Leveraging Taxonomy Similarity for Next Activity Prediction in Patient Treatment

• URL: http://arxiv.org/abs/2503.07638v2
• Date: Mon, 17 Mar 2025 13:52:26 GMT
• Title: Leveraging Taxonomy Similarity for Next Activity Prediction in Patient Treatment
• Authors: Martin Kuhn, Joscha Grüger, Tobias Geyer, Ralph Bergmann,
• Abstract summary: Next-activity-prediction (NAP) can be used as a promising technique to support physicians in treatment planning.<n>The use of patient data poses many challenges due to its knowledge-intensive character, high variability and scarcity of medical data.<n>This article examines the use of the knowledge encoded in a graph to improve and explain the prediction of the next activity in the treatment process.
• Score: 0.22499166814992436
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The rapid progress in modern medicine presents physicians with complex challenges when planning patient treatment. Techniques from the field of Predictive Business Process Monitoring, like Next-activity-prediction (NAP) can be used as a promising technique to support physicians in treatment planning, by proposing a possible next treatment step. Existing patient data, often in the form of electronic health records, can be analyzed to recommend the next suitable step in the treatment process. However, the use of patient data poses many challenges due to its knowledge-intensive character, high variability and scarcity of medical data. To overcome these challenges, this article examines the use of the knowledge encoded in taxonomies to improve and explain the prediction of the next activity in the treatment process. This study proposes the TS4NAP approach, which uses medical taxonomies (ICD-10-CM and ICD-10-PCS) in combination with graph matching to assess the similarities of medical codes to predict the next treatment step. The effectiveness of the proposed approach will be evaluated using event logs that are derived from the MIMIC-IV dataset. The results highlight the potential of using domain-specific knowledge held in taxonomies to improve the prediction of the next activity, and thus can improve treatment planning and decision-making by making the predictions more explainable.

Related papers

• Automatic Prediction of Stroke Treatment Outcomes: Latest Advances and Perspectives [3.7570334364848073]
  Engaging and developing deep learning techniques can help to analyse large and diverse medical data.<n>Despite the common data standardisation challenge within medical image analysis domain, the future of deep learning in stroke outcome prediction lie in using multimodal information.<n>This review aims to provide researchers, clinicians, and policy makers with an up-to-date understanding of this rapidly evolving and promising field.
  arXiv  Detail & Related papers  (2024-12-06T07:06:21Z)
• Reasoning-Enhanced Healthcare Predictions with Knowledge Graph Community Retrieval [61.70489848327436]
  KARE is a novel framework that integrates knowledge graph (KG) community-level retrieval with large language models (LLMs) reasoning. Extensive experiments demonstrate that KARE outperforms leading models by up to 10.8-15.0% on MIMIC-III and 12.6-12.7% on MIMIC-IV for mortality and readmission predictions.
  arXiv  Detail & Related papers  (2024-10-06T18:46:28Z)
• A machine learning framework for interpretable predictions in patient pathways: The case of predicting ICU admission for patients with symptoms of sepsis [3.5280004326441365]
  PatWay-Net is an ML framework designed for interpretable predictions of admission to the intensive care unit for patients with sepsis. We propose a novel type of recurrent neural network and combine it with multi-layer perceptrons to process the patient pathways. We demonstrate its utility through a comprehensive dashboard that visualizes patient health trajectories, predictive outcomes, and associated risks.
  arXiv  Detail & Related papers  (2024-05-21T20:31:42Z)
• Enhancing Readmission Prediction with Deep Learning: Extracting Biomedical Concepts from Clinical Texts [0.26813152817733554]
  This study focuses on predicting patient readmission within less than 30 days using text mining techniques. Various machine learning and deep learning methods were employed to develop a classification model for this purpose.
  arXiv  Detail & Related papers  (2024-03-12T09:03:44Z)
• Exploring Runtime Decision Support for Trauma Resuscitation [7.3268099910347715]
  We develop a treatment recommender system to provide runtime next-minute activity predictions. The system uses patient context (e.g., demographics and vital signs) and process context (e.g., activities) to continuously predict activities that will be performed in the next minute. The best model achieved an average F1-score of 0.67 for 61 activity types.
  arXiv  Detail & Related papers  (2022-07-06T19:02:43Z)
• Adaptive Semi-Supervised Inference for Optimal Treatment Decisions with Electronic Medical Record Data [18.77246683875067]
  The optimal treatment regime that yields the greatest overall expected clinical outcome of interest has attracted a lot of attention. We consider estimation of the optimal treatment regime with electronic medical record data under a semi-supervised setting.
  arXiv  Detail & Related papers  (2022-03-04T13:54:35Z)
• Predicting Patient Readmission Risk from Medical Text via Knowledge Graph Enhanced Multiview Graph Convolution [67.72545656557858]
  We propose a new method that uses medical text of Electronic Health Records for prediction. We represent discharge summaries of patients with multiview graphs enhanced by an external knowledge graph. Experimental results prove the effectiveness of our method, yielding state-of-the-art performance.
  arXiv  Detail & Related papers  (2021-12-19T01:45:57Z)
• Optimal discharge of patients from intensive care via a data-driven policy learning framework [58.720142291102135]
  It is important that the patient discharge task addresses the nuanced trade-off between decreasing a patient's length of stay and the risk of readmission or even death following the discharge decision. This work introduces an end-to-end general framework for capturing this trade-off to recommend optimal discharge timing decisions. A data-driven approach is used to derive a parsimonious, discrete state space representation that captures a patient's physiological condition.
  arXiv  Detail & Related papers  (2021-12-17T04:39:33Z)
• Clinical Outcome Prediction from Admission Notes using Self-Supervised Knowledge Integration [55.88616573143478]
  Outcome prediction from clinical text can prevent doctors from overlooking possible risks. Diagnoses at discharge, procedures performed, in-hospital mortality and length-of-stay prediction are four common outcome prediction targets. We propose clinical outcome pre-training to integrate knowledge about patient outcomes from multiple public sources.
  arXiv  Detail & Related papers  (2021-02-08T10:26:44Z)
• 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)
• Estimating Counterfactual Treatment Outcomes over Time Through Adversarially Balanced Representations [114.16762407465427]
  We introduce the Counterfactual Recurrent Network (CRN) to estimate treatment effects over time. CRN uses domain adversarial training to build balancing representations of the patient history. We show how our model achieves lower error in estimating counterfactuals and in choosing the correct treatment and timing of treatment.
  arXiv  Detail & Related papers  (2020-02-10T20:47:36Z)

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.