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The halakhic practice does more than regulating the inner world; it takes part in forming it, generating a unique legal subject. But is there a unique halakhic Self? This article examines this question in the context of Tannaitic halakha, both Mishnaic and Midrashic. More specifically I ask whether one can speak of subjectivity in Tannaitic halakha. I study the relationship between anonymous halakhic rulings and specific positions presented in the name of individual sages or argued with the force of personal commitment. Through analyzing the “I” language in Tannaitic literature, in comparison with the rhetoric of prerabbinic halakha, I wish to advance the ongoing search for the rabbinic Self.

The Coronavirus disease 2019 (COVID-19) global pandemic has transformed almost every facet of human society throughout the world. Against an emerging, highly transmissible disease, governments worldwide have implemented non-pharmaceutical interventions (NPIs) to slow the spread of the virus. Examples of such interventions include community actions, such as school closures or restrictions on mass gatherings, individual actions including mask wearing and self-quarantine, and environmental actions such as cleaning public facilities. We present the Worldwide Non-pharmaceutical Interventions Tracker for COVID-19 (WNTRAC), a comprehensive dataset consisting of over 6,000 NPIs implemented worldwide since the start of the pandemic. WNTRAC covers NPIs implemented across 261 countries and territories, and classifies NPIs into a taxonomy of 16 NPI types. NPIs are automatically extracted daily from Wikipedia articles using natural language processing techniques and then manually validated to ensure accuracy and veracity. We hope that the dataset will prove valuable for policymakers, public health leaders, and researchers in modeling and analysis efforts to control the spread of COVID-19., Measurement(s) Preventive Intervention • Public Health Technology Type(s) natural language processing objective • Artificial Intelligence Sample Characteristic - Environment anthropogenic environment Sample Characteristic - Location Global Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.13999484

Background Assistive automatic seizure detection can empower human annotators to shorten patient monitoring data review times. We present a proof-of-concept for a seizure detection system that is sensitive, automated, patient-specific, and tunable to maximise sensitivity while minimizing human annotation times. The system uses custom data preparation methods, deep learning analytics and electroencephalography (EEG) data. Methods Scalp EEG data of 365 patients containing 171,745 s ictal and 2,185,864 s interictal samples obtained from clinical monitoring systems were analysed as part of a crowdsourced artificial intelligence (AI) challenge. Participants were tasked to develop an ictal/interictal classifier with high sensitivity and low false alarm rates. We built a challenge platform that prevented participants from downloading or directly accessing the data while allowing crowdsourced model development. Findings The automatic detection system achieved tunable sensitivities between 75.00% and 91.60% allowing a reduction in the amount of raw EEG data to be reviewed by a human annotator by factors between 142x, and 22x respectively. The algorithm enables instantaneous reviewer-managed optimization of the balance between sensitivity and the amount of raw EEG data to be reviewed. Interpretation This study demonstrates the utility of deep learning for patient-specific seizure detection in EEG data. Furthermore, deep learning in combination with a human reviewer can provide the basis for an assistive data labelling system lowering the time of manual review while maintaining human expert annotation performance. Funding IBM employed all IBM Research authors. Temple University employed all Temple University authors. The Icahn School of Medicine at Mount Sinai employed Eren Ahsen. The corresponding authors Stefan Harrer and Gustavo Stolovitzky declare that they had full access to all the data in the study and that they had final responsibility for the decision to submit for publication.

Background Computational models on the basis of deep neural networks are increasingly used to analyze health care data. However, the efficacy of traditional computational models in radiology is a matter of debate. Purpose To evaluate the accuracy and efficiency of a combined machine and deep learning approach for early breast cancer detection applied to a linked set of digital mammography images and electronic health records. Materials and Methods In this retrospective study, 52 936 images were collected in 13 234 women who underwent at least one mammogram between 2013 and 2017, and who had health records for at least 1 year before undergoing mammography. The algorithm was trained on 9611 mammograms and health records of women to make two breast cancer predictions: to predict biopsy malignancy and to differentiate normal from abnormal screening examinations. The study estimated the association of features with outcomes by using t test and Fisher exact test. The model comparisons were performed with a 95% confidence interval (CI) or by using the DeLong test. Results The resulting algorithm was validated in 1055 women and tested in 2548 women (mean age, 55 years ± 10 [standard deviation]). In the test set, the algorithm identified 34 of 71 (48%) false-negative findings on mammograms. For the malignancy prediction objective, the algorithm obtained an area under the receiver operating characteristic curve (AUC) of 0.91 (95% CI: 0.89, 0.93), with specificity of 77.3% (95% CI: 69.2%, 85.4%) at a sensitivity of 87%. When trained on clinical data alone, the model performed significantly better than the Gail model (AUC, 0.78 vs 0.54, respectively; P < .004). Conclusion The algorithm, which combined machine-learning and deep-learning approaches, can be applied to assess breast cancer at a level comparable to radiologists and has the potential to substantially reduce missed diagnoses of breast cancer. © RSNA, 2019 Online supplemental material is available for this article.

