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  1. Article ; Online: Comparison between machine learning methods for mortality prediction for sepsis patients with different social determinants

    Hanyin Wang / Yikuan Li / Andrew Naidech / Yuan Luo

    BMC Medical Informatics and Decision Making, Vol 22, Iss S2, Pp 1-

    2022  Volume 13

    Abstract: Abstract Background Sepsis is one of the most life-threatening circumstances for critically ill patients in the United States, while diagnosis of sepsis is challenging as a standardized criteria for sepsis identification is still under development. ... ...

    Abstract Abstract Background Sepsis is one of the most life-threatening circumstances for critically ill patients in the United States, while diagnosis of sepsis is challenging as a standardized criteria for sepsis identification is still under development. Disparities in social determinants of sepsis patients can interfere with the risk prediction performances using machine learning. Methods We analyzed a cohort of critical care patients from the Medical Information Mart for Intensive Care (MIMIC)-III database. Disparities in social determinants, including race, sex, marital status, insurance types and languages, among patients identified by six available sepsis criteria were revealed by forest plots with 95% confidence intervals. Sepsis patients were then identified by the Sepsis-3 criteria. Sixteen machine learning classifiers were trained to predict in-hospital mortality for sepsis patients on a training set constructed by random selection. The performance was measured by area under the receiver operating characteristic curve (AUC). The performance of the trained model was tested on the entire randomly conducted test set and each sub-population built based on each of the following social determinants: race, sex, marital status, insurance type, and language. The fluctuations in performances were further examined by permutation tests. Results We analyzed a total of 11,791 critical care patients from the MIMIC-III database. Within the population identified by each sepsis identification method, significant differences were observed among sub-populations regarding race, marital status, insurance type, and language. On the 5783 sepsis patients identified by the Sepsis-3 criteria statistically significant performance decreases for mortality prediction were observed when applying the trained machine learning model on Asian and Hispanic patients, as well as the Spanish-speaking patients. With pairwise comparison, we detected performance discrepancies in mortality prediction between Asian and White patients, Asians and ...
    Keywords Sepsis ; Machine learning ; Social determinants ; Disparity ; Mortality prediction ; Computer applications to medicine. Medical informatics ; R858-859.7
    Subject code 610
    Language English
    Publishing date 2022-06-01T00:00:00Z
    Publisher BMC
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Article ; Online: Multimodal machine learning in precision health

    Adrienne Kline / Hanyin Wang / Yikuan Li / Saya Dennis / Meghan Hutch / Zhenxing Xu / Fei Wang / Feixiong Cheng / Yuan Luo

    npj Digital Medicine, Vol 5, Iss 1, Pp 1-

    A scoping review

    2022  Volume 14

    Abstract: Abstract Machine learning is frequently being leveraged to tackle problems in the health sector including utilization for clinical decision-support. Its use has historically been focused on single modal data. Attempts to improve prediction and mimic the ... ...

    Abstract Abstract Machine learning is frequently being leveraged to tackle problems in the health sector including utilization for clinical decision-support. Its use has historically been focused on single modal data. Attempts to improve prediction and mimic the multimodal nature of clinical expert decision-making has been met in the biomedical field of machine learning by fusing disparate data. This review was conducted to summarize the current studies in this field and identify topics ripe for future research. We conducted this review in accordance with the PRISMA extension for Scoping Reviews to characterize multi-modal data fusion in health. Search strings were established and used in databases: PubMed, Google Scholar, and IEEEXplore from 2011 to 2021. A final set of 128 articles were included in the analysis. The most common health areas utilizing multi-modal methods were neurology and oncology. Early fusion was the most common data merging strategy. Notably, there was an improvement in predictive performance when using data fusion. Lacking from the papers were clear clinical deployment strategies, FDA-approval, and analysis of how using multimodal approaches from diverse sub-populations may improve biases and healthcare disparities. These findings provide a summary on multimodal data fusion as applied to health diagnosis/prognosis problems. Few papers compared the outputs of a multimodal approach with a unimodal prediction. However, those that did achieved an average increase of 6.4% in predictive accuracy. Multi-modal machine learning, while more robust in its estimations over unimodal methods, has drawbacks in its scalability and the time-consuming nature of information concatenation.
    Keywords Computer applications to medicine. Medical informatics ; R858-859.7
    Subject code 006
    Language English
    Publishing date 2022-11-01T00:00:00Z
    Publisher Nature Portfolio
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Article ; Online: Optimizing inpatient bed management in a rural community-based hospital

