Covid-19 risk factors: Statistical learning from German healthcare claims data

• URL: http://arxiv.org/abs/2102.02697v1
• Date: Thu, 4 Feb 2021 15:48:21 GMT
• Title: Covid-19 risk factors: Statistical learning from German healthcare claims data
• Authors: Roland Jucknewitz, Oliver Weidinger, Anja Schramm
• Abstract summary: We analyse prior risk factors for severe, critical or fatal courses of Covid-19 based on a retrospective cohort study using claims data of the AOK Bayern. As a methodological contribution, we avoid prior grouping and pre-selection of candidate risk factors and use fine-grained hierarchical information from medical classification systems.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We analyse prior risk factors for severe, critical or fatal courses of Covid-19 based on a retrospective cohort study using claims data of the AOK Bayern. As a methodological contribution, we avoid prior grouping and pre-selection of candidate risk factors and use fine-grained hierarchical information from medical classification systems for diagnoses, pharmaceuticals and procedures, using more than 33,000 covariates. Our approach is competitive to formal analyses using well-specified morbidity groups without needing prior subject-matter knowledge. The methodology and our published coefficients may be of interest for decision makers when prioritizing protective measures towards vulnerable subpopulations as well as for researchers aiming to adjust for confounders in studies of individual risk factors also for smaller cohorts.

