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168 results on '"Haimovich, Adrian"'

4. Dynamically Extracting Outcome-Specific Problem Lists from Clinical Notes with Guided Multi-Headed Attention

Author

Lovelace, Justin, Hurley, Nathan C., Haimovich, Adrian D., and Mortazavi, Bobak J.

Subjects
Computer Science - Information Retrieval, Computer Science - Machine Learning
Abstract

Problem lists are intended to provide clinicians with a relevant summary of patient medical issues and are embedded in many electronic health record systems. Despite their importance, problem lists are often cluttered with resolved or currently irrelevant conditions. In this work, we develop a novel end-to-end framework that first extracts diagnosis and procedure information from clinical notes and subsequently uses the extracted medical problems to predict patient outcomes. This framework is both more performant and more interpretable than existing models used within the domain, achieving an AU-ROC of 0.710 for bounceback readmission and 0.869 for in-hospital mortality occurring after ICU discharge. We identify risk factors for both readmission and mortality outcomes and demonstrate that our framework can be used to develop dynamic problem lists that present clinical problems along with their quantitative importance. We conduct a qualitative user study with medical experts and demonstrate that they view the lists produced by our framework favorably and find them to be a more effective clinical decision support tool than a strong baseline., Comment: To appear in the proceedings of the Machine Learning for Healthcare Conference (MLHC) 2020. Accepted papers can be viewed at https://www.mlforhc.org/accepted-papers

Published
2020

5. Explainable Prediction of Adverse Outcomes Using Clinical Notes

Author

Lovelace, Justin R., Hurley, Nathan C., Haimovich, Adrian D., and Mortazavi, Bobak J.

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

Clinical notes contain a large amount of clinically valuable information that is ignored in many clinical decision support systems due to the difficulty that comes with mining that information. Recent work has found success leveraging deep learning models for the prediction of clinical outcomes using clinical notes. However, these models fail to provide clinically relevant and interpretable information that clinicians can utilize for informed clinical care. In this work, we augment a popular convolutional model with an attention mechanism and apply it to unstructured clinical notes for the prediction of ICU readmission and mortality. We find that the addition of the attention mechanism leads to competitive performance while allowing for the straightforward interpretation of predictions. We develop clear visualizations to present important spans of text for both individual predictions and high-risk cohorts. We then conduct a qualitative analysis and demonstrate that our model is consistently attending to clinically meaningful portions of the narrative for all of the outcomes that we explore., Comment: Machine Learning for Health (ML4H) at NeurIPS 2019 - Extended Abstract

Published
2019

6. Visualization of Emergency Department Clinical Data for Interpretable Patient Phenotyping

Author

Hurley, Nathan C., Haimovich, Adrian D., Taylor, R. Andrew, and Mortazavi, Bobak J.

Subjects
Computer Science - Human-Computer Interaction, Computer Science - Machine Learning, Statistics - Machine Learning, 00A66 68U35 91C20
Abstract

Visual summarization of clinical data collected on patients contained within the electronic health record (EHR) may enable precise and rapid triage at the time of patient presentation to an emergency department (ED). The triage process is critical in the appropriate allocation of resources and in anticipating eventual patient disposition, typically admission to the hospital or discharge home. EHR data are high-dimensional and complex, but offer the opportunity to discover and characterize underlying data-driven patient phenotypes. These phenotypes will enable improved, personalized therapeutic decision making and prognostication. In this work, we focus on the challenge of two-dimensional patient projections. A low dimensional embedding offers visual interpretability lost in higher dimensions. While linear dimensionality reduction techniques such as principal component analysis are often used towards this aim, they are insufficient to describe the variance of patient data. In this work, we employ the newly-described non-linear embedding technique called uniform manifold approximation and projection (UMAP). UMAP seeks to capture both local and global structures in high-dimensional data. We then use Gaussian mixture models to identify clusters in the embedded data and use the adjusted Rand index (ARI) to establish stability in the discovery of these clusters. This technique is applied to five common clinical chief complaints from a real-world ED EHR dataset, describing the emergent properties of discovered clusters. We observe clinically-relevant cluster attributes, suggesting that visual embeddings of EHR data using non-linear dimensionality reduction is a promising approach to reveal data-driven patient phenotypes. In the five chief complaints, we find between 2 and 6 clusters, with the peak mean pairwise ARI between subsequent training iterations to range from 0.35 to 0.74.

Published
2019

12. SOFA score performs worse than age for predicting mortality in patients with COVID-19.

Author

Sherak, Raphael A. G., Sajjadi, Hoomaan, Khimani, Naveed, Tolchin, Benjamin, Jubanyik, Karen, Taylor, R. Andrew, Schulz, Wade, Mortazavi, Bobak J., and Haimovich, Adrian D.

