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Conference of the Association-for-Information-Systems (AMCIS)

作者： Zhao, Kai Xiao, Houping Rai, Arun Georgia State Univ Atlanta GA 30303 USA

ISBN: (纸本)9781733632546

We survey recent articles on mobile promotion and propose guidance on the usage of mobility models for such promotion. Our paper identifies human mobility features, including geo-spatial contexts and hyper-contexts, and surveys localization tools for collecting such mobility data. It overviews the machine learning and deep learning algorithms on human mobility and develops guidelines for the selection of predictors based on the type of mobile-promotion question as well as the mobility characteristics, context, and underlying nature of data. To the best of our knowledge, our paper is the first to synthesize how mobile promotions can systematically leverage the range of contextual and hyper-contextual attributes underlying human mobility and to provide a parsimonious set of guidelines on effectively using mobility prediction techniques for mobile promotions.

关键词： Survey mobile promotion mobile marketing mobility data mining predictive algorithms

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predictive Policing: Review of Benefits and Drawbacks

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INTERNATIONAL JOURNAL OF PUBLIC ADMINISTRATION 2019年第12期42卷 1031-1039页

作者： Meijer, Albert Wessels, Martijn Univ Utrecht Sch Governance Utrecht Netherlands

This literature review illuminates the conceptualization of predictive policing, and also its potential and realized benefits and drawbacks. The review shows a discrepancy between the considerable attention for potential benefits and drawbacks of predictive policing in the literature, and the empirical evidence that is available. The empirical evidence provides little support for the claimed benefits of predictive policing. Whereas some empirical studies conclude that predictive policing strategies lead to a decrease in crime, others find no effect. At the same time, there is no empirical evidence at all for the claimed drawbacks. We conclude that the current thrust of predictive policing initiatives is based on convincing arguments and anecdotal evidence rather than on systematic empirical research. We urge the research community to do independent tests of both positive and negative expectations to generate an evidence base for predictive policing.

关键词： predictive policing predictive algorithms big data literature review

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predictive Noise Shaping SAR ADC

Predictive Noise Shaping SAR ADC

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IEEE International Symposium on Circuits and Systems (IEEE ISCAS)

作者： Shakya, Jyotindra R. Vadakkan, Ajmal K. Temes, Gabor C. Oregon State Univ Dept Elect Engn & Comp Sci Corvallis OR 97330 USA

ISBN: (纸本)9781728103976

In this paper, a novel predictive first-order noise shaping SAR ADC is proposed. Noise-shaping (NS) SAR ADCs have recently gained much attention due to their low power requirements and wide dynamic range. However, for any oversampled ADC, there is a range that each sample will be within guaranteed by input bandwidth. Then SAR ADC operation needs to be performed only around this range. The prediction of range can be done in digital domain which can make better use of process scaling. This saves a significant amount (70%) of power in the A-D conversion. The simulation results show that 109.8 dB SNDR can be achieved with oversampling ratio of 32 while testing only 5 out of 12 bits in each conversion, employing second-order predictor and first order noise shaping.

关键词： Analog to Digital Conversion Noise Shaping predictive algorithms

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Dual-stream algorithms for dementia detection: Harnessing structured and unstructured electronic health record data, a novel approach to prevalence estimation

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Alzheimer's and Dementia 2025年第5期21卷 e70132页

作者： Collyer, Taya A. Liu, Ming Beare, Richard Andrew, Nadine E. Ung, David Carver, Alison Ilomaki, Jenni Bell, J. Simon Thrift, Amanda G. Rocca, Walter A. St Sauver, Jennifer L. Lu, Alicia Siostrom, Kristy Moran, Chris Roberts, Helene Chong, Trevor T.-J. Murray, Anne Ravipati, Tanya Srikanth, Velandai K. National Centre for Healthy Ageing Frankston VIC Australia Peninsula Clinical School School of Translational Medicine Monash University Frankston VIC Australia Developmental Imaging Murdoch Children's Research Institute Melbourne VIC Australia Centre for Medicine Use and Safety Faculty of Pharmacy and Pharmaceutical Sciences Monash University Parkville VIC Australia Department of Medicine School of Clinical Sciences at Monash Health Monash University Clayton VIC Australia Division of Epidemiology Department of Quantitative Health Sciences Mayo Clinic Rochester MN United States Department of Neurology Mayo Clinic Rochester MN United States Women's Health Research Center Mayo Clinic Rochester MN United States Department of Geriatric Medicine Peninsula Health Frankston VIC Australia Department of Neurology Monash Medical Centre Clayton VIC Australia Turner Institute for Brain and Mental Health School of Psychological Sciences Monash University Notting Hill VIC Australia Division of Geriatrics Department of Medicine Hennepin HealthCare Berman Centre for Outcomes and Clinical Research Hennepin Healthcare Research Institute Minneapolis MN United States Department of Neurology University of Minnesota Minneapolis MN United States

