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41 results on '"Caraballo, César"'

1. Learning to Ask Medical Questions using Reinforcement Learning

Author

Shaham, Uri, Zahavy, Tom, Caraballo, Cesar, Mahajan, Shiwani, Massey, Daisy, and Krumholz, Harlan

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Statistics - Machine Learning
Abstract

We propose a novel reinforcement learning-based approach for adaptive and iterative feature selection. Given a masked vector of input features, a reinforcement learning agent iteratively selects certain features to be unmasked, and uses them to predict an outcome when it is sufficiently confident. The algorithm makes use of a novel environment setting, corresponding to a non-stationary Markov Decision Process. A key component of our approach is a guesser network, trained to predict the outcome from the selected features and parametrizing the reward function. Applying our method to a national survey dataset, we show that it not only outperforms strong baselines when requiring the prediction to be made based on a small number of input features, but is also highly more interpretable. Our code is publicly available at \url{https://github.com/ushaham/adaptiveFS}.

Published
2020

3. A Neural Topic-Attention Model for Medical Term Abbreviation Disambiguation

Author

Li, Irene, Yasunaga, Michihiro, Nuzumlalı, Muhammed Yavuz, Caraballo, Cesar, Mahajan, Shiwani, Krumholz, Harlan, and Radev, Dragomir

Subjects
Computer Science - Computation and Language
Abstract

Automated analysis of clinical notes is attracting increasing attention. However, there has not been much work on medical term abbreviation disambiguation. Such abbreviations are abundant, and highly ambiguous, in clinical documents. One of the main obstacles is the lack of large scale, balance labeled data sets. To address the issue, we propose a few-shot learning approach to take advantage of limited labeled data. Specifically, a neural topic-attention model is applied to learn improved contextualized sentence representations for medical term abbreviation disambiguation. Another vital issue is that the existing scarce annotations are noisy and missing. We re-examine and correct an existing dataset for training and collect a test set to evaluate the models fairly especially for rare senses. We train our model on the training set which contains 30 abbreviation terms as categories (on average, 479 samples and 3.24 classes in each term) selected from a public abbreviation disambiguation dataset, and then test on a manually-created balanced dataset (each class in each term has 15 samples). We show that enhancing the sentence representation with topic information improves the performance on small-scale unbalanced training datasets by a large margin, compared to a number of baseline models.

Published
2019

5. Intercountry Differences in Guideline-Directed Medical Therapy and Outcomes Among Patients With Heart Failure

Author

Fuery, Michael A., Chouairi, Fouad, Januzzi, James L., Moe, Gordon W., Caraballo, Cesar, McCullough, Megan, Miller, P. Elliott, Reinhardt, Samuel W., Clark, Katherine, Oseran, Andrew, Milner, Aidan, Pacor, Justin, Kahn, Peter A., Singh, Avinainder, Ravindra, Neal, Guha, Avirup, Vadlamani, Lina, Kulkarni, Neeti S., Fiuzat, Mona, Felker, G. Michael, O’Connor, Christopher M., Ahmad, Tariq, Ezekowitz, Justin, and Desai, Nihar R.

Published
2021
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7. SalivaDirect: A simplified and flexible platform to enhance SARS-CoV-2 testing capacity

Author

Anastasio, Kelly, Askenase, Michael H., Batsu, Maria, Bickerton, Sean, Brower, Kristina, Bucklin, Molly L., Cahill, Staci, Cao, Yiyun, Courchaine, Edward, DeIuliis, Giuseppe, Earnest, Rebecca, Geng, Bertie, Goldman-Israelow, Benjamin, Handoko, Ryan, Khoury-Hanold, William, Kim, Daniel, Knaggs, Lynda, Kuang, Maxine, Lapidus, Sarah, Lim, Joseph, Linehan, Melissa, Lu-Culligan, Alice, Martin, Anjelica, Matos, Irene, McDonald, David, Minasyan, Maksym, Nakahata, Maura, Naushad, Nida, Nouws, Jessica, Obaid, Abeer, Odio, Camila, Oh, Ji Eun, Omer, Saad, Park, Annsea, Park, Hong-Jai, Peng, Xiaohua, Petrone, Mary, Prophet, Sarah, Rice, Tyler, Rose, Kadi-Ann, Sewanan, Lorenzo, Sharma, Lokesh, Shaw, Albert C., Shepard, Denise, Smolgovsky, Mikhail, Sonnert, Nicole, Strong, Yvette, Todeasa, Codruta, Valdez, Jordan, Velazquez, Sofia, Vijayakumar, Pavithra, White, Elizabeth B., Yang, Yexin, Vogels, Chantal B.F., Watkins, Anne E., Harden, Christina A., Brackney, Doug E., Shafer, Jared, Wang, Jianhui, Caraballo, César, Kalinich, Chaney C., Ott, Isabel M., Fauver, Joseph R., Kudo, Eriko, Lu, Peiwen, Venkataraman, Arvind, Tokuyama, Maria, Moore, Adam J., Muenker, M. Catherine, Casanovas-Massana, Arnau, Fournier, John, Bermejo, Santos, Campbell, Melissa, Datta, Rupak, Nelson, Allison, Dela Cruz, Charles S., Ko, Albert I., Iwasaki, Akiko, Krumholz, Harlan M., Matheus, J.D., Hui, Pei, Liu, Chen, Farhadian, Shelli F., Sikka, Robby, Wyllie, Anne L., and Grubaugh, Nathan D.