Subjects
COVID-19, COVID-19 pandemic, SOFAS, INDEPENDENT variables, BLACK white differences
Abstract

The use of the Sequential Organ Failure Assessment (SOFA) score, originally developed to describe disease morbidity, is commonly used to predict in-hospital mortality. During the COVID-19 pandemic, many protocols for crisis standards of care used the SOFA score to select patients to be deprioritized due to a low likelihood of survival. A prior study found that age outperformed the SOFA score for mortality prediction in patients with COVID-19, but was limited to a small cohort of intensive care unit (ICU) patients and did not address whether their findings were unique to patients with COVID-19. Moreover, it is not known how well these measures perform across races. In this retrospective study, we compare the performance of age and SOFA score in predicting in-hospital mortality across two cohorts: a cohort of 2,648 consecutive adult patients diagnosed with COVID-19 who were admitted to a large academic health system in the northeastern United States over a 4-month period in 2020 and a cohort of 75,601 patients admitted to one of 335 ICUs in the eICU database between 2014 and 2015. We used age and the maximum SOFA score as predictor variables in separate univariate logistic regression models for in-hospital mortality and calculated area under the receiver operator characteristic curves (AU-ROCs) and area under precision-recall curves (AU-PRCs) for each predictor in both cohorts. Among the COVID-19 cohort, age (AU-ROC 0.795, 95% CI 0.762, 0.828) had a significantly better discrimination than SOFA score (AU-ROC 0.679, 95% CI 0.638, 0.721) for mortality prediction. Conversely, age (AU-ROC 0.628 95% CI 0.608, 0.628) underperformed compared to SOFA score (AU-ROC 0.735, 95% CI 0.726, 0.745) in non-COVID-19 ICU patients in the eICU database. There was no difference between Black and White COVID-19 patients in performance of either age or SOFA Score. Our findings bring into question the utility of SOFA score-based resource allocation in COVID-19 crisis standards of care. [ABSTRACT FROM AUTHOR]

Published
2024
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17. Automating risk stratification for geriatric syndromes in the emergency department.

Author

Haimovich, Adrian D., Shah, Manish N., Southerland, Lauren T., Hwang, Ula, and Patterson, Brian W.

Subjects
*COGNITION disorders, *HOSPITAL emergency services, *GERIATRIC assessment, *ARTIFICIAL intelligence, *RISK assessment, *AUTOMATION, *ACCIDENTAL falls, *ELECTRONIC health records, *ELDER care, *PALLIATIVE treatment
Abstract

Background: Geriatric emergency department (GED) guidelines endorse screening older patients for geriatric syndromes in the ED, but there have been significant barriers to widespread implementation. The majority of screening programs require engagement of a clinician, nurse, or social worker, adding to already significant workloads at a time of record‐breaking ED patient volumes, staff shortages, and hospital boarding crises. Automated, electronic health record (EHR)‐embedded risk stratification approaches may be an alternate solution for extending the reach of the GED mission by directing human actions to a smaller subset of higher risk patients. Methods: We define the concept of automated risk stratification and screening using existing EHR data. We discuss progress made in three potential use cases in the ED: falls, cognitive impairment, and end‐of‐life and palliative care, emphasizing the importance of linking automated screening with systems of healthcare delivery. Results: Research progress and operational deployment vary by use case, ranging from deployed solutions in falls screening to algorithmic validation in cognitive impairment and end‐of‐life care. Conclusions: Automated risk stratification offers a potential solution to one of the most pressing problems in geriatric emergency care: identifying high‐risk populations of older adults most appropriate for specific GED care. Future work is needed to realize the promise of improved care with less provider burden by creating tools suitable for widespread deployment as well as best practices for their implementation and governance. [ABSTRACT FROM AUTHOR]

Published
2024
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23. Genomically Recoded Organisms Expand Biological Functions

Author

Lajoie, Marc J., Rovner, Alexis J., Goodman, Daniel B., Aerni, Hans-Rudolf, Haimovich, Adrian D., Kuznetsov, Gleb, Mercer, Jaron A., Wang, Harris H., Carr, Peter A., Mosberg, Joshua A., Rohland, Nadin, Schultz, Peter G., Jacobson, Joseph M., Rinehart, Jesse, Church, George M., and Isaacs, Farren J.

Published
2013
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26. Recoded organisms engineered to depend on synthetic amino acids

Author

Rovner, Alexis J., Haimovich, Adrian D., Katz, Spencer R., Li, Zhe, Grome, Michael W., Gassaway, Brandon M., Amiram, Miriam, Patel, Jaymin R., Gallagher, Ryan R., Rinehart, Jesse, and Isaacs, Farren J.

Subjects
Amino acids -- Production processes -- Physiological aspects -- Research, Genetically modified organisms -- Usage -- Research, Environmental issues, Science and technology, Zoology and wildlife conservation
Abstract

Genetically modified organisms (GMOs) are increasingly used in research and industrial systems to produce high-value pharmaceuticals, fuels and chemicals (1). Genetic isolation and intrinsic biocontainment would provide essential biosafety measures [...]