INTRODUCTION: Identifying individuals with dementia is crucial for prevalence estimation and service planning, but reliable, scalable methods are lacking. We developed novel set algorithms using both structured and unstructured electronic health record (EHR) data, applying Diagnostic and Statistical Manual of Mental Disorders criteria for dementia case identification. METHODS: Our cohort (n = 1082) included individuals aged ≥ 60 with dementia identified through specialist clinics and a comparison group without dementia. Clinicians from Australia and the United States informed predictor selection. We developed algorithms through a biostatistics stream for structured data and a natural language processing (NLP) stream for text, synthesizing results via logistic regression. RESULTS: The final structured model retained 16 variables (area under the receiver operating characteristic curve [AUC] 0.853, specificity 72.2%, sensitivity 80.6%). NLP classifiers (logistic regression, support vector machine, and random forest models) performed comparably. The final, combined model outperformed all others (AUC = 0.951, P < 0.001 for comparison to structured model). DISCUSSION: Embedding text-derived insights within algorithms trained on structured medical data significantly enhances dementia identification capacity. Highlights: Algorithmic tools for detection of individuals with dementia are available;however, previous work has used heterogeneous case definitions which are not clinically meaningful, and has relied on proxies such as diagnostic codes or medications for case ascertainment. We used a novel, dual-stream algorithmic development approach, simultaneously and separately modeling a clinically meaningful outcome (diagnosis of dementia according to specialized clinical impression) using structured and unstructured electronic health record datasets. Our clinically grounded case definition supported the inclusion of key structured variables (such as dementia International Clas

关键词： big data methods dementia prevalence electronic health record data natural language processing predictive algorithms

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Diligent inquiry of lower level atmospheric clouds attenuation on earth space path links for certain Indian localities

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2019年第6-Sup期22卷 14241-14251页

作者： Srinivas, Kandala Kalyana Ramana, Teppala Venkata VNR VJIET ECE Dept Hyderabad India GITAM ECE Dept Vishakhapatnam India

This paper presents the effect of lower level atmospheric clouds for originating atmospheric attenuation, at Ku, K and Ka bands in the satellite communication path links. The clouds which are of low altitude (Cumulus, Cumulonimbus, Stratus, Nimbostratus) are majorly responsible for cloud attenuation. The physical cause of cloud attenuation is the total liquid water content. The details of the various existing cloud attenuation models and their advantages and disadvantages has been discussed. In this study, the attention has been calculated by using most popular, recent and well recognized models (e.g., Dissanayaka Allnut Haidara model, Salonen and Uppala model and International Telecommunication Union-Recommendations model). In this paper variation of frequency with cloud delay, Height of the cloud versus cloud attenuation, elevation angle versus cloud attenuation of four different low level clouds, frequency versus slant path attenuation, elevation angle and temperature versus cloud attenuation, cloud liquid water content versus cloud attenuation by using different models has been calculated, and comparison of different cloud attenuation has been reported. From the analysis, it has been realized that for the prediction of cloud attenuation in the frequency range of 10-60 GHz, a 0 degrees C effective cloud temperature must be used for frequencies below 40 GHz and a -1 degrees C effective cloud temperature must be used for frequencies above 40 GHz. In this paper it has been proved that SU-model values are very nearer to ITU-R model values.

关键词： Cloud attenuation Lower level atmospheric clouds predictive algorithms

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Human Mobility from theory to practice: Data, Models and Applications 19

Human Mobility from theory to practice: Data, Models and App...