Published
2021
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10. Association of marital/partner status with hospital readmission among young adults with acute myocardial infarction

Author

Zhu, Cenjing, primary, Dreyer, Rachel P., additional, Li, Fan, additional, Spatz, Erica S., additional, Caraballo, César, additional, Mahajan, Shiwani, additional, Raparelli, Valeria, additional, Leifheit, Erica C., additional, Lu, Yuan, additional, Krumholz, Harlan M., additional, Spertus, John A., additional, D’Onofrio, Gail, additional, Pilote, Louise, additional, and Lichtman, Judith H., additional

Published
2024
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11. Calcium channel blockers and clinical outcomes in patients with continuous‐flow left ventricular assist devices

Author

Tie, Hongtao, primary, Li, Zhenhan, additional, Welp, Henryk, additional, Guha, Avirup, additional, Caraballo, César, additional, Deschka, Heinz, additional, Shi, Rui, additional, Zheng, Xiaoqing, additional, Martens, Sven, additional, Sindermann, Jürgen, additional, Chen, Dan, additional, Wu, Qingchen, additional, and Martens, Sabrina, additional

Published
2023
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14. Calcium channel blockers and clinical outcomes in patients with continuous‐flow left ventricular assist devices.

Author

Tie, Hongtao, Li, Zhenhan, Welp, Henryk, Guha, Avirup, Caraballo, César, Deschka, Heinz, Shi, Rui, Zheng, Xiaoqing, Martens, Sven, Sindermann, Jürgen, Chen, Dan, Wu, Qingchen, and Martens, Sabrina

Subjects
HEART assist devices, CALCIUM antagonists, TREATMENT effectiveness, BLOOD pressure, KIDNEY diseases
Abstract

Aims: Current guidelines suggest calcium channel blockers (CCBs) as the second or third option for blood pressure management in patients with left ventricular assist device (LVAD). However, the clinical outcomes of patients with LVAD who receive CCBs remain unclear. Our study aims to analyse the association of CCBs with clinical outcomes in patients after LVAD implantation. Methods and results: This is a retrospective analysis based on the Interagency Registry for Mechanically Assisted Circulatory Support (INTERMACS) from 2006 to 2017, and adult patients who were alive with LVAD and CCB treatment information at 6 months after implantation were included. Among 10 717 patients, 1369 received CCBs 6 months after implantation, and there was an increasing trend of CCB use after LVAD. Patients receiving CCB therapy at 6 months had a similar 5 year survival rate to those not receiving CCB [49.6%, 95% confidence interval (CI): 47.5–51.7% vs. 51.1%, 95% CI: 45.3–56.7%]. In both Cox and competing risk regressions after adjusting for confounding factors, CCB treatment at 6 months after implantation was not associated with long‐term mortality [hazard ratio (HR): 1.03, 95% CI: 0.91–1.17, P = 0.624 and subdistribution HR (SHR): 1.07, 95% CI: 0.95–1.22, P = 0.260]. Consistently, in time‐varying models, CCB treatment was not linked to long‐term mortality (HR: 0.97, 95% CI: 0.87–1.09, P = 0.682 and SHR: 1.05, 95% CI: 0.94–1.18, P = 0.359). This null association remained in subgroup analysis according to device strategy and propensity‐matching analyses. Neurological dysfunction, stroke, bleeding, rehospitalization, and renal dysfunction were more likely to occur among those with CCB when compared with those without CCB treatment. Conclusions: In patients with LVAD, CCB therapy fails to show benefits in long‐term survival and is associated with increased incidences of neurological dysfunction, bleeding, renal dysfunction, and rehospitalization. [ABSTRACT FROM AUTHOR]

Published
2024
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16. Sex Difference in Outcomes of Acute Myocardial Infarction in Young Patients

