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1,130 results on '"Berlowitz, Dan"'

1. PaniniQA: Enhancing Patient Education Through Interactive Question Answering

Author

Cai, Pengshan, Yao, Zonghai, Liu, Fei, Wang, Dakuo, Reilly, Meghan, Zhou, Huixue, Li, Lingxi, Cao, Yi, Kapoor, Alok, Bajracharya, Adarsha, Berlowitz, Dan, and Yu, Hong

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract

Patient portal allows discharged patients to access their personalized discharge instructions in electronic health records (EHRs). However, many patients have difficulty understanding or memorizing their discharge instructions. In this paper, we present PaniniQA, a patient-centric interactive question answering system designed to help patients understand their discharge instructions. PaniniQA first identifies important clinical content from patients' discharge instructions and then formulates patient-specific educational questions. In addition, PaniniQA is also equipped with answer verification functionality to provide timely feedback to correct patients' misunderstandings. Our comprehensive automatic and human evaluation results demonstrate our PaniniQA is capable of improving patients' mastery of their medical instructions through effective interactions, Comment: Accepted to TACL 2023. Equal contribution for the first two authors. This arXiv version is a pre-MIT Press publication version

Published
2023

2. Early Prediction of Alzheimers Disease Leveraging Symptom Occurrences from Longitudinal Electronic Health Records of US Military Veterans

Author

Li, Rumeng, Wang, Xun, Berlowitz, Dan, Silver, Brian, Hu, Wen, Keating, Heather, Goodwin, Raelene, Liu, Weisong, Lin, Honghuang, and Yu, Hong

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Computation and Language
Abstract

Early prediction of Alzheimer's disease (AD) is crucial for timely intervention and treatment. This study aims to use machine learning approaches to analyze longitudinal electronic health records (EHRs) of patients with AD and identify signs and symptoms that can predict AD onset earlier. We used a case-control design with longitudinal EHRs from the U.S. Department of Veterans Affairs Veterans Health Administration (VHA) from 2004 to 2021. Cases were VHA patients with AD diagnosed after 1/1/2016 based on ICD-10-CM codes, matched 1:9 with controls by age, sex and clinical utilization with replacement. We used a panel of AD-related keywords and their occurrences over time in a patient's longitudinal EHRs as predictors for AD prediction with four machine learning models. We performed subgroup analyses by age, sex, and race/ethnicity, and validated the model in a hold-out and "unseen" VHA stations group. Model discrimination, calibration, and other relevant metrics were reported for predictions up to ten years before ICD-based diagnosis. The study population included 16,701 cases and 39,097 matched controls. The average number of AD-related keywords (e.g., "concentration", "speaking") per year increased rapidly for cases as diagnosis approached, from around 10 to over 40, while remaining flat at 10 for controls. The best model achieved high discriminative accuracy (ROCAUC 0.997) for predictions using data from at least ten years before ICD-based diagnoses. The model was well-calibrated (Hosmer-Lemeshow goodness-of-fit p-value = 0.99) and consistent across subgroups of age, sex and race/ethnicity, except for patients younger than 65 (ROCAUC 0.746). Machine learning models using AD-related keywords identified from EHR notes can predict future AD diagnoses, suggesting its potential use for identifying AD risk using EHR notes, offering an affordable way for early screening on large population., Comment: 24 pages

Published
2023

4. Enhancing the prediction of disease outcomes using electronic health records and pretrained deep learning models

Author

Yang, Zhichao, Liu, Weisong, Berlowitz, Dan, and Yu, Hong

Subjects
Computer Science - Artificial Intelligence, Computer Science - Computers and Society, Computer Science - Machine Learning
Abstract

Question: Can an encoder-decoder architecture pretrained on a large dataset of longitudinal electronic health records improves patient outcome predictions? Findings: In this prognostic study of 6.8 million patients, our denoising sequence-to-sequence prediction model of multiple outcomes outperformed state-of-the-art models scuh pretrained BERT on a broad range of patient outcomes, including intentional self-harm and pancreatic cancer. Meaning: Deep bidirectional and autoregressive representation improves patient outcome prediction.

