varunr14/newsummary huggingface dataset:
Despite recent progress in prediction and prevention heart disease remains a leading cause of death . One preliminary step in heart disease prediction and prevention is risk factor identification . Many studies have been proposed to identify risk factors associated with heart disease however none have attempted to identify all risk factors . In 2014 the National Center of Informatics for Integrating Biology and Beside issued a clinical natural language processing challenge that involved a track for identifying heart disease risk factors in clinical texts over time . This track aimed to identify medically relevant information related to heart disease risk and track the progression over sets of longitudinal patient medical records . Identification of tags and attributes associated with disease presence and progression risk factors and medications in patient medical history were required . Our participation led to development of a hybrid pipeline system based on both machine learning based and rule based approaches . Evaluation using the challenge corpus revealed that our system achieved an F1 score of 92.68 making it the top ranked system of the 2014 i2b2 clinical NLP challenge . 


Summary: We proposed a hybrid system to automatically identify heart disease risk factors. We divided different types of risk factors into three categories according to their descriptions. Our system achieves an F score of 92.86 on 2014 i2b2 corpus which is top ranked.
Advertisement options are a recent development in online advertising . Simply an ad option is a first look contract in which a publisher or search engine grants an advertiser a right but not obligation to enter into transactions to purchase impressions or clicks from a specific ad slot at a pre specified price on a specific delivery date . Such a structure provides advertisers with more flexibility of their guaranteed deliveries . The valuation of ad options is an important topic and previous studies on ad options pricing have been mostly restricted to the situations where the underlying prices follow a geometric Brownian motion . This assumption is reasonable for sponsored search however some studies have also indicated that it is not valid for display advertising . In this paper we address this issue by employing a stochastic volatility model and discuss a lattice framework to approximate the proposed SV model in option pricing . Our developments are validated by experiments with real advertising data we find that the SV model has a better fitness over the GBM model we validate the proposed lattice model via two sequential Monte Carlo simulation methods we demonstrate that advertisers are able to flexibly manage their guaranteed deliveries by using the proposed options and publishers can have an increased revenue when some of their inventories are sold via ad options . 


Summary: A new advertisement option that allows an advertiser to pay a fixed CPM CPC to purchase impressions or clicks. The fixed payment can be different to the underlying ad format. The proposed option can be priced under the lattice framework for both SV and GBM underlying models. The studied model is validated by two advertising datasets.
Online reviews as one kind of quality indicator of products or service are becoming increasingly important in influencing purchase decisions of prospective consumers on electronic commerce websites . With the fast growth of the Chinese e commerce industry it is thus indispensable to design effective online review systems for e commerce websites in the Chinese context by taking into account cultural factors . In this paper we conduct two empirical studies on online reviews . Firstly we study how culture differences across countries impact the way in which consumers provide online reviews . Secondly we investigate the impact of online reviews on product sales in the Chinese context and show that directly copying the ideas of successful online review systems in the USA will deteriorate the effectiveness of the systems in China . Finally we propose several suggestions for the development of effective online review systems in the Chinese context based on the results of our two empirical studies and the findings in previous studies . 


Summary: Consumers reviewing behavior is affected by culture differences across countries. Chinese are less engaged less negative and value negation more in review systems. Review volume emotional tendency and spotlight review positively affect book sales. Length and Reviewer rank negatively affect sales. Helpful votes have no impact. Effective review systems in China need care about review format and voting system.
Background Drug repositioning is the process of finding new indications for existing drugs . Its importance has been dramatically increasing recently due to the enormous increase in new drug discovery cost . However most of the previous molecular centered drug repositioning work is not able to reflect the end point physiological activities of drugs because of the inherent complexity of human physiological systems . Methods Here we suggest a novel computational framework to make inferences for alternative indications of marketed drugs by using electronic clinical information which reflects the end point physiological results of drug s effects on the biological activities of humans . In this work we use the concept of complementarity between clinical disease signatures and clinical drug effects . With this framework we establish disease related clinical variable vectors and drug related clinical variable vectors by applying two methodologies . Finally we assign a repositioning possibility score to each disease drug pair by the calculation of complementarity and association between clinical states of disease signatures and clinical effects of drugs . A total of 717 clinical variables in the electronic clinical dataset are considered in this study . Results The statistical significance of our prediction results is supported through two benchmark datasets . We discovered not only lots of known relationships between diseases and drugs but also many hidden disease drug relationships . For example glutathione and edetic acid may be investigated as candidate drugs for asthma treatment . We examined prediction results by using statistical experiments and presented evidences for those with already published literature . Conclusion The results show that electronic clinical information is a feasible data resource and utilizing the complementarity between clinical signatures of disease and clinical effects of drugs is a potentially predictive concept in drug repositioning research . It makes the proposed approach useful to identity novel relationships between diseases and drugs that have a high probability of being biologically valid . 


