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algorithmic prediction of failure modes in healthcare

INTERNATIONAL JOURNAL FOR QUALITY IN HEALTH CARE 2021年第1期33卷 mzaa151页

作者： Kobo-Greenhut, Ayala Sharlin, Ortal Adler, Yael Peer, Nitza Eisenberg, Vered H. Barbi, Merav Levy, Talia Ben Shlomo, Izhar Eyal, Zimlichman Zefat Acad Coll Israel Ctr Failure Modes Med Syst Program Emergency Med Jrusalem St 11 IL-13206 Safed Israel Sheba Med Ctr Hosp Management Qual & Safety Dept Ramat Gan Israel Sheba Med Ctr Div Diagnost Imaging Ramat Gan Israel

Background: Preventing medical errors is crucial, especially during crises like the COVID-19 pandemic. Failure Modes and Effects Analysis (FMEA) is the most widely used prospective hazard analysis in healthcare. FMEA relies on brainstorming by multi-disciplinary teams to identify hazards. This approach has two major weaknesses: significant time and human resource investments, and lack of complete and error-free results. Objectives: To introduce the algorithmic prediction of failure modes in healthcare (APFMH) and to examine whether APFMH is leaner in resource allocation in comparison to the traditional FMEA and whether it ensures the complete identification of hazards. Methods: The patient identification during imaging process at the emergency department of Sheba Medical Center was analyzed by FMEA and APFMH, independently and separately. We compared between the hazards predicted by APFMH method and the hazards predicted by FMEA method;the total participants' working hours invested in each process and the adverse events, categorized as 'patient identification', before and after the recommendations resulted from the above processes were implemented. Results: APFMH is more effective in identifying hazards (P<0.0001) and is leaner in resources than the traditional FMEA: the former used 21 h whereas the latter required 63 h. Following the implementation of the recommendations, the adverse events decreased by 44% annually (P=0.0026). Most adverse events were preventable, had all recommendations been fully implemented. Conclusion: In light of our initial and limited-size study, APFMH is more effective in identifying hazards (P<0.0001) and is leaner in resources than the traditional FMEA. APFMH is suggested as an alternative to FMEA since it is leaner in time and human resources, ensures more complete hazard identification and is especially valuable during crisis time, when new protocols are often adopted, such as in the current days of the COVID-19 pandemic.

关键词： algorithmic prediction failure modes healthcare FMEA APFMH

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algorithmic prediction in policing: assumptions, evaluation, and accountability

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POLICING & SOCIETY 2018年第7期28卷 806-822页

作者： Moses, Lyria Bennett Chan, Janet UNSW Australia Fac Law Sydney NSW Australia Data Decis Cooperat Res Ctr Sydney NSW Australia

The goal of predictive policing is to forecast where and when crimes will take place in the future. The idea has captured the imagination of law enforcement agencies around the world. Many agencies are purchasing software tools with the goal of reducing crime by mapping the likely locations of future crime to guide the deployment of police resources. Yet the claims and promises of predictive policing have not been subject to critical examination. This paper provides a review of the theories, techniques, and assumptions embedded in various predictive tools and highlights three key issues about the use of algorithmic prediction. Assumptions: The algorithms used to gain predictive insights build on assumptions about accuracy, continuity, the irrelevance of omitted variables, and the primary importance of particular information (such as location) over others. In making decisions based on these algorithms, police are also directed towards particular kinds of decisions and responses to the exclusion of others. Evaluation: Media coverage of these technologies implies that they are successful in reducing crime. However, these claims are not necessarily based on independent, peer reviewed evaluations. While some evaluations have been conducted, additional rigorous and independent evaluations are needed to understand more fully the effect of predictive policing programmes. Accountability: The use of predictive software can undermine the ability for individual officers or law enforcement agencies to give an account of their decisions in important ways. The paper explores how this accountability gap might be reduced.

关键词： Predictive policing big data algorithmic prediction policing innovation

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From pool to profile: Social consequences of algorithmic prediction in insurance

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Big Data and Society 2020年第2期7卷

作者： Cevolini, Alberto Esposito, Elena University of Modena and Reggio Emilia Reggio Emilia Italy Bielefeld University Bielefeld Germany University of Bologna Bologna Italy

The use of algorithmic prediction in insurance is regarded as the beginning of a new era, because it promises to personalise insurance policies and premiums on the basis of individual behaviour and level of risk. The core idea is that the price of the policy would no longer refer to the calculated uncertainty of a pool of policyholders, with the consequence that everyone would have to pay only for her real exposure to risk. For insurance, however, uncertainty is not only a problem – shared uncertainty is a resource. The availability of individual risk information could undermine the principle of risk-pooling and risk-spreading on which insurance is based. The article examines this disruptive change first by exploring the possible consequences of the use of predictive algorithms to set insurance premiums. Will it endanger the principle of mutualisation of risks, producing new forms of discrimination and exclusion from coverage? In a second step, we analyse how the relationship between the insurer and the policyholder changes when the customer knows that the company has voluminous, and continuously updated, data about her real behaviour. © The Author(s) 2020.

