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  1. Book: Building cultural competency for improved diabetes care

    Hsu, William C.

    (The journal of family practice ; 2007,Sept., Suppl.)

    2007  

    Author's details William C. Hsu
    Series title The journal of family practice ; 2007,Sept., Suppl.
    Collection
    Language English
    Size S31 S.
    Publisher Dowden Health Media
    Publishing place S.l.
    Publishing country United States
    Document type Book
    HBZ-ID HT015470005
    Database Catalogue ZB MED Medicine, Health

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    In stock of ZB MED Cologne/Königswinter

    Shelf markLoan statusLocation
    Zs A 1274 -2007,Sept.,Suppl.- not available for loan Zeitschriften-Lesesaal

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  2. Article ; Online: Transportability Analysis-A Tool for Extending Trial Results to a Representative Target Population.

    Inoue, Kosuke / Hsu, William

    JAMA network open

    2024  Volume 7, Issue 1, Page(s) e2346302

    Language English
    Publishing date 2024-01-02
    Publishing country United States
    Document type Journal Article
    ISSN 2574-3805
    ISSN (online) 2574-3805
    DOI 10.1001/jamanetworkopen.2023.46302
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: AMPRO-HPCC: A Machine-Learning Tool for Predicting Resources on Slurm HPC Clusters.

    Tanash, Mohammed / Andresen, Daniel / Hsu, William

    ADVCOMP ... the ... International Conference on Advanced Engineering Computing and Applications in Sciences

    2023  Volume 2021, Page(s) 20–27

    Abstract: Determining resource allocations (memory and time) for submitted jobs in High Performance Computing (HPC) systems is a challenging process even for computer scientists. HPC users are highly encouraged to overestimate resource allocation for their ... ...

    Abstract Determining resource allocations (memory and time) for submitted jobs in High Performance Computing (HPC) systems is a challenging process even for computer scientists. HPC users are highly encouraged to overestimate resource allocation for their submitted jobs, so their jobs will not be killed due to insufficient resources. Overestimating resource allocations occurs because of the wide variety of HPC applications and environment configuration options, and the lack of knowledge of the complex structure of HPC systems. This causes a waste of HPC resources, a decreased utilization of HPC systems, and increased waiting and turnaround time for submitted jobs. In this paper, we introduce our first ever implemented fully-offline, fully-automated, stand-alone, and open-source Machine Learning (ML) tool to help users predict memory and time requirements for their submitted jobs on the cluster. Our tool involves implementing six ML discriminative models from the scikit-learn and Microsoft LightGBM applied on the historical data (sacct data) from Simple Linux Utility for Resource Management (Slurm). We have tested our tool using historical data (saact data) using HPC resources of Kansas State University (Beocat), which covers the years from January 2019 - March 2021, and contains around 17.6 million jobs. Our results show that our tool achieves high predictive accuracy
    Language English
    Publishing date 2023-01-23
    Publishing country United States
    Document type Journal Article
    ISSN 2308-4499
    ISSN (online) 2308-4499
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article: CTFlow: Mitigating Effects of Computed Tomography Acquisition and Reconstruction with Normalizing Flows.

    Wei, Leihao / Yadav, Anil / Hsu, William

    Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention

    2023  Volume 14226, Page(s) 413–422

    Abstract: Mitigating the effects of image appearance due to variations in computed tomography (CT) acquisition and reconstruction parameters is a challenging inverse problem. We present CTFlow, a normalizing flows-based method for harmonizing CT scans acquired and ...

