Artificial Intelligence

New Course Creates Ethical Leaders for an AI-Driven Future

Andrew J Schappert — Tue, 10 Dec 2024 19:04:10 +0000

New Course Creates Ethical Leaders for an AI-Driven Future Andrew J Schappert Tue, 12/10/2024 - 14:04

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Jesse Kirkpatrick: ‘The interdisciplinary approach ensures that participants develop insights applicable across public and private sectors, enabling them to lead responsibly in the AI-driven future.’ Photo provided.

While the debates continue over artificial intelligence’s possible impacts on privacy, economics, education, and job displacement, perhaps the largest question regards the ethics of AI. Bias, accountability, transparency, and governance of the powerful technology are aspects that have yet to be fully answered.

A new cross-disciplinary course at ��AV is designed to prepare students to tackle the ethical, societal, and governance challenges presented by AI. The course, AI: Ethics, Policy, and Society, will draw expertise from the Schar School of Policy and Government, the College of Engineering and Computing (CEC), and the College of Humanities and Social Sciences (CHSS).

The master’s degree-level course begins in spring 2025 and will be taught by Jesse Kirkpatrick, a research associate professor in the CEC, the Department of Philosophy, and codirector of the Mason Autonomy and Robotics Center.

The course is important now, said Kirkpatrick, because “artificial intelligence is transforming industries, reshaping societal norms, and challenging long-standing ethical frameworks. This course provides critical insights into the ethical, societal, and policy implications of AI at a time when these technologies are increasingly deployed in areas like healthcare, criminal justice, and national defense.”

Debates about bias in AI systems, the governance of autonomous decision-making, and the risks of misinformation “underscore the urgency of equipping students and professionals with the tools to address the opportunities and challenges responsibly,” he added.

This course is designed for students and professionals from diverse fields, including policy, computer science, engineering, law, philosophy, and business.

“Occupations such as AI developers, policymakers, ethicists, legal advisors, and technology strategists will benefit greatly,” Kirkpatrick said. “The interdisciplinary approach ensures that participants develop insights applicable across public and private sectors, enabling them to lead responsibly in the AI-driven future.”

The course is open to George Mason students and is a core component of the university’s new graduate certificate in Responsible AI, making it an essential step for those pursuing advanced study or leadership roles in ethical AI design and governance.

In addition to critical readings and written assignments, the course incorporates hands-on components such as workshops, interactive discussions, and practical tools includes algorithmic audits, ethical toolkits, and risk management frameworks.

“Students will also engage in scenario-building exercises and present collaborative projects that apply ethical AI principles to real-world challenges,” Kirkpatrick said. “The course also features distinguished guest speakers from academia, industry, and government, providing students with diverse perspectives on AI.”

As the codirector of the Mason Autonomy and Robotics Center, Kirkpatrick is engaged in “Responsible AI” initiatives.

“I bring a unique blend of academic expertise and practical experience,” he said. “My work spans creating ethical AI frameworks, consulting on AI policy, and teaching at the intersection of ethics, technology, and public policy.

“This course reflects my commitment to equipping students with the knowledge and tools to address the profound ethical challenges and opportunities posed by AI technologies in society.”

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Interprofessional George Mason researchers awarded more than $1 million to improve outcomes for patients with depression

Jennifer Pocock — Tue, 10 Dec 2024 18:25:35 +0000

Interprofessional George Mason researchers awarded more than $1 million to improve outcomes for patients with depression Jennifer Pocock Tue, 12/10/2024 - 13:25

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Farrokh Alemi, PhD

Kevin Lybarger

Alison Evans Cuellar, PhD, MBA

Özlem Uzuner

Researchers Farrokh Alemi and Kevin Lybarger receive ��AV’s first Patient-Centered Outcomes Research Institute (PCORI) award to develop innovative Artificial Intelligence (AI) technology, including large language models, for improving antidepressant recommendations.

