检索结果-内蒙古大学图书馆

Effect of a predictive Analytics-Targeted Program in Patients on Opioids: a Stepped-Wedge Cluster Randomized Controlled Trial

引用

JOURNAL OF GENERAL INTERNAL MEDICINE 2023年第2期38卷 375-381页

作者： Strombotne, Kiersten L. Legler, Aaron Minegishi, Taeko Trafton, Jodie A. Oliva, Elizabeth M. Lewis, Eleanor T. Sohoni, Pooja Garrido, Melissa M. Pizer, Steven D. Frakt, Austin B. Boston Univ Publ Hlth Dept Hlth Law Policy & Management Boston MA 02118 USA VA Boston Healthcare Syst Partnered Evidence Based Policy Resource Ctr Boston MA USA Northeastern Univ Bouve Coll Hlth Sci Boston MA 02115 USA Vet Hlth Adm Program Evaluat & Resource Ctr Off Mental Hlth & Suicide Prevent Menlo Pk CA USA Vet Hlth Adm Ctr Innovat Implementat VA Palo Alto Hlth Care Syst Menlo Pk CA USA Stanford Univ Med Sch Dept Psychiat & Behav Sci Palo Alto CA 94304 USA Harvard TH Chan Sch Publ Hlth Dept Hlth Policy & Management Cambridge MA USA

Background Risk of overdose, suicide, and other adverse outcomes are elevated among sub-populations prescribed opioid analgesics. To address this, the Veterans Health Administration (VHA) developed the Stratification Tool for Opioid Risk Mitigation (STORM)-a provider-facing dashboard that utilizes predictive analytics to stratify patients prescribed opioids based on risk for overdose/suicide. Objective To evaluate the impact of the case review mandate on serious adverse events (SAEs) and all-cause mortality among high-risk Veterans. Design A 23-month stepped-wedge cluster randomized controlled trial in all 140 VHA medical centers between 2018 and 2020. Participants A total of 44,042 patients actively prescribed opioid analgesics with high STORM risk scores (i.e., percentiles 1% to 5%) for an overdose or suicide-related event. Intervention A mandate requiring providers to perform case reviews on opioid analgesic-prescribed patients at high risk of overdose/suicide. Main Measures Nine serious adverse events (SAEs), case review completion, number of risk mitigation strategies, and all-cause mortality. Key Results Mandated review inclusion was associated with a significant decrease in all-cause mortality within 4 months of inclusion (OR: 0.78;95% CI: 0.65-0.94). There was no detectable effect on SAEs. Stepped-wedge analyses found that mandated review patients were five times more likely to receive a case review than non-mandated patients with similar risk (OR: 5.1;95% CI: 3.64-7.23) and received more risk mitigation strategies than non-mandated patients (0.498;CI: 0.39-0.61). Conclusions Among VHA patients prescribed opioid analgesics, identifying high risk patients and mandating they receive an interdisciplinary case review was associated with a decrease in all-cause mortality. Results suggest that providers can leverage predictive analytic-targeted population health approaches and interdisciplinary collaboration to improve patient outcomes.

关键词： opioids predictive algorithms serious adverse events mortality risk mitigation veterans

来源：评论

学校读者我要写书评

暂无评论

A comparative study of predictive algorithms for time series forecasting 3rd

A comparative study of predictive algorithms for time series...

引用

3rd IEEE International Colloquium on Information Science and Technology (CIST)

作者： Melyem, Ouahilal Ismail, Jellouli Mohammed, El Mohajir UAE Fac Sci Tetouan Morocco USMBA Fac Sci Fes Morocco

ISBN: (纸本)9781479959792

Forecasting is an important activity in economics, finance, marketing and various other domains like environmental and social sciences. There are several methods for making forecasts, but they all fall into two categories: causal methods and time series methods. In many cases, predictive algorithms implementing time series are good candidates for forecasting. In this paper we run a comparative study of three of these algorithms: Linear Regression, Support Vector Machines and Multilayer Perceptron in order to determine their performances in term of implementing times series for predictive systems. To assess the performance of these algorithms, we have conducted experiments over four representative datasets. The results exhibit that linear regression produced the best forecasts. The other two algorithms show a good behavior as well.

