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  1. Article ; Online: Machine learning prediction of mild cognitive impairment and its progression to Alzheimer's disease.

    Fouladvand, Sajjad / Noshad, Morteza / Periyakoil, V J / Chen, Jonathan H

    Health science reports

    2023  Volume 6, Issue 10, Page(s) e1438

    Language English
    Publishing date 2023-10-18
    Publishing country United States
    Document type Journal Article
    ISSN 2398-8835
    ISSN (online) 2398-8835
    DOI 10.1002/hsr2.1438
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: The ChatGPT therapist will see you now: Navigating generative artificial intelligence's potential in addiction medicine research and patient care.

    Tate, Steven / Fouladvand, Sajjad / Chen, Jonathan H / Chen, Chwen-Yuen Angie

    Addiction (Abingdon, England)

    2023  Volume 118, Issue 12, Page(s) 2249–2251

    MeSH term(s) Humans ; Addiction Medicine ; Artificial Intelligence ; Patient Care
    Language English
    Publishing date 2023-09-21
    Publishing country England
    Document type Editorial ; Research Support, N.I.H., Extramural
    ZDB-ID 1141051-6
    ISSN 1360-0443 ; 0965-2140
    ISSN (online) 1360-0443
    ISSN 0965-2140
    DOI 10.1111/add.16341
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  3. Article ; Online: Machine learning prediction of mild cognitive impairment and its progression to Alzheimer's disease

    Sajjad Fouladvand / Morteza Noshad / V. J. Periyakoil / Jonathan H. Chen

    Health Science Reports, Vol 6, Iss 10, Pp n/a-n/a (2023)

    2023  

    Keywords Alzheimer's disease ; machine learning ; mild cognitive impairment ; Medicine ; R
    Language English
    Publishing date 2023-10-01T00:00:00Z
    Publisher Wiley
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  4. Article ; Online: Session Introduction: Artificial Intelligence in Clinical Medicine: Generative and Interactive Systems at the Human-Machine Interface.

    Fouladvand, Sajjad / Pierson, Emma / Jankovic, Ivana / Ouyang, David / Chen, Jonathan H / Daneshjou, Roxana

    Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing

    2023  Volume 29, Page(s) 1–7

    Abstract: Artificial Intelligence (AI) models are substantially enhancing the capability to analyze complex and multi-dimensional datasets. Generative AI and deep learning models have demonstrated significant advancements in extracting knowledge from unstructured ... ...

    Abstract Artificial Intelligence (AI) models are substantially enhancing the capability to analyze complex and multi-dimensional datasets. Generative AI and deep learning models have demonstrated significant advancements in extracting knowledge from unstructured text, imaging as well as structured and tabular data. This recent breakthrough in AI has inspired research in medicine, leading to the development of numerous tools for creating clinical decision support systems, monitoring tools, image interpretation, and triaging capabilities. Nevertheless, comprehensive research is imperative to evaluate the potential impact and implications of AI systems in healthcare. At the 2024 Pacific Symposium on Biocomputing (PSB) session entitled "Artificial Intelligence in Clinical Medicine: Generative and Interactive Systems at the Human-Machine Interface", we spotlight research that develops and applies AI algorithms to solve real-world problems in healthcare.
    MeSH term(s) Humans ; Artificial Intelligence ; Computational Biology ; Algorithms ; Clinical Medicine
    Language English
    Publishing date 2023-12-31
    Publishing country United States
    Document type Journal Article
    ISSN 2335-6936
    ISSN (online) 2335-6936
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Article ; Online: Mild Cognitive Impairment: Data-Driven Prediction, Risk Factors, and Workup.

    Fouladvand, Sajjad / Noshad, Morteza / Goldstein, Mary Kane / Periyakoil, V J / Chen, Jonathan H

    AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science

    2023  Volume 2023, Page(s) 167–175

    Abstract: Over 78 million people will suffer from dementia by 2030, emphasizing the need for early identification of patients with mild cognitive impairment (MCI) at risk, and personalized clinical evaluation steps to diagnose potentially reversible causes. Here, ... ...

