An Urban Population Health Observatory for Disease Causal Pathway Analysis and Decision Support: Underlying Explainable Artificial Intelligence Model

• URL: http://arxiv.org/abs/2208.04144v1
• Date: Tue, 26 Jul 2022 15:59:22 GMT
• Title: An Urban Population Health Observatory for Disease Causal Pathway Analysis and Decision Support: Underlying Explainable Artificial Intelligence Model
• Authors: Whitney S Brakefield, Nariman Ammar, Arash Shaban-Nejad
• Abstract summary: This study seeks to expand our existing Urban Population Health Observatory (UPHO) system. A cohesive approach that employs machine learning and semantic/logical inference reveals pathways leading to diseases. The application of UPHO could help reduce health disparities and improve urban population health.
• Score: 0.966840768820136
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This study sought to (1) expand our existing Urban Population Health Observatory (UPHO) system by incorporating a semantics layer; (2) cohesively employ machine learning and semantic/logical inference to provide measurable evidence and detect pathways leading to undesirable health outcomes; (3) provide clinical use case scenarios and design case studies to identify socioenvironmental determinants of health associated with the prevalence of obesity, and (4) design a dashboard that demonstrates the use of UPHO in the context of obesity surveillance using the provided scenarios. The system design includes a knowledge graph generation component that provides contextual knowledge from relevant domains of interest. This system leverages semantics using concepts, properties, and axioms from existing ontologies. In addition, we used the publicly available US Centers for Disease Control and Prevention 500 Cities data set to perform multivariate analysis. A cohesive approach that employs machine learning and semantic/logical inference reveals pathways leading to diseases. In this study, we present 2 clinical case scenarios and a proof-of-concept prototype design of a dashboard that provides warnings, recommendations, and explanations and demonstrates the use of UPHO in the context of obesity surveillance, treatment, and prevention. While exploring the case scenarios using a support vector regression machine learning model, we found that poverty, lack of physical activity, education, and unemployment were the most important predictive variables that contribute to obesity in Memphis, TN. The application of UPHO could help reduce health disparities and improve urban population health. The expanded UPHO feature incorporates an additional level of interpretable knowledge to enhance physicians, researchers, and health officials' informed decision-making at both patient and community levels.

Related papers

• A Survey of Models for Cognitive Diagnosis: New Developments and Future Directions [66.40362209055023]
  This paper aims to provide a survey of current models for cognitive diagnosis, with more attention on new developments using machine learning-based methods. By comparing the model structures, parameter estimation algorithms, model evaluation methods and applications, we provide a relatively comprehensive review of the recent trends in cognitive diagnosis models.
  arXiv  Detail & Related papers  (2024-07-07T18:02:00Z)
• Health-LLM: Personalized Retrieval-Augmented Disease Prediction System [43.91623010448573]
  We propose an innovative framework, Heath-LLM, which combines large-scale feature extraction and medical knowledge trade-off scoring. Compared to traditional health management applications, our system has three main advantages. It integrates health reports and medical knowledge into a large model to ask relevant questions to large language model for disease prediction.
  arXiv  Detail & Related papers  (2024-02-01T16:40:32Z)
• Agent-Based Model: Simulating a Virus Expansion Based on the Acceptance of Containment Measures [65.62256987706128]
  Compartmental epidemiological models categorize individuals based on their disease status. We propose an ABM architecture that combines an adapted SEIRD model with a decision-making model for citizens. We illustrate the designed model by examining the progression of SARS-CoV-2 infections in A Coruna, Spain.
  arXiv  Detail & Related papers  (2023-07-28T08:01:05Z)
• Safe AI for health and beyond -- Monitoring to transform a health service [51.8524501805308]
  We will assess the infrastructure required to monitor the outputs of a machine learning algorithm. We will present two scenarios with examples of monitoring and updates of models.
  arXiv  Detail & Related papers  (2023-03-02T17:27:45Z)
• Human Health Indicator Prediction from Gait Video [34.24448186464565]
  We propose to employ gait videos to predict health indicators, which are more prevalent in surveillance and home monitoring scenarios. To better suit the health indicator prediction task, we bring forward Global-Local Aware aNdsymmetric Centro (GLANCE) module. Experiments demonstrate that the proposed paradigm achieves state-of-the-art results for predicting health indicators on MoVi.
  arXiv  Detail & Related papers  (2022-12-25T19:10:37Z)
• On Curating Responsible and Representative Healthcare Video Recommendations for Patient Education and Health Literacy: An Augmented Intelligence Approach [5.545277272908999]
  One in three U.S. adults use the Internet to diagnose or learn about a health concern. Health literacy divides can be exacerbated by algorithmic recommendations.
  arXiv  Detail & Related papers  (2022-07-13T01:54:59Z)
• MIMO: Mutual Integration of Patient Journey and Medical Ontology for Healthcare Representation Learning [49.57261599776167]
  We propose an end-to-end robust Transformer-based solution, Mutual Integration of patient journey and Medical Ontology (MIMO) for healthcare representation learning and predictive analytics.
  arXiv  Detail & Related papers  (2021-07-20T07:04:52Z)
• An Urban Population Health Observatory System to Support COVID-19 Pandemic Preparedness, Response, and Management: Design and Development Study [0.8602553195689513]
  This study sought to redefine the Healthy People 2030 SDoH taxonomy to accommodate the COVID-19 pandemic. We aim to implement a prototype for the Urban Population Health Observatory (UPHO), a web-based platform that integrates classified group-level SDoH indicators to individual- and aggregate-level population health data.
  arXiv  Detail & Related papers  (2021-06-16T16:48:55Z)
• Collaborative Graph Learning with Auxiliary Text for Temporal Event Prediction in Healthcare [16.40827965484983]
  We propose a collaborative graph learning model to explore patient-disease interactions and medical domain knowledge. Our solution is able to capture structural features of both patients and diseases. We conduct experiments on two important healthcare problems to show the competitive prediction performance of the proposed method.
  arXiv  Detail & Related papers  (2021-05-16T23:11:11Z)
• MET: Multimodal Perception of Engagement for Telehealth [52.54282887530756]
  We present MET, a learning-based algorithm for perceiving a human's level of engagement from videos. We release a new dataset, MEDICA, for mental health patient engagement detection.
  arXiv  Detail & Related papers  (2020-11-17T15:18:38Z)
• Assessing the Severity of Health States based on Social Media Posts [62.52087340582502]
  We propose a multiview learning framework that models both the textual content as well as contextual-information to assess the severity of the user's health state. The diverse NLU views demonstrate its effectiveness on both the tasks and as well as on the individual disease to assess a user's health.
  arXiv  Detail & Related papers  (2020-09-21T03:45:14Z)

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.