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Validation Study of a predictive algorithm to Evaluate Opioid Use Disorder in a Primary Care Setting

HEALTH SERVICES RESEARCH AND MANAGERIAL EPIDEMIOLOGY 2017年 4卷

作者： Sharma, Maneesh Lee, Chee Kantorovich, Svetlana Tedtaotao, Maria Smith, Gregory A. Brenton, Ashley Good Samaritan Hosp Baltimore MD USA Proove Biosci Inc Irvine CA USA Zoe Family Care Lynn Haven FL USA Red Pill Med Inc Redondo Beach CA USA

Background: Opioid abuse in chronic pain patients is a major public health issue. Primary care providers are frequently the first to prescribe opioids to patients suffering from pain, yet do not always have the time or resources to adequately evaluate the risk of opioid use disorder (OUD). Purpose: This study seeks to determine the predictability of aberrant behavior to opioids using a comprehensive scoring algorithm ("profile") incorporating phenotypic and, more uniquely, genotypic risk factors. Methods and Results: In a validation study with 452 participants diagnosed with OUD and 1237 controls, the algorithm successfully categorized patients at high and moderate risk of OUD with 91.8% sensitivity. Regardless of changes in the prevalence of OUD, sensitivity of the algorithm remained >90%. Conclusion: The algorithm correctly stratifies primary care patients into low-, moderate-, and high-risk categories to appropriately identify patients in need for additional guidance, monitoring, or treatment changes.

关键词： opioid use disorder addiction personalized medicine pharmacogenetics genetic testing predictive algorithm primary care precision medicine

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Application of Regularized Ranking and Collaborative Filtering in predictive Alarm algorithm for Nocturnal Hypoglycemia Prevention 9

Application of Regularized Ranking and Collaborative Filteri...

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9th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems - Technology and Applications (IDAACS)

作者： Kriukova, Galyna Shvai, Nadiya Pereverzyev, Sergei V. Natl Univ Kyiv Mohyla Acad Fac Comp Sci Dept Math Skovorody Str 2 Kiev Ukraine Austrian Acad Sci Johann Radon Inst Computat & Appl Math Altenberger Str 69 Linz Austria

ISBN: (纸本)9781538606971

In this paper we propose a predictive alarm ranking algorithm for prevention of nocturnal hypoglycemia (NH) in patients with insulin-treated diabetes. We adapt collaborative filtering method for prognosis of risk of NH occurrence in diabetic patients. We illustrate the proposed algorithm and test it to quantify its accuracy with medical records obtained within the framework of the European FP7-funded project DIAdvisor. Results show that the algorithm with proper ranking and preprocessed data outperforms well-known NH-prediction approaches in terms of sensitivity, specificity and F1-score.

关键词： predictive algorithm regularization ranking collaborative filtering

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A multimodal approach to depression diagnosis: insights from machine learning algorithm development in primary care

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EUROPEAN ARCHIVES OF PSYCHIATRY AND CLINICAL NEUROSCIENCE 2025年 1-14页

作者： Eder, Julia Dong, Mark Sen Woehler, Melanie Simon, Maria S. Glocker, Catherine Pfeiffer, Lisa Gaus, Richard Wolf, Johannes Mestan, Kadir Krcmar, Helmut Koutsouleris, Nikolaos Schneider, Antonius Gensichen, Jochen Musil, Richard Falkai, Peter POKAL Grp Ludwig Maximilians Univ Munchen LMU Univ Hosp Dept Psychiat & Psychotherapy Nussbaumstr 7 D-80336 Munich Germany Grad Program POKAL Predictors & Outcomes Primary C Munich Germany German Ctr Mental Hlth DZPG Partner Site Munich Augsburg Munich Germany Tech Univ Munich TUM Chair Informat Syst D-85748 Garching Germany Kings Coll London Inst Psychiat Psychol & Neurosci London England Tech Univ Munich Inst Gen Practice & Hlth Serv Res Sch Med Munich Germany Ludwig Maximilians Univ Munchen Inst Gen Practice & Family Med Munich Germany Oberberg Fachklin Bad Tolz Bad Tolz Germany Max Planck Inst Psychiat Munich Germany