    Abstract Over 78 million people will suffer from dementia by 2030, emphasizing the need for early identification of patients with mild cognitive impairment (MCI) at risk, and personalized clinical evaluation steps to diagnose potentially reversible causes. Here, we leverage real-world electronic health records in the observational medical outcomes partnership (OMOP) data model to develop machine learning models to predict MCI up to a year in advance of recorded diagnosis. Our experimental results with logistic regression, random forest, and xgboost models trained and evaluated on more than 531K patient visits show random forest model can predict MCI onset with ROC-AUC of 68.2±0.7. We identify the clinical factors mentioned in clinician notes that are most predictive of MCI. Using similar association mining techniques, we develop a data-driven list of clinical procedures commonly ordered in the workup of MCI cases, that could be used as a basis for guidelines and clinical order set templates.
    Language English
    Publishing date 2023-06-16
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2676378-3
    ISSN 2153-4063 ; 2153-4063
    ISSN (online) 2153-4063
    ISSN 2153-4063
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  6. Article: Leveraging Artificial Intelligence to Predict Health Belief Model and COVID-19 Vaccine Uptake Using Survey Text from US Nurses.

    Omranian, Samaneh / Khoddam, Alireza / Campos-Castillo, Celeste / Fouladvand, Sajjad / McRoy, Susan / Rich-Edwards, Janet

    Behavioral sciences (Basel, Switzerland)

    2024  Volume 14, Issue 3

    Abstract: We investigated how artificial intelligence (AI) reveals factors shaping COVID-19 vaccine hesitancy among healthcare providers by examining their open-text comments. We conducted a longitudinal survey starting in Spring of 2020 with 38,788 current and ... ...

    Abstract We investigated how artificial intelligence (AI) reveals factors shaping COVID-19 vaccine hesitancy among healthcare providers by examining their open-text comments. We conducted a longitudinal survey starting in Spring of 2020 with 38,788 current and former female nurses in three national cohorts to assess how the pandemic has affected their livelihood. In January and March-April 2021 surveys, participants were invited to contribute open-text comments and answer specific questions about COVID-19 vaccine uptake. A closed-ended question in the survey identified vaccine-hesitant (VH) participants who either had no intention or were unsure of receiving a COVID-19 vaccine. We collected 1970 comments from VH participants and trained two machine learning (ML) algorithms to identify behavioral factors related to VH. The first predictive model classified each comment into one of three health belief model (HBM) constructs (barriers, severity, and susceptibility) related to adopting disease prevention activities. The second predictive model used the words in January comments to predict the vaccine status of VH in March-April 2021; vaccine status was correctly predicted 89% of the time. Our results showed that 35% of VH participants cited barriers, 17% severity, and 7% susceptibility to receiving a COVID-19 vaccine. Out of the HBM constructs, the VH participants citing a barrier, such as allergic reactions and side effects, had the most associated change in vaccine status from VH to later receiving a vaccine.
    Language English
    Publishing date 2024-03-07
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2651997-5
    ISSN 2076-328X
    ISSN 2076-328X
    DOI 10.3390/bs14030217
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  7. Article ; Online: Predicting premature discontinuation of medication for opioid use disorder from electronic medical records.

    Lopez, Ivan / Fouladvand, Sajjad / Kollins, Scott / Chen, Chwen-Yuen Angie / Bertz, Jeremiah / Hernandez-Boussard, Tina / Lembke, Anna / Humphreys, Keith / Miner, Adam S / Chen, Jonathan H

    AMIA ... Annual Symposium proceedings. AMIA Symposium

    2024  Volume 2023, Page(s) 1067–1076

    Abstract: Medications such as buprenorphine-naloxone are among the most effective treatments for opioid use disorder, but limited retention in treatment limits long-term outcomes. In this study, we assess the feasibility of a machine learning model to predict ... ...

