Construction of extra-large scale screening tools for risks of severe mental illnesses using real world healthcare data

• URL: http://arxiv.org/abs/2212.10320v1
• Date: Tue, 20 Dec 2022 15:12:12 GMT
• Title: Construction of extra-large scale screening tools for risks of severe mental illnesses using real world healthcare data
• Abstract summary: The prevalence of severe mental illnesses (SMIs) in the United States is approximately 3% of the whole population. A scalable machine learning based tool was developed to conduct population-level risk screening for SMIs.
• Score: 0.5671051073036456
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Importance: The prevalence of severe mental illnesses (SMIs) in the United States is approximately 3% of the whole population. The ability to conduct risk screening of SMIs at large scale could inform early prevention and treatment. Objective: A scalable machine learning based tool was developed to conduct population-level risk screening for SMIs, including schizophrenia, schizoaffective disorders, psychosis, and bipolar disorders,using 1) healthcare insurance claims and 2) electronic health records (EHRs). Design, setting and participants: Data from beneficiaries from a nationwide commercial healthcare insurer with 77.4 million members and data from patients from EHRs from eight academic hospitals based in the U.S. were used. First, the predictive models were constructed and tested using data in case-control cohorts from insurance claims or EHR data. Second, performance of the predictive models across data sources were analyzed. Third, as an illustrative application, the models were further trained to predict risks of SMIs among 18-year old young adults and individuals with substance associated conditions. Main outcomes and measures: Machine learning-based predictive models for SMIs in the general population were built based on insurance claims and EHR.