Farrokh Alemi and Kevin Lybarger

AI will soon receive a dose of empathy with the goal of helping to match people with depression to their best-fit medication. A team led by Farrokh Alemi, a professor in the College of Public Health (CPH), and Kevin Lybarger, an assistant professor in the College of Engineering and Computing (CEC), received $1,049,998 in research funding from the Patient-Centered Outcomes Research Institute (PCORI) to continue their work on developing an AI system that helps patients find the right depression medications.

With this funding support, Co-PIs Alemi and Lybarger will hone large language models (LLMs) to address known challenges in AI, including mitigating biases, reducing the potential for inaccurate information, and incorporating an empathetic tone, according to Alemi.

The new study will introduce an innovative way for AI to help patients make medication decisions. The AI system will engage patients in natural-language conversations to collect information about their medical history. The system will draw upon more than 10 million patient experiences with 15 different oral antidepressants and a National Institutes of Health All of Us database, which includes records from more than 80,000 participants with major depressive disorders, to help create a plan that is statistically likely to succeed. Alemi and Lybarger believe this will help alleviate the trial and error that can lead to negative patient outcomes.

The researchers will also introduce a first-of-its-kind patient simulator capable of mimicking various medical, linguistic, and behavioral characteristics. This simulator will be used to test and refine the AI system by simulating diverse patient scenarios, including infrequent but critical events such as suicidal ideation, to ensure the system’s recommendations are safe, culturally sensitive, and empathetic.

“This study was selected for its potential to address a high-priority methodological gap in patient-centered comparative clinical effectiveness research,” said PCORI Executive Director Nakela L. Cook. “We look forward to following the study’s progress and working with George Mason to share the results.”

This is the first PCORI-funded study that George Mason has received. “Depression is a major public health problem and we are excited to see the development of new AI-based decision tools, leveraging the multidisciplinary talents of our college to help tackle it,” said CPH Associate Dean of Research Alison Cuellar.

"This innovative study promises to generate methodologies for using AI for medical decision-support and for empowering patients to make critical health decisions beyond mental health,” says Özlem Uzuner, chair of CEC’s Department of Information Sciences and Technology.

This study is one of the latest funded by PCORI to examine which medical treatments work best, where and when treatment falls flat, and how to address the gaps. These studies deliver results that guide researchers in planning future studies and provide patients, their caregivers, and clinicians with the evidence-based information needed to make better-informed health and health care decisions.

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Jaffer appointed to new Virginia Artificial Intelligence Task Force

Colleen Rich — Wed, 23 Oct 2024 14:37:14 +0000

Jaffer appointed to new Virginia Artificial Intelligence Task Force Colleen Rich Wed, 10/23/2024 - 10:37

Virginia governor Glenn Youngkin has appointed Jamil N. Jaffer, the founder and executive director of the National Security Institute (NSI) at ��AV’s Antonin Scalia Law School, to his new Artificial Intelligence (AI) Task Force, which will work with legislators to regulate rapidly advancing AI technology. Jaffer is one of 10 appointees to the task force, alongside NSI Fellow Zach Graves.

Jamil Jaffer. Photo by Cristian Torres/Office of University Branding

The AI Task Force brings together a group of the most prominent AI experts in the commonwealth to provide advice to policy makers as they work to establish Virginia as a leader in the AI field, while balancing the need to create safeguards that promote responsible technology use. These experts will also provide guidance to state agencies on integrating AI, ensuring that any AI solutions implemented are both efficient and cost effective

“The AI Task Force is a vital component of Virginia’s efforts to lead in the rapidly advancing field of artificial intelligence, providing crucial guidance to policymakers as they balance innovation with responsible technology use,” said Ken Randall, Allison and Dorothy Rouse Dean and ��AV Foundation professor at the Antonin Scalia Law School. “We are confident that his expertise, alongside NSI Fellow Zach Graves and other distinguished members, will significantly contribute to shaping Virginia’s AI future.”

“I look forward to collaborating with the distinguished members of the Task Force to provide actionable insights that promote responsible AI use and drive meaningful advancements in both the public and private sectors,” said Jaffer.

