antidepressants

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

In This Story

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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College of Public Health receives NIH grant to pilot AI chatbot for African Americans with depression

Mary Cunningham — Mon, 10 Jun 2024 17:03:05 +0000

College of Public Health receives NIH grant to pilot AI chatbot for African Americans with depression Mary Cunningham Mon, 06/10/2024 - 13:03

In This Story

Farrokh Alemi, PhD

Janusz Wojtusiak, PhD

Kevin Lybarger

As a leader in innovative health solutions, ��AV’s College of Public Health received a National Institutes of Health (NIH) AIM-AHEAD program grant to pilot an artificial intelligence (AI) chatbot for Black and African Americans with depression. Professor Farrokh Alemi will enhance his first-of-its-kind, evidence-based artificial intelligence tool to address the medication needs of African Americans with depression.

Professor of Health Informatics Farrokh Alemi

The existing AI tool recommends antidepressants for 16,775 general-population patient subgroups, each representing a unique combination of medical history. For each of these subgroups, the current project will analyze the effectiveness and appropriateness of the recommendations for African Americans, using the NIH All of Us database and existing published literature.

To the researchers' knowledge, this is the first research focused on developing and evaluating an antidepressant recommendation system for Black and African American people.

“Antidepressant medications are a first-line treatment for depression; however, a majority of depressed patients do not experience improvement with their first antidepressant. Additionally, minority populations, including Black and African Americans, are not well represented in antidepressant studies, contributing to reduced antidepressant effectiveness in these populations,” said Alemi. “There is a significant need to synthesize available evidence regarding antidepressant effectiveness and provide personalized treatment recommendations, and this project addresses a major gap in the management of Black and African Americans with depression.”

Researchers will develop a Knowledge-enhanced Antidepressant Recommendation Dialogue System (KARDS), which will engage users in a back-and-forth conversation to acquire the patient information needed to identify the appropriate antidepressant medication. The AI will provide the patient with a list of recommended medications, list of the relevant studies, and an explanation for the medication decisions. The system will automatically send the patient’s clinician a brief point-of-care recommendation and explanation, with an option to examine a complete record of the conversation and the supporting evidence.

“Chatbots—or patient-facing dialogue systems like the one we will create—hold transformative potential for the health care sector and are increasingly prominent in psychiatric applications, predominantly through therapy-bot implementations,” said Alemi. “Our chatbot will help improve the detailed, time-consuming, medical history intake process, and provide point-of-care summary and prescription recommendations to the patients’ clinicians. The chatbot will make patients more comfortable because the natural language modality provides an intuitive, empathetic, stigma-free interface.”

Once the AI chatbot is developed, the team will test the dialogue system with Black and African American patients to evaluate system functionality and user preferences. Additionally, the project will train a Black or African American doctoral or master’s student in AI, expanding the available workforce and building the community’s capacity to address AI.

Alemi will lead the research team, which includes Janusz Wojtusiak, a George Mason professor of Health Informatics and the director of the Machine Learning and Inference Laboratory, and Kevin Lybarger, a George Mason assistant professor in the Department of Information Sciences and Technology in the College of Engineering and Computing. All three members have collaborated previously to diagnose COVID at home from presenting symptoms.

The $70,906 grant is part of the NIH’s AIM-AHEAD (Artificial Intelligence/Machine Learning Consortium to Advance Health Equity and Researcher Diversity) program, which aims “to establish mutually beneficial and coordinated partnerships to increase the participation and representation of researchers and communities currently underrepresented in the development of AI/machine learning models and enhance the capabilities of this emerging technology, beginning with electronic health record data.”

Innovate for Good is an ongoing series that examines how George Mason faculty in the College of Public Health are harnessing technology to improve health outcomes.

If you have stories to share as part of the Innovate for Good series, email Mary Cunningham at mcunni7@gmu.edu.

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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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In the George

ChooseTherapy.com: Farrok Alemi Discusses Antidepressants.

eander6 — Thu, 24 Mar 2022 18:26:29 +0000

ChooseTherapy.com: Farrok Alemi Discusses Antidepressants. eander6 Thu, 03/24/2022 - 14:26

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Farrokh Alemi, PhD

How Long Does It Take for Antidepressants to Work?

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Verywell Health: Dr. Farrokh Alemi comments on antidepressants and a new website that predicts which antidepressant will work best for a patient.

eander6 — Thu, 18 Nov 2021 16:29:16 +0000

Verywell Health: Dr. Farrokh Alemi comments on antidepressants and a new website that predicts which antidepressant will work best for a patient. eander6 Thu, 11/18/2021 - 11:29

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Farrokh Alemi, PhD

This Website Could Help You Find the Right Antidepressant on Your First Try.