关键词： algorithmic prediction information asymmetry Insurance of Things InsurTech profiling shared uncertainty

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On calibration error of randomized forecasting algorithms

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THEORETICAL COMPUTER SCIENCE 2009年第19期410卷 1781-1795页

作者： V'yugin, Vladimir V. Russian Acad Sci Inst Informat Transmiss Problems Moscow 127994 Russia

It has been recently shown that calibration with an error less than Delta > 0 is almost surely guaranteed with a randomized forecasting algorithm, where forecasts are obtained by random rounding the deterministic forecasts up to Delta. We show that this error cannot be improved for a vast majority of sequences: we prove that, using a probabilistic algorithm, we can effectively generate with probability close to one a sequence "resistant" to any randomized rounding forecasting with an error Much smaller than Delta. We also reformulate this result by means of a probabilistic game. (C) 2009 Elsevier B.V. All rights reserved.

关键词： Machine learning Universal prediction Randomized prediction algorithmic prediction Calibration Randomized rounding

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Advances of mRNA vaccine in tumor: a maze of opportunities and challenges

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BIOMARKER RESEARCH 2023年第1期11卷 1-25页

作者： Yuan, Yuan Gao, Fan Chang, Ying Zhao, Qiu He, Xingxing Wuhan Univ Dept Gastroenterol Zhongnan Hosp Wuhan Peoples R China Huazhong Univ Sci & Technol Tongji Hosp Tongji Med Coll Dept Gastroenterol Wuhan Peoples R China Hubei Clin Ctr Wuhan Peoples R China Key Lab Intestinal & Colorectal Dis Wuhan Peoples R China

High-frequency mutations in tumor genomes could be exploited as an asset for developing tumor vaccines. In recent years, with the tremendous breakthrough in genomics, intelligence algorithm, and in-depth insight of tumor immunology, it has become possible to rapidly target genomic alterations in tumor cell and rationally select vaccine targets. Among a variety of candidate vaccine platforms, the early application of mRNA was limited by instability low efficiency and excessive immunogenicity until the successful development of mRNA vaccines against SARS-COV-2 broken of technical bottleneck in vaccine preparation, allowing tumor mRNA vaccines to be prepared rapidly in an economical way with good performance of stability and efficiency. In this review, we systematically summarized the classification and characteristics of tumor antigens, the general process and methods for screening neoantigens, the strategies of vaccine preparations and advances in clinical trials, as well as presented the main challenges in the current mRNA tumor vaccine development.

关键词： mRNA vaccine Tumor antigens algorithmic prediction Sequence optimization Adjuvants mRNA delivery Tumor immunotherapy

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Human-Artificial Intelligence Collaboration in prediction: A Field Experiment in the Retail Industry

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JOURNAL OF MANAGEMENT INFORMATION SYSTEMS 2023年第4期40卷 1071-1098页

作者： Revilla, Elena Saenz, Maria Jesus Seifter, Matthias Ma, Ye IE Business Sch Madrid Spain Digital Supply Chain Transformat MIT CTL Cambridge MA USA IE Business Sch Maria de Molina 12 Madrid 28006 Spain

This study investigates the role of human intervention in artificial intelligence/machine learning (AIML)-driven predictions. By doing so, we distinguish between three different types of human-AIML collaboration: automation, adjustable automation, and augmentation. We theorize that prediction uncertainty and time horizon represent two critical determinants of forecast accuracy. Based on a field experiment involving AIML-driven demand forecasts approximately 1,888 stock-keeping units in the retail industry, we rely on a multivalued treatment effect methodology to measure the effects of human-AIML collaboration on forecast accuracy. Our findings show that human intervention complements AIML-driven forecasts most effectively (augmentation) in predictions with long time horizons and low uncertainty. However human intervention is least likely to contribute to the effectiveness of AIML predictions (automation) in environments with short time horizons and high uncertainty. We discuss implications for extant theory and propose a framework outlining the conditions in which human intervention is most likely to add predictive value to human-AIML collaborations.