Related papers

• PRIME-CVD: A Parametrically Rendered Informatics Medical Environment for Education in Cardiovascular Risk Modelling [0.93496537653413]
  PRIME-CVD is a medical environment designed explicitly for medical education.<n>It comprises two openly accessible synthetic data assets representing a cohort of 50,000 adults undergoing primary prevention for cardiovascular disease.<n>Data Asset 1 provides a clean, analysis-ready cohort suitable for exploratory analysis, stratification, and survival modelling, while Data Asset 2 restructures the same cohort into a relational, EMR-style database with realistic structural and lexical heterogeneity.<n>Because all individuals and events are generated de novo, PRIME-CVD preserves realistic subgroup imbalance and risk gradients while ensuring negligible disclosure risk.
  arXiv  Detail & Related papers  (2026-03-11T05:57:22Z)
• Predicting first-episode homelessness among US Veterans using longitudinal EHR data: time-varying models and social risk factors [4.832969778918575]
  We analyzed data from 4,276,403 Veterans Affairs patients to predict first-episode homelessness occurring 3-12 months later in 2017.<n> incorporating social and behavioral factors into longitudinal models improved precision-recall area under the curve (PR-AUC) by 15-30%.<n>These results demonstrate that longitudinal, socially informed EHR modeling concentrates homelessness risk into actionable strata, enabling targeted and data-informed prevention strategies for at-risk veterans.
  arXiv  Detail & Related papers  (2026-02-02T19:46:46Z)
• An Analytical Approach to Privacy and Performance Trade-Offs in Healthcare Data Sharing [1.2179548969182572]
  Older adults, frequently hospitalized patients, and racial minorities are vulnerable to privacy attacks.<n>We evaluate three anonymization methods-$k$-anonymity, the technique by Zheng et al., and the MO-OBAM model-based on their ability to reduce re-identification risk.
  arXiv  Detail & Related papers  (2025-08-25T21:36:47Z)
• 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)
• Machine Learning Solutions Integrated in an IoT Healthcare Platform for Heart Failure Risk Stratification [0.16863755729554883]
  The management of chronic Heart Failure (HF) presents significant challenges in modern healthcare.<n>We present a predictive model founded on Machine Learning (ML) techniques to identify patients at HF risk.
  arXiv  Detail & Related papers  (2025-04-07T14:07:05Z)
• Foundation Model of Electronic Medical Records for Adaptive Risk Estimation [6.248030496243407]
  The U.S. allocates nearly 18% of its GDP to healthcare but experiences lower life expectancy and higher preventable death rates compared to other high-income nations. We developed the Enhanced Transformer for Health Outcome Simulation (ETHOS), an AI model that tokenizes patient health timelines from EHRs. The Adaptive Risk Estimation System (ARES) leverages ETHOS to compute dynamic, personalized risk probabilities for clinician-defined critical events.
  arXiv  Detail & Related papers  (2025-02-10T03:22:39Z)
• DeLLiriuM: A large language model for delirium prediction in the ICU using structured EHR [1.4699314771635081]
  Delirium is an acute confusional state that has been shown to affect up to 31% of patients in the intensive care unit (ICU) We develop and validate DeLLiriuM on ICU admissions from 104,303 patients pertaining to 195 hospitals across three large databases.
  arXiv  Detail & Related papers  (2024-10-22T18:56:31Z)
• Automatic prediction of mortality in patients with mental illness using electronic health records [0.5957022371135096]
  This paper addresses the persistent challenge of predicting mortality in patients with mental diagnoses. Data from patients with mental disease diagnoses were extracted from the well-known clinical MIMIC-III data set. Four machine learning algorithms were used, with results indicating that Random Forest and Support Vector Machine models outperformed others.
  arXiv  Detail & Related papers  (2023-10-18T17:21:01Z)
• MedDiffusion: Boosting Health Risk Prediction via Diffusion-based Data Augmentation [58.93221876843639]
  This paper introduces a novel, end-to-end diffusion-based risk prediction model, named MedDiffusion. It enhances risk prediction performance by creating synthetic patient data during training to enlarge sample space. It discerns hidden relationships between patient visits using a step-wise attention mechanism, enabling the model to automatically retain the most vital information for generating high-quality data.
  arXiv  Detail & Related papers  (2023-10-04T01:36:30Z)
• Clinical Deterioration Prediction in Brazilian Hospitals Based on Artificial Neural Networks and Tree Decision Models [56.93322937189087]
  An extremely boosted neural network (XBNet) is used to predict clinical deterioration (CD) The XGBoost model obtained the best results in predicting CD among Brazilian hospitals' data.
  arXiv  Detail & Related papers  (2022-12-17T23:29:14Z)
• Foresight -- Deep Generative Modelling of Patient Timelines using Electronic Health Records [46.024501445093755]
  Temporal modelling of medical history can be used to forecast and simulate future events, estimate risk, suggest alternative diagnoses or forecast complications. We present Foresight, a novel GPT3-based pipeline that uses NER+L tools (i.e. MedCAT) to convert document text into structured, coded concepts.
  arXiv  Detail & Related papers  (2022-12-13T19:06:00Z)
• MedML: Fusing Medical Knowledge and Machine Learning Models for Early Pediatric COVID-19 Hospitalization and Severity Prediction [27.352097332678213]
  We respond to the national Pediatric COVID-19 data challenge with a novel machine learning model, MedML. MedML extracts the most predictive features based on medical knowledge and propensity scores from over 6 million medical concepts. We evaluate MedML across 143,605 patients for the hospitalization prediction task and 11,465 patients for the severity prediction task.
  arXiv  Detail & Related papers  (2022-07-25T15:56:14Z)
• Benchmarking Predictive Risk Models for Emergency Departments with Large Public Electronic Health Records [7.928862476020428]
  There is an absence of widely accepted ED benchmarks based on large-scale public EHR. We proposed a public ED benchmark suite and obtained a benchmark dataset containing over 500,000 ED visits episodes from 2011 to 2019. Our codes are open-source so that anyone with access to MIMIC-IV-ED could follow the same steps of data processing, build the benchmarks, and reproduce the experiments.
  arXiv  Detail & Related papers  (2021-11-22T06:51:11Z)
• UNITE: Uncertainty-based Health Risk Prediction Leveraging Multi-sourced Data [81.00385374948125]
  We present UNcertaInTy-based hEalth risk prediction (UNITE) model. UNITE provides accurate disease risk prediction and uncertainty estimation leveraging multi-sourced health data. We evaluate UNITE on real-world disease risk prediction tasks: nonalcoholic fatty liver disease (NASH) and Alzheimer's disease (AD) UNITE achieves up to 0.841 in F1 score for AD detection, up to 0.609 in PR-AUC for NASH detection, and outperforms various state-of-the-art baselines by up to $19%$ over the best baseline.
  arXiv  Detail & Related papers  (2020-10-22T02:28:11Z)
• From predictions to prescriptions: A data-driven response to COVID-19 [42.57407485467993]
  We propose a comprehensive data-driven approach to understand the clinical characteristics of COVID-19. We build personalized calculators to predict the risk of infection and mortality. We propose an optimization model to re-allocate ventilators and alleviate shortages.
  arXiv  Detail & Related papers  (2020-06-30T03:34:00Z)
• Hemogram Data as a Tool for Decision-making in COVID-19 Management: Applications to Resource Scarcity Scenarios [62.997667081978825]
  COVID-19 pandemics has challenged emergency response systems worldwide, with widespread reports of essential services breakdown and collapse of health care structure. This work describes a machine learning model derived from hemogram exam data performed in symptomatic patients. Proposed models can predict COVID-19 qRT-PCR results in symptomatic individuals with high accuracy, sensitivity and specificity.
  arXiv  Detail & Related papers  (2020-05-10T01:45:03Z)
• 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.