MIMIC-IV, a freely accessible electronic health record dataset

Expanded Prostate Cancer Index Composite for Clinical Practice: Development and Validation of a Practical Health Related Quality of Life Instrument for Use in the Routine Clinical Care of Patients With Prostate Cancer

BackgroundRapid advances in mobile technologies and applications and the continued growth in digital network coverage have the potential to transform data collection in low- and middle-income countries. A common perception is that digital data collection (DDC) is faster and quickly adaptable.ObjectiveThe objective of this study was to test whether DDC is faster and more adaptable in a roadside environment. We conducted a reliability study comparing digital versus paper data collection in 3 cities in Ghana, Vietnam, and Indonesia observing road safety risk factors in real time.MethodsRoadside observation of helmet use among motorcycle passengers, seat belt use among 4-wheeler passengers, and speeding was conducted in Accra, Ghana; Ho Chi Minh City (HCMC), Vietnam; and Bandung, Indonesia. Two independent data collection teams were deployed to the same sites on the same dates and times, one using a paper-based data collection tool and the other using a digital tool. All research assistants were trained on paper-based data collection and DDC. A head-to-head analysis was conducted to compare the volume of observations, as well as the prevalence of each risk factor. Correlations (r) for continuous variables and kappa for categorical variables are reported with their level of statistical significance.ResultsIn Accra, there were 119 observation periods (90-min each) identical by date, time, and location during the helmet and seat belt use risk factor data collection and 118 identical periods observing speeding prevalence. In Bandung, there were 150 observation periods common to digital and paper data collection methods, whereas in HCMC, there were 77 matching observation periods for helmet use, 82 for seat belt use, and 84 for speeding. Data collectors using paper tools were more productive than their DDC counterparts during the study. The highest mean volume per session was recorded for speeding, with Bandung recording over 1000 vehicles on paper (paper: mean 1092 [SD 435]; digital: mean 807 [SD 261]); whereas the lowest volume per session was from HCMC for seat belts (paper: mean 52 [SD 28]; digital: mean 62 [SD 30]). Accra and Bandung showed good-to-high correlation for all 3 risk factors (r=0.52 to 0.96), with higher reliability in speeding and helmet use over seat belt use; HCMC showed high reliability for speeding (r=0.99) but lower reliability for helmet and seat belt use (r=0.08 to 0.32). The reported prevalence of risk factors was comparable in all cities regardless of the data collection method.ConclusionsDDC was convenient and reliable during roadside observational data collection. There was some site-related variability in implementing DDC methods, and generally the productivity was higher using the more familiar paper-based method. Even with low correlations between digital and paper data collection methods, the overall reported population prevalence was similar for all risk factors.