Published
2022

9. FGF23, Frailty, and Falls in SPRINT

Author

Jovanovich, Anna, Ginsberg, Charles, You, Zhiying, Katz, Ronit, Ambrosius, Walter T, Berlowitz, Dan, Cheung, Alfred K, Cho, Monique, Lee, Alexandra K, Punzi, Henry, Rehman, Shakaib, Roumie, Christianne, Supiano, Mark A, Wright, Clinton B, Shlipak, Michael, Ix, Joachim H, and Chonchol, Michel

Subjects
Aging, Clinical Trials and Supportive Activities, Kidney Disease, Hypertension, Clinical Research, Prevention, Accidental Falls, Aged, Biomarkers, Correlation of Data, Cross-Sectional Studies, Female, Fibroblast Growth Factor-23, Fibroblast Growth Factors, Frailty, Glomerular Filtration Rate, Humans, Male, Renal Insufficiency, Chronic, fibroblast growth factor 23, falls, frailty, chronic kidney disease, biomarkers, Medical and Health Sciences, Geriatrics
Abstract

Background/objectivesChronic kidney disease (CKD) is associated with frailty. Fibroblast growth factor 23 (FGF23) is elevated in CKD and associated with frailty among non-CKD older adults and individuals with human immunodeficiency virus. Whether FGF23 is associated with frailty and falls in CKD is unknown.DesignCross-sectional and longitudinal observational study.SettingSystolic Blood Pressure Intervention Trial (SPRINT), a randomized trial evaluating standard (systolic blood pressure [SBP]

Published
2021

11. Prevalence of Mild Cognitive Impairment and Alzheimer’s Disease Identified in Veterans in the United States

Author

Aguilar, Byron J., primary, Jasuja, Guneet K., additional, Li, Xuyang, additional, Shishova, Ekaterina, additional, Palacios, Natalia, additional, Berlowitz, Dan, additional, Morin, Peter, additional, O’Connor, Maureen K., additional, Nguyen, Andrew, additional, Reisman, Joel, additional, Leng, Yue, additional, Zhang, Raymond, additional, Monfared, Amir Abbas Tahami, additional, Zhang, Quanwu, additional, and Xia, Weiming, additional

Published
2024
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14. Effects of intensive versus standard blood pressure control on domain-specific cognitive function: a substudy of the SPRINT randomised controlled trial

Author

Rapp, Stephen R, Pajewski, Nicholas M, Auchus, Alexander P, Chelune, Gordon, Cheung, Alfred K, Cleveland, Maryjo L, Coker, Laura H, Crowe, Michael G, Cushman, William C, Cutler, Jeffery A, Davatzikos, Christos, Desiderio, Lisa, Doshi, Jimit, Erus, Guray, Fine, Lawrence J, Gaussoin, Sarah A, Harris, Darrin, Johnson, Karen C, Kimmel, Paul L, Tamura, Manjula K, Launer, Lenore J, Lerner, Alan J, Lewis, Cora E, Martindale-Adams, Jennifer, Moy, Claudia S, Nichols, Linda O, Oparil, Suzanne, Ogrocki, Paula K, Rahman, Mahboob, Nasrallah, Ilya M, Reboussin, David M, Rocco, Michael V, Sachs, Bonnie C, Sink, Kaycee M, Still, Carolyn H, Supiano, Mark A, Snyder, Joni K, Wadley, Virginia G, Walker, Jennifer, Weiner, Daniel E, Whelton, Paul K, Wilson, Valerie M, Woolard, Nancy, Wright, Jackson T, Jr., Wright, Clinton B, Williamson, Jeff D, Bryan, R Nick, Wilson, Valarie M, Whittle, Jeff C, Beddhu, Srinivasan, Berlowitz, Dan R, Bress, Adam P, Krousel-Wood, Marie, Miller, Eliza C, Rifkin, Dena E, Tamariz, Leonardo, Wolfgram, Dawn F, Yang, Mia, and Bryan, Robert Nick