关键词： time series forcasting predictive algorithms decision support comparative study

来源：评论

学校读者我要写书评

暂无评论

Intelligent predictive Algorithm Models of Refuting Information Forwarding Behaviors Considering Users' Personal Characteristics 24

Intelligent Predictive Algorithm Models of Refuting Informat...

引用

6th International Conference on Big-data Service and Intelligent Computation (BDSIC)

作者： Li, Zongmin Li, Zhixuan Sichuan Univ Business Sch Chengdu Peoples R China

ISBN: (纸本)9798400718069

The massive spread of misinformation on social media platforms not only leads to the generation of irrational behaviors of users, but also threatens the social order. Users occupy an important position in the governance process of misinformation. In this study, users' willingness to forward refuting information is taken as an important indicator to measure the effectiveness of misinformation governance. The neutralization technique and norm activation model are applied to misinformation governance. Psychological mechanism, normative ethics, demographic informatics characteristics, and misinformation refuting methods are all considered as indicators for predicting users' forwarding behaviors. Based on support vector machine, XGBoost, random forest and artificial neural network algorithms, four prediction models are constructed respectively. The results show that all four categories of predictive indicators have good predictability. Three types of indicators related to users' personal characteristics are more important than refuting methods. XGBoost and random forest algorithms are more suitable for constructing the prediction models in this paper. Finally, specific suggestions are proposed for the government department's misinformation governance.

关键词： Refuting information forwarding behaviors Psychological mechanism Normative ethics predictive algorithms

来源：评论

学校读者我要写书评

暂无评论

Forecasting energy consumption and carbon dioxide emission of Vietnam by prognostic models based on explainable machine learning and time series

引用

CLEAN TECHNOLOGIES AND ENVIRONMENTAL POLICY 2024年第12期26卷 4405-4431页

作者： Le, Thanh Tuan Sharma, Prabhakar Osman, Sameh M. Dzida, Marek Nguyen, Phuoc Quy Phong Tran, Minh Ho Cao, Dao Nam Tran, Viet Dung HUTECH Univ Inst Engn Ho Chi Minh City Vietnam Delhi Skill & Entrepreneurship Univ Dept Mech Engn Delhi 110089 India King Saud Univ Coll Sci Chem Dept POB 2455 Riyadh 11451 Saudi Arabia Gdansk Univ Technol Inst Naval Architecture Gdansk Poland Ho Chi Minh City Univ Transport PATET Res Grp Ho Chi Minh City Vietnam Dong A Univ Fac Automot Engn Danang Vietnam

This study assessed the usefulness of algorithms in estimating energy consumption and carbon dioxide emissions in Vietnam, in which the training dataset was used to train the models linear regression, random forest, XGBoost, and AdaBoost, allowing them to comprehend the patterns and relationships between population, GDP, and carbon dioxide emissions, energy consumption. The results revealed that random forest, XGBoost, and AdaBoost outperformed linear regression. Furthermore, for random forest, XGBoost, and AdaBoost, the coefficients of determination were higher, indicating a better fit to the data. Moreover, time series forecasting models such as autoregressive integrated moving average, seasonal autoregressive integrated moving average, and exponential smoothing were used to predict future energy consumption and carbon dioxide emissions in Vietnam. The models were trained and verified using historical data. The time series model findings showed that energy consumption rose steadily during the predicted timeframe. The autoregressive integrated moving average model predicted 162258.77 ktoe of energy use by 2050, whereas the seasonal autoregressive integrated moving average and exponential smoothing modes predicted 160673.8 ktoe and 153206.44 ktoe of energy use, respectively. By 2050, the autoregressive integrated moving average model anticipated 6.51 metric tons of carbon dioxide emissions per capita, the SARIMA model 7.769 metric tons, and the exponential smoothing model 6.22 metric tons. The findings show how machine learning techniques and time series models may be used to estimate energy usage and carbon dioxide emissions in Vietnam. These insights could assist Vietnam government in making informed judgments concerning energy planning and policy development.