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Medical Press: Research from Dr. Alemi on the effectiveness of certain antidepressants is cited.

eander6 — Tue, 26 Oct 2021 15:47:02 +0000

Medical Press: Research from Dr. Alemi on the effectiveness of certain antidepressants is cited. eander6 Tue, 10/26/2021 - 11:47

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Farrokh Alemi, PhD

New study helps those with depression pick antidepressant based on health history.

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New Study Helps Those With Depression Pick Antidepressant Based on Health History

mthomp7 — Sat, 23 Oct 2021 13:12:32 +0000

New Study Helps Those With Depression Pick Antidepressant Based on Health History mthomp7 Sat, 10/23/2021 - 09:12

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Farrokh Alemi, PhD

The study, by Farrokh Alemi, PhD and collaborators, analyzed data from more than 3.6 million patients with major depression who had more than 10 million antidepressant treatments.

Web site developed based on the study can help alleviate depression symptoms more effectively.

Although antidepressants are one of the most frequently taken medications in the United States (11% of the population takes antidepressants)[i] [ii], 60% of depressed patients do not benefit from their first antidepressant.[iii] The sales of antidepressants exceed several billion dollars annually.[iv]

According to a new study published today from ��AV, a person’s existing medical conditions can have an influence on which antidepressant may work best. The data from the study can help millions alleviate depression symptoms more effectively and is now available to clinicians and patients in an online database.

“Effectiveness of Common Antidepressants: A Post Market Release Study,” released today in EClinical Medicine (published by The Lancet), summarizes the experiences of patients who have multiple comorbidities, or medical conditions, with using 15 different antidepressants. The study, by Farrokh Alemi, PhD, professor in Mason College of Health and Human Services Department of Health Administration and Policy and collaborators, analyzed data from more than 3.6 million patients with major depression who had more than 10 million antidepressant treatments. Patients were broken into more than 16,000 subgroups based on conditions, demographic information, and prescribed medications to analyze their experiences using antidepressants.

Janusz Wojtusiak, PhD, associate professor, used the data to build a Web site designed to help individuals with depression find what will work best for them. By entering a patient’s medical history into the site, users can find similar cases in the data and receive recommendations for antidepressants based on the experience of symptom remission in other patients. Patients can share those recommendations with their clinicians to ensure their appropriateness for their individual case.

“Unlike with previously published randomized studies, the remission rates in the study differed significantly based on the subgroup’s medical history,” says Alemi. “The choice of the optimal antidepressant depended on the subgroup matched to the patient’s medical history.”

As an example, Alemi says, in the age and gender subgroups, the best antidepressant had an average remission rate of 50.78 percent, 1.5 times higher than the average antidepressant, which has a 30.3 percent remission rate. This was 20 times higher than the worst antidepressant used in the age and gender subgroup.

Alemi’s Mason colleagues in the study included Hua Min, PhD, associate professor, and Janusz Wojtusiak, PhD, associate professor in the Department of Health Administration and Policy; Melanie Yousefi, DNP, assistant professions in the School of Nursing. Laura Becker, MS, Christopher Hane, PhD, and Vijay Nori, PhD, from OptumLabs also collaborated. Alemi was also a visiting fellow at OptumLabs.

The researchers received a grant from the Robert Wood Johnson Foundation for the study. Virginia’s Commonwealth Health Research Board provided additional funds for the related website.

[i] Urquhart L. Top companies and drugs by sales in 2019. Nat Rev Drug Discov. 2020 Apr; 19(4): 228.

[ii] Piek E, van der Meer K, Nolen WA. Guideline recommendations for long-term treatment of depression with antidepressants in primary care--a critical review. Eur J Gen Pract. 2010 Jun;16(2):106-12.

[iii] Cheung AH, Zuckerbrot RA, Jensen PS, Ghalib K, Laraque D, Stein RE; GLAD-PC Steering Group. Guidelines for Adolescent Depression in Primary Care (GLAD-PC): II. Treatment and ongoing management. Pediatrics. 2007 Nov;120(5):e1313-26.

[iv] Gautam S, Jain A, Gautam M, Vahia VN, Grover S. Clinical Practice Guidelines for the management of Depression. Indian J Psychiatry. 2017 Jan;59(Suppl 1):S34-S50.

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

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College of Public Health receives NIH grant to pilot AI chatbot for African Americans with depression

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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

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New Study Helps Those With Depression Pick Antidepressant Based on Health History

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