Md. Noor-E-Alam

Northeastern University | NEU · Department of Mechanical and Industrial Engineering

p>Opioid Use Disorder (OUD) is a major public health crisis in the US affecting over 2.4 million Americans across all age groups and backgrounds. Previous studies have developed machine learning (ML) and deep learning (DL) models with strong predictive performance for OUD; however, bias related to sociodemographic features remains unaddressed. In t...

p>Opioid Use Disorder (OUD) is a major public health crisis in the US affecting over 2.4 million Americans across all age groups and backgrounds. Previous studies have developed machine learning (ML) and deep learning (DL) models with strong predictive performance for OUD; however, bias related to sociodemographic features remains unaddressed. In t...

p>Opioid Use Disorder (OUD) is a major public health crisis in the US affecting over 2.4 million Americans across all age groups and backgrounds. Previous studies have developed machine learning (ML) and deep learning (DL) models with strong predictive performance for OUD; however, bias related to sociodemographic features remains unaddressed. In t...

Background: Although buprenorphine/naloxone has been demonstrated to be an effective treatment for patients with opioid use disorder (OUD), treatment retention has been a challenge. This study extends what is presently a limited literature regarding patients' experiences with this medication and the implications for treatment retention. Methods: Th...

Unquantified sources of uncertainty in observational causal analyses can break the integrity of the results. One would never want another analyst to repeat a calculation with the same data set, using a seemingly identical procedure, only to find a different conclusion. However, as we show in this work, there is a typical source of uncertainty that...

Opioid Use Disorder (OUD) has reached an epidemic level in the US. Diversion of unused prescription opioids to secondary users and black market significantly contributes to the abuse and misuse of these highly addictive drugs, leading to the increased risk of OUD and accidental opioid overdose within communities. Hence, it is critical to design eff...

Identifying cause-effect relations among variables is a key step in the decision-making process. Whereas causal inference requires randomized experiments, researchers and policy makers are increasingly using observational studies to test causal hypotheses due to the wide availability of data and the infeasibility of experiments. The matching method...

Providing first aid and other supplies (e.g., epi-pens, medical supplies, dry food, water) during and after a disaster is always challenging. The complexity of these operations increases when the transportation, power, and communications networks fail, leaving people stranded and unable to communicate their locations and needs. The advent of emergi...

Background: While buprenorphine/naloxone (buprenorphine) has been demonstrated to be an effective medication for treating opioid use disorder (OUD), an important question exists about how long patients should remain in treatment. Objective: To examine the relationship between treatment duration and patient outcomes for individuals with OUD who h...

Policymakers, practitioners, and researchers around the globe have been acting in a coordinated manner, yet remaining independent, to achieve the seventeen Sustainable Development Goals (SDGs) defined by the United Nations. Remarkably, SDG-centric activities have manifested a huge information silo known as big data. In most cases, a relevant subset...

Opioid use disorder (OUD) has reached an epidemic level in the US. The unintended flow of unused prescription opioids to secondary users and black market has exacerbated the public health risk. In this paper, we aim to address this critical public health problem by designing strategies for return and safe disposal of unused prescription opioids. We...

Background Buprenorphine is a widely used treatment option for patients with opioid use disorder (OUD). Premature discontinuation from this treatment has many negative health and societal consequences. Objective To develop and evaluate a machine learning based two-stage clinical decision-making framework for predicting which patients will disconti...

Feature selection is an extensively studied technique in the machine learning literature where the main objective is to identify the subset of features that provides the highest predictive power. However, in causal inference, our goal is to identify the set of variables that are associated with both the treatment variable and outcome (i.e., the con...

In this paper, we define a new, special second order cone as a type-k second order cone. We focus on the case of k=2, which can be viewed as a second order conic optimization (SOCO) problem with an additional complicating variable. For this new problem, we develop the necessary prerequisites, based on previous work for traditional SOCO problem. We...