Leading Cancer Researcher Joins BIDMCBOSTON - Pier Paolo Pandolfi, MD, PhD, a leading scientist in the field of cancer genetics, will join the faculty of Beth Israel Deaconess Medical Center (BIDMC). Pandolfi comes to BIDMC from New York's Memorial Sloan-Kettering Cancer Center, where he holds the Albert C. Foster Chair in Cancer Biology and Genetics and is Professor of Molecular Biology and Genetics at Weill Graduate School of Medical Sciences as well as Professor of Pathology at Weill Medical College of Cornell University."We are extremely pleased to announce that after a far-reaching international search, we have recruited Dr. Pandolfi to BIDMC and Harvard Medical School," said BIDMC Chief Academic Officer Jeffrey S. Flier, MD, adding that Pandolfi's laboratory will begin its move to BIDMC over the summer. "Dr. Pandolfi's reputation among cancer geneticists is world-renowned, and the research from his laboratory has been seminal in defining the molecular mechanisms and genetics underlying a number of types of cancer."In his new position, Pandolfi will maintain joint appointments in the Departments of Medicine and Pathology at BIDMC, including serving as Director of a new Cancer Genetics program. He will also serve as Associate Director of Basic Research for BIDMC's Cancer Center and will be a Professor of Medicine and Pathology at Harvard Medical School (HMS)."Dr. Pandolfi, at a relatively early age, has become a world leader in cancer genetics and in cancer cell biology," notes Lewis Cantley, PhD, Chief of the Division of Signal Transduction at BIDMC and Professor of Medicine and Systems Biology at HMS. "His research has led to major breakthroughs in our understanding of how mutations in oncogenes and tumor suppressor genes result in leukemias, lymphomas and solid tumors. In his new roles as Associate Director of BIDMC's Cancer Center and head of the cancer genetics program, Dr. Pandolfi will greatly facilitate our mission to translate breakthroughs in the molecular diagnosis of cancer into individualized treatment for patients."In 1998, Pandolfi's laboratory uncovered the molecular underpinnings of acute promyelocytic leukemia (APL), findings that led to the development and testing of novel therapeutic strategies for this once-fatal disease, now readily curable. His work has also extended to lymphomas and solid tumors, most recently a greater understanding of the role of the PTEN tumor suppressor gene in prostate cancer."Beth Israel Deaconess offers the perfect scientific environment for translational cancer research," says Pandolfi. "I'm thrilled to be joining the Harvard Medical School community, where the opportunities for collaboration among leading scientific investigators are so robust."Existing cancer treatments already utilize targeted therapy," he adds. "Beginning with the use of retinoic acid in APL, and now extending to the use of Gleevec for the treatment of chronic myeloid leukemia, cancer therapies have been routinely prescribed based on the individual patient.As our fundamental understanding of cancers continues to develop, I'm extremely optimistic that we will indeed produce a new class of pharmaceutical agents specifically tailored to individuals' genetic profiles." "This recruitment reflects BIDMC's deep commitment to cancer research," adds BIDMC President and CEO Paul Levy. "Dr. Pandolfi is an extraordinarily accomplished scientist performing leading-edge research. His arrival will further strengthen and enhance the efforts of our world-class investigators who are working in pursuit of customized cancer treatments."A native of Rome, Pandolfi received his MD in 1989 and PhD in 1995, both from the University of Perugia, Italy. He completed post-graduate work at the Royal Postgraduate Medical School, University of London, before joining the faculty of Memorial Sloan-Kettering Cancer Center and the Weill Graduate School of Medical Sciences at Cornell University in 1994.Pandolfi is the recipient of numerous awards and honors, including a National Institutes of Health/National Cancer Institute MERIT Award in 2005, the Leukemia and Lymphoma Society of America Stohlman Scholar Award in 2002 and the Weizmann Institute of Science: Sergio Lombroso Prize for Cancer Research in 2001. In 2006, Pandolfi was elected as a member of the American Society for Clinical Investigation and the American Association of Physicians. He presently serves on the editorial of boards of the medical journals Blood, Cancer Science and The Journal of Clinical Investigation.Beth Israel Deaconess Medical Center is a patient care, teaching and research affiliate of Harvard Medical School and ranks third among independent hospitals nationwide in National Institutes of Health (NIH) funding. BIDMC is clinically affiliated with the Joslin Diabetes Center and is a research partner of the Dana-Farber/Harvard Cancer Center. BIDMC is the official hospital of the Boston Red Sox.For more information, visit http://www.bidmc.harvard.edu.

WEO Newsletter.

Use of Administrative Claims Data to Estimate Treatment Effects for 30 Versus 12 Months of Dual Antiplatelet Therapy After Percutaneous Coronary Intervention: Findings From the EXTEND-DAPT Study.

Journal of Magnetic Resonance ImagingVolume 10, Issue 5 p. 713-720 Original ResearchFree Access Coronary MRA: A clinical experience in the United States Peter G. Danias MD, PhD, Corresponding Author Peter G. Danias MD, PhD Charles A. Dana Research Institute and the Harvard-Thorndike Laboratory, Department of Medicine, Cardiovascular Division, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts 02215.Beth Israel Deaconess Medical Center, 330 Brookline Avenue, Boston, MA 02215.Search for more papers by this authorMatthias Stuber PhD, Matthias Stuber PhD Charles A. Dana Research Institute and the Harvard-Thorndike Laboratory, Department of Medicine, Cardiovascular Division, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts 02215. Philips Medical Systems, Best, The Netherlands.Search for more papers by this authorRobert R. Edelman MD, Robert R. Edelman MD Department of Radiology, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts, 02215.Search for more papers by this authorWarren J. Manning MD, Warren J. Manning MD Charles A. Dana Research Institute and the Harvard-Thorndike Laboratory, Department of Medicine, Cardiovascular Division, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts 02215. Department of Radiology, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts, 02215.Search for more papers by this author Peter G. Danias MD, PhD, Corresponding Author Peter G. Danias MD, PhD Charles A. Dana Research Institute and the Harvard-Thorndike Laboratory, Department of Medicine, Cardiovascular Division, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts 02215.Beth Israel Deaconess Medical Center, 330 Brookline Avenue, Boston, MA 02215.Search for more papers by this authorMatthias Stuber PhD, Matthias Stuber PhD Charles A. Dana Research Institute and the Harvard-Thorndike Laboratory, Department of Medicine, Cardiovascular Division, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts 02215. Philips Medical Systems, Best, The Netherlands.Search for more papers by this authorRobert R. Edelman MD, Robert R. Edelman MD Department of Radiology, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts, 02215.Search for more papers by this authorWarren J. Manning MD, Warren J. Manning MD Charles A. Dana Research Institute and the Harvard-Thorndike Laboratory, Department of Medicine, Cardiovascular Division, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts 02215. Department of Radiology, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts, 02215.Search for more papers by this author First published: 04 November 1999 https://doi.org/10.1002/(SICI)1522-2586(199911)10:5<713::AID-JMRI15>3.0.CO;2-ICitations: 11AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Citing Literature Volume10, Issue5Special Issue: Cardiovascular MRINovember 1999Pages 713-720 ReferencesRelatedInformation