    Abstract Mitigating the effects of image appearance due to variations in computed tomography (CT) acquisition and reconstruction parameters is a challenging inverse problem. We present CTFlow, a normalizing flows-based method for harmonizing CT scans acquired and reconstructed using different doses and kernels to a target scan. Unlike existing state-of-the-art image harmonization approaches that only generate a single output, flow-based methods learn the explicit conditional density and output the entire spectrum of plausible reconstruction, reflecting the underlying uncertainty of the problem. We demonstrate how normalizing flows reduces variability in image quality and the performance of a machine learning algorithm for lung nodule detection. We evaluate the performance of CTFlow by 1) comparing it with other techniques on a denoising task using the AAPM-Mayo Clinical Low-Dose CT Grand Challenge dataset, and 2) demonstrating consistency in nodule detection performance across 186 real-world low-dose CT chest scans acquired at our institution. CTFlow performs better in the denoising task for both peak signal-to-noise ratio and perceptual quality metrics. Moreover, CTFlow produces more consistent predictions across all dose and kernel conditions than generative adversarial network (GAN)-based image harmonization on a lung nodule detection task. The code is available at https://github.com/hsu-lab/ctflow.
    Language English
    Publishing date 2023-10-01
    Publishing country Germany
    Document type Journal Article
    DOI 10.1007/978-3-031-43990-2_39
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  5. Article ; Online: pLM4ACE: A protein language model based predictor for antihypertensive peptide screening

    Du, Zhenjiao / Ding, Xingjian / Hsu, William / Munir, Arslan / Xu, Yixiang / Li, Yonghui

    Food Chemistry. 2024 Jan. 15, v. 431, p. 137162

    2024  , Page(s) 137162

    Abstract: Angiotensin-I converting enzyme (ACE) regulates the renin-angiotensin system and is a drug target in clinical treatment for hypertension. This study aims to develop a protein language model (pLM) with evolutionary scale modeling (ESM-2) embeddings that ... ...

    Abstract Angiotensin-I converting enzyme (ACE) regulates the renin-angiotensin system and is a drug target in clinical treatment for hypertension. This study aims to develop a protein language model (pLM) with evolutionary scale modeling (ESM-2) embeddings that is trained on experimental data to screen peptides with strong ACE inhibitory activity. Twelve conventional peptide embedding approaches and five machine learning (ML) modeling methods were also tested for performance comparison. Among the 65 classifiers tested, logistic regression with ESM-2 embeddings showed the b
    Keywords angiotensin I ; drugs ; enzymes ; food chemistry ; hypertension ; models ; regression analysis ; renin-angiotensin system
    Language English
    Dates of publication 2024-0115
    Size p. 137162
    Publishing place Elsevier BV
    Document type Article ; Online
    ZDB-ID 243123-3
    ISSN 1873-7072 ; 0308-8146
    ISSN (online) 1873-7072
    ISSN 0308-8146
    DOI 10.1016/j.foodchem.2023.137162
    Database NAL-Catalogue (AGRICOLA)

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    In stock of ZB MED Bonn / Germany

    Z 3634: Show issues

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  6. Article ; Online: Using Radiomics for Risk Stratification: Where We Need to Go.

    Hsu, William / Sohn, Jae Ho

    Radiology

    2021  Volume 302, Issue 2, Page(s) 435–437

    MeSH term(s) Humans ; Tomography, X-Ray Computed
    Language English
    Publishing date 2021-11-02
    Publishing country United States
    Document type Editorial ; Research Support, N.I.H., Extramural ; Comment
    ZDB-ID 80324-8
    ISSN 1527-1315 ; 0033-8419
    ISSN (online) 1527-1315
    ISSN 0033-8419
    DOI 10.1148/radiol.2021212085
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    In stock of ZB MED Cologne/Königswinter

    Ue I Zs.106: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 2021: Bestellungen von Artikeln über das Online-Bestellformular
    ab Jg. 2022: Lesesaal (EG)

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  7. Article ; Online: Medication adherence prediction through temporal modelling in cardiovascular disease management.

    Hsu, William / Warren, James R / Riddle, Patricia J

    BMC medical informatics and decision making

    2022  Volume 22, Issue 1, Page(s) 313

    Abstract: Background: Chronic conditions place a considerable burden on modern healthcare systems. Within New Zealand and worldwide cardiovascular disease (CVD) affects a significant proportion of the population and it is the leading cause of death. Like other ... ...