    Abstract Medications such as buprenorphine-naloxone are among the most effective treatments for opioid use disorder, but limited retention in treatment limits long-term outcomes. In this study, we assess the feasibility of a machine learning model to predict retention vs. attrition in medication for opioid use disorder (MOUD) treatment using electronic medical record data including concepts extracted from clinical notes. A logistic regression classifier was trained on 374 MOUD treatments with 68% resulting in potential attrition. On a held-out test set of 157 events, the full model achieved an area under the receiver operating characteristic curve (AUROC) of 0.77 (95% CI: 0.64-0.90) and AUROC of 0.74 (95% CI: 0.62-0.87) with a limited model using only structured EMR data. Risk prediction for opioid MOUD retention vs. attrition is feasible given electronic medical record data, even without necessarily incorporating concepts extracted from clinical notes.
    MeSH term(s) Humans ; Electronic Health Records ; Area Under Curve ; Machine Learning ; Opioid-Related Disorders/drug therapy ; ROC Curve ; Analgesics, Opioid/therapeutic use
    Chemical Substances Analgesics, Opioid
    Language English
    Publishing date 2024-01-11
    Publishing country United States
    Document type Journal Article
    ISSN 1942-597X
    ISSN (online) 1942-597X
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  8. Article ; Online: Identifying Opioid Use Disorder from Longitudinal Healthcare Data using a Multi-stream Transformer.

    Fouladvand, Sajjad / Talbert, Jeffery / Dwoskin, Linda P / Bush, Heather / Meadows, Amy Lynn / Peterson, Lars E / Roggenkamp, Steve K / Kavuluru, Ramakanth / Chen, Jin

    AMIA ... Annual Symposium proceedings. AMIA Symposium

    2022  Volume 2021, Page(s) 476–485

    Abstract: Opioid Use Disorder (OUD) is a public health crisis costing the US billions of dollars annually in healthcare, lost workplace productivity, and crime. Analyzing longitudinal healthcare data is critical in addressing many real-world problems in healthcare. ...

    Abstract Opioid Use Disorder (OUD) is a public health crisis costing the US billions of dollars annually in healthcare, lost workplace productivity, and crime. Analyzing longitudinal healthcare data is critical in addressing many real-world problems in healthcare. Leveraging the real-world longitudinal healthcare data, we propose a novel multi-stream transformer model called MUPOD for OUD identification. MUPOD is designed to simultaneously analyze multiple types of healthcare data streams, such as medications and diagnoses, by attending to segments within and across these data streams. Our model tested on the data from 392,492 patients with long-term back pain problems showed significantly better performance than the traditional models and recently developed deep learning models.
    MeSH term(s) Analgesics, Opioid/therapeutic use ; Delivery of Health Care ; Health Facilities ; Humans ; Opioid-Related Disorders/epidemiology
    Chemical Substances Analgesics, Opioid
    Language English
    Publishing date 2022-02-21
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ISSN 1942-597X
    ISSN (online) 1942-597X
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  9. Article ; Online: T cells dominate peripheral inflammation in a cross-sectional analysis of obesity-associated diabetes.

    Pugh, Gabriella H / Fouladvand, Sajjad / SantaCruz-Calvo, Sara / Agrawal, Madhur / Zhang, Xiaohua Douglas / Chen, Jin / Kern, Philip A / Nikolajczyk, Barbara S

    Obesity (Silver Spring, Md.)

    2022  Volume 30, Issue 10, Page(s) 1983–1994

    Abstract: Objective: Myeloid cells dominate metabolic disease-associated inflammation (metaflammation) in mouse obesity, but the contributions of myeloid cells to the peripheral inflammation that fuels sequelae of human obesity are untested. This study used ... ...