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World Wide Web Conference (WWW)

作者： Simini, Filippo Barlacchi, Gianni Pellungrini, Roberto Pappalardo, Luca Univ Bristol Dept Engn Math Bristol Avon England Fdn Bruno Kessler Trento Italy Univ Trento Trento Italy Univ Pisa Dept Comp Sci Pisa Italy CNR ISTI Pisa Italy

ISBN: (纸本)9781450366755

The inclusion of tracking technologies in personal devices opened the doors to the analysis of large sets of mobility data like GPS traces and call detail records. This tutorial presents an overview of both modeling principles of human mobility and machine learning models applicable to specific problems. We review the state of the art of five main aspects in human mobility: (1) human mobility data landscape;(2) key measures of individual and collective mobility;(3) generative models at the level of individual, population and mixture of the two;(4) next location prediction algorithms;(5) applications for social good. For each aspect, we show experiments and simulations using the Python library "scikit-mobility" developed by the presenters of the tutorial.

关键词： Human Mobility Artificial Intelligence Data Science Generative Models predictive algorithms

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Face To Face with Next Flu Pandemic with a Wiener-Series-Based Machine Learning: Fast Decisions to Tackle Rapid Spread 9

Face To Face with Next Flu Pandemic with a Wiener-Series-Bas...

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9th IEEE Annual Computing and Communication Workshop and Conference (CCWC)

作者： Nieto-Chaupis, Huber Univ Ciencias & Humanidades Ctr Res eHlth Lima Peru

ISBN: (纸本)9781728105543

It's well known the potential arrival of the AH1N1 flu disease can be realized in any large city, as well as its unknown impact and consequences on the people. Depending upon the strength of the propagation of virus, clearly it might fade away any scheme of preparedness has been designed. The experience of the 2009 worldwide flu pandemic have served to improve and test newest methodologies that target to toughen the resilience of the public health systems. In this paper we focus on the usage of a Machine Learning algorithm as an advantageous computational system aimed to support fast and effective decisions in epochs where a flu virus has initialized its spreading in a large or middlesize city. For this end the algorithm uses the formalism of the Wiener series that allows us to estimate predictions and thus manage decisions through these computational methodologies. In order to test the efficiency of the algorithm we used the 2009 Peruvian data where the flu A(H1N1) was spreading in Lima city with a velocity of 40 cases per week. We present simulations by which the usage of Machine Learning algorithms might be of importance to minimize undesired errors and optimize resources of public health services on those epochs where the velocity of spreading and number of contagious reaches their top values.

关键词： convolution theory flu pandemic machine learning predictive algorithms public health Wiener series

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Accurate Prediction of Coronary Heart Disease for Patients With Hypertension From Electronic Health Records With Big Data and Machine-Learning Methods: Model Development and Performance Evaluation

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JMIR MEDICAL INFORMATICS 2020年第7期8卷 e17257页

作者： Du, Zhenzhen Yang, Yujie Zheng, Jing Li, Qi Lin, Denan Li, Ye Fan, Jianping Cheng, Wen Chen, Xie-Hui Cai, Yunpeng Chinese Acad Sci Shenzhen Inst Adv Technol 1068 Xueyuan Blvd Shenzhen Peoples R China Wuhan Res Inst Posts & Telecommun Fiberhome Technol Coll Wuhan Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Shenzhen Hlth Informat Ctr Shenzhen Peoples R China Chinese Acad Med Sci FuWai Hosp Shenzhen Peoples R China

Background: Predictions of cardiovascular disease risks based on health records have long attracted broad research interests. Despite extensive efforts, the prediction accuracy has remained unsatisfactory. This raises the question as to whether the data insufficiency, statistical and machine-learning methods, or intrinsic noise have hindered the performance of previous approaches, and how these issues can be alleviated. Objective: Based on a large population of patients with hypertension in Shenzhen, China, we aimed to establish a high-precision coronary heart disease (CHD) prediction model through big data and machine-learning Methods: Data from a large cohort of 42,676 patients with hypertension, including 20,156 patients with CHD onset, were investigated from electronic health records (EHR5) 1-3 years prior to CHD onset (for CHD-positive cases) or during a disease-free follow-up period of more than 3 years (for CHD-negative cases). The population was divided evenly into independent training and test datasets. Various machine-learning methods were adopted on the training set to achieve high-accuracy prediction models and the results were compared with traditional statistical methods and well-known risk scales. Comparison analyses were performed to investigate the effects of training sample size, factor sets, and modeling approaches on the prediction performance. Results: An ensemble method, XGBoost, achieved high accuracy in predicting 3-year CHD onset for the independent test dataset with an area under the receiver operating characteristic curve (AUC) value of 0.943. Comparison analysis showed that nonlinear models (K-nearest neighbor AUC 0.908, random forest AUC 0.938) outperform linear models (logistic regression AUC 0.865) on the same datasets, and machine-learning methods significantly surpassed traditional risk scales or fixed models (eg, Framingham cardiovascular disease risk models). Further analyses revealed that using time-dependent features obtained fro

关键词： coronary heart disease machine learning electronic health records predictive algorithms hypertension

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Modelling and Forecasting of Photovoltaic Generation for Microgrid Applications: from Theory to Validation

Modelling and Forecasting of Photovoltaic Generation for Mic...