Summary: We suggest a computational framework to find new uses of existing drugs. We use the complementarity between clinical disease signatures and clinical drug effects. The statistical significance of prediction results is supported through two benchmark datasets.
Studies have shown that perceptual maps derived from online consumer generated data are effective for depicting market structure such as demonstrating positioning of competitive brands . However most text mining algorithms would require manual reading to merge extracted product features with synonyms . In response Topic modeling is introduced to group synonyms together under a topic automatically leading to convenient and accurate evaluation of brands based on consumers online reviews . To ensure the feasibility of employing Topic modeling in assessing competitive brands we developed a unique and novel framework named WVAP based on Scree plot technique . WVAP can filter the noises in posterior distribution obtained from Topic modeling and improve accuracy in brand evaluation . A case study exploring online reviews of mobile phones is conducted . We extract topics to reflect the features of the cell phones with a qualified validity . In addition to perceptual maps derived by multi dimensional scaling for product positioning we also rank these products by TOPSIS so as to visualize the market structure from different perspectives . Our case study of cell phones shows that the proposed framework is effective in mining online reviews and providing insights into the competitive landscape . 


Summary: Apply Topic modeling to group synonyms under a topic to avoid human intervention and improve automatic market structure generation. Develop the WVAP method to filter noises in Topic modeling results to elicit market structure. Besides perceptual maps of product positioning the proposed framework can provide rankings of products.
Owing to the limited information possessed by patients there exists significant information asymmetry between patients and physicians . In addition as services are intangible inseparable and heterogeneous patients are difficult to judge the physicians service quality . With the development of online healthcare services healthcare websites currently provide more information for patients such as patient generated and system generated information . Those kinds of information can reflect the quality of physicians service outcome and delivery process to help patients to select physicians . However there is scant research on the role of patient generated and system generated information in patients online behavior . Collecting data from a healthcare website this paper develops a two equation model to verify the effects of two kinds of information on patients search evaluation and decision making on healthcare websites . The results of our empirical research show that positive patient generated and system generated information on physicians service quality positively impact patients reactions at different stages . Moreover we also find that synergies between patient generated and system generated information are positively associated with patients decisions to consult a physician . These findings provide valuable contributions to the online healthcare research . 


Summary: Patients can access information on healthcare websites to determinate physicians quality. We verify the effects of physicians information on patients online reactions. Physicians information can be divided into patient generated information and system generated information. We find that the physicians information positively impact patients reactions at different stages. Moreover synergies between two kinds of information are positively associated with patients decisions.
In e commerce applications vendors can construct detailed profiles about customers preferences which is known as buyer profiling . These profiles can then be used by vendors in order to perform practices such as price discrimination poor judgment etc . The use of pseudonyms and specially changing pseudonyms from time to time are known to minimize profiling minimizing the capacity of vendors to perform such practices in turn . Although there are some frameworks and tools that support pseudonym change there are few proposals that suggest or directly change the pseudonym in an automated fashion . Instead users are usually provided with the mechanisms to change pseudonyms but without any advise on when they should actually use these mechanisms . In this paper we present an approach to control buyer profiling by means of automated pseudonym changes performed according to human privacy attitudes . We also present an application scenario and an evaluation of our proposal . 