“No-shows”, defined as missed appointments or late cancellations, is a central problem in healthcare systems. It has appeared to intensify during the COVID-19 pandemic and the nonpharmaceutical interventions, such as closures, taken to slow its spread. No-shows interfere with patients’ continuous care, lead to inefficient utilization of medical resources, and increase healthcare costs. We present a comprehensive analysis of no-shows for breast imaging appointments made during 2020 in a large medical network in Israel. We applied advanced machine learning methods to provide insights into novel and known predictors. Additionally, we employed causal inference methodology to infer the effect of closures on no-shows, after accounting for confounding biases, and demonstrate the superiority of adversarial balancing over inverse probability weighting in correcting these biases. Our results imply that a patient’s perceived risk of cancer and the COVID-19 time-based factors are major predictors. Further, we reveal that closures impact patients over 60, but not patients undergoing advanced diagnostic examinations.

Characterization of lesions by artificial intelligence (AI) has been the subject of extensive research. In recent years, many studies demonstrated the ability of convolution neural networks (CNNs) to successfully distinguish between malignant and benign breast lesions in mammography (MG) images. However, to date, no study has assessed the specific sub-type of lesions in MG images, as detailed in histolopathology reports. We present a method for finer classification of breast lesions in MG images into multiple pathology sub-types. Our approach works well with radiologists’ diagnostic workflow, and uses data available in radiology reports. The proposed Dual-Radiology Dual-Resolution Network (Du-Rad Du-Res Net) receives dual input from the radiologist and dual image resolutions. The radiologist input includes annotation of the lesion area and semantic radiology features; the dual image resolutions comprise a low resolution of the entire mammogram and a high resolution of the lesion area. The network estimates the likelihood of malignancy, as well as the associated pathological sub-type. We show that the combined input of the lesion region of interest (ROI) and the entire mammogram is important for optimizing the model’s performance. We tested the AI in a reader study on a dataset of 100 heldout cases. The AI outperformed three breast radiologists in the task of lesion histopathology sub-typing.

Although a plethora of research articles on AI methods on COVID-19 medical imaging are published, their clinical value remains unclear. We conducted the largest systematic review of the literature addressing the utility of AI in imaging for COVID-19 patient care. By keyword searches on PubMed and preprint servers throughout 2020, we identified 463 manuscripts and performed a systematic meta-analysis to assess their technical merit and clinical relevance. Our analysis evidences a significant disparity between clinical and AI communities, both in the focus on imaging modalities (AI experts neglected CT and Ultrasound, favoring X-Ray) and performed tasks (71.9% of AI papers centered on diagnosis). The vast majority of manuscripts were found deficient regarding potential use in clinical practice, but 2.7% (N=12) publications were assigned a high maturity level and are summarized in greater detail. We provide an itemized discussion of the challenges in developing clinically relevant AI solutions with recommendations and remedies., Graphical Abstract, In medical imaging, artificial intelligence (AI) has the potential to save time and cost in the healthcare system by advancing rapid patient stratification. We conducted the largest systematic review (N=463) of AI methodology on medical imaging data from COVID-19 patients. AI experts largely failed to focus on clinically relevant tasks and imaging modalities and only few publications are found valuable. On the roadmap toward developing robust and clinically valuable AI solutions, we identify key challenges and provide possible remedies.

Objective Digital Technology has become a pervasive, even ubiquitous part of our daily lives, affecting almost every aspect of our lives. Although the uptake of digital technology in health care has lagged behind other sectors, today, digital health is already becoming a cornerstone of developed health systems all over the world. Hence, the question is not whether we should adopt digital technology in health care, but how to do it most effectively. Digitally enabled remote care, or telemedicine has been available for many years but large-scale adoption has been slow. COVID-19 has caused a quantum leap in this area and particularly in the area of chronic disease and cancer care. The objective of this article is to briefly review the literature on the use of digitally enabled remote health care, in general and in cancer care specifically, with a focus on nursing practice, and to define the questions that need to be asked to guide effective implementation. Data Sources Review of the literature and the experience of the authors. Conclusion There is increasing uptake of digitally enabled remote care. A growing body of evidence suggests that care delivered via telemedicine can be both safe and effective, in some cases with better outcomes than conventional face-to-face care. However, tele-oncology has not yet become standard practice. Digital health solutions need to be integrated into the patient pathway and in health care team practices for optimal supportive care in oncology in line with appropriate guidelines. Training education and formative evaluation are required to guide effective implementation. Formulating the right questions to ask is a critical starting point. Implications for Nursing Practice There is very little debate today about the centrality of the role of the oncology nurse in coordinating care, guiding the patient, and providing ongoing support. Mobile technology provides an opportunity for monitoring and support through a minimally burdensome, maximally accessible approach. Moreover, smartphones and applications allow for repeated evaluation of adherence and symptoms in real time, ideally enhancing care for patients. However, even with the growing acceptance of “nursing telepractice”, there are challenges and barriers to overcome to mainstream digital health into oncology nursing practice. Telemedicine services go much further than simply digitizing traditionally analogue health care processes and services, they fundamentally reorganize processes, procedures, and services. Thus, in addition to training and education, nursing tele-oncology demands a service transformation.