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作者： ENRICA SCOLARI Swiss Federal Institute of Technology in Lausanne

学位级别：博士

The penetration of stochastic renewable generation in modern power systems requires to reconsider conventional practices to ensure the reliable operation of the electrical network. Decentralized control schemes for distributed energy resources (DERs) have gained attention to support the grid operation. In order to cope with the uncertainties of the DERs, predictive schemes that leverage on forecast of renewable generation recently came into prominence. The period of the control action typically depends on the availability of the reserve in the grid. For the case of microgrids, their limited physical extension and the lack of spatial smoothing imply fast power fluctuations and the necessity of coupling energy management strategies with real-time control. Among the DERs, small-scale photovoltaic (PV) systems are expected to represent most of the future available capacity, and consequently, solar resource assessment and power forecasting are of fundamental importance. This thesis focuses on developing forecasting methods and generation models to support the integration of photovoltaic systems in microgrids, considering short-term temporal hori- zons (below one hour) and fine spatial resolution (single site installations). In particular, we aim at computing probabilistic prediction intervals (PIs), i. e., we include information accounting for the intrinsic uncertainty of the prediction. In this respect, nonparametric tools to deliver PIs from sub-second to intra-hour forecasting horizons are proposed and benchmarked. They forecast the AC power and/or the global horizontal irradiance (GHI) by extracting selected endogenous influential variables from historical time-series. The meth- ods are shown to outperform available state-of-the-art techniques and are able to capture the fastest fluctuations of small-scale PV plants. Then, we investigate how the inclusion of features from ground all-sky images can be used to improve time-series-based forecasting tools, thanks to iden

关键词： Microgrids Photovoltaics Solar forecasting Irradiance estimation Prediction intervals Stochastic resources predictive algorithms All-sky imager Machine learning Artificial intelligence

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Improving Detection of Rapid Cystic Fibrosis Disease Progression-Early Translation of a predictive Algorithm Into a Point-of-Care Tool

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IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE 2019年 7卷 2800108-2800108页

作者： Szczesniak, Rhonda D. Brokamp, Cole Su, Weiji Mcphail, Gary L. Pestian, John Clancy, John P. Cincinnati Childrens Hosp Med Ctr Div Biostat & Epidemiol Cincinnati OH 45229 USA Cincinnati Childrens Hosp Med Ctr Div Pulm Med Cincinnati OH 45229 USA Cincinnati Childrens Hosp Med Ctr Div Biomed Informat Cincinnati OH 45229 USA

The clinical course of cystic fibrosis (CF) lung disease is marked by acute drops of lung function, defined clinically as rapid decline. As such, lung function is monitored routinely through pulmonary function testing, producing hundreds of measurements over the lifespan of an individual patient. Point-of-care technologies aimed at improving detection of rapid decline have been limited. Our aim in this early translational study is to develop and translate a predictive algorithm into a prototype prognostic tool for improved detection of rapid decline. The predictive algorithm was developed, validated and checked for 6-month, 1-year, and 2-year forecast accuracies using data on demographic and clinical characteristics from 30 879 patients aged 6 years and older who were followed in the U.S. Cystic Fibrosis Foundation Patient Registry from 2003 to 2015. Predictions of rapid decline based on the algorithm were compared to a detection algorithm currently being used at a CF center with 212 patients who received care between 2012-2017. The algorithm was translated into a prototype web application using RShiny, which resulted from an iterative development and refinement based on clinician feedback. The study showed that the algorithm had excellent predictive accuracy and earlier detection of rapid decline, compared to the current approach, and yielded a prototype platform with the potential to serve as a viable point-of-care tool. Future work includes implementation of this clinical prototype, which will be evaluated prospectively under real-world settings, with the aim of improving the pre-visit planning process for CF point of care. Likely extensions to other point-of-care settings are discussed.

关键词： Decision support systems longitudinal data analysis patient monitoring predictive algorithms user centered design

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