Published
2020
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18. Development and validation of a natural language processing system that extracts cognitive test results from clinical notes

Author

Chen, Jinying, primary, Li, Xuyang, additional, Aguilar, Byron J., additional, Shishova, Ekaterina, additional, Morin, Peter J, additional, Berlowitz, Dan, additional, Miller, Donald R, additional, O'Connor, Maureen K., additional, Nguyen, Andrew H., additional, Zhang, Raymond, additional, Monfared, Amir Abbas Tahami, additional, Zhang, Quanwu, additional, and Xia, Weiming, additional

Published
2023
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19. Diagnosis of Severity of Alzheimer’s Disease from Clinical notes in the Veterans Affairs Healthcare System

Author

Aguilar, Byron J., primary, Shishova, Ekaterina, additional, Li, Xuyang, additional, Nguyen, Andrew H., additional, Morin, Peter J, additional, Berlowitz, Dan, additional, O'Connor, Maureen K., additional, Miller, Donald R, additional, Jasuja, Guneet, additional, Zhang, Raymond, additional, Monfared, Amir Abbas Tahami, additional, Zhang, Quanwu, additional, and Xia, Weiming, additional

Published
2023
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20. Building a neuropsychiatric testing database for veterans with mild cognitive impairment and Alzheimer’s disease with unstructured electronic health records

Author

Li, Xuyang, primary, Chen, Jinying, additional, Aguilar, Byron J., additional, Shishova, Ekaterina, additional, Morin, Peter J, additional, Berlowitz, Dan, additional, Miller, Donald R, additional, O'Connor, Maureen K., additional, Nguyen, Andrew Huy, additional, Zhang, Raymond, additional, Monfared, Amir Abbas Tahami, additional, Zhang, Quanwu, additional, and Xia, Weiming, additional

Published
2023
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21. Identification of Alzheimer’s disease in Veteran patients using clinical notes from electronic health records

Author

Miller, Donald R, primary, Aguilar, Byron J., additional, Li, Xuyang, additional, Shishova, Ekaterina, additional, Jasuja, Guneet, additional, Berlowitz, Dan, additional, Morin, Peter J, additional, O'Connor, Maureen K., additional, Nguyen, Andrew H., additional, Christiansen, Cindy, additional, Zhang, Haixin, additional, Monfared, Amir Abbas Tahami, additional, Zhang, Quanwu, additional, and Xia, Weiming, additional

Published
2023
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22. Estimating the prevalence of mild cognitive impairment from clinical notes in U.S. Veterans

Author

Jasuja, Guneet, primary, Li, Xuyang, additional, Aguilar, Byron J., additional, Shishova, Ekaterina, additional, Morin, Peter J, additional, Berlowitz, Dan, additional, Miller, Donald R, additional, O'Connor, Maureen K., additional, Nguyen, Andrew H., additional, Zhang, Raymond, additional, Monfared, Amir Abbas Tahami, additional, Zhang, Quanwu, additional, and Xia, Weiming, additional

Published
2023
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23. Comparative identification of Veterans with mild cognitive impairment or Alzheimer’s disease extracted from clinical notes and administrative data

Author

Berlowitz, Dan, primary, Aguilar, Byron J., additional, Li, Xuyang, additional, Shishova, Ekaterina, additional, Jasuja, Guneet, additional, Morin, Peter J, additional, Miller, Donald R, additional, O'Connor, Maureen K., additional, Nguyen, Andrew H., additional, Zhang, Raymond, additional, Monfared, Amir Abbas Tahami, additional, Zhang, Quanwu, additional, and Xia, Weiming, additional