Jaffer is also director of the National Security Law and Policy Program and director of the Cyber, Intelligence, and National Security LLM Program at Scalia Law. He teaches classes on counterterrorism, intelligence, surveillance, cybersecurity, and other national security matters.

He has served as an outside advisor to the Cyberspace Solarium Commission and has testified before committees of the U.S. Senate and House of Representatives on China, cybersecurity, counterterrorism, and other national security matters.

Jaffer has also appeared as a subject matter expert on a range of national television and radio outlets and various print and online publications.

Also on the task force are John Bailey, founder of Vestigo Partners; Bill Cleveland, former vice mayor, Alexandria; Richard Cullatta, CEO, International Society for Technology in Education; Sam Hammond, senior economist, Foundation for American Innovation; Tim Hwang, Senior Technology Fellow, Institute for Progress; Lori Jennings, founder, Jennings ProSearch; Paige Kowalski, executive vice president, Data Quality Campaign; and Naren Ramakrishnan, professor of engineering, Virginia Tech.

Listen to Jamil Jaffer and George Mason President Gregory Washington discuss the U.S.'s position on the global stage, the power of the American Dream, and more on the Access to Excellence podcast.

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New research utilizes machine learning to address social isolation among alzheimer’s caregivers

mthomp7 — Thu, 10 Oct 2024 17:22:37 +0000

New research utilizes machine learning to address social isolation among alzheimer’s caregivers mthomp7 Thu, 10/10/2024 - 13:22

A new study from the College of Public Health at ��AV, led by Professor Janusz Wojtusiak and Health Services Research doctoral candidate Ghaida Alsadah, explores the use of machine learning to predict social isolation among caregivers of individuals with Alzheimer’s disease and related disorders.

This research, which has been selected for funding by the Commonwealth of Virginia's Alzheimer's and Related Diseases Research Award Fund (ARDRAF), marks a significant advance in leveraging artificial intelligence (AI) to address a critical public health issue.

Professor Janusz Wojtusiak uses machine learning to predict social isolation among caregivers of individuals with Alzheimer’s disease and related disorders.

Alsadah has played a pivotal role in this study, contributing her expertise as a doctoral candidate to develop and refine machine learning models. These models analyze data from the Health and Retirement Study (HRS), National Health and Aging Trends Study (NHATS), and Behavioral Risk Factor Surveillance System (BRFSS) to identify and predict social isolation trajectories among caregivers. The study’s key finding is that AI-driven methods offer a novel approach to detecting social isolation, potentially leading to targeted interventions.

“This research is groundbreaking in its application of machine learning to predict social isolation among caregivers, an area previously underexplored,” said Wojtusiak. “The potential to develop AI-based interventions could significantly enhance the well-being of caregivers who often face profound social and emotional challenges.”

The study aims to construct predictive models for social isolation, adapt them for Medicare claims data, and simulate their application across large populations. The goal is to create a framework for AI-based interventions to address loneliness among caregivers effectively.

The innovation of this work lies in its use of machine learning to analyze and predict social isolation—a new approach with the potential to transform current understanding and interventions. Wojtusiak and Alsadah’s research is set to significantly impact health informatics and caregiver support. “Predicting Social Isolation Among Alzheimer’s Caregivers Using Machine Learning” will be published in an upcoming issue of a leading health informatics journal, emphasizing the importance of innovative approaches to complex public health challenges.

Additional contributors include College of Public Health alumna Mary Louise Pomeroy, a postdoctoral research fellow at Johns Hopkins University, who provided valuable expertise on social isolation and relevant datasets.

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Schar School Welcomes Thema Monroe-White

Andrew J Schappert — Wed, 04 Sep 2024 15:54:33 +0000

Schar School Welcomes Thema Monroe-White Andrew J Schappert Wed, 09/04/2024 - 11:54

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‘We’ve talked about the data harm. I want to get at the solutions.’ – Thema Monroe-White

Thema Monroe-White joins ��AV this fall as an associate professor in both the Schar School and the College of Engineering and Computing. She is an artificial intelligence (AI) and policy justice scholar and the recipient of a prestigious $1 million CAREER grant, awarded by the National Science Foundation (NSF) to early-career faculty who are trailblazers within their field.