关键词： Human-AI collaboration AIML algorithmic prediction uncertainty time horizon demand forecasting machine learning

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Predictive complexity and information

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JOURNAL OF COMPUTER AND SYSTEM SCIENCES 2005年第4期70卷 539-554页

作者： Vyugin, MV V'yugin, VV Russian Acad Sci Inst Informat Transmiss Problems Moscow 101447 Russia Univ London Royal Holloway & Bedford New Coll Dept Comp Sci Egham TW20 0EX Surrey England

The notions of predictive complexity and of corresponding amount of information are considered. Predictive complexity is a generalization of Kolmogorov complexity which bounds the ability of any algorithm to predict elements of a sequence of outcomes. We consider predictive complexity for a wide class of bounded loss functions which are generalizations of square-loss function. Relations between unconditional KG(x) and conditional KG(x vertical bar y) predictive complexities are studied. We define an algorithm which has some "expanding property". It transforms with positive probability sequences of given predictive complexity into sequences of essentially bigger predictive complexity. A concept of amount of predictive information IG(y : x) is studied. We show that this information is noncommutative in a very strong sense and present asymptotic relations between values IG(y : x), IG(x : y), KG(x) and KG(y). (c) 2004 Elsevier Inc. All rights reserved.

关键词： machine learning algorithmic prediction predictive complexity predictive information loss functions Kolmogorov complexity expanding property

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Predictive complexity and information

Predictive complexity and information

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15th Annual Conference on Computational Learning Theory

作者： Vyugin, MV V'yugin, VV Russian Acad Sci Inst Informat Transmiss Problems Moscow 101447 Russia Univ London Royal Holloway & Bedford New Coll Dept Comp Sci Egham TW20 0EX Surrey England

关键词： machine learning algorithmic prediction predictive complexity predictive information loss functions Kolmogorov complexity expanding property

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CAN A PREDICTED FUTURE STILL BE AN OPEN FUTURE? algorithmic FORECASTS AND ACTIONABILITY IN PRECISION MEDICINE

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HISTORY AND THEORY 2024年第1期63卷 4-24页

作者： Esposito, Elena Hofmann, Dominik Coloni, Costanza Bielefeld Univ Bielefeld Germany Univ Bologna Bologna Italy Univ Cambridge Cambridge England

The openness of the future is rightly considered one of the qualifying aspects of the temporality of modern society. The open future, which does not yet exist in the present, implies radical unpredictability. This article discusses how, in the last few centuries, the resulting uncertainty has been managed with probabilistic tools that compute present information about the future in a controlled way. The probabilistic approach has always been plagued by three fundamental problems: performativity, the need for individualization, and the opacity of predictions. We contrast this approach with recent forms of algorithmic forecasting, which seem to turn these problems into resources and produce an innovative form of prediction. But can a predicted future still be an open future? We explore this specific contemporary modality of historical futures by examining the recent debate about the notion of actionability in precision medicine, which focuses on a form of individualized prediction that enables direct intervention in the future it predicts.

关键词： open future predictability probability calculus algorithmic prediction precision medicine actionability performativity individualization

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Social determinants of health in the Big Data mode of population health risk calculation

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BIG DATA & SOCIETY 2021年第2期8卷

作者： Rowe, Rachel Univ New South Wales Sydney NSW Australia

Amidst the climate of crisis surrounding the rise in opioid-related overdose in the USA, early in 2019, Google and Deloitte launched 'Opioid360'. Here came a platform combining browser histories, credit, insurance, social media, and traditional survey data to sell the service of risk calculation in population health. Opioid360's approach to automating risk calculation not only promised to identify persons 'at risk' of opioid dependence, but also paved the way for broader applications anticipating common chronic diseases and coordinating logistical operations involved in pandemic response. Beginning with this experimental platform, this paper develops an analysis of the Big Data mode of risk calculation - an epistemological and political shift that involves technology companies, investors, insurers, governments, and public health institutions. The analysis focuses on the re-emergence of 'social determinants of health' (SDOH) in the rhetoric accompanying novel analytic platforms that estimate, calculate, and compute individual health risks. While the treatment of SDOH has always been a site of political contestation within the discipline of public health, powerful interests are crystallising around the concept and instrumentalising it in platforms that sell algorithmic prediction. Silicon Valley's breed of asset-oriented technoscience appears not only to be amplifying the behaviouralist elements of public health. Among the stakes of the Big Data mode is the paradoxical retreat from changing social conditions that contribute to the prevalence of health and illness in populations;and instead, the promotion of an apparatus for pricing and exchanging individual risk or excluding from services those who bear risk most acutely.

关键词： Social determinants of health population health algorithmic prediction digital platforms financial logics risk

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