Author

Sawano, Mitsuaki, primary, Lu, Yuan, additional, Caraballo, César, additional, Mahajan, Shiwani, additional, Dreyer, Rachel, additional, Lichtman, Judith H., additional, D’Onofrio, Gail, additional, Spatz, Erica, additional, Khera, Rohan, additional, Onuma, Oyere, additional, Murugiah, Karthik, additional, Spertus, John A., additional, and Krumholz, Harlan M., additional

Published
2023
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20. Hemodynamic differences between women and men with elevated blood pressure in China: A non-invasive assessment of 45,082 adults using impedance cardiography

Author

Caraballo, César, primary, Mahajan, Shiwani, additional, Gu, Jianlei, additional, Lu, Yuan, additional, Spatz, Erica S., additional, Dreyer, Rachel P., additional, Zhang, MaoZhen, additional, Sun, NingLing, additional, Ren, Yihong, additional, Zheng, Xin, additional, Zhao, Hongyu, additional, Lu, Hui, additional, Ma, Zheng J., additional, and Krumholz, Harlan M., additional

Published
2022
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21. Evaluation of Temporal Trends in Racial and Ethnic Disparities in Sleep Duration Among US Adults, 2004-2018

Author

Caraballo, César, primary, Mahajan, Shiwani, additional, Valero-Elizondo, Javier, additional, Massey, Daisy, additional, Lu, Yuan, additional, Roy, Brita, additional, Riley, Carley, additional, Annapureddy, Amarnath R., additional, Murugiah, Karthik, additional, Elumn, Johanna, additional, Nasir, Khurram, additional, Nunez-Smith, Marcella, additional, Forman, Howard P., additional, Jackson, Chandra L., additional, Herrin, Jeph, additional, and Krumholz, Harlan M., additional

Published
2022
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22. Cost-Related Medication Nonadherence in Adults With Diabetes in the United States: The National Health Interview Survey 2013–2018

Author

Taha, Mohamad B., primary, Valero-Elizondo, Javier, additional, Yahya, Tamer, additional, Caraballo, César, additional, Khera, Rohan, additional, Patel, Kershaw V., additional, Ali, Hyeon Ju R., additional, Sharma, Garima, additional, Mossialos, Elias, additional, Cainzos-Achirica, Miguel, additional, and Nasir, Khurram, additional

Published
2022
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24. Tracking Self-reported Symptoms and Medical Conditions on Social Media During the COVID-19 Pandemic: Infodemiological Study

Author

Ding, Qinglan, primary, Massey, Daisy, additional, Huang, Chenxi, additional, Grady, Connor B, additional, Lu, Yuan, additional, Cohen, Alina, additional, Matzner, Pini, additional, Mahajan, Shiwani, additional, Caraballo, César, additional, Kumar, Navin, additional, Xue, Yuchen, additional, Dreyer, Rachel, additional, Roy, Brita, additional, and Krumholz, Harlan M, additional

Published
2021
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25. Engagement With COVID-19 Public Health Measures in the United States: A Cross-sectional Social Media Analysis from June to November 2020

Author

Massey, Daisy, primary, Huang, Chenxi, additional, Lu, Yuan, additional, Cohen, Alina, additional, Oren, Yahel, additional, Moed, Tali, additional, Matzner, Pini, additional, Mahajan, Shiwani, additional, Caraballo, César, additional, Kumar, Navin, additional, Xue, Yuchen, additional, Ding, Qinglan, additional, Dreyer, Rachel, additional, Roy, Brita, additional, and Krumholz, Harlan, additional

Published
2021
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26. SalivaDirect: A simplified and flexible platform to enhance SARS-CoV-2 testing capacity