Background and Purpose-The association of light to moderate alcohol consumption with risk of ischemic stroke remains uncertain, as are the roles of potentially mediating factors and modification by apolipoprotein E (apoE) genotype. Methods-We studied the prospective association of alcohol consumption and risk of ischemic stroke among 4410 participants free of cardiovascular disease at baseline in the Cardiovascular Health Study, a population-based cohort study of older adults from 4 US communities. Participants reported their consumption of alcoholic beverages yearly. Results-During an average follow-up period of 9.2 years, 434 cases of incident ischemic stroke occurred. Compared with long-term abstainers, the multivariate relative risks of ischemic stroke were 0.85 (95% CI, 0.63 to 1.13), 0.75 (95% CI, 0.53 to 1.06), 0.82 (95% CI, 0.51 to 1.30), and 1.03 (95% CI, 0.68 to 1.57) among consumers of 1, 1 to 6, 7 to 13, and 14 drinks per week (P quadratic trend 0.06). ApoE genotype appeared to modify the alcohol-ischemic stroke relationship (P interaction 0.08), with generally lower risks among drinkers than abstainers in apoE4-negative participants but higher risks among drinkers than abstainers among apoE4-positive participants. We could not identify candidate mediators among lipid, inflammatory, and prothrombotic factors. Conclusions-In this study of older adults, the association of alcohol use and risk of ischemic stroke was U-shaped, with modestly lower risk among consumers of 1 to 6 drinks per week. However, apoE genotype may modify this association, and even moderate alcohol intake may be associated with an increased risk of ischemic stroke among apoE4-positive older adults. (Stroke.

Background: Undiagnosed diabetes and prediabetes present a significant global health challenge. Artificial Intelligence-enabled electrocardiography (AI-ECG) has shown promise in identifying subtle ECG changes in a wide range of subclinical diseases. Opportunistic ECG screening could identify prediabetic patients, enabling early interventions to prevent T2DM and adverse cardiovascular events. Aims: To develop the AI-ECG Risk Estimator to diagnose prevalent T2DM and predict future T2DM (AIRE-DM) Methods: AIRE-DM was trained on a real-world secondary care cohort from Beth Israel Deaconess Medical Center (BIDMC) of 1,163,401 ECGs and externally validated in the UK Biobank (UKB, N = 65,606). AIRE-DM employs a residual neural network architecture with a discrete-time survival loss function. Results: AIRE-DM accurately identifies prevalent T2DM (AUROC: BIDMC – 0.712 (0.705-0.719), UKB - 0.731 (0.725 - 0.741) and predicts future T2DM (C-index: BIDMC - 0.666 (0.658-0.675), UKB 0.689 (0.663-0.715). In subjects without T2DM, the high-risk quartile shows a markedly increased risk of future T2DM (HR: BIDMC - 4.67 (4.01-5.45), UKB - 10.10 (5.87-17.40), adjusted for age and sex. Adding AIRE-DM to clinical risk factors in BIDMC and to the American Diabetes Association (ADA) score in the UKB significantly enhanced predictive accuracy for future T2DM (C-index improvement: BIDMC - 0.0359 (0.0354-0.0363), UKB: 0.0337 (0.0324-0.0350), continuous net reclassification index: BIDMC - 0.407 (0.360-0.445), UKB - 0.391 (0.259-0.503)). Using phenome- and genome-wide association studies, we identified biologically plausible associations for AIRE-DM, including glucose regulation, cardiac morphology, diastolic dysfunction, arterial stiffness and lipid metabolism. We identified variants adjacent to CASQ2, TBX3, NOS1AP, TKT, VGLL2 and PRDM6, which are known regulators of cardiac morphology, arterial stiffness and glucose metabolism. Conclusion: AIRE-DM can predict future T2DM in non-diabetics and enhances T2DM risk prediction when integrated with clinical risk scores. Its application holds promise for early identification of individuals at high risk of T2DM, enabling early lifestyle and pharmacological interventions.