Opioid overdose epidemic is a national public health crisis in the US. Little is known about how large-scale data analytics can be leveraged to help physicians predict whether a prescription opioid user will develop opioid use disorder. To that end, we proposed a machine learning framework for identifying potential risk factors of opioid use disord...

Background Research has shown buprenorphine/naloxone to be an effective medication for treating individuals with opioid use disorder. At the same time, treatment discontinuation rates are reportedly high though but much of the extant evidence comes from studies of the Medicaid population. Objectives To examine the pattern and determinants of bupre...

Randomized Kaczmarz, Motzkin Method and Sampling Kaczmarz Motzkin (SKM) algorithms are commonly used iterative techniques for solving a system of linear inequalities (i.e., \(Ax \le b\)). As linear systems of equations represent a modeling paradigm for solving many optimization problems, these randomized and iterative techniques are gaining popular...

Decisions for a variable renewable resource generator’s commitment in the energy market are typically made in advance when little information is obtainable about wind availability and market prices. Much research has been published recommending various frameworks for addressing this issue. However, these frameworks are limited as they do not consid...

Decisions for a variable renewable resource generator commitment in the energy market oftentimes must be made ahead of time when little information is available about availability and market prices. Much research has been published recommending various frameworks for addressing this issue, however, they are not comprehensive since they do not take...

Recently proposed adaptive Sketch & Project (SP) methods connect several well-known projection methods such as Randomized Kaczmarz (RK), Randomized Block Kaczmarz (RBK), Motzkin Relaxation (MR), Randomized Coordinate Descent (RCD), Capped Coordinate Descent (CCD), etc. into one framework for solving linear systems. In this work, we first propose a...

Identifying cause-effect relation among variables is a key step in the decision-making process. While causal inference requires randomized experiments, researchers and policymakers are increasingly using observational studies to test causal hypotheses due to the wide availability of observational data and the infeasibility of experiments. The match...

We develop two greedy sampling rules for the Sketch & Project method for solving linear feasibility problems. The proposed greedy sampling rules generalize the existing max-distance sampling rule and uniform sampling rule and generate faster variants of Sketch & Project methods. We also introduce greedy capped sampling rules that improve the existi...

The recently proposed Sampling Kaczmarz Motzkin (SKM) algorithm performs well in comparison with the state-of-the-art methods in solving large-scale Linear Feasibility (LF) problems. To explore the concept of momentum in the context of solving LF problems, in this work, we propose a momentum induced algorithm called Momentum Sampling Kaczmarz Motzk...

The Sampling Kaczmarz Motzkin (SKM) algorithm is a generalized method for solving large-scale linear systems of inequalities. Having its root in the relaxation method of Agmon, Schoenberg, and Motzkin and the randomized Kaczmarz method, SKM outperforms the state-of-the-art methods in solving large-scale Linear Feasibility (LF) problems. Motivated b...

In prior literature regarding facility location problems, there has been little explicit acknowledgement of problems arising from concave (non-convex) regions. This issue extends to computational geometry as a whole, as there is a distinct deficiency in existing center finding techniques amidst work on forbidden regions. In this paper, we present a...

Randomized Kaczmarz (RK), Motzkin Method (MM) and Sampling Kaczmarz Motzkin (SKM) algorithms are commonly used iterative techniques for solving linear system of inequalities (i.e., $Ax \leq b$). As linear systems of equations represents a modeling paradigm for solving many optimization problems, these randomized and iterative techniques are gaining...

Following a disaster, humanitarian logistics providers have two major responsibilities: locating trapped people and routing aid to them. These difficult operations are further hindered by failures in the transportation and communications networks, often rendered unusable by the disaster. In this work, we propose a two-echelon vehicle routing proble...

Background: The brand name Suboxone and its generic formulation buprenorphine/naloxone is a medication for treating opioid use disorder. While this medication has been shown to be effective, little research has examined the extent to which it is being prescribed and under what circumstances. Objective: This study examined patterns of prescription c...

Thus far, limited research has been performed on resilient supplier selection—a problem that requires simultaneous consideration of a set of numerical and linguistic evaluation criteria, which are substantially different from traditional supplier selection problem. Essentially, resilient supplier selection entails key sourcing decision for an organ...