The global COVID-19 pandemic has accelerated the development of numerous digital technologies in medicine from telemedicine to remote monitoring. Concurrently, the pandemic has resulted in huge pressures on healthcare systems. Medical imaging (MI) from chest radiographs to computed tomography and ultrasound of the thorax have played an important role in the diagnosis and management of the coronavirus infection.We conducted the, to date, largest systematic review of the literature addressing the utility of Artificial Intelligence (AI) in MI for COVID-19 management. Through keyword matching on PubMed and preprint servers, including arXiv, bioRxiv and medRxiv, 463 papers were selected for a meta-analysis, with manual reviews to assess the clinical relevance of AI solutions. Further, we evaluated the maturity of the papers based on five criteria assessing the state of the field: peer-review, patient dataset size and origin, algorithmic complexity, experimental rigor and clinical deployment.In 2020, we identified 4977 papers on MI in COVID-19, of which 872 mentioned the term AI. 2039 papers of the 4977 were specific to imaging modalities with a majority of 83.8% focusing on CT, while 10% involved CXR and 6.2% used LUS. Meanwhile, the AI literature predominantly analyzed CXR data (49.7%), with 38.7% using CT and 1.5% LUS. Only a small portion of the papers were judged as mature (2.7 %). 71.9% of AI papers centered on disease detection.This review evidences a disparity between clinicians and the AI community, both in the focus on imaging modalities and performed tasks. Therefore, in order to develop clinically relevant AI solutions, rigorously validated on large-scale patient data, we foresee a need for improved collaboration between the two communities ensuring optimal outcomes and allocation of resources. AI may aid clinicians and radiologists by providing better tools for localization and quantification of disease features and changes thereof, and, with integration of clinical data, may provide better diagnostic performance and prognostic value.

Real-world healthcare data hold the potential to identify therapeutic solutions for progressive diseases by efficiently pinpointing safe and efficacious repurposing drug candidates. This approach circumvents key early clinical development challenges, particularly relevant for neurological diseases, concordant with the vision of the 21st Century Cures Act. However, to-date, these data have been utilized mainly for confirmatory purposes rather than as drug discovery engines. Here, we demonstrate the usefulness of real-world data in identifying drug repurposing candidates for disease- modifying effects, specifically candidate marketed drugs that exhibit beneficial effects on Parkinson’s disease (PD) progression. We performed an observational study in cohorts of ascertained PD patients extracted from two large medical databases, Explorys SuperMart (N=88,867) and IBM MarketScan Research Databases (N=106,395); and applied two conceptually different, well-established causal inference methods to estimate the effect of hundreds of drugs on delaying dementia onset as a proxy for slowing PD progression. Using this approach, we identified two drugs that manifested significant beneficial effects on PD progression in both datasets: rasagiline, narrowly indicated for PD motor symptoms; and zolpidem, a psycholeptic. Each confers its effects through distinct mechanisms, which we explored via a comparison of estimated effects within the drug classification ontology. We conclude that analysis of observational healthcare data, emulating otherwise costly, large, and lengthy clinical trials, can highlight promising repurposing candidates, to be validated in prospective registration trials, for common, late-onset progressive diseases for which disease-modifying therapeutic solutions are scarce.

We investigate the potential contribution of an AI system as a safety net application for radiologists in breast cancer screening. As a safety net, the AI alerts on cases suspected to be malignant which the radiologist did not recommend for a recall. We analyzed held-out data of 2,638 exams enriched with 90 missed cancers. In screening mammography settings, we show that a system alerting on 11 out of every 1,000 cases, could detect up to 10.7% of the radiologists’ missed cancers. Thus, significantly increasing radiologist’s sensitivity to 80.3%, while only slightly decreasing their specificity to 95.3%. Importantly, the safety net demonstrated a significant contribution to their performance even when radiologists utilized both mammography and ultrasound images. In those settings, it would have alerted 8.5 times per 1,000 cases, and detected 11.7% of the radiologists’ missed cancers. In an analysis of the missed cancers by an expert, we found that most of the cancers detected by the AI were visible post-hoc. Finally, we performed a reader study with five radiologists over 120 exams, 10 of which were originally missed cancers. The AI safety net was able to assist 3 out of the 5 radiologists in detecting missed cancers without raising any false alerts.

The last chapter of tractate Berakhot (“Benedictions”) is exceptional. It does not discuss the set daily liturgy, as does the rest of the tractate, but rather presents benedictions to be said following encounters with various phenomena, both seen and heard. These benedictions carry none of the permanence of the fixed liturgy, but are reflexive to the world around them. This makes the closing chapter a rare depository of information about the rabbinic world view. In this list of phenomena and their appropriate liturgical responses, the discerning reader can detect the manner in which the rabbis theologically conceptualize the world around them. This article focuses on the benediction that opens the chapter—for miracles—and the discussions about it in the two Talmuds. A close reading of these discussions reveals some of the ways in which rabbis in Roman Palestine and Sassanian Babylonia explained miracles and account for their (non)existence in their own reality.

The goy has been present in Jewish discourses since antiquity. Despite this, its birth and history have received almost no scholarly attention. In this paper we shift the focus from the various historical attitudes towards the goy, to the very constitution of the concept and the dichotomy it constructs. We claim that scholars have been anachronistically reading Jewish (or Judaean) texts from the centuries before the common era as if they contained the Jew/goy distinction. Through a series of readings in texts like Jubilees, Pseudo-Aristeas, Joseph and Aseneth, 1-4 Maccabees, the Damascus Document, we seek to demonstrate the plurality of options for separation that existed before the Jew/goy discourse took over.

