Proceedings of the International AAAI Conference on Web and Social Media
Gold farming and real money trade refer to a set of illicit practices in massively multiplayer on... more Gold farming and real money trade refer to a set of illicit practices in massively multiplayer online games (MMOGs) whereby players accumulate virtual resources to sell for “real world” money. Prior work has examined trade relationships formed by gold farmers but not the trust relationships which exist between members of these organizations. We adopt a hypergraph approach to model the multi-modal relationships of gold farmers granting other players permission to use and modify objects they own. We argue these permissions reflect underlying trust relationships which can be analyzed using network analysis methods. We compare farmers’ trust networks to the trust networks of both unidentified farmers and typical players. Our results demonstrate that gold farmers’ networks are different from trust networks of normal players whereby farmers trust highly-central non-farmer players but not each other. These findings have implications for augmenting detection methods and re-evaluating theori...
Proceedings of the AAAI Conference on Artificial Intelligence
There is a large body of work on the evolution of graphs in various domains, which shows that man... more There is a large body of work on the evolution of graphs in various domains, which shows that many real graphs evolve in a similar manner. In this paper we study a novel type of network formed by mentor-apprentice relationships in a massively multiplayer online role playing game. We observe that some of the static and dynamic laws which have been observed in many other real world networks are not observed in this network. Consequently well known graph generators like Preferential Attachment, Forest Fire, Butterfly, RTM, etc., cannot be applied to such mentoring networks. We propose a novel generative model to generate networks with the characteristics of mentoring networks.
Death is an inevitable part of life and while it cannot be delayed indefinitely it is possible to... more Death is an inevitable part of life and while it cannot be delayed indefinitely it is possible to predict with some certainty when the health of a person is going to deteriorate. In this paper, we predict risk of mortality for patients from two large hospital systems in the Pacific Northwest. Using medical claims and electronic medical records (EMR) data we greatly improve prediction for risk of mortality and explore machine learning models with explanations for end of life predictions. The insights that are derived from the predictions can then be used to improve the quality of patient care towards the end of life.
There are more than nine hundred Accountable Care Organizations (ACOs) in the United States, both... more There are more than nine hundred Accountable Care Organizations (ACOs) in the United States, both in the public and private sector, serving millions of patients across the country in a process to transition from fee-for-service to a value-based-care model for healthcare delivery in an effort to contain expenditures. Identifying fraud, waste, and abuse resulting in superfluous expenditures associated with care delivery is central to the success of ACOs and for making the cost of healthcare sustainable. In theory, such expenditures should be easily identifiable with large amounts of historical data. However, to the best of our knowledge there is no data mining framework that systematically addresses the problem of identifying unwarranted variation in expenditures on high dimensional claims data using unsupervised machine learning techniques. In this paper we propose methods to uncover unwarranted variation in healthcare spending by automatically extracting reference groups of peerprov...
Prediction of diabetes and its various complications has been studied in a number of settings, bu... more Prediction of diabetes and its various complications has been studied in a number of settings, but a comprehensive overview of problem setting for diabetes prediction and care management has not been addressed in the literature. In this document we seek to remedy this omission in literature with an encompassing overview of diabetes complication prediction as well as situating this problem in the context of real world healthcare management. We illustrate various problems encountered in real world clinical scenarios via our own experience with building and deploying such models. In this manuscript we illustrate a Machine Learning (ML) framework for addressing the problem of predicting Type 2 Diabetes Mellitus (T2DM) together with a solution for risk stratification, intervention and management. These ML models align with how physicians think about disease management and mitigation, which comprises these four steps: Identify, Stratify, Engage, Measure.
Explainable models in Artificial Intelligence are often employed to ensure transparency and accou... more Explainable models in Artificial Intelligence are often employed to ensure transparency and accountability of AI systems. The fidelity of the explanations are dependent upon the algorithms used as well as on the fidelity of the data. Many real world datasets have missing values that can greatly influence explanation fidelity. The standard way to deal with such scenarios is imputation. This can, however, lead to situations where the imputed values may correspond to a setting which refer to counterfactuals. Acting on explanations from AI models with imputed values may lead to unsafe outcomes. In this paper, we explore different settings where AI models with imputation can be problematic and describe ways to address such scenarios.