    Abstract Objective: Myeloid cells dominate metabolic disease-associated inflammation (metaflammation) in mouse obesity, but the contributions of myeloid cells to the peripheral inflammation that fuels sequelae of human obesity are untested. This study used unbiased approaches to rank contributions of myeloid and T cells to peripheral inflammation in people with obesity across the spectrum of metabolic health.
    Methods: Peripheral blood mononuclear cells (PBMCs) from people with obesity with or without prediabetes or type 2 diabetes were stimulated with T cell-targeting CD3/CD28 or myeloid-targeting lipopolysaccharide for 20 to 72 hours to assess cytokine production using Bio-Plex. Bioinformatic modeling ranked cytokines with respect to their predictive power for metabolic health. Intracellular tumor necrosis factor α was quantitated as a classical indicator of metaflammation.
    Results: Cytokines increased over 72 hours following T cell-, but not myeloid-, targeted stimulation to indicate that acute myeloid inflammation may shift to T cell inflammation over time. T cells contributed more tumor necrosis factor α to peripheral inflammation regardless of metabolic status. Bioinformatic combination of cytokines from all cohorts, stimuli, and time points indicated that T cell-targeted stimulation was most important for differentiating inflammation in diabetes, consistent with previous identification of a mixed T helper type 1/T helper type 17 cytokine profile in diabetes.
    Conclusions: T cells dominate peripheral inflammation in obesity; therefore, targeting T cells may be an effective approach for prevention/management of metaflammation.
    MeSH term(s) Animals ; CD28 Antigens ; Cross-Sectional Studies ; Cytokines/metabolism ; Diabetes Mellitus, Type 2/complications ; Humans ; Inflammation/metabolism ; Leukocytes, Mononuclear/metabolism ; Lipopolysaccharides ; Mice ; Obesity/complications ; Obesity/metabolism ; T-Lymphocytes/metabolism ; Tumor Necrosis Factor-alpha/metabolism
    Chemical Substances CD28 Antigens ; Cytokines ; Lipopolysaccharides ; Tumor Necrosis Factor-alpha
    Language English
    Publishing date 2022-09-07
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, N.I.H., Extramural
    ZDB-ID 2230457-5
    ISSN 1930-739X ; 1071-7323 ; 1930-7381
    ISSN (online) 1930-739X
    ISSN 1071-7323 ; 1930-7381
    DOI 10.1002/oby.23528
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  10. Article ; Online: High-throughput target trial emulation for Alzheimer's disease drug repurposing with real-world data.

    Zang, Chengxi / Zhang, Hao / Xu, Jie / Zhang, Hansi / Fouladvand, Sajjad / Havaldar, Shreyas / Cheng, Feixiong / Chen, Kun / Chen, Yong / Glicksberg, Benjamin S / Chen, Jin / Bian, Jiang / Wang, Fei

    Nature communications

    2023  Volume 14, Issue 1, Page(s) 8180

    Abstract: Target trial emulation is the process of mimicking target randomized trials using real-world data, where effective confounding control for unbiased treatment effect estimation remains a main challenge. Although various approaches have been proposed for ... ...

    Abstract Target trial emulation is the process of mimicking target randomized trials using real-world data, where effective confounding control for unbiased treatment effect estimation remains a main challenge. Although various approaches have been proposed for this challenge, a systematic evaluation is still lacking. Here we emulated trials for thousands of medications from two large-scale real-world data warehouses, covering over 10 years of clinical records for over 170 million patients, aiming to identify new indications of approved drugs for Alzheimer's disease. We assessed different propensity score models under the inverse probability of treatment weighting framework and suggested a model selection strategy for improved baseline covariate balancing. We also found that the deep learning-based propensity score model did not necessarily outperform logistic regression-based methods in covariate balancing. Finally, we highlighted five top-ranked drugs (pantoprazole, gabapentin, atorvastatin, fluticasone, and omeprazole) originally intended for other indications with potential benefits for Alzheimer's patients.
    MeSH term(s) Humans ; Alzheimer Disease/drug therapy ; Drug Repositioning ; Propensity Score ; Atorvastatin/therapeutic use
    Chemical Substances Atorvastatin (A0JWA85V8F)
    Language English
    Publishing date 2023-12-11
    Publishing country England
    Document type Journal Article
    ZDB-ID 2553671-0
    ISSN 2041-1723 ; 2041-1723
    ISSN (online) 2041-1723
    ISSN 2041-1723
    DOI 10.1038/s41467-023-43929-1
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