The purpose of this study was to find out the digital competencies of vocational high school students and to derive implications for increasing the digital competencies of vocational high school students by identifying what competencies currently need to be improved most urgently. The results are as follows. First, the digital competency of vocational high school students was defined as the competency, including knowledge, skills, attitudes, and thinking skills that vocational high school students must have in common to perform their duties using various digital technologies after entering the labor market after graduation. Second, 5 major areas of vocational high school students' digital competencies were derived: 'understanding and responding to changes according to digital transformation', 'digital data collection and management', 'digital-based communication and collaboration’, ‘digital content development and utilization’, and 'digital-based problem solving' and minor 11 areas were derived. Third, ‘communication through digital technology’ and ‘digital data search and collection’ were identified as areas with high level of performance and importance, but ‘digital content development’ was identified as areas with low level of performance and importance. Fourth, competencies with high demand for improvement include ‘digital data search and collection’, ‘communication through digital technology’, ‘sharing and collaboration through digital technology’, and ‘digital content utilization’. In the suggestion, it was suggested that long-term research should be conducted to find ways to increase digital competencies of vocational high school students, and specific policy should be established that focus on improving students' foundational common competency.

Journal of Cardiovascular ElectrophysiologyVolume 16, Issue 6 p. 625-628 T-Wave Alternans: Does Size Matter RICHARD L. VERRIER Ph.D. , RICHARD L. VERRIER Ph.D. Cardiovascular Division, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USASearch for more papers by this authorKEVIN F. KWAKU M.D., Ph.D., KEVIN F. KWAKU M.D., Ph.D. Cardiovascular Division, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USASearch for more papers by this authorBRUCE D. NEARING Ph.D. , BRUCE D. NEARING Ph.D. Cardiovascular Division, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USASearch for more papers by this authorMARK E. JOSEPHSON M.D., MARK E. JOSEPHSON M.D. Cardiovascular Division, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USASearch for more papers by this author RICHARD L. VERRIER Ph.D. , RICHARD L. VERRIER Ph.D. Cardiovascular Division, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USASearch for more papers by this authorKEVIN F. KWAKU M.D., Ph.D., KEVIN F. KWAKU M.D., Ph.D. Cardiovascular Division, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USASearch for more papers by this authorBRUCE D. NEARING Ph.D. , BRUCE D. NEARING Ph.D. Cardiovascular Division, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USASearch for more papers by this authorMARK E. JOSEPHSON M.D., MARK E. JOSEPHSON M.D. Cardiovascular Division, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USASearch for more papers by this author First published: 12 April 2005 https://doi.org/10.1111/j.1540-8167.2005.50115.xCitations: 25 Richard L. Verrier, Ph.D., F.A.C.C., Associate Professor of Medicine, Harvard Medical School, Beth Israel Deaconess Medical Center, Harvard-Thorndike Electrophysiology Institute, Harvard Institutes of Medicine, 77 Avenue Louis Pasteur, Room 223, Boston MA 02115. Fax: 617-975-5270; E-mail: [email protected] J Cardiovasc Electrophysiol, Vol. 16, pp. 1-4, June 2005. Drs. Verrier and Nearing are the inventors of the Modified Moving Average Analysis method for T-wave Alternans (United States patent #6,169,919), which has been licensed by GE Medical Systems. Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Citing Literature Volume16, Issue6June 2005Pages 625-628 RelatedInformation

When Residents Speak Out

BackgroundRoadside observational studies play a fundamental role in designing evidence-informed strategies to address the pressing global health problem of road traffic injuries. Paper-based data collection has been the standard method for such studies, although digital methods are gaining popularity in all types of primary data collection.ObjectiveThis study aims to understand the reliability, productivity, and efficiency of paper vs digital data collection based on three different road user behaviors: helmet use, seatbelt use, and speeding. It also aims to understand the cost and time efficiency of each method and to evaluate potential trade-offs among reliability, productivity, and efficiency.MethodsA total of 150 observational sessions were conducted simultaneously for each risk factor in Mumbai, India, across two rounds of data collection. We matched the simultaneous digital and paper observation periods by date, time, and location, and compared the reliability by subgroups and the productivity using Pearson correlations (r). We also conducted logistic regressions separately by method to understand how similar results of inferential analyses would be. The time to complete an observation and the time to obtain a complete dataset were also compared, as were the total costs in US dollars for fieldwork, data entry, management, and cleaning.ResultsProductivity was higher in paper than digital methods in each round for each risk factor. However, the sample sizes across both methods provided a precision of 0.7 percentage points or smaller. The gap between digital and paper data collection productivity narrowed across rounds, with correlations improving from r=0.27-0.49 to 0.89-0.96. Reliability in risk factor proportions was between 0.61 and 0.99, improving between the two rounds for each risk factor. The results of the logistic regressions were also largely comparable between the two methods. Differences in regression results were largely attributable to small sample sizes in some variable levels or random error in variables where the prevalence of the outcome was similar among variable levels. Although data collectors were able to complete an observation using paper more quickly, the digital dataset was available approximately 9 days sooner. Although fixed costs were higher for digital data collection, variable costs were much lower, resulting in a 7.73% (US $3011/38,947) lower overall cost.ConclusionsOur study did not face trade-offs among time efficiency, cost efficiency, statistical reliability, and descriptive comparability when deciding between digital and paper, as digital data collection proved equivalent or superior on these domains in the context of our project. As trade-offs among cost, timeliness, and comparability—and the relative importance of each—could be unique to every data collection project, researchers should carefully consider the questionnaire complexity, target sample size, implementation plan, cost and logistical constraints, and geographical contexts when making the decision between digital and paper.