Summary: We present a novel automated buyer profiling control mechanism. It is based on pseudonym changes performed according to human privacy attitudes. Agents automatically decide whether reusing or changing their pseudonyms. We present the results of an extensive experimental evaluation of the mechanism based on an application scenario.
Introduction While mammography notably contributes to earlier detection of breast cancer it has its limitations including a large number of false positive exams . Improved radiology education could potentially contribute to alleviating this issue . Toward this goal in this paper we propose an algorithm for modeling of false positive error making among radiology trainees . Identifying troublesome locations for the trainees could focus their training and in turn improve their performance . Methods The algorithm proposed in this paper predicts locations that are likely to result in a false positive error for each trainee based on the previous annotations made by the trainee . The algorithm consists of three steps . First the suspicious false positive locations are identified in mammograms by Difference of Gaussian filter and suspicious regions are segmented by computer vision based segmentation algorithms . Second 133 features are extracted for each suspicious region to describe its distinctive characteristics . Third a random forest classifier is applied to predict the likelihood of the trainee making a false positive error using the extracted features . The random forest classifier is trained using previous annotations made by the trainee . We evaluated the algorithm using data from a reader study in which 3 experts and 10 trainees interpreted 100 mammographic cases . Results The algorithm was able to identify locations where the trainee will commit a false positive error with accuracy higher than an algorithm that selects such locations randomly . Specifically our algorithm found false positive locations with 40 accuracy when only 1 location was selected for all cases for each trainee and 12 accuracy when 10 locations were selected . The accuracies for randomly identified locations were both 0 for these two scenarios . Conclusions In this first study on the topic we were able to build computer models that were able to find locations for which a trainee will make a false positive error in images that were not previously seen by the trainee . Presenting the trainees with such locations rather than randomly selected ones may improve their educational outcomes . 


Summary: A method for predicting trainees false positive locations in mammography is proposed. This is the first exploratory study on the topic using computer algorithms. Predictions are made using 133 imaging features and a random forest classifier. The predicted locations are more accurate than the locations selected randomly. The method can select educational material with more challenging locations.
Concept drift is a common phenomenon in stock market that can cause the devaluation of the knowledge learned from cross sectional analysis as the market changes over time in unforeseen ways . The widely used cross sectional regression analysis based on expert knowledge has obvious limitations in handling problems that involve concept drift and high dimensional data . To discover causal relations between portfolio selection factors and stock returns and identify concept drifts of these relations we apply a novel causal discovery technique called modified Additive Noise Model with Conditional Probability Table . In evaluation experiments we compares ANMCPT to the conventional cross sectional analysis approach in mining relationships between portfolio selection factors and stock returns . Results indicate that the factors selected by ANMCPT outperform the factors adopted in most previous cross sectional researches that followed the Fama French framework . To the best of our knowledge this paper is the first to compare causal inference technique with Fama French framework in concept drift mining of stock portfolio selection factors . Our causal inference based concept drift mining method provides a new approach to accurate knowledge discovery in stock market . 


Summary: Provide a model based on causal discovery technique ANMCPT for concept drift mining in cross sectional analysis. AVMCPT can discover causal in high dimensional and dynamic environment. ANMCPT outperform the classical Fama French framework. Concept drift phenomenon in China stock market is observed and exhibited clearly.
In Electronic Health Records much of valuable information regarding patients conditions is embedded in free text format . Natural language processing techniques have been developed to extract clinical information from free text . One challenge faced in clinical NLP is that the meaning of clinical entities is heavily affected by modifiers such as negation . A negation detection algorithm NegEx applies a simplistic approach that has been shown to be powerful in clinical NLP . However due to the failure to consider the contextual relationship between words within a sentence NegEx fails to correctly capture the negation status of concepts in complex sentences . Incorrect negation assignment could cause inaccurate diagnosis of patients condition or contaminated study cohorts . We developed a negation algorithm called DEEPEN to decrease NegEx s false positives by taking into account the dependency relationship between negation words and concepts within a sentence using Stanford dependency parser . The system was developed and tested using EHR data from Indiana University and it was further evaluated on Mayo Clinic dataset to assess its generalizability . The evaluation results demonstrate DEEPEN which incorporates dependency parsing into NegEx can reduce the number of incorrect negation assignment for patients with positive findings and therefore improve the identification of patients with the target clinical findings in EHRs . 