Author

Vogels, Chantal B.F., primary, Watkins, Anne E., additional, Harden, Christina A., additional, Brackney, Doug E., additional, Shafer, Jared, additional, Wang, Jianhui, additional, Caraballo, César, additional, Kalinich, Chaney C., additional, Ott, Isabel M., additional, Fauver, Joseph R., additional, Kudo, Eriko, additional, Lu, Peiwen, additional, Venkataraman, Arvind, additional, Tokuyama, Maria, additional, Moore, Adam J., additional, Muenker, M. Catherine, additional, Casanovas-Massana, Arnau, additional, Fournier, John, additional, Bermejo, Santos, additional, Campbell, Melissa, additional, Datta, Rupak, additional, Nelson, Allison, additional, Dela Cruz, Charles S., additional, Ko, Albert I., additional, Iwasaki, Akiko, additional, Krumholz, Harlan M., additional, Matheus, J.D., additional, Hui, Pei, additional, Liu, Chen, additional, Farhadian, Shelli F., additional, Sikka, Robby, additional, Wyllie, Anne L., additional, Grubaugh, Nathan D., additional, Anastasio, Kelly, additional, Askenase, Michael H., additional, Batsu, Maria, additional, Bickerton, Sean, additional, Brower, Kristina, additional, Bucklin, Molly L., additional, Cahill, Staci, additional, Cao, Yiyun, additional, Courchaine, Edward, additional, DeIuliis, Giuseppe, additional, Earnest, Rebecca, additional, Geng, Bertie, additional, Goldman-Israelow, Benjamin, additional, Handoko, Ryan, additional, Khoury-Hanold, William, additional, Kim, Daniel, additional, Knaggs, Lynda, additional, Kuang, Maxine, additional, Lapidus, Sarah, additional, Lim, Joseph, additional, Linehan, Melissa, additional, Lu-Culligan, Alice, additional, Martin, Anjelica, additional, Matos, Irene, additional, McDonald, David, additional, Minasyan, Maksym, additional, Nakahata, Maura, additional, Naushad, Nida, additional, Nouws, Jessica, additional, Obaid, Abeer, additional, Odio, Camila, additional, Oh, Ji Eun, additional, Omer, Saad, additional, Park, Annsea, additional, Park, Hong-Jai, additional, Peng, Xiaohua, additional, Petrone, Mary, additional, Prophet, Sarah, additional, Rice, Tyler, additional, Rose, Kadi-Ann, additional, Sewanan, Lorenzo, additional, Sharma, Lokesh, additional, Shaw, Albert C., additional, Shepard, Denise, additional, Smolgovsky, Mikhail, additional, Sonnert, Nicole, additional, Strong, Yvette, additional, Todeasa, Codruta, additional, Valdez, Jordan, additional, Velazquez, Sofia, additional, Vijayakumar, Pavithra, additional, White, Elizabeth B., additional, and Yang, Yexin, additional

Published
2021
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28. COVID-19 infections and outcomes in a live registry of heart failure patients across an integrated health care system

Author

Caraballo, César, primary, McCullough, Megan, additional, Fuery, Michael A., additional, Chouairi, Fouad, additional, Keating, Craig, additional, Ravindra, Neal G., additional, Miller, P. Elliott, additional, Malinis, Maricar, additional, Kashyap, Nitu, additional, Hsiao, Allen, additional, Wilson, F. Perry, additional, Curtis, Jeptha P., additional, Grant, Matthew, additional, Velazquez, Eric J., additional, Desai, Nihar R., additional, and Ahmad, Tariq, additional

Published
2020
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29. Cumulative Burden of Financial Hardship From Medical Bills Across the Spectrum of Diabetes Mellitus and Atherosclerotic Cardiovascular Disease Among Non‐Elderly Adults in the United States

Author

Mszar, Reed, primary, Grandhi, Gowtham R., additional, Valero‐Elizondo, Javier, additional, Caraballo, César, additional, Khera, Rohan, additional, Desai, Nihar, additional, Virani, Salim S., additional, Blankstein, Ron, additional, Blaha, Michael J., additional, and Nasir, Khurram, additional

Published
2020
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30. Clinical Implications of the New York Heart Association Classification

Author

Caraballo, César, primary, Desai, Nihar R., additional, Mulder, Hillary, additional, Alhanti, Brooke, additional, Wilson, F. Perry, additional, Fiuzat, Mona, additional, Felker, G. Michael, additional, Piña, Ileana L., additional, O'Connor, Christopher M., additional, Lindenfeld, Joanne, additional, Januzzi, James L., additional, Cohen, Lawrence S., additional, and Ahmad, Tariq, additional

Published
2019
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32. Performance Metrics for the Comparative Analysis of Clinical Risk Prediction Models Employing Machine Learning.

Author

Chenxi Huang, Shu-Xia Li, Caraballo, César, Masoudi, Frederick A., Rumsfeld, John S., Spertus, John A., Normand, Sharon-Lise T., Mortazavi, Bobak J., Krumholz, Harlan M., Huang, Chenxi, and Li, Shu-Xia

Abstract

Background: New methods such as machine learning techniques have been increasingly used to enhance the performance of risk predictions for clinical decision-making. However, commonly reported performance metrics may not be sufficient to capture the advantages of these newly proposed models for their adoption by health care professionals to improve care. Machine learning models often improve risk estimation for certain subpopulations that may be missed by these metrics.Methods and Results: This article addresses the limitations of commonly reported metrics for performance comparison and proposes additional metrics. Our discussions cover metrics related to overall performance, discrimination, calibration, resolution, reclassification, and model implementation. Models for predicting acute kidney injury after percutaneous coronary intervention are used to illustrate the use of these metrics.Conclusions: We demonstrate that commonly reported metrics may not have sufficient sensitivity to identify improvement of machine learning models and propose the use of a comprehensive list of performance metrics for reporting and comparing clinical risk prediction models. [ABSTRACT FROM AUTHOR]

Published
2021
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39. Asociación entre el sitio de infección y la mortalidad hospitalaria en pacientes con sepsis atendidos en urgencias de hospitales de tercer nivel en Medellín, Colombia.