General practitioners play an essential role in identifying depression and are often the first point of contact for patients. Current diagnostic tools, such as the Patient Health Questionnaire-9, provide initial screening but might lead to false positives. To address this, we developed a two-step machine learning model called Clinical 15, trained on a cohort of 581 participants using a nested cross-validation framework. The model integrates self-reported data from validated questionnaires within a study sample of patients presenting to general practitioners. Clinical 15 demonstrated a balanced accuracy of 88.2% and incorporates a traffic light system: green for healthy, red for depression, and yellow for uncertain cases. Gaussian mixture model clustering identified four depression subtypes, including an Immuno-Metabolic cluster characterized by obesity, low-grade inflammation, autonomic nervous system dysregulation, and reduced physical activity. The Clinical 15 algorithm identified all patients within the immuno-metabolic cluster as depressed, although 22.2% (30.8% across the whole dataset) were categorized as uncertain, leading to a yellow traffic light. The biological characterization of patients and monitoring of their clinical course may be used for differential risk stratification in the future. In conclusion, the Clinical 15 model provides a highly sensitive and specific tool to support GPs in diagnosing depression. Future algorithm improvements may integrate further biological markers and longitudinal data. The tool's clinical utility needs further evaluation through a randomized controlled trial, which is currently being planned. Additionally, assessing whether GPs actively integrate the algorithm's predictions into their diagnostic and treatment decisions will be critical for its practical adoption.

关键词： Depression diagnosis Primary care Depression subtypes Data-driven methods predictive algorithm

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predictive algorithms for adjuvant therapy: TransATAC

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STEROIDS 2011年第8期76卷 777-780页

作者： Dowsett, Mitch Salter, Janine Zabaglo, Lila Mallon, Elizabeth Howell, Antony Buzdar, Aman U. Forbes, John Pineda, S. Cuzick, Jack Royal Marsden Hosp Acad Dept Biochem London SW3 6JJ England Univ Glasgow Western Infirm Dept Pathol Glasgow G11 6NT Lanark Scotland Univ Manchester Christie NHS Trust Manchester Breast Ctr Manchester Lancs England Univ Texas MD Anderson Canc Ctr Houston TX 77030 USA Univ Newcastle Sch Med Practice & Populat Hlth Callaghan NSW 2308 Australia Queen Mary Univ London Wolfson Inst Prevent Med CR UK Ctr Epidemiol Math & Stat London EC1M 6BQ England

Estrogen receptor (ER) positive primary breast cancers have a wide range of clinical outcomes. Prediction of the likely course of the disease aids treatment decision-making. In the translational arm of the ATAC (anastrozole or tamoxifen alone or combined) trial (TransATAC) we have assessed individual and multiparameter biomarkers for their prediction of overall and distant recurrence. None of the biomarkers identified differential benefit for anastrozole versus tamoxifen. Each of ER, PgR, HER2 and Ki67 was associated with risk of recurrence. A combination of these to create a single predictor IHC4 was as informative as the 21-gene recurrence score (RS). Integration of each of these molecular profiles with classical clinicopathologic variables provided the most accurate prediction of outcome. (C) 2011 Elsevier Inc. All rights reserved.

关键词： predictive algorithm Endocrine therapy Aromatase inhibitor

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Implementing a transmural model of early palliative care in advanced dementia: the use of a hybrid effectiveness-implementation study design

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BMC GERIATRICS 2025年第1期25卷 1-10页

作者： Hum, Allyn Kaur, Palvinder Goh, Wen Yang Tay, Riyin Neo, Han Yee Koh, Mervyn Yong Hwang Ali, Noorhazlina Binte Lim, Wee Shiong Tan, Yu-Ling Jackie Wu, Huei Yaw Kannapiran, Palvannan Tan, Hwee Teng Robyn Sun, Yan Ong, Chin Ee Sachdev, Ravinder Singh Low, Zhi Jun Tey, Lee Hung Tan, Woan Shin Ding, Yew Yoong Tan Tock Seng Hosp Dept Palliat Med 11 Jalan Tan Tock Seng Singapore 308433 Singapore Natl Healthcare Grp Hlth Serv & Outcomes Res 3 Fusionopolis Link Singapore 138543 Singapore Tan Tock Seng Hosp Dept Geriatr Med 11 Jalan Tan Tock Seng Singapore 308433 Singapore Palliat Care Ctr Excellence Res & Educ 1 Tan Tock Seng Link Singapore 307382 Singapore Dover Pk Hosp TTSH Integrated Care Hub 1 Tan Tock Seng Link Singapore 307382 Singapore Tan Tock Seng Hosp Dept Gen Med 11 Jalan Tan Tock Seng Singapore 308433 Singapore Natl Univ Singapore Inst Policy Studies 1C Cluny Rd House 5 Singapore 259599 Singapore Tan Tock Seng Hosp Dept Clin & Hlth Informat Off 11 Jalan Tan Tock Seng Singapore 308433 Singapore Geriatr Educ & Res Inst 2 Yishun Cent 2 Singapore 768024 Singapore Woodlands Hlth Campus Palliat & Support Care 17 Woodlands Dr 17 Singapore 737628 Singapore