    Abstract Background: Chronic conditions place a considerable burden on modern healthcare systems. Within New Zealand and worldwide cardiovascular disease (CVD) affects a significant proportion of the population and it is the leading cause of death. Like other chronic diseases, the course of cardiovascular disease is usually prolonged and its management necessarily long-term. Despite being highly effective in reducing CVD risk, non-adherence to long-term medication continues to be a longstanding challenge in healthcare delivery. The study investigates the benefits of integrating patient history and assesses the contribution of explicitly temporal models to medication adherence prediction in the context of lipid-lowering therapy.
    Methods: Data from a CVD risk assessment tool is linked to routinely collected national and regional data sets including pharmaceutical dispensing, hospitalisation, lab test results and deaths. The study extracts a sub-cohort from 564,180 patients who had primary CVD risk assessment for analysis. Based on community pharmaceutical dispensing record, proportion of days covered (PDC) [Formula: see text] 80 is used as the threshold for adherence. Two years (8 quarters) of patient history before their CVD risk assessment is used as the observation window to predict patient adherence in the subsequent 5 years (20 quarters). The predictive performance of temporal deep learning models long short-term memory (LSTM) and simple recurrent neural networks (Simple RNN) are compared against non-temporal models multilayer perceptron (MLP), ridge classifier (RC) and logistic regression (LR). Further, the study investigates the effect of lengthening the observation window on the task of adherence prediction.
    Results: Temporal models that use sequential data outperform non-temporal models, with LSTM producing the best predictive performance achieving a ROC AUC of 0.805. A performance gap is observed between models that can discover non-linear interactions between predictor variables and their linear counter parts, with neural network (NN) based models significantly outperforming linear models. Additionally, the predictive advantage of temporal models become more pronounced when the length of the observation window is increased.
    Conclusion: The findings of the study provide evidence that using deep temporal models to integrate patient history in adherence prediction is advantageous. In particular, the RNN architecture LSTM significantly outperforms all other model comparators.
    MeSH term(s) Humans ; Cardiovascular Diseases/drug therapy ; Medication Adherence ; Hospitalization ; Neural Networks, Computer ; Pharmaceutical Preparations
    Chemical Substances Pharmaceutical Preparations
    Language English
    Publishing date 2022-11-29
    Publishing country England
    Document type Journal Article
    ZDB-ID 2046490-3
    ISSN 1472-6947 ; 1472-6947
    ISSN (online) 1472-6947
    ISSN 1472-6947
    DOI 10.1186/s12911-022-02052-9
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  8. Article ; Online: Multivariate Sequential Analytics for Cardiovascular Disease Event Prediction.

    Hsu, William / Warren, Jim / Riddle, Patricia

    Methods of information in medicine

    2022  Volume 61, Issue S 02, Page(s) e149–e171

    Abstract: Background: Automated clinical decision support for risk assessment is a powerful tool in combating cardiovascular disease (CVD), enabling targeted early intervention that could avoid issues of overtreatment or undertreatment. However, current CVD risk ... ...

    Abstract Background: Automated clinical decision support for risk assessment is a powerful tool in combating cardiovascular disease (CVD), enabling targeted early intervention that could avoid issues of overtreatment or undertreatment. However, current CVD risk prediction models use observations at baseline without explicitly representing patient history as a time series.
    Objective: The aim of this study is to examine whether by explicitly modelling the temporal dimension of patient history event prediction may be improved.
    Methods: This study investigates methods for multivariate sequential modelling with a particular emphasis on long short-term memory (LSTM) recurrent neural networks. Data from a CVD decision support tool is linked to routinely collected national datasets including pharmaceutical dispensing, hospitalization, laboratory test results, and deaths. The study uses a 2-year observation and a 5-year prediction window. Selected methods are applied to the linked dataset. The experiments performed focus on CVD event prediction. CVD death or hospitalization in a 5-year interval was predicted for patients with history of lipid-lowering therapy.
    Results: The results of the experiments showed temporal models are valuable for CVD event prediction over a 5-year interval. This is especially the case for LSTM, which produced the best predictive performance among all models compared achieving AUROC of 0.801 and average precision of 0.425. The non-temporal model comparator ridge classifier (RC) trained using all quarterly data or by aggregating quarterly data (averaging time-varying features) was highly competitive achieving AUROC of 0.799 and average precision of 0.420 and AUROC of 0.800 and average precision of 0.421, respectively.
    Conclusion: This study provides evidence that the use of deep temporal models particularly LSTM in clinical decision support for chronic disease would be advantageous with LSTM significantly improving on commonly used regression models such as logistic regression and Cox proportional hazards on the task of CVD event prediction.
    MeSH term(s) Humans ; Cardiovascular Diseases/epidemiology ; Risk Factors ; Risk Assessment/methods ; Neural Networks, Computer ; Multivariate Analysis
    Language English
    Publishing date 2022-12-23
    Publishing country Germany
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 3500-2
    ISSN 2511-705X ; 0026-1270
    ISSN (online) 2511-705X
    ISSN 0026-1270
    DOI 10.1055/s-0042-1758687
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 126: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (1.OG)
    ab Jg. 2022: Lesesaal (EG)