This chapter traces the developments of various terms denoting “others” in biblical literature. In much of the biblical corpus, Israel is still one goy among many, and the difference between it and its Others is neither binary nor stable. After a brief analysis of the dynamics of familial and ethnwic separations in Genesis and Exodus, this chapter concentrates on the priestly and Deuteronomistic modes of separating peoples, examines the novelty and limitedness of the Deuteronomistic legislation, where the nokhri (stranger) is systematically contrasted for the first time with the Israelite (referred to as “your brother”), and follows the various modes of separations and their rationales.

In the standard agnostic multiclass model, pairs are sampled independently from some underlying distribution. This distribution induces a conditional probability over the labels given an instance, and our goal in this paper is to learn this conditional distribution. Since even unconditional densities are quite challenging to learn, we give our learner access to pairs. Assuming a base learner oracle in this model, we might seek a boosting algorithm for constructing a strong learner. Unfortunately, without further assumptions, this is provably impossible. However, we give a new boosting algorithm that succeeds in the following sense: given a base learner guaranteed to achieve some average accuracy (i.e., risk), we efficiently construct a learner that achieves the same level of accuracy with arbitrarily high probability. We give generalization guarantees of several different kinds, including distribution-free accuracy and risk bounds. None of our estimates depend on the number of boosting rounds and some of them admit dimension-free formulations.

This paper studies the birth of the concept of the gentile (goy). We begin by presenting an outline of a genealogy of “goy,” as a word and a concept, from the Bible to rabbinic literature, and then focus on one chapter in this genealogy: the meaning and use of ethne in Paul's epistles. We claim that Paul takes a crucial part in the emergence of the goy in its new, non-ethnic, privatized and generalized sense. In contrast to the scholarly consensus, according to which Paul's simply borrows his binary distinction between Jews and ethne from an established Jewish tradition, we show that no such tradition existed, and that Paul in fact plays a key role in forming this new meaning of ethne /goyim and in consolidating the binary division between the Jews and their others. The paper reconstructs the discursive conditions and form of reasoning underlying Paul's novel rendering of ethne as generalized-individualized others. This calls for a thorough revision of the recent surge of interest in the teaching of Paul by historians, political-theologians and philosophers who all presuppose the Jew-Gentile division as a given.

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Objectives The EuResist expert system is a novel data-driven online system for computing the probability of 8-week success for any given pair of HIV-1 genotype and combination antiretroviral therapy regimen plus optional patient information. The objective of this study was to compare the EuResist system vs. human experts (EVE) for the ability to predict response to treatment. Methods The EuResist system was compared with 10 HIV-1 drug resistance experts for the ability to predict 8-week response to 25 treatment cases derived from the EuResist database validation data set. All current and past patient data were made available to simulate clinical practice. The experts were asked to provide a qualitative and quantitative estimate of the probability of treatment success. Results There were 15 treatment successes and 10 treatment failures. In the classification task, the number of mislabelled cases was six for EuResist and 6–13 for the human experts [mean±standard deviation (SD) 9.1±1.9]. The accuracy of EuResist was higher than the average for the experts (0.76 vs. 0.64, respectively). The quantitative estimates computed by EuResist were significantly correlated (Pearson r=0.695, P

The increasing interest in Jewish carnality and sexuality in recent years has influenced many areas of research, one of which is the rabbinic 'evil yetzer' (inclination). More and more studies discuss the rabbinic yetzer in sexual terms, and yetzer has become almost synonymous with ‘sexuality’. In this paper I wish to show that this perception lacks textual justification. Most rabbinic sources present the yetzer as the enemy of men, constantly dragging them to sin, and accounting for their difficulty to become and remain servants of God. The yetzer drags one to sexual sins just as it drags to any other sinful acts, as it is an antinomian entity, the enemy of Torah and its commandments. The sexualisation of the yetzer appear only in Babylonian sources, and for the main part is a post Amoraic development. The paper ends with an attempt to locate this phenomenon in a broader Babylonian context: a process of hyper-sexualisation which takes place in the Bavli alone.

The appearance in recent years of an impressive series of books, articles, and mainly dissertations on various aspects of the Mishnah collectively signifies something greater than the sum of its parts. These works herald the emergence of a new wave of Mishnah research. While differing significantly in their themes and methods, all the works discussed here share some basic methodological assumptions that are not shared by more “traditional” studies. Among these are a holistic attitude to the Mishnah as a composition; interest in questions of variegation of genre and style (narratives, rituals, lists, etc.); sensitivity to literary devices and techniques; and the use of new interpretive paradigms from rhetoric, cultural, and performative studies.

The scholarly consensus on the Rabbinic concept of the yetzer (inclination) takes for granted the existence of two yetzarim, good and evil, in every person. Some have noticed the marginality of the good yetzer in rabbinic discourse, as well as the fact that most sources discuss only one yetzer, but ascribed it to the inherent imbalance of power between the two yetzarim. In this paper I wish to rethink this scholarly consensus, while taking the single-yetzer model, which appears in almost all rabbinic sources, seriously. A systematic analysis of all rabbinic references to the yetzer, which attempts to distinguish as much as possible between sources, both chronologically (early or late), and geographically (Palestinian or Babylonian), yields a picture markedly different from the common view, and helps locate the (rather marginal) place of the two yetzarim in context of the rabbinic corpus in its entirety.

