CausalMed: Causality-Based Personalized Medication Recommendation Centered on Patient health state

• URL: http://arxiv.org/abs/2404.12228v3
• Date: Sun, 21 Jul 2024 03:55:46 GMT
• Title: CausalMed: Causality-Based Personalized Medication Recommendation Centered on Patient health state
• Authors: Xiang Li, Shunpan Liang, Yu Lei, Chen Li, Yulei Hou, Tengfei Ma,
• Abstract summary: CausalMed is a patient health state-centric model capable of enhancing the personalization of patient representations. Our method learns more personalized patient representation and outperforms state-of-the-art models in accuracy and safety.
• Score: 11.137353555292277
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Medication recommendation systems are developed to recommend suitable medications tailored to specific patient. Previous researches primarily focus on learning medication representations, which have yielded notable advances. However, these methods are limited to capturing personalized patient representations due to the following primary limitations: (i) unable to capture the differences in the impact of diseases/procedures on patients across various patient health states; (ii) fail to model the direct causal relationships between medications and specific health state of patients, resulting in an inability to determine which specific disease each medication is treating. To address these limitations, we propose CausalMed, a patient health state-centric model capable of enhancing the personalization of patient representations. Specifically, CausalMed first captures the causal relationship between diseases/procedures and medications through causal discovery and evaluates their causal effects. Building upon this, CausalMed focuses on analyzing the health state of patients, capturing the dynamic differences of diseases/procedures in different health states of patients, and transforming diseases/procedures into medications on direct causal relationships. Ultimately, CausalMed integrates information from longitudinal visits to recommend medication combinations. Extensive experiments on real-world datasets show that our method learns more personalized patient representation and outperforms state-of-the-art models in accuracy and safety.

Related papers

• Probabilistic Temporal Prediction of Continuous Disease Trajectories and Treatment Effects Using Neural SDEs [6.5527554277858275]
  We present the first causal temporal framework to model the continuous temporal evolution of disease progression via Neural Differential Equations (NSDE) Our results present the first successful uncertainty-based causal Deep Learning (DL) model to accurately predict future patient MS disability (e.g. EDSS) and treatment effects.
  arXiv  Detail & Related papers  (2024-06-18T17:22:55Z)
• Leave No Patient Behind: Enhancing Medication Recommendation for Rare Disease Patients [47.68396964741116]
  We propose a novel model called Robust and Accurate REcommendations for Medication (RAREMed) to enhance accuracy for rare diseases. It employs a transformer encoder with a unified input sequence approach to capture complex relationships among disease and procedure codes. It provides accurate drug sets for both rare and common disease patients, thereby mitigating unfairness in medication recommendation systems.
  arXiv  Detail & Related papers  (2024-03-26T14:36:22Z)
• Causal prediction models for medication safety monitoring: The diagnosis of vancomycin-induced acute kidney injury [0.282736966249181]
  Current best practice for the retrospective diagnosis of adverse drug events (ADEs) in hospitalized patients relies on a full patient chart review and a formal causality assessment by medical experts. Here, we pioneer a causal modeling approach using observational data to estimate a lower bound of the probability of causation (PC) We apply our method to the clinically relevant use-case of vancomycin-induced acute kidney injury in intensive care patients.
  arXiv  Detail & Related papers  (2023-11-15T17:29:24Z)
• DKINet: Medication Recommendation via Domain Knowledge Informed Deep Learning [12.609882335746859]
  Medication recommendation is a fundamental yet crucial branch of healthcare. Previous studies have primarily focused on learning patient representation from electronic health records. We propose a knowledge injection module that addresses the effective integration of domain knowledge with complex clinical manifestations.
  arXiv  Detail & Related papers  (2023-05-31T07:22:15Z)
• 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)
• Enriching Unsupervised User Embedding via Medical Concepts [51.17532619610099]
  Unsupervised user embedding aims to encode patients into fixed-length vectors without human supervisions. Medical concepts extracted from the clinical notes contain rich connections between patients and their clinical categories. We propose a concept-aware unsupervised user embedding that jointly leverages text documents and medical concepts from two clinical corpora.
  arXiv  Detail & Related papers  (2022-03-20T18:54:05Z)
• Conditional Generation Net for Medication Recommendation [73.09366442098339]
  Medication recommendation targets to provide a proper set of medicines according to patients' diagnoses, which is a critical task in clinics. We propose Conditional Generation Net (COGNet) which introduces a novel copy-or-predict mechanism to generate the set of medicines. We validate the proposed model on the public MIMIC data set, and the experimental results show that the proposed model can outperform state-of-the-art approaches.
  arXiv  Detail & Related papers  (2022-02-14T10:16:41Z)
• Towards Trustworthy Cross-patient Model Development [3.109478324371548]
  We study differences in model performance and explainability when trained for all patients and one patient at a time. The results show that patients' demographics has a large impact on the performance and explainability and thus trustworthiness.
  arXiv  Detail & Related papers  (2021-12-20T10:51:04Z)
• What Do You See in this Patient? Behavioral Testing of Clinical NLP Models [69.09570726777817]
  We introduce an extendable testing framework that evaluates the behavior of clinical outcome models regarding changes of the input. We show that model behavior varies drastically even when fine-tuned on the same data and that allegedly best-performing models have not always learned the most medically plausible patterns.
  arXiv  Detail & Related papers  (2021-11-30T15:52:04Z)
• Semi-Supervised Variational Reasoning for Medical Dialogue Generation [70.838542865384]
  Two key characteristics are relevant for medical dialogue generation: patient states and physician actions. We propose an end-to-end variational reasoning approach to medical dialogue generation. A physician policy network composed of an action-classifier and two reasoning detectors is proposed for augmented reasoning ability.
  arXiv  Detail & Related papers  (2021-05-13T04:14:35Z)
• 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)

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.