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  9. Article: HPCGCN: A Predictive Framework on High Performance Computing Cluster Log Data Using Graph Convolutional Networks.

    Bose, Avishek / Yang, Huichen / Hsu, William H / Andresen, Daniel

    Proceedings : ... IEEE International Conference on Big Data. IEEE International Conference on Big Data

    2022  Volume 2021, Page(s) 4113–4118

    Abstract: This paper presents a novel use case of Graph Convolutional Network (GCN) learning representations for predictive data mining, specifically from user/task data in the domain of high-performance computing (HPC). It outlines an approach based on a ... ...

    Abstract This paper presents a novel use case of Graph Convolutional Network (GCN) learning representations for predictive data mining, specifically from user/task data in the domain of high-performance computing (HPC). It outlines an approach based on a coalesced data set: logs from the Slurm workload manager, joined with user experience survey data from computational cluster users. We introduce a new method of constructing a heterogeneous unweighted HPC graph consisting of multiple typed nodes after revealing the manifold relations between the nodes. The GCN structure used here supports two tasks: i) determining whether a job will complete or fail and ii) predicting memory and CPU requirements by training the GCN semi-supervised classification model and regression models on the generated graph. The graph is partitioned into partitions using graph clustering. We conducted classification and regression experiments using the proposed framework on our HPC log dataset and evaluated predictions by our trained models against baselines using test_score, F1-score, precision, recall for classification, and R1 score for regression, showing that our framework achieves significant improvements.
    Language English
    Publishing date 2022-01-13
    Publishing country United States
    Document type Journal Article
    DOI 10.1109/bigdata52589.2021.9671370
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  10. Article: The Effects of a Fasting Mimicking Diet on Skin Hydration, Skin Texture, and Skin Assessment: A Randomized Controlled Trial.

    Maloh, Jessica / Wei, Min / Hsu, William C / Caputo, Sara / Afzal, Najiba / Sivamani, Raja K

    Journal of clinical medicine

    2023  Volume 12, Issue 5

    Abstract: Diet and nutrition have been shown to impact dermatological conditions. This has increased attention toward integrative and lifestyle medicine in the management of skin health. Emerging research around fasting diets, specifically the fasting-mimicking ... ...

    Abstract Diet and nutrition have been shown to impact dermatological conditions. This has increased attention toward integrative and lifestyle medicine in the management of skin health. Emerging research around fasting diets, specifically the fasting-mimicking diet (FMD), has provided clinical evidence for chronic inflammatory, cardiometabolic, and autoimmune diseases. In this randomized controlled trial, we evaluated the effects of a five-day FMD protocol, administrated once a month for three months, on facial skin parameters, including skin hydration and skin roughness, in a group of 45 healthy women between the ages of 35 to 60 years old over the course of 71 days. The results of the study revealed that the three consecutive monthly cycles of FMD resulted in a significant percentage increase in skin hydration at day 11 (
    Language English
    Publishing date 2023-02-21
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2662592-1
    ISSN 2077-0383
    ISSN 2077-0383
    DOI 10.3390/jcm12051710
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