Monroe-White focuses on the intersection of science, policy, and racial equity, working to combat bias within data science and AI by building a diverse scientific workforce. Her CAREER grant, entitled “Investigating Undergraduate Student Persistence Intentions for a Diverse Data Science Community,” is a huge step toward her goal of developing social inclusion in the scientific community. Monroe-White will investigate which interventions are most effective in encouraging underrepresented students to choose STEM majors, graduate, and subsequently stay in STEM careers. “I want to develop concrete, practical approaches for broadening participation in data science and related fields,” says Monroe-White. “Why do students persist in STEM, and how can we codify those conditions to encourage even more underrepresented students to pursue STEM careers?”

The CAREER grant builds on another NSF-funded project Monroe White brings to Mason, which examines how scientists’ background and experience influence the research topics they choose to pursue. Monroe-White's team has illustrated that a homogeneous workforce in STEM fields results in a less comprehensive exploration of scientific topics. Just as medical research focused on white male subjects until more women and researchers of color achieved authority in the field, Monroe-White's research exposes similar patterns in other fields. Preliminary research focused on scientists in the United States, and the team will now refine their algorithm and apply it to an international dataset.

Monroe-White brings her expertise in AI to George Mason at time when the university is investing in AI research and innovation. The Schar School recently secured two other seven-figure AI-related grants, one from the Department of Defense and another to create the nation’s first Center for AI Innovation for Economic Competitiveness (CAIIEC).

“Thema Monroe-White’s research understands the computational, social, and policy issues that underline artificial intelligence,” said J.P. Singh, Schar School Distinguished University Professor and principal investigator for CAIIEC. “She will greatly help us bridge disciplinary divides and train the next generation of student scholars to address the cutting-edge issues of our times.”

Monroe-White's research and teaching agenda focus both on revealing algorithmic bias and realizing AI’s capacity for positive social transformation. This will be the center of her contributions to George Mason’s new undergraduate program in technology policy, an innovative split between computer science and policy studies. In her fall course, AI: Ethics, Policy, & Society, Monroe-White asks, “Must AI tools reflect reality?” Algorithms learn from the data they are given and reflect the biases embedded in that data.

“We’ve talked about the data harms. I want to get at the solutions,” she said when asked about her goals at George Mason. “How do we round that corner and, instead of stereotyping subordination and omission, how do we get at empowerment and uplift?” AI is a powerful tool – one that has caused harm but also has enormous potential to reverse previous harms. As part of the Schar School, Monroe-White will train the next generation of computer scientists and policy practitioners to build that future.

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GenAI brings us closer to automating investment expertise

Jennifer Anzaldi — Thu, 22 Aug 2024 18:39:34 +0000

GenAI brings us closer to automating investment expertise Jennifer Anzaldi Thu, 08/22/2024 - 14:39

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Yi Cao

Long Chen

Large language models (LLMs) such as ChatGPT and Google Gemini excel at being trained on large data-sets to generate informative responses to prompts. Yi Cao, an assistant professor of accounting at the Donald G. Costello College of Business at ��AV, and Long Chen, associate professor and area chair of accounting at Costello, are actively exploring how individual investors can use LLMs to glean market insights from the dizzying array of available data about companies.

Long Chen and Yi Cao

Their new working paper, co-authored with Jennifer Wu Tucker of the University of Florida and Chi Wan of University of Massachusetts Boston, examines AI’s ability to identify “peer firms,” or product market competitors in an industry.

Cao explains the significance of selecting peers by relating this process to the real-estate market. “The capital market is similar to the real-estate market in that a firm’s value is partially determined by the value of its peers. In the real-estate market, we price a home based on the value of comparable properties in the neighborhood, or the so-called 'comps.' In our paper, we aim to leverage the power of LLMs to identify comps for evaluating firm value.”

