Predicting the dynamic nature of risk

The trusting peer in order to determine the likelihood of the loss in its resources might analyze the Risk before engaging in an interaction with any trusted peer. This likelihood of the loss in the resources is termed as Risk in the interaction. Risk analysis is important in e-commerce transactions because of the vast literature that argues that the decision to buy is based on the Risk-adjusted cost benefit analysis. If the trusting peer can determine the future Riskiness value or reputation of a trusted peer for the time period of its interaction, before engaging in an activity with it, then it can ease its decision making process of whether to interact with the trusted peer or not. In this paper we present such a novel method which predicts the dynamic nature of Risk and determines the future Riskiness value of the trusted peer, before the interaction starts, thus helping the trusting peer considerably in making its decision

Empirical Evidence on the Use of Credit Scoring for Predicting Insurance Losses with Psycho-social and Biochemical Explanations

An important development in personal lines of insurance in the United States is the use of credit history data for insurance risk classification to predict losses. This research presents the results of collaboration with industry conducted by a university at the request of its state legislature. The purpose was to see the viability and validity of the use of credit scoring to predict insurance losses given its controversial nature and criticism as redundant of other predictive variables currently used. Working with industry and government, this study analyzed more than 175,000 policyholders’ information for the relationship between credit score and claims. Credit scores were significantly related to incurred losses, evidencing both statistical and practical significance. We investigate whether the revealed relationship between credit score and incurred losses was explainable by overlap with existing underwriting variables or whether the credit score adds new information about losses not contained in existing underwriting variables. The results show that credit scores contain significant information not already incorporated into other traditional rating variables (e.g., age, sex, driving history). We discuss how sensation seeking and self-control theory provide a partial explanation of why credit scoring works (the psycho-social perspective). This article also presents an overview of biological and chemical correlates of risk taking that helps explain why knowing risk-taking behavior in one realm (e.g., risky financial behavior and poor credit history) transits to predicting risk-taking behavior in other realms (e.g., automobile insurance incurred losses). Additional research is needed to advance new nontraditional loss prediction variables from social media consumer information to using information provided by technological advances. The evolving and dynamic nature of the insurance marketplace makes it imperative that professionals continue to evolve predictive variables and for academics to assist with understanding the whys of the relationships through theory development.IC2 Institut

DeepCare: A Deep Dynamic Memory Model for Predictive Medicine

Personalized predictive medicine necessitates the modeling of patient illness and care processes, which inherently have long-term temporal dependencies. Healthcare observations, recorded in electronic medical records, are episodic and irregular in time. We introduce DeepCare, an end-to-end deep dynamic neural network that reads medical records, stores previous illness history, infers current illness states and predicts future medical outcomes. At the data level, DeepCare represents care episodes as vectors in space, models patient health state trajectories through explicit memory of historical records. Built on Long Short-Term Memory (LSTM), DeepCare introduces time parameterizations to handle irregular timed events by moderating the forgetting and consolidation of memory cells. DeepCare also incorporates medical interventions that change the course of illness and shape future medical risk. Moving up to the health state level, historical and present health states are then aggregated through multiscale temporal pooling, before passing through a neural network that estimates future outcomes. We demonstrate the efficacy of DeepCare for disease progression modeling, intervention recommendation, and future risk prediction. On two important cohorts with heavy social and economic burden -- diabetes and mental health -- the results show improved modeling and risk prediction accuracy.Comment: Accepted at JBI under the new name: "Predicting healthcare trajectories from medical records: A deep learning approach

DeepSoft: A vision for a deep model of software

Although software analytics has experienced rapid growth as a research area, it has not yet reached its full potential for wide industrial adoption. Most of the existing work in software analytics still relies heavily on costly manual feature engineering processes, and they mainly address the traditional classification problems, as opposed to predicting future events. We present a vision for \emph{DeepSoft}, an \emph{end-to-end} generic framework for modeling software and its development process to predict future risks and recommend interventions. DeepSoft, partly inspired by human memory, is built upon the powerful deep learning-based Long Short Term Memory architecture that is capable of learning long-term temporal dependencies that occur in software evolution. Such deep learned patterns of software can be used to address a range of challenging problems such as code and task recommendation and prediction. DeepSoft provides a new approach for research into modeling of source code, risk prediction and mitigation, developer modeling, and automatically generating code patches from bug reports.Comment: FSE 201

The choice of self-rated health measures matter when predicting mortality: evidence from 10 years follow-up of the Australian longitudinal study of ageing

BACKGROUND Self-rated health (SRH) measures with different wording and reference points are often used as equivalent health indicators in public health surveys estimating health outcomes such as healthy life expectancies and mortality for older adults. Whilst the robust relationship between SRH and mortality is well established, it is not known how comparable different SRH items are in their relationship to mortality over time. We used a dynamic evaluation model to investigate the sensitivity of time-varying SRH measures with different reference points to predict mortality in older adults over time. METHODS We used seven waves of data from the Australian Longitudinal Study of Ageing (1992 to 2004; N = 1733, 52.6% males). Cox regression analysis was used to evaluate the relationship between three time-varying SRH measures (global, age-comparative and self-comparative reference point) with mortality in older adults (65+ years). RESULTS After accounting for other mortality risk factors, poor global SRH ratings increased mortality risk by 2.83 times compared to excellent ratings. In contrast, the mortality relationship with age-comparative and self-comparative SRH was moderated by age, revealing that these comparative SRH measures did not independently predict mortality for adults over 75 years of age in adjusted models. CONCLUSIONS We found that a global measure of SRH not referenced to age or self is the best predictor of mortality, and is the most reliable measure of self-perceived health for longitudinal research and population health estimates of healthy life expectancy in older adults. Findings emphasize that the SRH measures are not equivalent measures of health status.This study was funded by the South Australian Health Commission, the Australian Rotary Health Research Fund, the US National Institute of Health (Grant No. AG 08523-02) and the National Health and Medical Research Council (NHMRC; Grant No.229936). KJA is supported by NHMRC Fellowship No.366756