Reading is a complex cognitive process, errors in which may assume diverse forms. In this study, introducing a novel approach, we use two families of probabilistic graphical models to analyze patterns of reading errors made by dyslexic people: an LDA-based model and two Naeve Bayes models which differ by their assumptions about the generation process of reading errors. The models are trained on a large corpus of reading errors. Results show that a Naeve Bayes model achieves highest accuracy compared to labels given by clinicians (AUC = 0.801 ± 0.05), thus providing the first automated and objective diagnosis tool for dyslexia which is solely based on reading errors data. Results also show that the LDA-based model best captures patterns of reading errors and could therefore contribute to the understanding of dyslexia and to future improvement of the diagnostic procedure. Finally, we draw on our results to shed light on a theoretical debate about the definition and heterogeneity of dyslexia. Our results support a model assuming multiple dyslexia subtypes, that of a heterogeneous view of dyslexia.

This paper explores the way several biblical stories of sexual misconduct are retold in The Testament of Reuben, and other, related, compositions. Through a close reading of these narratives we shows how, in contrast to the biblical narrative, the Testaments tends to expand the female characters' responsibility for causing the forbidden acts. This trend is further revealed as but part of a much broader transformation in which internal thoughts and inclinations rather than actions become the focus of the religious struggle. The stark misogyny of these Testaments, rather than merely a commonplace to be noted, is revealed here as a representation of a whole new era of sexual discourse; one which carries with it also a new economy of gender. This, finally, leads us to rethink Foucault's thesis regarding the Christian origins of sexuality in Late Antiquity.

Machine learning methods that can handle variable-size structured data such as sequences and graphs include Bayesian networks (BNs) and Recursive Neural Networks (RNNs). In both classes of models, the data is modeled using a set of observed and hidden variables associated with the nodes of a directed acyclic graph. In BNs, the conditional relationships between parent and child variables are probabilistic, whereas in RNNs they are deterministic and parameterized by neural networks. Here, we study the formal relationship between both classes of models and show that when the source nodes variables are observed, RNNs can be viewed as limits, both in distribution and probability, of BNs with local conditional distributions that have vanishing covariance matrices and converge to delta functions. Conditions for uniform convergence are also given together with an analysis of the behavior and exactness of Belief Propagation (BP) in 'deterministic' BNs. Implications for the design of mixed architectures and the corresponding inference algorithms are briefly discussed.

Mutual learning process between two parity feed-forward networks with discrete and continuous weights is studied analytically, and we find that the number of steps required to achieve full synchronization between the two networks in the case of discrete weights is finite. The synchronization process is shown to be non-self-averaging and the analytical solution is based on random auxiliary variables. The learning time of an attacker that is trying to imitate one of the networks is examined analytically and is found to be much longer than the synchronization time. Analytical results are found to be in agreement with simulations.

New modes of environmental regulation are said to have transcended the public/private divide. These new regulatory schemes - referred to as non-coercive orderings, self-regulation, co-regulation, metaregulation and social regulation - set aside the formal nature of the regulating entity, the regulated entity, and the tools of regulation. Instead of asking whether the means, objects and formulators of the regulation are public or private, the focus lies on the substance and effectiveness of the regulation in mitigating environmental harms. In this Article we argue that despite these claims, often advanced by new governance proponents, the public/private divide in in fact alive and well, informing and impacting the ways in which various regulatory schemes are justified and legitimated. We exemplify this argument through an analysis of the role of three entities in international environmental regulation: the state (and its perception as sovereign), local governments, and civil society entities (both NGOs and business corporations). This Article then suggests three consequences of the persistence of the public/private dichotomy and its denial: it produces a “tilt” towards the private; it tends to hide conflicts and disagreements, projecting an image of a frictionless world; and it prevents an imagination of a different world that transcends the structure of social life embedded in it.

Attorney fees fund litigation yet little is known about fees in most cases. Fee data are rarely available in the United States or in English rule, loser pays, jurisdictions. This Article analyzes fee awards in Israel, which vests judges with discretion to award fees, with loser pays operating as a norm. The 2641 cases studied constitute nearly all cases terminated by judgment in district courts in 2005, 2006, 2011, and 2012. Given many fee denials, and fees when awarded being well below client payments to attorneys, the fee system could reasonably be characterized as being more American than English. Fees were awarded to prevailing parties in 72.8 percent of cases. Judges often exercised their discretion to protect losing litigants, especially individuals, from having to pay fees. In tort cases won by individuals against corporate defendants, corporations paid their own fees plus plaintiffs’ fees in 99 percent of the cases; corporate defendants that prevailed in such cases paid their own fees 48 percent of the time. Asymmetry between plaintiffs and defendants existed. In cases with fee awards, the mean and median fee paid to prevailing plaintiffs was 110,000 shekels (NIS) and 31,000 NIS, respectively; the mean and median fee paid to prevailing defendants was 49,000 NIS and 25,000 NIS, respectively. Plaintiffs prevailed in 54.8 of cases between individuals but received 90 percent of the fees. Expected award amounts varied by case category and party status. Fees were significantly correlated with damages recoveries in plaintiff victories and with time on the docket. In contract and property cases, but not in tort cases, fees declined as a percent of recovery as the recovery increased.