This task is at least as difficult as it is essential. It takes much time, skill and effort to gather, aggregate and manage data to select peers. However, the researchers reasoned that LLMs could do a lot of the heavy lifting of data aggregation and analysis for the individual investors, and produce a list of peers comparable in validity to that identified by human experts.

“The advantage is in the capability to utilize all the information potentially out there so that it is at least performing as well as other traditional methods that can help us investors and researchers,” says Cao.

For the study, Chen and Cao employed Bard from Google, now known as “Gemini,” as their LLM of choice because “Bard has a greater ability to utilize its pre-training data, which is arguably larger than ChatGPT’s and with more parameters,” says Cao.

After defining “product market competition” and forming a prompt for Bard, the researchers instructed Bard to limit its knowledge pool to a specific year within the period 1981-2023, in order to avoid “look-ahead bias,” i.e., future information scrambling the results.

“We need to understand that LLMs are actually a very powerful, new tool, unmatched in their efficiency, ability to process vast amounts of information at a low cost, and accessibility to the general public.”

They limited focal firms to large, publicly listed companies as there is less data out there for smaller or private firms. In all, the data-set comprised over 300,000 focal firm-years.

On average, the LLM could generate about seven peer firms for a focal firm, a number that is similar to the SEC recommendations on how firms should disclose their segments.

The researchers then compared the LLM’s performance to the lists generated by three human experts for a set of 40 leading computer software companies. The average overlap was a little over 40 percent, greater than expected.

They also compared the AI-identified peer lists to two alternative systems for identifying peers: the federal government’s Standard Industrial Classification (SIC) codes and Text-based Network Industry Classification (TNIC), which compares firms based on linguistic similarities in their 10-K filings. The LLM’s output overlapped significantly with TNIC’s. Plus, the peers identified by the LLM were generally a better fit than those from SIC and TNIC, as their monthly stock returns hewed closer to the focal firm.

But TNIC outperformed the LLM in identifying peers for mid-sized firms within the sample, indicating that it is not a clear-cut case of universal LLM superiority.

“It’s especially beneficial for individual investors—as all the cost concerns that we’re talking about are especially relevant for them,” Chen adds.

Regarding the future of LLM, Chen states, “There are always costs and benefits associated with using generative AI. It is uncertain whether current systems will soon be obsolete.” When asked about the SEC adopting an AI tool for investors, Chen emphasizes that users need to understand the pros and cons of using AI to make their informed judgments “because AI cannot be held responsible for the information it provides or for how it is utilized.”

Chen concludes, “We need to embrace this new technology, but we must recognize that it is not yet in a perfect state. Competition to improve the technology is fierce. Our findings might just represent the lower bound of the effectiveness of the technology.”

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Online learning expert suggests best practices for promoting academic honesty

Jennifer Pocock — Tue, 13 Aug 2024 17:44:33 +0000

Online learning expert suggests best practices for promoting academic honesty Jennifer Pocock Tue, 08/13/2024 - 13:44

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John Cantiello, PhD

The growing popularity of easy-to-use artificial intelligence (AI) interfaces has reignited discussions about cheating in academics. Academic dishonesty, however, is a long-standing concern for faculty and a burgeoning area of research for online coursework. Whether cheating is more or less prevalent in remote coursework has been the topic of debate since online coursework went mainstream in the early 2000s. While online learning provides access to education for nontraditional students and in nontraditional circumstances (such as the COVID-19 pandemic), it’s important to ensure that students are authentically learning the material.

John Cantiello. Photo provided

To understand the prevalence of cheating and help teachers address it, John Cantiello, a professor of the Department of Health Administration and Policy, reviewed the existing literature on the topic. In the Journal of Health Administration Education, Cantiello, who was honored last year as an online teacher of distinction by the Stearns Center of Teaching and Learning, reports that many methods for preventing academic dishonesty are effective.

Not everyone agrees on what constitutes “dishonesty,” he points out. While some forms of cheating are clear-cut—having others do assignments or take tests in the place of the student, plagiarism, or copying answers, others are not. Up for debate are the use of tools like artificial intelligence, or consulting publications that, in a professional setting, would be available to them while solving problems.