关键词： Energy consumption CO2 emission Time series forecasting predictive algorithms Energy plan Emission policy

来源：评论

学校读者我要写书评

暂无评论

Prediction of Locally Stationary Data Using Expert Advice

引用

PROBLEMS OF INFORMATION TRANSMISSION 2024年第1期60卷 35-52页

作者： V'yugin, V. V. Trunov, V. G. Zukhba, R. D. Russian Acad Sci Kharkevich Inst Informat Transmiss Problems Moscow Russia Moscow Inst Phys & Technol Moscow Russia

We address the lifelong machine learning problem. Within the game-theoretic approach, in the calculation of the next prediction we use no assumptions on the stochastic nature of a source that generates the data flow: the source can be either analog, or algorithmic, or probabilistic;its parameters can change at random times;when constructing a prediction model, only structural assumptions are used about the nature of data generation. We present an online forecasting algorithm for a locally stationary time series. We also obtain an estimate for the efficiency of the proposed algorithm. The obtained estimates for the regret of the algorithm are illustrated by results of numerical experiments.

关键词： lifelong machine learning predictive algorithms supervised learning adaptive online forecasting algorithms predictions with expert advice regret aggregation algorithm Fixed Share method Mixing Past Posteriors (MPP) method

来源：评论

学校读者我要写书评

暂无评论

Statistical and Probabilistic Insights into the Internal Stability of an MSE Wall

引用

INTERNATIONAL JOURNAL OF GEOMECHANICS 2024年第12期24卷 04024289-04024289页

作者： Verma, Harshal Mishra, Partha Narayan Manna, Bappaditya Williams, David Univ Queensland Indian Inst Technol IIT Delhi Acad Res UQIDAR New Delhi 110016 India IIT Delhi Dept Civil Engn New Delhi 110016 India Univ Queensland Sch Civil Engn Brisbane Qld 4072 Australia IIT Kanpur Dept Civil Engn Geotech Engn Kanpur 208016 Uttar Pradesh India

Mechanically stabilized earth (MSE) walls are widely used to stabilize steep slopes, especially when the crest is subjected to static or dynamic loading. These structures are frequently used in highway and railway structures where they are subjected to dynamic loading and seasonal variation. With rapid urbanization and a growing population, especially in developing countries, the transportation infrastructure is growing at a tremendous speed. Compared with conventional concrete retaining walls, MSE walls emit less carbon dioxide (CO2) and require less area. In addition, the cost of their construction is low, and less earthwork is required. The other advantage of MSE walls in transportation structures is their better performance under dynamic (or seismic) loads and shorter construction time. This paper discusses the pullout behavior of geogrid, which is an important parameter for determining the internal stability of an MSE wall. The internal stability of MSE walls has two major failure types: (1) pullout;and (2) rupture. In this paper, different data-driven algorithms were used to predict the outcome of the geogrid pullout test and to understand the influence of various physical parameters. This paper utilizes statistical models and machine learning (ML) algorithms, for example, correlation analysis, linear regression analysis with one and multiple variables, tree-based ensemble algorithms, artificial neural network (ANN), support vector machine (SVM), and adaptive neuro fuzzy inference systems (ANFIS) for predictive modeling. In total, 201 data sets from geogrid pullout tests were collected from the literature, which considered 12 features. The performance of the developed model was examined using the coefficient of determination (R-2) and later compared using a Taylor diagram, Willmott's index of agreement, the root mean square error standard deviation ratio (RSR), and mean absolute percentage error (MAPE). The performance of the ANFIS was superior to any other mo

关键词： Geogrid Mechanically stabilized earth wall Monte Carlo simulation predictive algorithms Probabilistic analysis Pullout test Regression analysis