Under the current policy decision making paradigm we make or evaluate a policy decision by intervening different socio-economic parameters and analyzing the impact of those interventions. This process involves identifying the causal relation between interventions and outcomes. Matching method is one of the popular techniques to identify such causal...

Supplier selection problem has gained extensive attention in the prior studies. However, research based on Fuzzy Multi-Attribute Decision Making (F-MADM) approach in ranking resilient suppliers in logistic 4.0 is still in its infancy. Traditional MADM approach fails to address the resilient supplier selection problem in logistic 4.0 primarily becau...

Objective: To examine patterns and determinants of nonindex readmissions for Medicare as well as non-Medicare patients both before and immediately after the adoption of Medicare's Hospital Readmission Reduction Program (HRRP) in 2012. Nonindex readmissions are readmissions to hospitals that are different from the one from which the patient was dis...

Supplier selection problem has gained extensive attention in the prior studies. However, research based on Fuzzy Multi-Attribute Decision Making (F-MADM) approach in ranking resilient suppliers in logistic 4.0 is still in its infancy. Traditional MADM approach fails to address the resilient supplier selection problem in logistic 4.0 primarily becau...

Addiction and overdose related to prescription opioids have reached an epidemic level in the U.S., creating an unprecedented national crisis. This has been exacerbated partly due to the lack of tools for physicians to help predict whether or not a patient will develop opioid use disorder. Prior research lacks the investigation of how machine learni...

Under current policy decision making paradigm, we make or evaluate a policy decision by intervening different socio-economic parameters and analyzing the impact of those interventions. This process involves identifying the causal relation between interventions and outcomes. Matching method is one of the popular techniques to identify such causal re...

The Sampling Kaczmarz-Motzkin (SKM) algorithm is a generalized method for solving large-scale linear system of inequalities. Having its root in the relaxation method of Agmon, Motzkin and the randomized Kaczmarz method, SKM outperforms the state-of-the-art methods in solving large-scale linear feasibility problems. Motivated by the success of SKM m...

In this paper, we consider a variant of a truckload open vehicle routing problem with time windows, which is suitable for modeling vehicle routing operations during a humanitarian crisis. We present two integer linear programming models to formulate the problem. The first one is an arc-based mixed integer linear programming model that can be solved...

Renewable energy plants can participate in the energy pool market including day-ahead, intraday and balancing markets. The aim of this work is to develop a decision-making framework for a Wind and Storage Power Plant participating in the pool market to handle the uncertainty associated with the parameters of energy price and available wind energy,...

A vast number of causal inference studies test hypotheses on treatment effects after treatment cases are matched with similar control cases. The quality of matched data is usually evaluated according to some metric, such as balance; however the same level of match quality can be achieved by different matches on the same data. Crucially, matches tha...

People with serious mental illness (SMI)—for example, schizophrenia, bipolar disorder, and major depression—experience pronounced challenges in psychological and social functioning that often co-occur with physical health conditions. They receive inferior quality of medical care compared with patients without SMI.¹ Risk-adjusted 30-day readmissions...

Data centers are special-purpose facilities that enable customers to perform cloud based real-time online transactions and rigorous computing operations. Service levels of data center facilities are characterized by response time between query and action, which to a large extent depends on data center location and data travel distance. Another aspe...

During natural or anthropogenic disasters, humanitarian organizations (HO) are faced with the time sensitive task of sending critical resources from multiple depots to the affected areas that can be scattered across a region. This responsibility includes the quick acquisition of vehicles from the local market and the preparation of pickup and deliv...

Resilient supplier selection problem is a key decision problem for an organization to gain competitive advantage. In the presence of multiple conflicting evaluation criteria, contradicting decision makers, and imprecise information sources, this problem becomes even more difficult to solve with the classical optimization approaches. Multi-Criteria...