Fairness in AI and machine learning systems has become a fundamental problem in the accountabilit... more Fairness in AI and machine learning systems has become a fundamental problem in the accountability of AI systems. While the need for accountability of AI models is near ubiquitous, healthcare in particular is a challenging field where accountability of such systems takes upon additional importance, as decisions in healthcare can have life altering consequences. In this paper we present preliminary results on fairness in the context of classification parity in healthcare. We also present some exploratory methods to improve fairness and choosing appropriate classification algorithms in the context of healthcare.
Background Thirty-day hospital readmissions are a quality metric for health care systems. Predict... more Background Thirty-day hospital readmissions are a quality metric for health care systems. Predictive models aim to identify patients likely to readmit to more effectively target preventive strategies. Many risk of readmission models have been developed on retrospective data, but prospective validation of readmission models is rare. To the best of our knowledge, none of these developed models have been evaluated or prospectively validated in a military hospital. Objectives The objectives of this study are to demonstrate the development and prospective validation of machine learning (ML) risk of readmission models to be utilized by clinical staff at a military medical facility and demonstrate the collaboration between the U.S. Department of Defense's integrated health care system and a private company. Methods We evaluated multiple ML algorithms to develop a predictive model for 30-day readmissions using data from a retrospective cohort of all-cause inpatient readmissions at Madig...
Adversarial behavioral has been observed in many different contexts. In this paper we address the... more Adversarial behavioral has been observed in many different contexts. In this paper we address the problem of adversarial behavior in the context of clandestine networks. We use data from a massively multiplayer online role playing game to illustrate the behavioral and structural signatures of deviant players change over time as a response to" policing" activities of the game administrators. Preliminary results show that the behavior of the deviant players and their affiliates show co-evolutionary behavior and the timespan within ...
Proceedings of the AAAI Conference on Artificial Intelligence
Death is an inevitable part of life and while it cannot be delayed indefinitely it is possible to... more Death is an inevitable part of life and while it cannot be delayed indefinitely it is possible to predict with some certainty when the health of a person is going to deteriorate. In this paper, we predict risk of mortality for patients from two large hospital systems in the Pacific Northwest. Using medical claims and electronic medical records (EMR) data we greatly improve prediction for risk of mortality and explore machine learning models with explanations for end of life predictions. The insights that are derived from the predictions can then be used to improve the quality of patient care towards the end of life.
Proceedings of the International AAAI Conference on Web and Social Media
Gold farming and real money trade refer to a set of illicit practices in massively multiplayer on... more Gold farming and real money trade refer to a set of illicit practices in massively multiplayer online games (MMOGs) whereby players accumulate virtual resources to sell for “real world” money. Prior work has examined trade relationships formed by gold farmers but not the trust relationships which exist between members of these organizations. We adopt a hypergraph approach to model the multi-modal relationships of gold farmers granting other players permission to use and modify objects they own. We argue these permissions reflect underlying trust relationships which can be analyzed using network analysis methods. We compare farmers’ trust networks to the trust networks of both unidentified farmers and typical players. Our results demonstrate that gold farmers’ networks are different from trust networks of normal players whereby farmers trust highly-central non-farmer players but not each other. These findings have implications for augmenting detection methods and re-evaluating theori...
Proceedings of the AAAI Conference on Artificial Intelligence
There is a large body of work on the evolution of graphs in various domains, which shows that man... more There is a large body of work on the evolution of graphs in various domains, which shows that many real graphs evolve in a similar manner. In this paper we study a novel type of network formed by mentor-apprentice relationships in a massively multiplayer online role playing game. We observe that some of the static and dynamic laws which have been observed in many other real world networks are not observed in this network. Consequently well known graph generators like Preferential Attachment, Forest Fire, Butterfly, RTM, etc., cannot be applied to such mentoring networks. We propose a novel generative model to generate networks with the characteristics of mentoring networks.
Death is an inevitable part of life and while it cannot be delayed indefinitely it is possible to... more Death is an inevitable part of life and while it cannot be delayed indefinitely it is possible to predict with some certainty when the health of a person is going to deteriorate. In this paper, we predict risk of mortality for patients from two large hospital systems in the Pacific Northwest. Using medical claims and electronic medical records (EMR) data we greatly improve prediction for risk of mortality and explore machine learning models with explanations for end of life predictions. The insights that are derived from the predictions can then be used to improve the quality of patient care towards the end of life.