来源：评论

学校读者我要写书评

暂无评论

An introduction to machine learning and generative artificial intelligence for otolaryngologists-head and neck surgeons: a narrative review

引用

EUROPEAN ARCHIVES OF OTO-RHINO-LARYNGOLOGY 2024年第5期281卷 2723-2731页

作者： Alter, Isaac L. Chan, Karly Lechien, Jerome Rameau, Anais Weill Cornell Med Coll Sean Parker Inst Voice Dept Otolaryngol Head & Neck Surg 240 E 59 St New York NY 10022 USA Paris Saclay Univ Univ Versailles St Quentin en Yvelines Hop FochDept Otorhinolaryngol Head & Neck Surg Sch MedUFR Simone Veil Paris France Univ Mons UMons UMONS Res Inst Hlth & Sci Technol Fac Med Dept Human Anat & Expt Oncol Mons Belgium

PurposeDespite the robust expansion of research surrounding artificial intelligence (AI) and machine learning (ML) and their applications to medicine, these methodologies often remain opaque and inaccessible to many otolaryngologists. Especially, with the increasing ubiquity of large-language models (LLMs), such as ChatGPT and their potential implementation in clinical practice, clinicians may benefit from a baseline understanding of some aspects of AI. In this narrative review, we seek to clarify underlying concepts, illustrate applications to otolaryngology, and highlight future directions and limitations of these *** literature regarding AI principles and otolaryngologic applications of ML and LLMs was reviewed via search in PubMed and Google *** recent strides have been made in otolaryngology research utilizing AI and ML, across all subspecialties, including neurotology, head and neck oncology, laryngology, rhinology, and sleep surgery. Potential applications suggested by recent publications include screening and diagnosis, predictive tools, clinical decision support, and clinical workflow improvement via LLMs. Ongoing concerns regarding AI in medicine include ethical concerns around bias and data sharing, as well as the "black box" problem and limitations in *** implementations of AI in otolaryngology are rapidly expanding. While implementation in clinical practice remains theoretical for most of these tools, their potential power to influence the practice of otolaryngology is *** of evidence4.

关键词： Artificial intelligence Machine learning Large language models Natural language processing Diagnostic tools predictive algorithms Clinical decision support tools Otolaryngology

来源：评论

学校读者我要写书评

暂无评论

Development and validation of prediction models for stroke and myocardial infarction in type 2 diabetes based on health insurance claims: does machine learning outperform traditional regression approaches?

引用

CARDIOVASCULAR DIABETOLOGY 2025年第1期24卷 1-13页

作者： Stephan, Anna-Janina Hanselmann, Michael Bajramovic, Medina Schosser, Simon Laxy, Michael Tech Univ Munich TUM Sch Med & Hlth Professorship Publ Hlth & Prevent Munich Germany German Ctr Diabet Res DZD Munich Germany Ludwig Maximilians Univ Munchen Dept Stat Munich Germany

BackgroundDigitalization and big health system data open new avenues for targeted prevention and treatment strategies. We aimed to develop and validate prediction models for stroke and myocardial infarction (MI) in patients with type 2 diabetes based on routinely collected high-dimensional health insurance claims and compared predictive performance of traditional regression with state-of-the-art machine learning including deep learning *** used German health insurance claims from 2014 to 2019 with 287 potentially relevant literature-derived variables to predict 3-year risk of MI and stroke. Following a train-test split approach, we compared the performance of logistic methods with and without forward selection, LASSO-regularization, random forests (RF), gradient boosting (GB), multi-layer-perceptrons (MLP) and feature-tokenizer transformers (FTT). We assessed discrimination (Areas Under the Precision-Recall and Receiver-Operator Curves, AUPRC and AUROC) and *** n = 371,006 patients with type 2 diabetes (mean age: 67.2 years), 3.5% (n = 13,030) had MIs and 3.4% (n = 12,701) strokes. AUPRCs were 0.035 (MI) and 0.034 (stroke) for a null model, between 0.082 (MLP) and 0.092 (GB) for MI, and between 0.061 (MLP) and 0.073 (GB) for stoke. AUROCs were 0.5 for null models, between 0.70 (RF, MLP, FTT) and 0.71 (all other models) for MI, and between 0.66 (MLP) and 0.69 (GB) for stroke. All models were well *** performance of claims-based models reached a ceiling at around 0.09 AUPRC and 0.7 AUROC. While for AUROC this performance was comparable to existing epidemiological models incorporating clinical information, comparison of other, potentially more relevant metrics, such as AUPRC, sensitivity and Positive predictive Value was hampered by lack of reporting in the literature. The fact that machine learning including deep learning methods did not outperform more traditional approaches may suggest that feature