Author

Caraballo, César, Ascuntar, Johana, Hincapié, Carolina, Restrepo, Camilo, Bernal, Elisa, and Jaimes, Fabián

Abstract

Objective: To determine the association between the primary site of infection and in-hospital mortality as the main outcome, or the need for admission to the intensive care unit as a secondary outcome, in patients with sepsis admitted to the emergency department. Methods: This was a secondary analysis of a multicenter prospective cohort. Patients included in the study were older than 18 years with a diagnosis of severe sepsis or septic shock who were admitted to the emergency departments of three tertiary care hospitals. Of the 5022 eligible participants, 2510 were included. Multiple logistic regression analysis was performed for mortality. Results: The most common site of infection was the urinary tract, present in 27.8% of the cases, followed by pneumonia (27.5%) and intra-abdominal focus (10.8%). In 5.4% of the cases, no definite site of infection was identified on admission. Logistic regression revealed a significant association between the following sites of infection and in-hospital mortality when using the urinary infection group as a reference: pneumonia (OR 3.4; 95%CI, 2.2 - 5.2; p < 0.001), skin and soft tissues (OR 2.6; 95%CI, 1.4 - 5.0; p = 0.003), bloodstream (OR 2.0; 95%CI, 1.1 - 3.6; p = 0.018), without specific focus (OR 2.0; 95%CI, 1.1 - 3.8; p = 0.028), and intra-abdominal focus (OR 1.9; 95%CI, 1.1 - 3.3; p = 0.024). Conclusions: There is a significant association between the different sites of infection and in-hospital mortality or the need for admission to an intensive care unit in patients with sepsis or septic shock. Urinary tract infection shows the lowest risk, which should be considered in prognostic models of these conditions. [ABSTRACT FROM AUTHOR]

Published
2019
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40. Cause-Specific Mortality Rates Among the US Black Population.

Author

Arun AS, Caraballo C, Sawano M, Lu Y, Khera R, Yancy CW, and Krumholz HM

Subjects
Humans, United States epidemiology, Male, Female, Middle Aged, Adult, Aged, Mortality trends, Adolescent, Young Adult, Black or African American statistics & numerical data, Cause of Death trends
Published
2024
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41. Organ Dysfunction in Sepsis: An Ominous Trajectory From Infection To Death.

Author

Caraballo C and Jaimes F

Subjects
Cost of Illness, Humans, Microcirculation, Multiple Organ Failure blood, Oxygen, Perfusion, Sepsis blood, Multiple Organ Failure complications, Multiple Organ Failure mortality, Sepsis complications, Sepsis mortality
Abstract

Sepsis is a highly complex and lethal syndrome with highly heterogeneous clinical manifestations that makes it difficult to detect and treat. It is also one of the major and most urgent global public health challenges. More than 30 million people are diagnosed with sepsis each year, with 5 million attributable deaths and long-term sequalae among survivors. The current international consensus defines sepsis as a life-threatening organ dysfunction caused by a dysregulated host response to an infection. Over the past decades substantial research has increased the understanding of its pathophysiology. The immune response induces a severe macro and microcirculatory dysfunction that leads to a profound global hypoperfusion, injuring multiple organs. Consequently, patients with sepsis might present dysfunction of virtually any system, regardless of the site of infection. The organs more frequently affected are kidneys, liver, lungs, heart, central nervous system, and hematologic system. This multiple organ failure is the hallmark of sepsis and determines patients' course from infection to recovery or death. There are tools to assess the severity of the disease that can also help to guide treatment, like the Sequential Organ Failure Assessment (SOFA) score. However, sepsis disease process is vastly heterogeneous, which could explain why interventions targeted to directly intervene its mechanisms have shown unsuccessful results and predicting outcomes with accuracy is still elusive. Thus, it is required to implement strong public health strategies and leverage novel technologies in research to improve outcomes and mitigate the burden of sepsis and septic shock worldwide., (Copyright ©2019, Yale Journal of Biology and Medicine.)

Published
2019

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