PREMIER: Personalized REcommendation for Medical prescrIptions from Electronic Records

• URL: http://arxiv.org/abs/2008.13569v1
• Date: Fri, 28 Aug 2020 04:48:32 GMT
• Title: PREMIER: Personalized REcommendation for Medical prescrIptions from Electronic Records
• Authors: Suman Bhoi, Lee Mong Li, Wynne Hsu
• Abstract summary: We design a two-stage attention-based personalized medication recommender system called PREMIER. Our system takes into account the interactions among drugs in order to minimize the adverse effects for the patient. Experiment results on MIMIC-III and a proprietary outpatient dataset show that PREMIER outperforms state-of-the-art medication recommendation systems.
• Score: 8.365167718547296
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The broad adoption of Electronic Health Records (EHR) has led to vast amounts of data being accumulated on a patient's history, diagnosis, prescriptions, and lab tests. Advances in recommender technologies have the potential to utilize this information to help doctors personalize the prescribed medications. In this work, we design a two-stage attention-based personalized medication recommender system called PREMIER which incorporates information from the EHR to suggest a set of medications. Our system takes into account the interactions among drugs in order to minimize the adverse effects for the patient. We utilize the various attention weights in the system to compute the contributions from the information sources for the recommended medications. Experiment results on MIMIC-III and a proprietary outpatient dataset show that PREMIER outperforms state-of-the-art medication recommendation systems while achieving the best tradeoff between accuracy and drug-drug interaction. Two case studies are also presented demonstrating that the justifications provided by PREMIER are appropriate and aligned to clinical practices.

Related papers

• 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)
• Knowledge-Induced Medicine Prescribing Network for Medication Recommendation [9.591674293850557]
  This study proposes a novel Knowledge-Induced Medicine Prescribing Network (KindMed) to recommend medicines. We leverage hierarchical sequence learning to discover and fuse temporal dynamics of clinical (i.e., diagnosis and procedures) and medicine streams across patients' historical admissions to foster personalized recommendations. We validated the effectiveness of our KindMed on the augmented real-world EHR cohorts, achieving improved recommendation performances against a handful of graph-driven baselines.
  arXiv  Detail & Related papers  (2023-10-23T04:15:39Z)
• ADRNet: A Generalized Collaborative Filtering Framework Combining Clinical and Non-Clinical Data for Adverse Drug Reaction Prediction [49.56476929112382]
  Adverse drug reaction (ADR) prediction plays a crucial role in both health care and drug discovery. We propose ADRNet, a generalized collaborative filtering framework combining clinical and non-clinical data for drug-ADR prediction.
  arXiv  Detail & Related papers  (2023-08-03T11:28:12Z)
• Knowledge-Driven New Drug Recommendation [88.35607943144261]
  We develop a drug-dependent multi-phenotype few-shot learner to bridge the gap between existing and new drugs. EDGE eliminates the false-negative supervision signal using an external drug-disease knowledge base. Results show that EDGE achieves 7.3% improvement on the ROC-AUC score over the best baseline.
  arXiv  Detail & Related papers  (2022-10-11T16:07:52Z)
• 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)
• RecoMed: A Knowledge-Aware Recommender System for Hypertension Medications [1.2633386045916444]
  This paper aims to develop a medicine recommender system called RecoMed to aid the physician in the prescription process of hypertension. A list of recommended medicines is provided as the system's output, and physicians can choose one or more of the medicines based on the patient's clinical symptoms.
  arXiv  Detail & Related papers  (2022-01-09T08:01:41Z)
• MeSIN: Multilevel Selective and Interactive Network for Medication Recommendation [9.173903754083927]
  We propose a multilevel selective and interactive network (MeSIN) for medication recommendation. First, an attentional selective module (ASM) is applied to assign flexible attention scores to different medical codes embeddings. Second, we incorporate a novel interactive long-short term memory network (InLSTM) to reinforce the interactions of multilevel medical sequences in EHR data.
  arXiv  Detail & Related papers  (2021-04-22T12:59:50Z)
• 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.