“This paper illustrates varying results on the frequency of cheating in online, college-level courses,” Cantiello said. “Tools and strategies for preventing academic dishonesty vary in effectiveness, but some show significant success.”

Cantiello examined many different interventions to prevent online academic dishonesty, including frequent discussions on what constitutes cheating and plagiarism (e.g., can they use AI tools in this course?), technology (e.g., biometric hardware and software such as Turnitin or Examity), and varied testing strategies (e.g., limiting testing times, multiple versions of tests, and leaking fake answers online).

He suggests that a combination of strategies is the best way to limit cheating. “A holistic approach to course design that incorporates these elements and includes a variety of learning activities fosters an effective learning environment where academic integrity can be upheld,” Cantiello said. However, he says, more research is needed on how often and in what capacity cheating happens.

“Preventing Academic Dishonesty in Online Courses: Best Practices to Discourage Cheating” was published online in March 2024 in the Journal of Health Administration Education. Renee Hotchkiss Geschke is co-author on the paper.

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George Mason to Launch Nation’s First Center for Small Business AI Innovation with $1M Grant

Andrew J Schappert — Fri, 28 Jun 2024 14:40:17 +0000

George Mason to Launch Nation’s First Center for Small Business AI Innovation with $1M Grant Andrew J Schappert Fri, 06/28/2024 - 10:40

In a groundbreaking move set to bolster the economic competitiveness of small and medium enterprises (SMEs) across Virginia, ��AV has been awarded a $1 million grant to establish the nation’s first Center for AI Innovation for Economic Competitiveness (CAIIEC).

J.P. Singh: ‘Small and medium enterprises are the backbone of the U.S. economy at the local level.’ Photo by Ron Aira/Office of University Branding

This pioneering initiative aims to place Virginia at the forefront of artificial intelligence (AI) adoption among SMEs, which often fly under the radar in discussions dominated by federal and big-tech AI advancements.

The $1 million grant, which was sponsored by Senators Mark Warner and Tim Kaine, is directed through the U.S. Small Business Administration. The center will draw expertise from several George Mason colleges, including the Schar School of Policy and Government, the College of Humanities and Social Sciences, and the College of Engineering and Computing.

“Small and medium enterprises are the backbone of the U.S. economy at the local level,” said the Schar School’s J.P. Singh, the Distinguished University Professor serving as principal investigator. “We will work with SMEs to strengthen their efforts in AI adoption all over Virginia and learn from global cases such as Germany, where its famed SMEs have been called ‘the hidden champions.’”

“I'm excited George Mason’s Center for AI Innovation for Economic Competitiveness is receiving $1 million in federal funding, which I was proud to have helped secure in the recent government funding bill,” said Senator Kaine. “This will help prepare small businesses and Virginians to harness AI and lead to new opportunities for economic growth and jobs in the commonwealth.”

The new center promises to provide much-needed leadership, support, and training to these enterprises, ensuring they do not get left behind in the rapidly evolving AI landscape. George Mason’s interdisciplinary team, comprising computer scientists, public policy experts, economists, environmental and medical scientists, and ethicists, is uniquely positioned to drive this initiative.

“I am proud to have helped secure funding for the Center for AI Innovation for Economic Competitiveness at George Mason,” said Senator Warner in a statement. “This investment will help position Virginia as a leader in AI development by creating programs to help small businesses and communities thrive in the digital market, marking a huge win for Virginians across the commonwealth.”

The university has long been a leader in AI research, and the center aims to extend this expertise beyond academia to make a tangible impact on local businesses.

Amarda Shehu: ‘This is much needed to better position industry in an innovative and competitive environment.’ Photo by Office of University Branding

“Here is another great example of how George Mason continues to deliver on our commitment to access and community-engaged technology diffusion, partnering with diverse communities in new and innovated ways,” said Vice President for Research, Innovation, and Economic Impact Andre Marshall. “What better way to broadly support our local and national economy by providing our small businesses with the disruptive tech and best practices that will shape our future?”