Introduction: Highly active antiretroviral therapy (HAART) has been shown to be effective in many recent trials. However, there is limited data on time trends of HAART efficacy after treatment change. Methods: Data from different European cohorts were compiled within the EuResist Project. The efficacy of HAART defined by suppression of viral replication at 24 weeks after therapy switch was analyzed considering previous treatment modifications from 1999 to 2008. Results: Altogether, 12,323 treatment change episodes in 7,342 patients were included in the analysis. In 1999, HAART after treatment switch was effective in 38.0% of the patients who had previously undergone 1–5 therapies. This figure rose to 85.0% in 2008. In patients with more than 5 previous therapies, efficacy rose from 23.9 to 76.2% in the same time period. In patients with detectable viral load at therapy switch, the efficacy rose from 23.3 to 66.7% with 1–5 previous treatments and from 14.4 to 55.6% with more than 5 previous treatments. Conclusion: The results of this large cohort show that the outcome of HAART switch has improved considerably over the last years. This result was particularly observed in the context after viral rebound. Thus, changing HAART is no longer associated with a high risk of treatment failure.

Chronic diseases constitute the leading cause of mortality in the western world, have a major impact on the patients' quality of life, and comprise the bulk of healthcare costs. Nowadays, healthcare data management systems integrate large amounts of medical information on patients, including diagnoses, medical procedures, lab test results, and more. Sophisticated analysis methods are needed for utilizing these data to assist in patient management and to enhance treatment quality at reduced costs. In this study, we take a first step towards better disease management of diabetic patients by applying state-of-the art methods to anticipate the patient's future health condition and to identify patients at high risk. Two relevant outcome measures are explored: the need for emergency care services and the probability of the treatment producing a sub-optimal result, as defined by domain experts. By identifying the high-risk patients our prediction system can be used by healthcare providers to prepare both financially and logistically for the patient needs. To demonstrate a potential downstream application for the identified high-risk patients, we explore the association between the physician treating these patients and the treatment outcome, and propose a system that can assist healthcare providers in optimizing the match between a patient and a physician.Our work formulates the problem and examines the performance of several learning models on data from several thousands of patients. We further describe a pilot system built on the results of this analysis. We show that the risk for the two considered outcomes can be evaluated from patients' characteristics and that features of the patient-physician match improve the prediction accuracy for the treatment's success. These results suggest that personalized medicine can be valuable for high risk patients and raise interesting questions for future improvements.

The creation of a "market for virtue" and social responsibility is dependent on the flow of information from the corporation to the responsible agents. To achieve a free flow of information, excessive, missing and unreliable information must be avoided. More generally, a market for virtue should make it possible to create the appropriate means to signal true commitments and enable informed agents to know how to effectively use their limited resources for deploying market power that rewards and sanctions the corporations that deserve such responses. The underlying assumption of this query is that if corporations in fact compete among themselves in the dimension of social responsibility, then some corporations will identify ways of demonstrating credibility of both efforts and results. The purpose of publishing codes of conduct and annual Corporate Social Responsibility (CSR) reports would be to improve the level of information that is needed for the market for virtue to function. In order to assess the existence of a competitive market for virtue, the Article analyzes the codes and associated documents of sixteen multinational companies in three industries — textile, petrochemical and automobile. The Article finds some differences between labor and environmental norms, both within each of the sectors, as well as between sectors. However, the major finding casts doubt on the very assumption that labor and environmental norms are shaped by a market for virtue. Contrarily, the Article sheds new light on alternative reasons for developing social norms in corporations and rethinks the purpose of CSR reports.

BACKGROUND: Although genotypic resistance testing (GRT) is recommended to guide combination antiretroviral therapy (cART), funding and/or facilities to perform GRT may not be available in low to middle income countries. Since treatment history (TH) impacts response to subsequent therapy, we investigated a set of statistical learning models to optimise cART in the absence of GRT information.METHODS AND FINDINGS: The EuResist database was used to extract 8-week and 24-week treatment change episodes (TCE) with GRT and additional clinical, demographic and TH information. Random Forest (RF) classification was used to predict 8- and 24-week success, defined as undetectable HIV-1 RNA, comparing nested models including (i) GRT+TH and (ii) TH without GRT, using multiple cross-validation and area under the receiver operating characteristic curve (AUC). Virological success was achieved in 68.2% and 68.0% of TCE at 8- and 24-weeks (n = 2,831 and 2,579), respectively. RF (i) and (ii) showed comparable performances, with an average (st.dev.) AUC 0.77 (0.031) vs. 0.757 (0.035) at 8-weeks, 0.834 (0.027) vs. 0.821 (0.025) at 24-weeks. Sensitivity analyses, carried out on a data subset that included antiretroviral regimens commonly used in low to middle income countries, confirmed our findings. Training on subtype B and validation on non-B isolates resulted in a decline of performance for models (i) and (ii).CONCLUSIONS: Treatment history-based RF prediction models are comparable to GRT-based for classification of virological outcome. These results may be relevant for therapy optimisation in areas where availability of GRT is limited. Further investigations are required in order to account for different demographics, subtypes and different therapy switching strategies.