Published
2023
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24. Protocol for development of the fourth edition of Prevention and Treatment of Pressure Ulcers/Injuries: Clinical Practice Guideline using GRADE methods

Author

Haesler, Emily, primary, Cuddigan, Janet, additional, Carville, Keryln, additional, Moore, Zena, additional, Kottner, Jan, additional, Ayello, Elizabeth A., additional, Berlowitz, Dan, additional, Carruth, Althea, additional, Chang, Yee Yee, additional, Cox, Jill, additional, Creehan, Sue, additional, Nixon, Jane, additional, Ngan, Hau Lan, additional, and Balzer, Katrin, additional

Published
2023
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28. Variation in telehealth use for endocrine care: Patterns and predictors under the “new normal”

Author

Vimalananda, Varsha G, primary, Arao, Kevin, additional, Qian, Shirley, additional, Leibowitz, Alison, additional, Zupa, Margaret F, additional, Benzer, Justin, additional, Fincke, B Graeme, additional, Zocchi, Mark, additional, Meterko, Mark, additional, Berlowitz, Dan, additional, Sitter, Kailyn E, additional, and Wormwood, Jolie B, additional

Published
2023
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34. Intentional Self-Harm Among US Veterans With Traumatic Brain Injury or Posttraumatic Stress Disorder: Retrospective Cohort Study From 2008 to 2017

Author

Rawat, Bhanu Pratap Singh, primary, Reisman, Joel, additional, Pogoda, Terri K, additional, Liu, Weisong, additional, Rongali, Subendhu, additional, Aseltine Jr, Robert H, additional, Chen, Kun, additional, Tsai, Jack, additional, Berlowitz, Dan, additional, Yu, Hong, additional, and Carlson, Kathleen F, additional

Published
2023
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35. Evidence for Age Bias Contributing to Therapeutic Inertia in Blood Pressure Management: A Secondary Analysis of SPRINT

Author

Zheutlin, Alexander R., primary, Addo, Daniel K., additional, Jacobs, Joshua A., additional, Derington, Catherine G., additional, Herrick, Jennifer S., additional, Greene, Tom H., additional, Stulberg, Eric L., additional, Berlowitz, Dan R., additional, Williamson, Jeff D., additional, Pajewski, Nicholas M., additional, Supiano, Mark A., additional, and Bress, Adam P., additional

Published
2023
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40. Protocol for the Development of the Fourth Edition of the Prevention and Treatment of Pressure Ulcers/Injuries: Clinical Practice GuidelineUsing GRADE Methods

Author

Haesler, Emily, Cuddigan, Janet, Carville, Keryln, Moore, Zena, Kottner, Jan, Ayello, Elizabeth A., Berlowitz, Dan, Carruth, Althea, Yee, Chang Yee, Cox, Jill, Creehan, Sue, Nixon, Jane, Ngan, Hau Lan, and Balzer, Katrin

Abstract

The National Pressure Injury Advisory Panel, European Pressure Ulcer Advisory Panel, and the Pan Pacific Pressure Injury Alliance are commencing a new (fourth) edition of the Prevention and Treatment of Pressure Ulcers/Injuries: Clinical Practice Guideline. The fourth edition of the International Pressure Injury (PI) Guideline will be developed using GRADE methods to ensure a rigorous process consistent with evolving international standards. Clinical questions will address prevention and treatment of PIs, identification of individuals at risk of PIs, assessment of skin and tissues, and PI assessment. Implementation considerations supporting application of the guidance in clinical practice will be developed. The guideline development process will be overseen by a guideline governance group and methodologist; the guideline development team will include health professionals, educators, researchers, individuals with or at risk of PIs, and informal carers.This article presents the project structure and processes to be used to undertake a systematic literature search, appraise risk of bias of the evidence, and aggregate research findings. The methods detail how certainty of evidence will be evaluated; presentation of relative benefits, risks, feasibility, acceptability, and resource requirements; and how recommendations will be made and graded. The methods outline transparent processes of development that combine scientific research with best clinical practice. Strong involvement from health professionals, educators, individuals with PIs, and informal carers will enhance the guideline’s relevance and facilitate uptake. This update builds on previous editions to ensure consistency and comparability, with methodology changes improving the guideline’s quality and clarity.