Published
2015

30. Analysis of Health Trajectories Leading to Adverse Opioid-Related Events

Author

Gilson, Aidan S., Chartash, David, Chang, David, Hawk, Kathryn, D'Onofrio, Gail, Haimovich, Adrian D., Fiellin, David A., and Taylor, R. Andrew

Subjects
Analgesics, Opioid, Humans, Articles
Abstract

Identifying patient risk factors leading to adverse opioid-related events (AOEs) may enable targeted risk-based interventions, uncover potential causal mechanisms, and enhance prognosis. In this article, we aim to discover patient diagnosis, procedure, and medication event trajectories associated with AOEs using large-scale data mining methods. The individual temporally preceding factors associated with the highest relative risk (RR) for AOEs were opioid withdrawal therapy agents, toxic encephalopathy, problems related to housing and economic circumstances, and unspecified viral hepatitis, with RR of 33.4, 26.1, 19.9, and 18.7, respectively. Patient cohorts with a socioeconomic or mental health code had a larger RR for over 75% of all identified trajectories compared to the average population. By analyzing health trajectories leading to AOEs, we discover novel, temporally-connected combinations of diagnoses and health service events that significantly increase risk of AOEs, including natural histories marked by socioeconomic and mental health diagnoses.

Published
2021

34. sj-pdf-1-aic-10.1177_0310057X211024258 - Supplemental material for Risk factor identification and predictive models for central line requirements for patients on vasopressors

Author

Haimovich, Adrian D, Jiang, Ruoyi, Taylor, Richard A, and Belsky, Justin B

Subjects
FOS: Clinical medicine, 110305 Emergency Medicine
Abstract

Supplemental material, sj-pdf-1-aic-10.1177_0310057X211024258 for Risk factor identification and predictive models for central line requirements for patients on vasopressors by Adrian D Haimovich in Anaesthesia and Intensive Care

Published
2021
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36. Clinical characteristics and outcomes for 7,995 patients with SARS-CoV-2 infection

Author

McPadden, Jacob, primary, Warner, Frederick, additional, Young, H. Patrick, additional, Hurley, Nathan C., additional, Pulk, Rebecca A., additional, Singh, Avinainder, additional, Durant, Thomas J. S., additional, Gong, Guannan, additional, Desai, Nihar, additional, Haimovich, Adrian, additional, Taylor, Richard Andrew, additional, Gunel, Murat, additional, Dela Cruz, Charles S., additional, Farhadian, Shelli F., additional, Siner, Jonathan, additional, Villanueva, Merceditas, additional, Churchwell, Keith, additional, Hsiao, Allen, additional, Torre, Charles J., additional, Velazquez, Eric J., additional, Herbst, Roy S., additional, Iwasaki, Akiko, additional, Ko, Albert I., additional, Mortazavi, Bobak J., additional, Krumholz, Harlan M., additional, and Schulz, Wade L., additional

Published
2021
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39. Open Science in Emergency Medicine Research

Author

Taylor, R. Andrew, primary, Haimovich, Adrian D., additional, Horng, Steven, additional, Hinson, Jeremiah, additional, Levin, Scott, additional, Porturas, Thomas, additional, Du, Katherine, additional, Kornblith, Aaron, additional, and Hall, Michael Kennedy, additional

Published
2020
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40. Clinical Characteristics and Outcomes for 7,995 Patients with SARS-CoV-2 Infection

Author

McPadden, Jacob, primary, Warner, Frederick, additional, Young, H. Patrick, additional, Hurley, Nathan C., additional, Pulk, Rebecca A., additional, Singh, Avinainder, additional, Durant, Thomas JS, additional, Gong, Guannan, additional, Desai, Nihar, additional, Haimovich, Adrian, additional, Taylor, Richard Andrew, additional, Gunel, Murat, additional, Cruz, Charles S. Dela, additional, Farhadian, Shelli F., additional, Siner, Jonathan, additional, Villanueva, Merceditas, additional, Churchwell, Keith, additional, Hsiao, Allen, additional, Torre, Charles J., additional, Velazquez, Eric J., additional, Herbst, Roy S., additional, Iwasaki, Akiko, additional, Ko, Albert I., additional, Mortazavi, Bobak J., additional, Krumholz, Harlan M., additional, and Schulz, Wade L., additional

Published
2020
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49. Evolution of translation machinery in recoded bacteria enables multi-site incorporation of nonstandard amino acids

Author

Amiram, Miriam, primary, Haimovich, Adrian D, additional, Fan, Chenguang, additional, Wang, Yane-Shih, additional, Aerni, Hans-Rudolf, additional, Ntai, Ioanna, additional, Moonan, Daniel W, additional, Ma, Natalie J, additional, Rovner, Alexis J, additional, Hong, Seok Hoon, additional, Kelleher, Neil L, additional, Goodman, Andrew L, additional, Jewett, Michael C, additional, Söll, Dieter, additional, Rinehart, Jesse, additional, and Isaacs, Farren J, additional

Published
2015
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