Incorporating Accuracy and Diversity in a News Recommender System

Raza, Shaina; Bashir, Syed Raza; Naseem, Usman; D., Liu, Dora; Reji, Deepak John

doi:10.1109/dsaa54385.2022.10032442

Cited by 1 publication

(3 citation statements)

References 37 publications

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“…Two-stage retriever-ranker approach [11]: The two-stage approach involves first selecting a subset of items that are likely to be of interest to the user, followed by ranking those items to provide a personalized recommendation.…”

Section: Methodsmentioning

confidence: 99%

“…The feedback can be used to refine the recommendations and improve the accuracy of the RS over time. -Two-stage retriever-ranker approach [11]: The two-stage approach involves first selecting a subset of items that are likely to be of interest to the user, followed by ranking those items to provide a personalized recommendation. -Pipeline [13]: A machine learning pipeline is a series of steps that are taken to process and analyze data, train a model, evaluate its performance, and deploy it for use in a production environment.…”

Section: Preliminariesmentioning

confidence: 99%

“…We contribute by proposing a novel two-stage recommendation framework for clinical decision-making that combines multiple deep-learning models to learn representations of clinical notes. Our framework employs a retriever-ranker [9]- [11] approach, which uses a retriever model in the first stage to retrieve a set of candidate items (such as diagnoses or medications) from clinical notes and a ranker model in the second stage to recommend the most relevant items to the user (healthcare practitioner). The two-stage approach improves the accuracy and efficiency of the RS by reducing the number of irrelevant items that need to be ranked.…”

Section: Introductionmentioning

confidence: 99%

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Improving Clinical Decision Making with a Two-Stage Recommender System: A Case Study on MIMIC-III Dataset

Raza

2023

Preprint

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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 basedon the publicly available MIMIC dataset of electronic health records. The first stage of the framework employs a deep neural networkbased 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. 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.

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Section: Methodsmentioning

confidence: 99%

Section: Preliminariesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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