Published
2024
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45. Alzheimer's Disease Stage Transitions Among United States Veterans.

Author

Morin, Peter, Aguilar, Byron J., Li, Xuyang, Chen, Jinying, Berlowitz, Dan, Zhang, Raymond, Tahami Monfared, Amir Abbas, Zhang, Quanwu, and Xia, Weiming

Subjects
ALZHEIMER'S disease, MILD cognitive impairment, MONTREAL Cognitive Assessment, DISEASE progression, MINI-Mental State Examination, APOLIPOPROTEIN E4
Abstract

Background: Alzheimer's disease (AD) and related dementias are progressive neurological disorders with stage-specific clinical features and challenges. An important knowledge gap is the "window of time" within which patients transition from mild cognitive impairment or mild AD to moderate or severe AD. Better characterization/establishment of transition times would help clinicians initiating treatments, including anti-amyloid therapy. Objective: To describe cognitive test score-based AD stage transitions in Veterans with AD in the US Veterans Affairs Healthcare System (VAHS). Methods: This retrospective analysis (2010–2019) identified Veterans with AD from the VAHS Electronic Health Record (EHR) notes. AD stage was based on Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), or Saint Louis University Mental Status (SLUMS) Examination scores in the EHR. Results: We identified 296,519 Veterans with cognitive test-based AD staging. Over the 10-year study, the proportion of veterans with MMSE scores declined from 24.9% to 9.5% while those with SLUMS rose from 9.0% to 17.8%; and MoCA rose from 5.0% to 25.4%. The average forward transition times between each stage were approximately 2–4 years, whether assessed by MMSE, MoCA, or SLUMS. Conclusion: The average transition time for cognitive test-based assessments of initial cognitive decline, early-stage AD, and moderate/severe AD in the VAHS is 2–4 years. In view of the short window for introducing disease-modifying therapy and the significant benefits of early treatment of AD, our data suggest a critical need for treatment guidelines in the management of AD. [ABSTRACT FROM AUTHOR]

Published
2024
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46. Quality Improvement Efforts in VA Community Living Centers Following Public Reporting of Performance.

Author

Davila, Heather, Mills, Whitney L., Clark, Valerie, Hartmann, Christine W., Sullivan, Jennifer L., Mohr, David C., Baughman, Amy W., Berlowitz, Dan R., and Pimentel, Camilla B.

Subjects
RESEARCH, RESEARCH methodology, PRIVATE sector, INTERVIEWING, NURSING care facilities, QUALITATIVE research, COMPARATIVE studies, QUALITY assurance, RESEARCH funding
Abstract

For two decades, the U.S. government has publicly reported performance measures for most nursing homes, spurring some improvements in quality. Public reporting is new, however, to Department of Veterans Affairs nursing homes (Community Living Centers [CLCs]). As part of a large, public integrated healthcare system, CLCs operate with unique financial and market incentives. Thus, their responses to public reporting may differ from private sector nursing homes. In three CLCs with varied public ratings, we used an exploratory, qualitative case study approach involving semi-structured interviews to compare how CLC leaders (n = 12) perceived public reporting and its influence on quality improvement. Across CLCs, respondents said public reporting was helpful for transparency and to provide an "outside perspective" on CLC performance. Respondents described employing similar strategies to improve their public ratings: using data, engaging staff, and clearly defining staff roles vis-à-vis quality improvement, although more effort was required to implement change in lower performing CLCs. Our findings augment those from prior studies and offer new insights into the potential for public reporting to spur quality improvement in public nursing homes and those that are part of integrated healthcare systems. [ABSTRACT FROM AUTHOR]

Published
2024
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