BackgroundPeople with dementia receive differential access to palliative care services despite suffering from a significant burden of the disease in the advanced stage. Professional and familial caregivers may not view dementia as a terminal illness and are less likely to engage in end-of-life care discussions. Healthcare providers also face challenges coordinating palliative care in the community for dementia, demonstrating a lack of understanding of the needs of patients living with advanced dementia, and resources available to support them within the *** aim of this study is to implement a transmural model of care in a tertiary care setting where patients living with advanced dementia (PLAD) at risk of deterioration in one year are identified early to receive tailored palliative care interventions using a predictive ***/DesignThe updated medical research council (MRC) framework for complex interventions is used to guide the development and implementation of the transmural model which incorporates a predictive algorithm in clinical practice, with interventions tailored for at risk PLAD both within, and beyond the tertiary care setting. The PROgnostic Model for Advanced DEmentia (PRO-MADE) developed to predict one-year all-cause mortality in PLAD presenting to an acute care hospital was embedded into the electronic health records of the tertiary care setting as a mathematical equation. predictive modeling markup language in the digital records platform is used to calculate the risk score for PLAD by inputting the predictors. The clinical team is alerted of at risk PLAD through timed directive prompts, with advice on management given through tailored care bundles. The study will adopt a mixed methods approach in a Type 1 effectiveness-implementation study design to concurrently study the effectiveness of the transmural model in practice, and the barriers and facilitators to its *** implementation of a transmural mo

关键词： Advanced dementia predictive algorithm Transmural Early palliative care Implementation

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predictive algorithms for browser support of habitual user activities on the Web 05

Predictive algorithms for browser support of habitual user a...

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IEEE/WIC/ACM International Conference on Web Intelligence

作者： Brank, J Frayling, NM Frayling, A Smyth, G Jozef Stefan Inst Ljubljana Slovenia

ISBN: (纸本)076952415X

Routine user activities on the Web result in the revisilation of Web sites and pages. Standard browser applications provide limited support for this type of habitual behaviour. They typically expose lists of visited VRLs that are automatically recorded by the system or manually created by the user, such as bookmarks. Studies have shown that these approaches are not successful in supporting routine user activities. Informed by our user research we designed a browser feature that automatically exposes candidate URLs for revisitation by the user. In this paper we describe and evaluate the algorithms that we use to model the user's habitual behaviour. We demonstrate how a structured navigation history model facilitates the discovery of relevant usage patterns and supports predictive algorithms that are applicable to relatively short personal navigation histories.

关键词： Web sites information retrieval online front-ends URL Web page Web site browser support habitual user activity navigation history model personal navigation history predictive algorithm routine user activity user habitual behaviour

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HapLogic: A predictive Human Leukocyte Antigen-Matching algorithm to Enhance Rapid Identification of the Optimal Unrelated Hematopoietic Stem Cell Sources for Transplantation

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BIOLOGY OF BLOOD AND MARROW TRANSPLANTATION 2016年第11期22卷 2038-2046页

作者： Dehn, Jason Setterholm, Michelle Buck, Kelly Kempenich, Jane Beduhn, Beth Gragert, Loren Madbouly, Abeer Fingerson, Stephanie Maiers, Martin Natl Marrow Donor Program Minneapolis MN USA Tulane Univ Sch Med Dept Pathol & Lab Med Tulane Canc Ctr 1430 Tulane Ave New Orleans LA 70112 USA

The search for a suitable human leukocyte antigen (HLA)-matched unrelated adult stem cell donor (URD) or umbilical cord blood unit (UCB) is a complex process. The National Marrow Donor Program (NMDP) developed a search algorithm known as HapLogic, which is currently provided within the NMDP Traxis application. The HapLogic algorithm has been in use since 2006 and has advanced URD/UCB HLA-matching technology. The algorithm has been shown to have high predictive accuracy, which can streamline URD/UCB selection and drive efficiencies in the search process to the benefit of the stem cell transplantation community. Here, we describe the fundamental components of the NMDP matching algorithm, output, validation, and future directions. (C) 2016 American Society for Blood and Marrow Transplantation.

关键词： HLA matching predictive algorithm Donor selection Cord blood unit selection

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A CNN_LSTM_KAN Based Genetic algorithm for Photovoltaic Power Generation Revenue Prediction 25th

A CNN_LSTM_KAN Based Genetic Algorithm for Photovoltaic Pow...