This chapter provides a review of the challenges machine-learning specialists face when trying to assist virologists by generating an automatic prediction of an outcome of HIV therapy. Optimizing HIV therapies is crucial since the virus rapidly develops mutations to evade drug pressures. Modern anti-HIV regimens comprise multiple drugs in order to prevent, or at least delay, the development of resistance mutations. In recent years, large databases have been collected to allow the automatic analysis of relations between the virus genome other clinical and demographical information, and the failure or success of a therapy. The EuResist integrated database (EID) collected from about 18500 patients and 65000 different therapies is probably one of the largest clinical genomic databases. Only one third of the therapies in the EID contain therapy response information and only 5% of the therapy records have response information as well as genotypic data. This leads to two specific challenges (a) semi-supervised learning --- a setting where many samples are available but only a small proportion of them are labeled and (b) missing data. We review a novel solution for the first setting: a novel dimensionality reduction framework that binds information theoretic considerations with geometrical constraints over the simplex. The dimensionality reduction framework is formulated to find optimal low dimensional geometric embedding of the simplex that preserves pairwise distances. This novel similarity-based clustering solution was tested on toy data and textual data. We show that this solution, although it outperforms other methods and provides good results on a small sample of the Euresist data, is impractical for the large EuResist dataset. In addition, we review a generative-discriminative prediction system that successfully overcomes the missing value challenge. Apart from a review of the EuResist project and related challenges, this chapter provides an overview of recent developments in the field of machine learning-based prediction methods for HIV drug resistance.

This chapter attempts to decipher the different ways in which time is conceived, narrated and constructed in Mekilta Shirata . It begins with a simple question: what allows the homilist to join the biblical victory song? The question becomes more disturbing in light of the past/future model which prevails in this tractate. It considers the possibility of two rival models: "succession" vs. "sandwich" and shows that there is no real contradiction between the two. Through a close reading of one homily author suggests that the homiletical activity itself as the mechanism that allows the homilist to transcend the past/future model and join himself the biblical hymn. The chapter can be seen as an exercise in comparative methodology to tannaitic midrash. A hermeneutical-rhetorical analysis, concentrating on the midrashic process itself, reveals the mechanism that enables the very act of joining the biblical song wholeheartedly. Keywords: biblical victory song; homilist; Mekilta Shirata ; one homily; revelation; tannaitic midrash

Mutual learning of a pair of tree parity machines with continuous and discrete weight vectors is studied analytically. The analysis is based on a mapping procedure that maps the mutual learning in tree parity machines onto mutual learning in noisy perceptrons. The stationary solution of the mutual learning in the case of continuous tree parity machines depends on the learning rate where a phase transition from partial to full synchronization is observed. In the discrete case the learning process is based on a finite increment and a full synchronized state is achieved in a finite number of steps. The synchronization of discrete parity machines is introduced in order to construct an ephemeral key-exchange protocol. The dynamic learning of a third tree parity machine (an attacker) that tries to imitate one of the two machines while the two still update their weight vectors is also analyzed. In particular, the synchronization times of the naive attacker and the flipping attacker recently introduced in Ref. 9 are analyzed. All analytical results are found to be in good agreement with simulation results.

Modern computer systems generate an enormous number of logs. IBM Mining Effectively Large Output Data Yield (MELODY) is a unique and innovative solution for handling these logs and filtering out the anomalies and failures. MELODY can detect system errors early on and avoid subsequent crashes by identifying the root causes of such errors. By analyzing the logs leading up to a problem, MELODY can pinpoint when and where things went wrong and visually present them to the user, ensuring that corrections are accurately and effectively done. We present the MELODY solution and describe its architecture, algorithmic components, functions, and benefits. After being trained on a large portion of relevant data, MELODY provides alerts of abnormalities in newly arriving log files or in streams of logs. The solution is being used by IBM services groups that support IBM xSeries® servers on a regular basis. MELODY was recently tested with ten large IBM customers who use zSeries® machines and was found to be extremely useful for the information technology experts in those companies. They found that the solution's ability to reduce extensively large log data to manageable sets of highlighted messages saved them time and helped them make better use of the data.

Rising costs, decreasing quality of care, diminishing productivity, and increasing complexity have all contributed to the present state of the healthcare industry. The interactions between payers (e.g., insurance companies and health plans) and providers (e.g., hospitals and laboratories) are growing and are becoming more complicated. The constant upsurge in and enhanced complexity of diagnostic and treatment information has made the clinical decision-making process more difficult. Medical transaction charges are greater than ever. Population-specific financial requirements are increasing the economic burden on the entire system. Medical insurance and identity theft frauds are on the rise. The current lack of comparative cost analytics hampers systematic efficiency. Redundant and unnecessary interventions add to medical expenditures that add no value. Contemporary payment models are antithetic to outcome-driven medicine. The rate of medical errors and mistakes is high. Slow inefficient processes and the lack of best practice support for care delivery do not create productive settings. Information technology has an important role to play in approaching these problems. This paper describes IBM Research's approach to helping address these issues, i.e., the evidence-based healthcare platform.

