Digital Twins for Patient Care via Knowledge Graphs and Closed-Form Continuous-Time Liquid Neural Networks

• URL: http://arxiv.org/abs/2307.04772v1
• Date: Sat, 8 Jul 2023 12:52:31 GMT
• Title: Digital Twins for Patient Care via Knowledge Graphs and Closed-Form Continuous-Time Liquid Neural Networks
• Authors: Logan Nye
• Abstract summary: Digital twins are readily gaining traction in industries like manufacturing, supply chain logistics, and civil infrastructure. The challenge of modeling complex diseases with multimodal patient data and the computational complexities of analyzing it have stifled digital twin adoption in the biomedical vertical. This paper proposes a novel framework for addressing the barriers to clinical twin modeling created by computational costs and modeling complexities.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Digital twin technology has is anticipated to transform healthcare, enabling personalized medicines and support, earlier diagnoses, simulated treatment outcomes, and optimized surgical plans. Digital twins are readily gaining traction in industries like manufacturing, supply chain logistics, and civil infrastructure. Not in patient care, however. The challenge of modeling complex diseases with multimodal patient data and the computational complexities of analyzing it have stifled digital twin adoption in the biomedical vertical. Yet, these major obstacles can potentially be handled by approaching these models in a different way. This paper proposes a novel framework for addressing the barriers to clinical twin modeling created by computational costs and modeling complexities. We propose structuring patient health data as a knowledge graph and using closed-form continuous-time liquid neural networks, for real-time analytics. By synthesizing multimodal patient data and leveraging the flexibility and efficiency of closed form continuous time networks and knowledge graph ontologies, our approach enables real time insights, personalized medicine, early diagnosis and intervention, and optimal surgical planning. This novel approach provides a comprehensive and adaptable view of patient health along with real-time analytics, paving the way for digital twin simulations and other anticipated benefits in healthcare.

Related papers

• Patient-centered data science: an integrative framework for evaluating and predicting clinical outcomes in the digital health era [0.0]
  This study proposes a novel, integrative framework for patient-centered data science in the digital health era. We developed a multidimensional model that combines traditional clinical data with patient-reported outcomes, social determinants of health, and multi-omic data to create comprehensive digital patient representations.
  arXiv  Detail & Related papers  (2024-07-31T02:36:17Z)
• Digital Twin Generators for Disease Modeling [2.341540989979203]
  A patient's digital twin is a computational model that describes the evolution of their health over time. Digital twins have the potential to revolutionize medicine by enabling individual-level computer simulations of human health.
  arXiv  Detail & Related papers  (2024-05-02T17:23:04Z)
• Clairvoyance: A Pipeline Toolkit for Medical Time Series [95.22483029602921]
  Time-series learning is the bread and butter of data-driven *clinical decision support* Clairvoyance proposes a unified, end-to-end, autoML-friendly pipeline that serves as a software toolkit. Clairvoyance is the first to demonstrate viability of a comprehensive and automatable pipeline for clinical time-series ML.
  arXiv  Detail & Related papers  (2023-10-28T12:08:03Z)
• A digital twin framework for civil engineering structures [0.6249768559720122]
  The digital twin concept represents an appealing opportunity to advance condition-based and predictive maintenance paradigms. This work proposes a predictive digital twin approach to the health monitoring, maintenance, and management planning of civil engineering structures.
  arXiv  Detail & Related papers  (2023-08-02T21:38:36Z)
• 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)
• AutoPrognosis 2.0: Democratizing Diagnostic and Prognostic Modeling in Healthcare with Automated Machine Learning [72.2614468437919]
  We present a machine learning framework, AutoPrognosis 2.0, to develop diagnostic and prognostic models. We provide an illustrative application where we construct a prognostic risk score for diabetes using the UK Biobank. Our risk score has been implemented as a web-based decision support tool and can be publicly accessed by patients and clinicians worldwide.
  arXiv  Detail & Related papers  (2022-10-21T16:31:46Z)
• Modelling Patient Trajectories Using Multimodal Information [0.0]
  We propose a solution to model patient trajectories that combines different types of information and considers the temporal aspect of clinical data. The developed solution was evaluated on two different clinical outcomes, unexpected patient readmission and disease progression.
  arXiv  Detail & Related papers  (2022-09-09T10:20:54Z)
• 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)
• BiteNet: Bidirectional Temporal Encoder Network to Predict Medical Outcomes [53.163089893876645]
  We propose a novel self-attention mechanism that captures the contextual dependency and temporal relationships within a patient's healthcare journey. An end-to-end bidirectional temporal encoder network (BiteNet) then learns representations of the patient's journeys. We have evaluated the effectiveness of our methods on two supervised prediction and two unsupervised clustering tasks with a real-world EHR dataset.
  arXiv  Detail & Related papers  (2020-09-24T00:42:36Z)
• Graph representation forecasting of patient's medical conditions: towards a digital twin [0.0]
  We show the results of the investigation of pathological effects of overexpression of ACE2 across different signalling pathways in multiple tissues on cardiovascular functions. We provide a proof of concept of integrating a large set of composable clinical models using molecular data.
  arXiv  Detail & Related papers  (2020-09-17T13:49:48Z)
• Learning Dynamic and Personalized Comorbidity Networks from Event Data using Deep Diffusion Processes [102.02672176520382]
  Comorbid diseases co-occur and progress via complex temporal patterns that vary among individuals. In electronic health records we can observe the different diseases a patient has, but can only infer the temporal relationship between each co-morbid condition. We develop deep diffusion processes to model "dynamic comorbidity networks"
  arXiv  Detail & Related papers  (2020-01-08T15:47:08Z)

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.