The center will both train and encourage students at George Mason to work with SMEs in Virginia, Singh added, highlighting the initiative’s educational component.

Co-principal investigator Amarda Shehu, a professor in the Department of Computer Science and a longtime AI researcher, emphasized the practical benefits the center will offer.

“As an AI researcher, I see firsthand how fast AI is moving,” said Shehu, who is also associate dean for AI Innovation in the College of Engineering and Computing and associate vice president for research at George Mason's Institute for Digital Innovation (IDIA). “I also get to see through my various interactions with industry that such a pace can be challenging, particularly for small businesses. So, we thought about how Research 1 universities, such as George Mason, can unlock the power of AI for small and medium enterprises.”

This center will do that, she said.

“To respond to AI technologies and regulatory requirements moving rapidly, the center will develop an AI toolkit to help small and medium enterprises capture and operationalize the value of AI for the enterprise,” she said. “This is much needed to better position industry in an innovative and competitive environment.”

The center is a collaboration between the Schar School, IDIA, the Institute for Philosophy and Public Policy, and the Center for Regional Analysis (CRA). These four entities will leverage their existing collaborative connections to jointly oversee and manage the project.

Project leaders include Singh, who is also codirector of George Mason’s Center for Advancing Human-Machine Partnership and a Richard von Weizsäcker Fellow with the Robert Bosch Academy, Berlin; Shehu; Terry Clower, CRA director; Naoru Koizumi, Schar School professor and associate dean of research and grants; and Jesse Kirkpatrick, research associate professor and codirector of the Mason Autonomy and Robotics Center.

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George Mason researchers harness the power of artificial intelligence to match patients with the most effective antidepressant for their unique needs

Mary Cunningham — Wed, 29 May 2024 19:46:58 +0000

George Mason researchers harness the power of artificial intelligence to match patients with the most effective antidepressant for their unique needs Mary Cunningham Wed, 05/29/2024 - 15:46

Researchers in ��AV’s College of Public Health have leveraged the power of artificial intelligence (AI) analytical models to match a patient’s medical history to the most effective antidepressant, allowing patients to find symptom relief sooner. The free website, MeAgainMeds.com, provides evidence-based recommendations, allowing clinicians and patients to find the optimal antidepressant the first time.

Farrokh Alemi, principal investigator and professor of health informatics at ��AV’s College of Public Health

“Many people with depression must try multiple antidepressants before finding the right one that alleviates their symptoms. Our website reduces the number of medications that patients are asked to try. The system recommends to the patient what has worked for at least 100 other patients with the same exact relevant medical history,” said Farrokh Alemi, principal investigator and professor of health informatics at ��AV’s College of Public Health.

AI helped to simplify the very complex task of making the thousands of guidelines easily accessible to patients and clinicians. The guidelines that researchers created are complicated because of the amount of clinical information that is relevant in prescribing an antidepressant; AI seamlessly simplifies the task.

With AI at its core, MeAgainMeds.com analyzes clinician or patient responses to a few anonymous medical history questions to determine which oral antidepressant would best meet the specific needs. The website does not ask for any personal identifiable information and it does not prescribe medication changes. Patients are advised to visit their primary health care provider for any changes in medication.

In 2018, the Centers for Disease Control reported that more than 13% of adults use antidepressants, and the number has only increased since the pandemic and other epidemics since 2020. This website could help millions of people find relief more quickly.

Alemi and his team analyzed 3,678,082 patients who took 10,221,145 antidepressants. The oral antidepressants analyzed were amitriptyline, bupropion, citalopram, desvenlafaxine, doxepin, duloxetine, escitalopram, fluoxetine, mirtazapine, nortriptyline, paroxetine, sertraline, trazodone, and venlafaxine. From the data, they created 16,770 subgroups of at least 100 cases, using reactions to prior antidepressants, current medication, history of physical illness, history of mental illness, key procedures, and other information. The subgroups and remission rates drive the AI to produce an evidence-based medication recommendation.