    Brian N. Bartlett / Nadine N. Vanhoudt / Hanyin Wang / Ashley A. Anderson / Danielle L. Juliar / Jennifer M. Bartelt / April D. Lanz / Pawan Bhandari / Gokhan Anil

    BMC Health Services Research, Vol 23, Iss 1, Pp 1-

    a quality improvement initiative

    2023  Volume 10

    Abstract: Abstract Background Appropriate use of available inpatient beds is an ongoing challenge for US hospitals. Historical capacity goals of 80% to 85% may no longer serve the intended purpose of maximizing the resources of space, staff, and equipment. ... ...

    Abstract Abstract Background Appropriate use of available inpatient beds is an ongoing challenge for US hospitals. Historical capacity goals of 80% to 85% may no longer serve the intended purpose of maximizing the resources of space, staff, and equipment. Numerous variables affect the input, throughput, and output of a hospital. Some of these variables include patient demand, regulatory requirements, coordination of patient flow between various systems, coordination of processes such as bed management and patient transfers, and the diversity of departments (both inpatient and outpatient) in an organization. Methods Mayo Clinic Health System in the Southwest Minnesota region of the US, a community-based hospital system primarily serving patients in rural southwestern Minnesota and part of Iowa, consists of 2 postacute care and 3 critical access hospitals. Our inpatient bed usage rates had exceeded 85%, and patient transfers from the region to other hospitals in the state (including Mayo Clinic in Rochester, Minnesota) had increased. To address these quality gaps, we used a blend of Agile project management methodology, rapid Plan-Do-Study-Act cycles, and a proactive approach to patient placement in the medical-surgical units as a quality improvement initiative. Results During 2 trial periods of the initiative, the main hub hospital (Mayo Clinic Health System hospital in Mankato) and other hospitals in the region increased inpatient bed usage while reducing total out-of-region transfers. Conclusion Our novel approach to proactively managing bed capacity in the hospital allowed the region’s only tertiary medical center to increase capacity for more complex and acute cases by optimizing the use of historically underused partner hospital beds.
    Keywords Acute care beds ; Capacity constraints ; Community hospital ; Inpatient bed management optimization ; Patient transfers ; Public aspects of medicine ; RA1-1270
    Subject code 360
    Language English
    Publishing date 2023-09-01T00:00:00Z
    Publisher BMC
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  4. Article ; Online: 2020 The clinical implications of a positive prostate cancer screen in patients undergoing a cardiac transplant evaluation

    Vaibhav Kumar / Hanyin Wang / David DeNofrio / David Kent

    Journal of Clinical and Translational Science, Vol 2, Pp 48-

    2018  Volume 48

    Abstract: OBJECTIVES/SPECIFIC AIMS: Screening the general population for prostate cancer with prostate specific antigen (PSA) continues to be controversial. Patients with advanced heart failure undergoing evaluation for suitability for cardiac transplantation are ... ...

