Clinical decision making is a challenging and time-consuming task that involves integrating a vast amount of patient data, including medical history, test results, and notes from clinicians. To assist this process, clinical recommender systems have been developed to provide personalized recommendations to healthcare practitioners. However, creating effective clinical recommender systems is complex due to the diversity and intricacy of clinical data and the need for customized recommendations. In this paper, we propose a two-stage recommender framework for clinical decision making based on the publicly available MIMIC dataset of electronic health records. The first stage of the framework employs a deep neural network-based model to retrieve a set of candidate items, such as diagnosis, medication, and prescriptions, from the patient's electronic health records. The model is trained to extract relevant information from clinical notes using a pre-trained language model. The second stage of the framework utilizes a deep learning model to rank and recommend the most pertinent items to healthcare providers. The model considers the patient's medical history and the context of the current visit to offer personalized recommendations. To evaluate the proposed model, we compared it to various baseline models using multiple evaluation metrics, including precision and macro-average F1 score. The findings indicate that the proposed model achieved a precision of 89% and a macro-average F1 score of approximately 84%, indicating its potential to improve clinical decision making and reduce information overload for healthcare providers. The paper also discusses challenges, such as data availability, privacy, and bias, and suggests areas for future research in this field.

The authors have declared no competing interest.

This study did not receive any funding

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https://physionet.org/content/mimiciii/1.4/