“By matching patients to the subgroups, clinicians can prescribe the medication that works best for people with similar medical history,” said Alemi. The researchers and website recommend that patients who use the site take the information to their clinicians, who will ultimately decide whether to prescribe the recommended medicine.

Alemi and his team tested a protype version of the site in 2023, which they advertised on social media. At that time, 1,500 patients used the website. Their goal is to improve the website and expand its user base. The initial research was funded by the Commonwealth of Virginia and by the Robert Wood Johnson Foundation.

The researchers’ most recent paper in a series of papers on response to antidepressants analyzed 2,467 subgroups of patients who had received psychotherapy. “Effectiveness of Antidepressants in Combination with Psychotherapy” was published online in March 2024. Additional authors include Tulay G Soylu from Temple University, and Mary Cannon and Conor McCandless from Royal College of Surgeons in Dublin, Ireland.

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Professor applies statistics and AI to land use modeling and real estate pricing

Teresa Donnellan — Wed, 29 May 2024 16:18:12 +0000

Professor applies statistics and AI to land use modeling and real estate pricing Teresa Donnellan Wed, 05/29/2024 - 12:18

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Abolfazl Safikhani

��AV statistics professor Abolfazl Safikhani recently applied his cutting-edge, interdisciplinary research to analyzing land use dynamics and property pricing shifts over time, work that underscores the transformative potential of data-driven insights, especially in urban planning and real estate.

Safikhani earned bachelor’s and master’s degrees in mathematics before earning a doctorate in statistics.

“I decided to do a PhD in statistics because throughout the master’s I had become more and more interested in connecting real world problems to data. And I'm very happy that I made that decision,” he said.

Abolfazl Safikhani

Along with a former colleague at the University of Florida in the urban planning department, Safikhani applied machine learning techniques to a dataset comprising millions of land parcels in Florida. The two endeavored to decipher the intricate dynamics of land use transformations over time and predict future developments with unprecedented accuracy. Their predictions surpassed 98% accuracy.

But the team didn't stop with successful predictions. They recognized the importance of understanding the underlying mechanisms driving these predictions. With the addition of a new collaborator, Tianshu Feng in George Mason’s Systems Engineering and Operations Research Department, the researchers aim to present their land use analysis software as explainable artificial intelligence (XAI). By elucidating the black box of machine learning algorithms, Safikhani hopes local government decision-makers and urban planners can confidently leverage the software to optimize resource allocation effectively.

Another of Safikhani’s projects considers land use and value specifically concerning the price of residential real estate. Safikhani’s own experience buying real estate in Fairfax County, Virginia, in 2022, inspired this project. When he asked his real estate agent to estimate a fair price of a certain house, the agent came back with an estimate based on the price of three comparable local properties that had recently sold. Ever a “quant guy,” Safikhani said, he thought there could be a better way: applying the idea of transfer learning.

“The big idea of transfer learning is, within your big data set, try to find areas that have similar dynamics to your area of interest. And then use that similarity to improve your prediction,” Safikhani explained. “So, imagine that there is a little neighborhood somewhere in DC or somewhere in Maryland or somewhere in California that has dynamics very similar to the specific neighborhood where you want to buy a house in Northern Virginia. Once you account for some changes, let's say, regulations and things that are different, then the remaining dynamics are their similarities.”

He continued, “If you only use your neighborhood, you can have three data points. If you use another, similar neighborhood, it's going to be 20. If you use neighborhoods from other places over the 50 states of the U.S., you may end up getting a thousand data points.”

Safikhani is working with a colleague from the University of California – Los Angeles to bring in funding to develop this pricing software. Their preliminary results show the benefit of their proposed model versus current pricing systems.

Safikhani's research is poised to revolutionize sectors like urban planning and real estate. In fact, his research has attracted the attention of startups keen to translate his findings into real estate–disrupting tools.

“It seems there's actually a growing interest in having such AI tools that would understand land use development and then really match it with pricing,” he said. “And sooner or later, this [technology] is going to come out. Platforms like Zillow are doing a good job, but there's much more that can be done.”