    Abstract OBJECTIVES/SPECIFIC AIMS: Screening the general population for prostate cancer with prostate specific antigen (PSA) continues to be controversial. Patients with advanced heart failure undergoing evaluation for suitability for cardiac transplantation are often requested to undergo prostate cancer screening, with guiding evidence generated from the general population. The objective of this study is to determine the clinical implications of a positive prostate cancer screen result in this patient population. METHODS/STUDY POPULATION: A retrospective cohort study was performed on all men that were referred to a tertiary care cardiac transplant center between January 2000 and December 2015. Patients were classified as having either a “positive screen” (PSA≥4 ng/mL) or a “negative screen” (PSA<4 ng/mL) at the point of evaluation. The primary outcome of time to listing for cardiac transplant (days) was calculated from the date of referral to the date of listing. A multivariable Cox proportional hazards model was developed to assess the association between a positive prostate cancer test result and listing for cardiac transplantation. RESULTS/ANTICIPATED RESULTS: Among the 704 patients included in this study, 66 men (9.4%) had a positive prostate cancer screen result. Men with a positive prostate cancer screen were approximately 4 year older (mean 58.5 vs. 54.1 years), more likely to have a diagnosis of Ischemic Cardiomyopathy (74% vs. 53%) and require continuous mechanical support (61% vs. 16%) at the point of transplant evaluation. The median time for listing for cardiac transplant was greater in patients with a positive PSA (119 vs. 48 days, p<0.05). After adjusting for age, renal function, clinical status at evaluation, history of COPD, and year of referral, patients with a positive prostate cancer screen had a reduced hazards ratio (HR) for progressing to cardiac transplant listing compared with those with a negative screen (HR 0.58, 95%CI: 0.38–0.91). DISCUSSION/SIGNIFICANCE OF IMPACT: Screening patients ...
    Keywords Medicine ; R
    Subject code 610
    Language English
    Publishing date 2018-06-01T00:00:00Z
    Publisher Cambridge University Press
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  5. Article ; Online: Embryotoxicity Caused by DON-Induced Oxidative Stress Mediated by Nrf2/HO-1 Pathway

    Miao Yu / Liangkai Chen / Zhao Peng / Di Wang / Yadong Song / Hanyin Wang / Ping Yao / Hong Yan / Andreas K. Nüssler / Liegang Liu / Wei Yang

    Toxins, Vol 9, Iss 6, p

    2017  Volume 188

    Abstract: Deoxynivalenol (DON) belongs to the type B group of trichothecenes family, which is composed of sesquiterpenoid metabolites produced by Fusarium and other fungi in grain. DON may cause various toxicities, such as cytotoxicity, immunotoxicity, ... ...

    Abstract Deoxynivalenol (DON) belongs to the type B group of trichothecenes family, which is composed of sesquiterpenoid metabolites produced by Fusarium and other fungi in grain. DON may cause various toxicities, such as cytotoxicity, immunotoxicity, genotoxicity as well as teratogenicity and carcinogenicity. In the present study, we focus on a hypothesis that DON alters the expressions of Nrf2/HO-1 pathway by inducing embryotoxicity in C57BL/6 mouse (5.0, 2.5, 1.0, and 0 mg/kg/day) and BeWo cell lines (0 and 50 nM; 3 h, 12 h and 24 h). Our results indicate that DON treatment in mice during pregnancy leads to ROS accumulation in the placenta, which results in embryotoxicity. At the same time Nrf2/HO-1 pathway is up-regulated by ROS to protect placenta cells from oxidative damage. In DON-treated BeWo cells, the level of ROS has time–effect and dose–effect relationships with HO-1 expression. Moderate increase in HO-1 protects the cell from oxidative damage, while excessive increase in HO-1 aggravates the oxidative damage, which is called in some studies the “threshold effect”. Therefore, oxidative stress may be the critical molecular mechanism for DON-induced embryotoxicity. Besides, Nrf2/HO-1 pathway accompanied by the “threshold effect” also plays an important role against DON-induced oxidative damage in this process.
    Keywords skeleton abnormalities ; reactive oxygen species ; anti-oxidative system ; Nrf2 translocation ; Medicine ; R
    Subject code 500
    Language English
    Publishing date 2017-06-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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