The growing healthcare industry is generating a large volume of useful data on patient demographics, treatment plans, payment, and insurance coverage—attracting the attention of clinicians and scientists alike. In recent years, a number of peer-reviewed articles have addressed different dimensions of data mining application in healthcare. However,...

In this paper, we identified a special second order cone as a type-k second order cone and developed a primal dual interior point algorithm for second-order type-2 cone optimization (SOCO) problems based on a family of kernel functions. We derived the following iteration bound for type-2 SOCO: \[\frac{L^\gamma}{\theta \kappa \gamma} \left[2N \psi\l...

A more comprehensive participation of renewable generators in the power market is being practiced in many countries. To add storage capability to these generators is also a major trend nowadays. Decisions concerning the participation in the power market have to be made several hours in advance, which is a key challenge for the renewable energy-base...

Interior point methods are widely used to solve linear programming problems. In this work, we present two accelerated primal affine scaling algorithms to achieve faster convergence for solving linear programming problems. For the first algorithm, we integrate nesterov's acceleration in the primal affine scaling method with an acceleration parameter...

Unexpected component failures in a mechanical system always cause loss of performance and functionality of the entire system. Condition-based maintenance decisions for a multi-component mechanical system are challenging because the interdependence of individual components' degradation is not fully understood and lack of physical models. Most existi...

Graphical information (visualized data, information, and knowledge generated from different investigations and experimentations) is a useful form of decision-relevant information in all fields of study. The usages of such information are expected to increase exponentially due to the advent of big data. Unfortunately, there are no formal methods ava...

This study deals with both a decision model for making decisions under epistemic uncertainty and how to use it for selecting optimal materials under the same uncertainty. In particular, the proposed decision model employs a set of possibilistic objective functions defined by fuzzy numbers to handle a set of conflicting criteria. In addition, the mo...

In condition-based maintenance, preventive replacement threshold and inspection scheme play important roles in maintenance performance. Major research considers cost as the main objective for measuring maintenance performance; here the average cost per unit time is used as the only objective in a single-unit system. The intention of this study was...

Facility location decisions are long term commitments that manufacturing and service industries require to make in accordance with their vision statement, competitive strategies, and with the provisions for future uncertainty. Such decisions involve huge investments, and once the decisions have been executed, recourse options are very costly. Healt...

Minimizing crowd volumes at Emergency Departments (ED) of healthcare delivery systems is of paramount importance to healthcare providers. One of the most promising option for doing so is the designing of at-home healthcare delivery system and its successful implementation to avoid the visits to the ED by patients with non-critical ailments. In this...

Advent of sophisticated technologies has drastically reduced the cost of data collection and storage but unless we exploit the data in order to gain meaningful insights, it is just the data. Data Analytics can be considered as an approach to carve out information of the very large data set. A lot of business decisions, in today's time, are taken ba...

Facility location decisions is one of the most crucial commitments that manufacturing and service industries face to impacts their interaction with the end users or customers. This is a long term decision and recourse option is very difficult once the decision has been implemented. By optimally placing the facilities considering the probable future...

A vast number of causal inference studies use matching techniques, where treatment cases are matched with similar control cases. For observational data in particular, we claim there is a major source of uncertainty that is essentially ignored in these tests, which is the way the assignments of matched pairs are constructed. It is entirely possible,...

We consider the decision problem of making causal conclusions from observational data. Typically , using standard matched pairs techniques, there is a source of uncertainty that is not usually quantified, namely the uncertainty due to the choice of the experimenter: two different reasonable ex-perimenters can easily have opposite results. In this w...

Grid-based location problems (GBLPs) can be used to solve location problems in business, engineering, resource exploitation, and even in the field of medical sciences. To solve these decision problems, an integer linear programming (ILP) model is designed and developed to provide the optimal solution for GBLPs considering fixed cost criteria. Preli...

At present, wireless communications are an integral part of day to day life. To make this communication effective and efficient, we have to place our transmitters in such a way that we can provide reliable service with a minimum cost. However, properly accomplishing this becomes a very difficult and computationally complex task when real-world cons...