There are more than nine hundred Accountable Care Organizations (ACOs) in the United States, both... more There are more than nine hundred Accountable Care Organizations (ACOs) in the United States, both in the public and private sector, serving millions of patients across the country in a process to transition from fee-for-service to a value-based-care model for healthcare delivery in an effort to contain expenditures. Identifying fraud, waste, and abuse resulting in superfluous expenditures associated with care delivery is central to the success of ACOs and for making the cost of healthcare sustainable. In theory, such expenditures should be easily identifiable with large amounts of historical data. However, to the best of our knowledge there is no data mining framework that systematically addresses the problem of identifying unwarranted variation in expenditures on high dimensional claims data using unsupervised machine learning techniques. In this paper we propose methods to uncover unwarranted variation in healthcare spending by automatically extracting reference groups of peerprov...
Prediction of diabetes and its various complications has been studied in a number of settings, bu... more Prediction of diabetes and its various complications has been studied in a number of settings, but a comprehensive overview of problem setting for diabetes prediction and care management has not been addressed in the literature. In this document we seek to remedy this omission in literature with an encompassing overview of diabetes complication prediction as well as situating this problem in the context of real world healthcare management. We illustrate various problems encountered in real world clinical scenarios via our own experience with building and deploying such models. In this manuscript we illustrate a Machine Learning (ML) framework for addressing the problem of predicting Type 2 Diabetes Mellitus (T2DM) together with a solution for risk stratification, intervention and management. These ML models align with how physicians think about disease management and mitigation, which comprises these four steps: Identify, Stratify, Engage, Measure.
Explainable models in Artificial Intelligence are often employed to ensure transparency and accou... more Explainable models in Artificial Intelligence are often employed to ensure transparency and accountability of AI systems. The fidelity of the explanations are dependent upon the algorithms used as well as on the fidelity of the data. Many real world datasets have missing values that can greatly influence explanation fidelity. The standard way to deal with such scenarios is imputation. This can, however, lead to situations where the imputed values may correspond to a setting which refer to counterfactuals. Acting on explanations from AI models with imputed values may lead to unsafe outcomes. In this paper, we explore different settings where AI models with imputation can be problematic and describe ways to address such scenarios.
Fairness in AI and machine learning systems has become a fundamental problem in the accountabilit... more Fairness in AI and machine learning systems has become a fundamental problem in the accountability of AI systems. While the need for accountability of AI models is near ubiquitous, healthcare in particular is a challenging field where accountability of such systems takes upon additional importance, as decisions in healthcare can have life altering consequences. In this paper we present preliminary results on fairness in the context of classification parity in healthcare. We also present some exploratory methods to improve fairness and choosing appropriate classification algorithms in the context of healthcare.
Background Thirty-day hospital readmissions are a quality metric for health care systems. Predict... more Background Thirty-day hospital readmissions are a quality metric for health care systems. Predictive models aim to identify patients likely to readmit to more effectively target preventive strategies. Many risk of readmission models have been developed on retrospective data, but prospective validation of readmission models is rare. To the best of our knowledge, none of these developed models have been evaluated or prospectively validated in a military hospital. Objectives The objectives of this study are to demonstrate the development and prospective validation of machine learning (ML) risk of readmission models to be utilized by clinical staff at a military medical facility and demonstrate the collaboration between the U.S. Department of Defense's integrated health care system and a private company. Methods We evaluated multiple ML algorithms to develop a predictive model for 30-day readmissions using data from a retrospective cohort of all-cause inpatient readmissions at Madig...
Adversarial behavioral has been observed in many different contexts. In this paper we address the... more Adversarial behavioral has been observed in many different contexts. In this paper we address the problem of adversarial behavior in the context of clandestine networks. We use data from a massively multiplayer online role playing game to illustrate the behavioral and structural signatures of deviant players change over time as a response to" policing" activities of the game administrators. Preliminary results show that the behavior of the deviant players and their affiliates show co-evolutionary behavior and the timespan within ...
Proceedings of the AAAI Conference on Artificial Intelligence
Death is an inevitable part of life and while it cannot be delayed indefinitely it is possible to... more Death is an inevitable part of life and while it cannot be delayed indefinitely it is possible to predict with some certainty when the health of a person is going to deteriorate. In this paper, we predict risk of mortality for patients from two large hospital systems in the Pacific Northwest. Using medical claims and electronic medical records (EMR) data we greatly improve prediction for risk of mortality and explore machine learning models with explanations for end of life predictions. The insights that are derived from the predictions can then be used to improve the quality of patient care towards the end of life.
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Papers by Muhammad A Ahmad