Education•Chicago, Illinois, United States•
About: Chicago-Kent College of Law is a education organization based out in Chicago, Illinois, United States. It is known for research contribution in the topics: Supreme court & Comparative law. The organization has 101 authors who have published 563 publications receiving 5671 citations. The organization is also known as: IIT Chicago-Kent College of Law.
Papers published on a yearly basis
TL;DR: This study shows that there is a wide range of potential causes of adverse events that should be considered, and that careful attention must be paid to errors with interactive or administrative causes.
Abstract: Summary Background Data about the frequency of adverse events related to inappropriate care in hospitals come from studies of medical records as if they represented a true record of adverse events. In a prospective, observational design we analysed discussion of adverse events during the care of all patients admitted to three units of a large, urban teaching hospital affiliated to a university medical school. Discussion took place during routine clinical meetings. We undertook the study to enhance understanding of the incidence and scope of adverse events as a basis for preventing them. Methods Ethnographers trained in qualitative observational research attended day-shift, weekday, regularly scheduled attending rounds, residents' work rounds, nursing shift changes, case conferences, and other scheduled meetings in three study units as well as various departmental and section meetings. They recorded all adverse events during patient care discussed at these meetings and developed a classification scheme to code the data. Data were collected about health-care providers' own assessments about the appropriateness of the care that patients received to assess the nature and impact of adverse events and how health-care providers and patients responded to the adverse events. Findings Of the 1047 patients in the study, 185 (17·7%) were said to have had at least one serious adverse event; having an initial event was linked to the seriousness of the patient's underlying illness. Patients with long stays in hospital had more adverse events than those with short stays. The likelihood of experiencing an adverse event increased about 6% for each day of hospital stay. 37·8% of adverse events were caused by an individual, 15·6% had interactive causes, and 9·8% were due to administrative decisions. Although 17·7% of patients experienced serious events that led to longer hospital stays and increased costs to the patients, only 1·2% (13) of the 1047 patients made claims for compensation. Interpretation This study shows that there is a wide range of potential causes of adverse events that should be considered, and that careful attention must be paid to errors with interactive or administrative causes. Health-care providers' own discussions of adverse events can be a good source of data for proactive error prevention.
TL;DR: The accelerated pace of gene discovery and molecular medicine portend a future in which information about a plethora of disease genes can be readily obtained, and so does the potential for discrimination in health insurance coverage for an ever increasing number of Americans.
Abstract: The accelerated pace of gene discovery and molecular medicine portend a future in which information about a plethora of disease genes can be readily obtained. As at-risk populations are identified, research can be done to determine effective prevention and treatment strategies that will lower the personal, social and perhaps the financial costs of disease in the future. We all carry genes that predispose to common illnesses. In many circumstances knowing this information can be beneficial, as it allows individualized strategies to be designed to reduce the risk of illness. But, as knowledge about the genetic basis of common disorders grows, so does the potential for discrimination in health insurance coverage for an ever increasing number of Americans.
TL;DR: Despite its enormous importance in the evolution of competition law in Europe, ordoliberal thought has received little attention in the English-speaking world, and it remains all but unknown in the United States as mentioned in this paper.
Abstract: Despite its enormous importance in the evolution of competition law in Europe, ordoliberal thought — and German neo-liberal thought generally — has received little attention in the English-speaking world, and it remains all but unknown in the United States. Moreover, except in Germany, awareness of these ideas has been confined almost exclusively to economists, while lawyers and political scientists have seldom been exposed to them. Finally, there has been little modern study of the impact of these ideas on the development of European thought.In this article I address this critically important gap in our understanding of European thought and institutions. I seek to contribute to a fuller understanding of ordoliberal thought, particularly among non-German readers and among lawyers and policy analysts — to whom (together with economists) this body of thought has been addressed. In addition, I sketch the roles these ideas have played in the evolution of German and European legal thought and institutions, in general, and competition law, in particular.
TL;DR: A time-evolving random forest classifier that leverages unique feature engineering to predict more than 240,000 justice votes and 28,000 cases outcomes over nearly two centuries and outperforms null models at both the justice and case level under both parametric and non-parametric tests.
Abstract: Building upon developments in theoretical and applied machine learning, as well as the efforts of various scholars including Guimera and Sales-Pardo (2011), Ruger et al. (2004), and Martin et al. (2004), we construct a model designed to predict the voting behavior of the Supreme Court of the United States. Using the extremely randomized tree method first proposed in Geurts, et al. (2006), a method similar to the random forest approach developed in Breiman (2001), as well as novel feature engineering, we predict more than sixty years of decisions by the Supreme Court of the United States (1953-2013). Using only data available prior to the date of decision, our model correctly identifies 69.7% of the Court’s overall affirm/reverse decisions and correctly forecasts 70.9% of the votes of individual justices across 7,700 cases and more than 68,000 justice votes. Our performance is consistent with the general level of prediction offered by prior scholars. However, our model is distinctive as it is the first robust, generalized, and fully predictive model of Supreme Court voting behavior offered to date. Our model predicts six decades of behavior of thirty Justices appointed by thirteen Presidents. With a more sound methodological foundation, our results represent a major advance for the science of quantitative legal prediction and portend a range of other potential applications, such as those described in Katz (2013).
TL;DR: The authors used a time-evolving random forest classifier that leverages unique feature engineering to predict more than 240,000 justice votes and 28,000 cases outcomes over nearly two centuries (1816-2015).
Abstract: Building on developments in machine learning and prior work in the science of judicial prediction, we construct a model designed to predict the behavior of the Supreme Court of the United States in a generalized, out-of-sample context. To do so, we develop a time-evolving random forest classifier that leverages unique feature engineering to predict more than 240,000 justice votes and 28,000 cases outcomes over nearly two centuries (1816-2015). Using only data available prior to decision, our model outperforms null (baseline) models at both the justice and case level under both parametric and non-parametric tests. Over nearly two centuries, we achieve 70.2% accuracy at the case outcome level and 71.9% at the justice vote level. More recently, over the past century, we outperform an in-sample optimized null model by nearly 5%. Our performance is consistent with, and improves on the general level of prediction demonstrated by prior work; however, our model is distinctive because it can be applied out-of-sample to the entire past and future of the Court, not a single term. Our results represent an important advance for the science of quantitative legal prediction and portend a range of other potential applications.
Showing all 101 results
|Lori B. Andrews||27||118||3413|
|Frederick M. Abbott||24||132||2207|
|Daniel Martin Katz||20||77||1215|
|Randy E. Barnett||19||135||1701|
|A. Dan Tarlock||18||142||1220|
|Richard W. Wright||17||47||979|
|Claire A. Hill||17||107||1059|
|Michael James Bommarito||16||55||849|
|Richard L. Hasen||16||131||1009|
|Graeme B. Dinwoodie||16||101||887|
|David J. Gerber||15||100||1241|
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