Summary: Utilizing Stanford dependency relation to further analyze the negation status of clinical concepts negated by NegEx. Improvement of NegEx algorithm by decreasing the number of false positives. Comparison of NegEx and DEEPEN on clinical reports from two different clinical settings.
Despite the huge growth potential that has been predicted for in app purchases and the mobile game market little is known about what motivates game players to make such purchases . The purpose of this paper is to build a research model based on the loyalty literature and studies of value theory to identify the antecedents of in app purchase intention in the context of mobile games . The proposed model was empirically evaluated using a web survey of 3309 mobile game players 813 nonpaying players and 2496 paying players . Structural equation modeling was used to assess the research model . The results reveal that loyalty to the mobile game has significant influence on a player s intention to make an in app purchase . The perceived values of the game have direct influence on the loyalty of all players but appear to have relatively little impact on the purchase intentions of nonpaying players . Two values were found to have a direct impact on a player s intention to make an in app purchase . Specifically our study revealed differences between paying users and nonpaying users . This study provides a better understanding of how the values influence loyalty among all players of the game and the purchase intentions of paying and nonpaying players . Further insights into mobile game app marketing strategies are provided as well . 


Summary: This study investigates the in app purchase intention and mobile game loyalty. The paying players intention is determined by playfulness good price and reward. The nonpaying players is only determined by good price.
Objective Some phase 1 clinical trials offer strong financial incentives for healthy individuals to participate in their studies . There is evidence that some individuals enroll in multiple trials concurrently . This creates safety risks and introduces data quality problems into the trials . Our objective was to construct a privacy preserving protocol to track phase 1 participants to detect concurrent enrollment . Design A protocol using secure probabilistic querying against a database of trial participants that allows for screening during telephone interviews and on site enrollment was developed . The match variables consisted of demographic information . Measurement The accuracy of the matching and its computational performance in seconds were measured under simulated environments . Accuracy was also compared to non secure matching methods . Results The protocol performance scales linearly with the database size . At the largest database size of 20 000 participants a query takes under 20s on a 64 cores machine . Sensitivity precision and negative predictive value of the queries were consistently at or above 0.9 and were very similar to non secure versions of the protocol . Conclusion The protocol provides a reasonable solution to the concurrent enrollment problems in phase 1 clinical trials and is able to ensure that personal information about participants is kept secure . 


Summary: We present a privacy preserving protocol to detect concurrent trial participants. We present a name representation scheme resilient to frequency attacks. The accuracy of the protocol is similar to standard non secure methods. For a database size of 20 000 the private query time is under 40s on 32 cores.
In this study a novel approach via the composite of fuzzy controllers and dithers is presented . According to this approach we can synthesize a set of fuzzy controllers and find appropriate dithers to stabilize nonlinear multiple time delay interconnected systems . A robustness design of model based fuzzy control is first proposed to overcome the effect of modeling errors between the NMTD interconnected subsystems and Takagi Sugeno fuzzy models . In terms of Lyapunov s direct method a delay dependent stability criterion is then derived to guarantee the asymptotic stability of NMTD interconnected systems . Based on this criterion and the decentralized control scheme a set of model based fuzzy controllers is synthesized via the technique of parallel distributed compensation to stabilize the NMTD interconnected system . When the designed fuzzy controllers can not stabilize the NMTD interconnected systems a batch of high frequency signals is simultaneously introduced to stabilize it . If the frequencies of dithers are high enough the outputs of the dithered interconnected system and those of its corresponding mathematical model the relaxed interconnected system can be made as close as desired . This makes it possible to obtain a rigorous prediction of the stability of the dithered interconnected system based on the one of the relaxed interconnected system . Finally a numerical example is given to illustrate the feasibility of our approach . 


Summary: A novel approach via the composite of fuzzy controllers and dithers is presented. We can synthesize a set of fuzzy controllers and find appropriate dithers to stabilize nonlinear multiple time delay NMTD interconnected systems. When the designed fuzzy controllers cannot stabilize the NMTD interconnected systems a batch of high frequency signals commonly referred to as dithers is simultaneously introduced to stabilize it.