Background: The design of an optimal highly active antiretroviral therapy (HAART) customised on patient's background and viral genotyping, is still a challenge. EuResist has been the first data-driven system to be implemented as a free web-service for customised HAART optimisation, and was proven to be superior to all existing genotypic interpretation systems (GIS) since it takes into account not only genotype but multiple other variables. Data and methods: The EuResist database stores and updates periodically demographic, clinical, and genomic information of HIV+ patients from several countries in Western Europe. The EuResist system is trained on treatment change episodes (TCE) drawn from the EuResist data base, composed of a new drug regimen with a baseline HIV-1 RNA load and a CD4+ count, a baseline viral pol genotype, demographic and previous treatment information. Each TCE is associated to an HIV-1 RNA measurement after 8 weeks, which is used for the definition of virological success (below 500 copies/ml or >2 Log10 reduction from baseline HIV-1 RNA). The system is a combination of three independent machine learning models (based on logistic regression, random forests, and Bayesian networks). The 2010 update has been trained on >5,000 TCE, composed of 20 FDA/EMEA approved nucleoside/tide, non-nucleoside, and protease inhibitors, including the recently approved compounds (RAC) tipranavir, etravirine and darunavir. Results: The EuResist combined system performance in predicting the correct virological success of a TCE after 8-weeks (validation set, n=561) exhibited an area under the receiver operating characteristic (AUROC) of 0.8 (whereas Stanford HIVdb GIS assessed to 0.73, p=0.002). The inclusion of therapy history, clinical, and demographic covariates was shown to increase significantly prediction performance. In the subset of regimens containing RAC (n=151), the EuResist AUROC was 0.7 and Stanford HIVdb AUROC was 0.63, not allowing to assess a significant difference owing to the small sample size. See Figure ​1. Conclusions: Based on patient’s information and virus genotype, the EuResist web-service ranks the most effective HAART regimens by the probability to achieve an undetectable HIV-1 RNA load after 8 weeks. Thus, it might be useful in clinical practice. The 2010 update includes also RAC and achieved fair performance, which is expected to increase with expanding training data. Supplement: Abstracts of the Tenth International Congress on Drug Therapy in HIV Infection http://www.biomedcentral.com/content/pdf/1758-2652-13-S4-info.pdf Conference: Tenth International Congress on Drug Therapy in HIV Infection 7-11 November 2010 Glasgow, UK (Published: 8 November 2010) doi:10.1186/1758-2652-13-S4-O6 Cite this article as: Pironti et al.: O116. The EuResist expert model for customised HAART optimisation: 2010 update and extension to newest compounds. Journal of the International AIDS Society 2010 13(Suppl 4):O6. Full text: PubMed Central: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3112868/

Purpose A UCB–IBM collaboration explored the application of machine learning to large claims databases to construct an algorithm for antiepileptic drug (AED) choice for individual patients. Methods Claims data were collected between January 2006 and September 2011 for patients with epilepsy > 16 years of age. A subset of patient claims with a valid index date of AED treatment change (new, add, or switch) were used to train the AED prediction model by retrospectively evaluating an index date treatment for subsequent treatment change. Based on the trained model, a model-predicted AED regimen with the lowest likelihood of treatment change was assigned to each patient in the group of test claims, and outcomes were evaluated to test model validity. Results The model had 72% area under receiver operator characteristic curve, indicating good predictive power. Patients who were given the model-predicted AED regimen had significantly longer survival rates (time until a treatment change event) and lower expected health resource utilization on average than those who received another treatment. The actual prescribed AED regimen at the index date matched the model-predicted AED regimen in only 13% of cases; there were large discrepancies in the frequency of use of certain AEDs/combinations between model-predicted AED regimens and those actually prescribed. Conclusions Chances of treatment success were improved if patients received the model-predicted treatment. Using the model's prediction system may enable personalized, evidence-based epilepsy care, accelerating the match between patients and their ideal therapy, thereby delivering significantly better health outcomes for patients and providing health-care savings by applying resources more efficiently. Our goal will be to strengthen the predictive power of the model by integrating diverse data sets and potentially moving to prospective data collection.

BackgroundThe extreme flexibility of the HIV type-1 (HIV-1) genome makes it challenging to build the ideal antiretroviral treatment regimen. Interpretation of HIV-1 genotypic drug resistance is evolving from rule-based systems guided by expert opinion to data-driven engines developed through machine learning methods.MethodsThe aim of the study was to investigate linear and non-linear statistical learning models for classifying short-term virological outcome of antiretroviral treatment. To optimize the model, different feature selection methods were considered. Robust extra-sample error estimation and different loss functions were used to assess model performance. The results were compared with widely used rule-based genotypic interpretation systems (Stanford HIVdb, Rega and ANRS).ResultsA set of 3,143 treatment change episodes were extracted from the EuResist database. The dataset included patient demographics, treatment history and viral genotypes. A logistic regression model using high order interaction variables performed better than rule-based genotypic interpretation systems (accuracy 75.63% versus 71.74–73.89%, area under the receiver operating characteristic curve [AUC] 0.76 versus 0.68–0.70) and was equivalent to a random forest model (accuracy 76.16%, AUC 0.77). However, when rule-based genotypic interpretation systems were coupled with additional patient attributes, and the combination was provided as input to the logistic regression model, the performance increased significantly, becoming comparable to the fully data-driven methods.ConclusionsPatient-derived supplementary features significantly improved the accuracy of the prediction of response to treatment, both with rule-based and data-driven interpretation systems. Fully data-driven models derived from large-scale data sources show promise as antiretroviral treatment decision support tools.