关键词： Machine learning Health insurance Claims database analysis predictive algorithms Prediction model Risk scores Type 2 diabetes Myocardial infarction Stroke Logistic regression Deep learning

来源：评论

学校读者我要写书评

暂无评论

AI and suicide risk prediction: Facebook live and its aftermath

引用

AI & SOCIETY 2024年第4期39卷 2155-2167页

作者： Peralta, Dolores Univ Alabama Sch Lib & Informat Studies Tuscaloosa AL 35487 USA

As suicide rates increase worldwide, the mental health industry has reached an impasse in attempts to assess patients, predict risk, and prevent suicide. Traditional assessment tools are no more accurate than chance, prompting the need to explore new avenues in artificial intelligence (AI). Early studies into these tools show potential with higher accuracy rates than previous methods alone. Medical researchers, computer scientists, and social media companies are exploring these avenues. While Facebook leads the pack, its efforts stem from scrutiny following suicides and suicide attempts broadcasted on Facebook Live. The company continues to face scrutiny for ethical, privacy, and safety concerns associated with the quick rollout of proprietary AI technology that seems more focused on the optics of the company than the protection of its billions of users. This paper explores some of these issues, including a lack of transparency, questionable data practices, escalation to law enforcement, little to no regulation, potential for bias, increased viewership of troubling content, and worsening thoughts of suicide among vulnerable Facebook users. Despite AI tools showing promise in improving the accuracy of suicide risk predictions over traditional methods, without regulation, external review, and increased protection of users and data, these tools have the potential to cause more harm than good in the hands of powerful companies like Facebook.

关键词： Artificial intelligence (AI) Facebook predictive algorithms Social media Suicide risk prediction

来源：评论

学校读者我要写书评

暂无评论

Harnessing Hybrid Random Search algorithms for Intelligent State Control in Technological Processes

Harnessing Hybrid Random Search Algorithms for Intelligent S...

引用

International Conference on Intelligent and Fuzzy Systems (INFUS)

作者： Musaev, A. A. Grigoriev, D. A. ITMO Univ St Petersburg Russia St Petersburg State Univ SPBU Ctr Econometr & Business Analyt CEBA St Petersburg Russia

ISBN: (纸本)9783031671913;9783031671920

This article presents a research focused on improving the control of stochastic search procedures within an e-neighborhood surrounding current monitoring outcomes of control parameters in technological processes. The research aimed to enhance the effectiveness of control measures through the utilization of state-of-the-art Random Search (RS) technology. A mathematical structure was established to define an e-neighborhood for manipulating parameters and setting boundaries for the search space. Various methods were explored for selecting manipulation parameters using operator-driven processes via Human-Machine Interface (HMI) tools. The RS-Control Module's functional structure accounted for the dynamics of controlled parameter evolution using a sliding observation window. The RS-Optimization Module's software implementation allowed for forecasting output parameters and assessing forecast accuracy using quality indicators. The implementation of the main program provided a user-friendly interface for adjusting process parameters, optimizing criteria, and monitoring control based on RS forecasts. The research demonstrated the effectiveness of the proposed approach in improving output and technological parameters in industrial processes.

关键词： Stationarity violation Random Search algorithms Hybrid algorithms predictive algorithms

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：