MD-Manifold: A Medical-Distance-Based Representation Learning Approach for Medical Concept and Patient Representation

• URL: http://arxiv.org/abs/2305.00553v1
• Date: Sun, 30 Apr 2023 18:58:32 GMT
• Title: MD-Manifold: A Medical-Distance-Based Representation Learning Approach for Medical Concept and Patient Representation
• Authors: Shaodong Wang and Qing Li and Wenli Zhang
• Abstract summary: Representing medical concepts for healthcare analytical tasks requires incorporating medical domain knowledge and prior data information. Our proposed framework, MD-Manifold, introduces a novel approach to medical concept and patient representation. It includes a new data augmentation approach, concept distance metric, and patient-patient network to incorporate crucial medical domain knowledge and prior data information.
• Score: 6.795388490479779
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Effectively representing medical concepts and patients is important for healthcare analytical applications. Representing medical concepts for healthcare analytical tasks requires incorporating medical domain knowledge and prior information from patient description data. Current methods, such as feature engineering and mapping medical concepts to standardized terminologies, have limitations in capturing the dynamic patterns from patient description data. Other embedding-based methods have difficulties in incorporating important medical domain knowledge and often require a large amount of training data, which may not be feasible for most healthcare systems. Our proposed framework, MD-Manifold, introduces a novel approach to medical concept and patient representation. It includes a new data augmentation approach, concept distance metric, and patient-patient network to incorporate crucial medical domain knowledge and prior data information. It then adapts manifold learning methods to generate medical concept-level representations that accurately reflect medical knowledge and patient-level representations that clearly identify heterogeneous patient cohorts. MD-Manifold also outperforms other state-of-the-art techniques in various downstream healthcare analytical tasks. Our work has significant implications in information systems research in representation learning, knowledge-driven machine learning, and using design science as middle-ground frameworks for downstream explorative and predictive analyses. Practically, MD-Manifold has the potential to create effective and generalizable representations of medical concepts and patients by incorporating medical domain knowledge and prior data information. It enables deeper insights into medical data and facilitates the development of new analytical applications for better healthcare outcomes.

Related papers

• MedKP: Medical Dialogue with Knowledge Enhancement and Clinical Pathway Encoding [48.348511646407026]
  We introduce the Medical dialogue with Knowledge enhancement and clinical Pathway encoding framework. The framework integrates an external knowledge enhancement module through a medical knowledge graph and an internal clinical pathway encoding via medical entities and physician actions.
  arXiv  Detail & Related papers  (2024-03-11T10:57:45Z)
• Patient-Centric Knowledge Graphs: A Survey of Current Methods, Challenges, and Applications [2.913761513290171]
  Patient-Centric Knowledge Graphs (PCKGs) represent an important shift in healthcare that focuses on individualized patient care. PCKGs integrate various types of health data to provide healthcare professionals with a comprehensive understanding of a patient's health. This literature review explores the methodologies, challenges, and opportunities associated with PCKGs.
  arXiv  Detail & Related papers  (2024-02-20T00:07:55Z)
• Multi-modal Graph Learning over UMLS Knowledge Graphs [1.6311327256285293]
  We propose a novel approach named Multi-Modal UMLS Graph Learning (MMUGL) for learning meaningful representations of medical concepts. These representations are aggregated to represent entire patient visits and then fed into a sequence model to perform predictions at the granularity of multiple hospital visits of a patient.
  arXiv  Detail & Related papers  (2023-07-10T10:16:57Z)
• Towards Medical Artificial General Intelligence via Knowledge-Enhanced Multimodal Pretraining [121.89793208683625]
  Medical artificial general intelligence (MAGI) enables one foundation model to solve different medical tasks. We propose a new paradigm called Medical-knedge-enhanced mulTimOdal pretRaining (MOTOR)
  arXiv  Detail & Related papers  (2023-04-26T01:26:19Z)
• Informing clinical assessment by contextualizing post-hoc explanations of risk prediction models in type-2 diabetes [50.8044927215346]
  We consider a comorbidity risk prediction scenario and focus on contexts regarding the patients clinical state. We employ several state-of-the-art LLMs to present contexts around risk prediction model inferences and evaluate their acceptability. Our paper is one of the first end-to-end analyses identifying the feasibility and benefits of contextual explanations in a real-world clinical use case.
  arXiv  Detail & Related papers  (2023-02-11T18:07:11Z)
• 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)
• AI Approaches in Processing and Using Data in Personalized Medicine [0.0]
  Advanced artificial intelligence techniques offer the opportunity to analyze such big data, consume them, and derive new knowledge to support personalized medical decisions. New approaches like those based on advanced machine learning, federated learning, transfer learning, explainable artificial intelligence open new paths for more quality use of health and medical data in future.
  arXiv  Detail & Related papers  (2022-07-26T11:11:39Z)
• 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)
• The Medkit-Learn(ing) Environment: Medical Decision Modelling through Simulation [81.72197368690031]
  We present a new benchmarking suite designed specifically for medical sequential decision making. The Medkit-Learn(ing) Environment is a publicly available Python package providing simple and easy access to high-fidelity synthetic medical data.
  arXiv  Detail & Related papers  (2021-06-08T10:38:09Z)
• 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)

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.