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25th International Conference on Parallel and Distributed Computing, Applications and Technologies, PDCAT 2024

作者： Ma, Ruikang Lin, Guanyu Dong, De Zhou, Rong Fan, Jianping Duan, Keliang Zhang, Jun Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Shenzhen China University of Chinese Academy of Sciences Beijing China Southern University of Science and Technology Guangdong Shenzhen China National Supercomputing Center in Shenzhen Guangdong Shenzhen China Beijing JH Eco-Energy Technology Co. Ltd. Beijing100160 China Xinjiang Technical Institute of Physics and Chemistry Chinese Academy of Sciences Ürümqi China

ISBN: (纸本)9789819642069

In order to improve the economic benefits of photovoltaic power generation systems, accurate prediction of photovoltaic power generation revenue has become particularly important. However, traditional prediction algorithms such as CNN and LSTM suffer from overfitting issues, while the latest emerging KAN neural network also has problems with parameter tuning difficulties and sensitivity to data quality. Based on these issues, this paper proposes a CNN_LSTM_KAN photovoltaic power generation revenue prediction algorithm based on genetic algorithm. The experimental results show that the algorithm outperforms traditional methods in both prediction accuracy and robustness, providing an effective solution for predicting the revenue of photovoltaic power generation systems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： genetic algorithm photovoltaics predictive algorithm

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A predictive model for identification of pediatric individuals with common variable immunodeficiency through electronic medical records

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Journal of Allergy and Clinical Immunology 2025年

作者： Alsaati, Nouf Penney, Chris Helbig, Ingo Sullivan, Kathleen E. Division of Allergy and Immunology Children's Hospital of Philadelphia Philadelphia Pa United States Division of Neurology Epilepsy NeuroGenetics Initiative (ENGIN) Children's Hospital of Philadelphia Philadelphia Pa United States Department of Biomedical and Health Informatics Children's Hospital of Philadelphia Philadelphia Pa United States Department of Neurology University of Pennsylvania Perelman School of Medicine Philadelphia Pa United States

Introduction: Common variable immunodeficiency (CVID) is characterized by recurrent sinopulmonary infections. However, in the pediatric population, recurrent sinopulmonary infections early in life are common, which can render key clinical features of CVID less distinctive. Accordingly, the diagnosis of CVID is often delayed owing to the heterogeneous nature of the presentation and the broad range of ages of onset. A 10-year lag in diagnosis has been found for CVID, and there is a critical need for improved time to diagnosis. Objective: Our aim was to utilize machine learning techniques to identify a clinical signature of CVID in a pediatric population. Methods: Our selected cohort included 112 individuals with CVID and 627 controls. The controls were restricted from having other medical conditions associated with infection. A machine learning data set was constructed by summing patient-level counts of clinical metrics. A total of 3 supervised machine learning classifiers were trained, tuned, and performance-tested. We validated our findings using a distinct control cohort with high medical complexity and tested a logistic regression approach. Results: Key features associated with CVID were chest radiograph count, number of antibiotic prescriptions, and number of common infections. Our Extreme Gradient Boosting (XGBoost) model best predicted eventual CVID diagnosis, with an F1 score of 0.77, a total of 21 of 29 CVID diagnoses classified correctly (8 false-negative results), and 179 of 183 patients without CVID correctly classified (4 false-positive results) up to 10 years before the eventual clinical diagnosis. Key features with a robust association with pediatric CVID were the frequency of common infections and antibiotic prescriptions. Conclusion: In spite of a high frequency of infections in the comparator population, the clinical signature of pediatric CVID was sufficiently distinctive to enable early identification. © 2025 American Academy of Allergy, Asthma & I

关键词： antibody deficiency artificial intelligence inborn error of immunity machine learning predictive algorithm Primary immunodeficiency

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Vehicle Collision Avoidance System Using Localization algorithm And predictive Analysis 6

Vehicle Collision Avoidance System Using Localization Algori...

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6th Annual Conference on Computational Science and Computational Intelligence (CSCI)

作者： John, Samuel Ndueso Noma-Osaghae, Etinosa Okokpujie, Kennedy Okereke, Chinonso Ananaba, Joshua Omoruyi, Osemwegie Nigeria Def Acad Dept Elect & Elect Engn Afaka Kaduna State Nigeria Covenant Univ Dept Elect & Informat Engn Ota Ogun State Nigeria

ISBN: (纸本)9781728155845

Road crashes account for over a million deaths around the world every year. It is one of the leading causes of death for young people between the ages of fifteen and twenty-ine. Road accidents cause a whooping loss of up to three percent of the many nations' Gross Domestic Product (GDP) and ninety percent of these accidents occur in low to middle income countries with a sizable fifty-four percent share of the world's vehicular population. One of the Sustainable Development Goals (SDG5) is the reduction of road accidents around the world by half of its current value by 2020. This goal becomes a hit if low to medium-income nations get safer roads. This paper proposes a collision avoidance system that provides drivers with an automated preemptive response to impending car accidents with the aid of distance predictive analysis via sensors connected to the braking system of the vehicle, which in turn slows down the speed of the vehicle or completely stops it from moving altogether. The proposed collision avoidance system makes use of ultrasonic sensors and a unique localization algorithm to deliver a largely user-based vehicular protection from collision.

关键词： wireless sensor network collision avoidance localization predictive algorithm

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