Related papers

• Competing Risks: Impact on Risk Estimation and Algorithmic Fairness [0.0]
  Survival analysis accounts for patients who do not experience the event of interest during the study period, known as censored patients.<n> competing risks are often treated as censoring, a practice frequently overlooked due to a limited understanding of its consequences.<n>Our work shows why treating competing risks as censoring introduces substantial bias in survival estimates, leading to systematic overestimation of risk and, critically, amplifying disparities.
  arXiv  Detail & Related papers  (2025-08-07T14:25:43Z)
• Adaptable Cardiovascular Disease Risk Prediction from Heterogeneous Data using Large Language Models [70.64969663547703]
  AdaCVD is an adaptable CVD risk prediction framework built on large language models extensively fine-tuned on over half a million participants from the UK Biobank.<n>It addresses key clinical challenges across three dimensions: it flexibly incorporates comprehensive yet variable patient information; it seamlessly integrates both structured data and unstructured text; and it rapidly adapts to new patient populations using minimal additional data.
  arXiv  Detail & Related papers  (2025-05-30T14:42:02Z)
• Identifying Heart Attack Risk in Vulnerable Population: A Machine Learning Approach [0.0]
  This study employs a hybrid machine learning approach to analyze epidemiological data in assessing 13 key heart attack risk factors and their susceptibility.<n>The study reveals strong association between the likelihood of experiencing a heart attack on the 13 risk factors studied.<n>The aggravated risk for postmenopausal patients indicates compromised individual risk factors due to estrogen depletion.
  arXiv  Detail & Related papers  (2025-05-27T12:51:04Z)
• HACSurv: A Hierarchical Copula-based Approach for Survival Analysis with Dependent Competing Risks [51.95824566163554]
  HACSurv is a survival analysis method that learns structures and cause-specific survival functions from data with competing risks. By capturing the dependencies between risks and censoring, HACSurv achieves better survival predictions.
  arXiv  Detail & Related papers  (2024-10-19T18:52:18Z)
• Automatic Extraction of Disease Risk Factors from Medical Publications [1.321009936753118]
  We present a novel approach to automating the identification of risk factors for diseases from medical literature. We first identify relevant articles, then classify them based on the presence of risk factor discussions, and finally extract specific risk factor information for a disease. Our contributions include the development of a comprehensive pipeline for the automated extraction of risk factors and the compilation of several datasets.
  arXiv  Detail & Related papers  (2024-07-10T05:17:55Z)
• Risk Factor Identification In Osteoporosis Using Unsupervised Machine Learning Techniques [0.0]
  The reliability of identified risk factors associated with osteoporosis is investigated using a new clustering-based method on electronic medical records. This study proposes utilizing a new CLustering Iterations Framework (CLIF) that includes an iterative clustering framework that can adapt any of the following three components: clustering, feature selection, and principal feature identification.
  arXiv  Detail & Related papers  (2024-05-24T18:53:28Z)
• Feasibility of Identifying Factors Related to Alzheimer's Disease and Related Dementia in Real-World Data [56.7069469207376]
  In total, we extracted 477 risk factors in 10 categories from 537 studies. Genetic testing for AD/ADRD is still not a common practice and is poorly documented in both structured and unstructured EHRs. Considering the constantly evolving research on AD/ADRD risk factors, literature mining via NLP methods offers a solution to automatically update our knowledge map.
  arXiv  Detail & Related papers  (2024-02-03T18:17:19Z)
• Incentive-Theoretic Bayesian Inference for Collaborative Science [59.15962177829337]
  We study hypothesis testing when there is an agent with a private prior about an unknown parameter. We show how the principal can conduct statistical inference that leverages the information that is revealed by an agent's strategic behavior.
  arXiv  Detail & Related papers  (2023-07-07T17:59:01Z)
• An explainable model to support the decision about the therapy protocol for AML [1.290382979353427]
  This paper presents the data analysis and an explainable machine-learning model to support the decision about the most appropriate therapy protocol. The results indicate that it is possible to use it to support the specialists' decisions safely.
  arXiv  Detail & Related papers  (2023-07-05T20:04:13Z)
• Evaluating COVID-19 vaccine allocation policies using Bayesian $m$-top exploration [53.122045119395594]
  We present a novel technique for evaluating vaccine allocation strategies using a multi-armed bandit framework. $m$-top exploration allows the algorithm to learn $m$ policies for which it expects the highest utility. We consider the Belgian COVID-19 epidemic using the individual-based model STRIDE, where we learn a set of vaccination policies.
  arXiv  Detail & Related papers  (2023-01-30T12:22:30Z)
• Mutual Information Assisted Ensemble Recommender System for Identifying Critical Risk Factors in Healthcare Prognosis [0.10262304700896199]
  This study proposes a feature recommender, as a part of a disease management system, that identifies and recommends the most important risk factors for an illness. Experiments have been conducted on four benchmark datasets of diverse diseases. The proposed recommender can identify and recommend risk factors that have the most discriminating power for detecting diseases.
  arXiv  Detail & Related papers  (2022-09-28T04:54:37Z)
• A Survey of Risk-Aware Multi-Armed Bandits [84.67376599822569]
  We review various risk measures of interest, and comment on their properties. We consider algorithms for the regret minimization setting, where the exploration-exploitation trade-off manifests. We conclude by commenting on persisting challenges and fertile areas for future research.
  arXiv  Detail & Related papers  (2022-05-12T02:20:34Z)
• On the Importance of Diversity in Re-Sampling for Imbalanced Data and Rare Events in Mortality Risk Models [0.0]
  The Surgical Outcome Risk Tool (SORT) is one of the tools developed to predict mortality risk throughout the entire period for major elective in-patient surgeries in the UK. In this study, we enhance the original SORT prediction model (SORT) by addressing the class imbalance within the dataset. Our proposed method investigates the application of diversity-based selection on top of common re-sampling techniques.
  arXiv  Detail & Related papers  (2020-12-15T09:45:35Z)
• Classification supporting COVID-19 diagnostics based on patient survey data [82.41449972618423]
  logistic regression and XGBoost classifiers, that allow for effective screening of patients for COVID-19 were generated. The obtained classification models provided the basis for the DECODE service (decode.polsl.pl), which can serve as support in screening patients with COVID-19 disease. This data set consists of more than 3,000 examples is based on questionnaires collected at a hospital in Poland.
  arXiv  Detail & Related papers  (2020-11-24T17:44:01Z)
• Predictive Modeling of ICU Healthcare-Associated Infections from Imbalanced Data. Using Ensembles and a Clustering-Based Undersampling Approach [55.41644538483948]
  This work is focused on both the identification of risk factors and the prediction of healthcare-associated infections in intensive-care units. The aim is to support decision making addressed at reducing the incidence rate of infections.
  arXiv  Detail & Related papers  (2020-05-07T16:13:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.