2) the right not to be subject to decisions based solely on automated profiling; (3) the right to be forgotten; (4) opportunities for public participation in data processing programs; and (5) robust implementation and enforcement tools. The interests of low-income people must be part of privacy lawmaking, and the GDPR is a useful template for thinking about how to meet their data privacy needs

Enabling Automated, Conversational Health Coaching with Human-Centered Artificial Intelligence

Health coaching is a promising approach to support self-management of chronic conditions like type 2 diabetes; however, there aren’t enough coaching practitioners to support those in need. Advances in Artificial Intelligence (AI) and Machine Learning (ML) have the potential to enable innovative, automated health coaching interventions, but important gaps remain in applying AI and ML to coaching interventions. This thesis aims to identify computational approaches and interactive technologies that enable automated health coaching systems. First, I utilized computational approaches that leverage individuals’ self-tracking and health data and used an expert system to translate ML inferences into personalized nutrition goal recommendations. The system, GlucoGoalie, was evaluated in multiple studies including a 4-week deployment study which demonstrated the feasibility of the approach. Second, I compared human-powered and automated/chatbot approaches to health coaching in a 3-week study which found that t2.coach — a scripted, theoretically-grounded chatbot designed through an iterative, user-centered process — cultivated a coach-like experience that had many similarities to the experience of messaging with actual health coaches, and outlined directions for automated, conversational coaching interventions. Third, I examined multiple AI approaches to enable micro-coaching dialogs — brief coaching conversations related to specific meals, to support achievement of nutrition goals — including a knowledge-based system for natural language understanding, and a data-driven, reinforcement learning approach for dialog management. Together, the results of these studies contribute methods and insights that take steps towards more intelligent conversational coaching systems, with resonance to research in informatics, human-computer interaction, and health coaching

Front-Line Physicians' Satisfaction with Information Systems in Hospitals

Day-to-day operations management in hospital units is difficult due to continuously varying situations, several actors involved and a vast number of information systems in use. The aim of this study was to describe front-line physicians' satisfaction with existing information systems needed to support the day-to-day operations management in hospitals. A cross-sectional survey was used and data chosen with stratified random sampling were collected in nine hospitals. Data were analyzed with descriptive and inferential statistical methods. The response rate was 65 % (n = 111). The physicians reported that information systems support their decision making to some extent, but they do not improve access to information nor are they tailored for physicians. The respondents also reported that they need to use several information systems to support decision making and that they would prefer one information system to access important information. Improved information access would better support physicians' decision making and has the potential to improve the quality of decisions and speed up the decision making process.Peer reviewe

CORPORATE SOCIAL RESPONSIBILITY IN ROMANIA

The purpose of this paper is to identify the main opportunities and limitations of corporate social responsibility (CSR). The survey was defined with the aim to involve the highest possible number of relevant CSR topics and give the issue a more wholesome perspective. It provides a basis for further comprehension and deeper analyses of specific CSR areas. The conditions determining the success of CSR in Romania have been defined in the paper on the basis of the previously cumulative knowledge as well as the results of various researches. This paper provides knowledge which may be useful in the programs promoting CSR.Corporate social responsibility, Supportive policies, Romania