Transplant Infectious DiseaseVolume 16, Issue 6 p. 1042-1043 Letter to the Editor Mycobacterium mucogenicum infections in immunocompromised hosts S. Chalkias, Corresponding Author S. Chalkias Division of Infectious Diseases, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA Correspondence to: Spyridon Chalkias, MD, Division of Infectious Diseases, Beth Israel Deaconess Medical Center, 110 Francis St, Suite GB, Boston, MA 02215, USA Tel: 617-632-7706 Fax: 617-632-7626 E-mail: [email protected]Search for more papers by this authorJ. Lu, J. Lu Division of Infectious Diseases, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USASearch for more papers by this authorC.D. Alonso, C.D. Alonso Division of Infectious Diseases, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USASearch for more papers by this author S. Chalkias, Corresponding Author S. Chalkias Division of Infectious Diseases, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA Correspondence to: Spyridon Chalkias, MD, Division of Infectious Diseases, Beth Israel Deaconess Medical Center, 110 Francis St, Suite GB, Boston, MA 02215, USA Tel: 617-632-7706 Fax: 617-632-7626 E-mail: [email protected]Search for more papers by this authorJ. Lu, J. Lu Division of Infectious Diseases, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USASearch for more papers by this authorC.D. Alonso, C.D. Alonso Division of Infectious Diseases, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USASearch for more papers by this author First published: 04 November 2014 https://doi.org/10.1111/tid.12308Citations: 1Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL No abstract is available for this article.Citing Literature Volume16, Issue6December 2014Pages 1042-1043 RelatedInformation

Clinical note text was used to build machine learning models for adults admitted to the ICU. Preprocessing strategies studied were none (raw text), cleaning text, stemming, term frequency-inverse document frequency vectorization, and creation of n-grams. Model performance was assessed by the area under the receiver operating characteristic curve. Models were trained and internally validated on University of California San Francisco data using 10-fold cross validation. These models were then externally validated on Beth Israel Deaconess Medical Center data. ICUs at University of California San Francisco and Beth Israel Deaconess Medical Center. Ten thousand patients in the University of California San Francisco training and internal testing dataset and 27,058 patients in the external validation dataset, Beth Israel Deaconess Medical Center. None. Mortality rate at Beth Israel Deaconess Medical Center and University of California San Francisco was 10.9% and 7.4%, respectively. Data are presented as area under the receiver operating characteristic curve (95% CI) for models validated at University of California San Francisco and area under the receiver operating characteristic curve for models validated at Beth Israel Deaconess Medical Center. Models built and trained on University of California San Francisco data for the prediction of inhospital mortality improved from the raw note text model (AUROC, 0.84; CI, 0.80-0.89) to the term frequency-inverse document frequency model (AUROC, 0.89; CI, 0.85-0.94). When applying the models developed at University of California San Francisco to Beth Israel Deaconess Medical Center data, there was a similar increase in model performance from raw note text (area under the receiver operating characteristic curve at Beth Israel Deaconess Medical Center: 0.72) to the term frequency-inverse document frequency model (area under the receiver operating characteristic curve at Beth Israel Deaconess Medical Center: 0.83). Differences in preprocessing strategies for note text impacted model discrimination. Completing a preprocessing pathway including cleaning, stemming, and term frequency-inverse document frequency vectorization resulted in the preprocessing strategy with the greatest improvement in model performance. Further study is needed, with particular emphasis on how to manage author implicit bias present in note text, before natural language processing algorithms are implemented in the clinical setting.

COVID-19-Associated Stress (Takotsubo) Cardiomyopathy.