Electronic health records based reinforcement learning for treatment optimizing

Li, Tianhao; Wang, Zhishun; Lü, Wei; Zhang, Qian; Li, Dengfeng

doi:10.1016/j.is.2021.101878

Cited by 37 publications

(37 citation statements)

References 32 publications

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“…The actor algorithm refers to the strategy function π θ ( s , a ), i.e., learning a strategy to get a higher return. The critic algorithm is represented via the value function V φ ( s ), which estimates the value function of the current strategy, i.e., evaluating how good the actor algorithm is [ 34 ]. The AC method may update the parameters in a single step rather than having to repeat the process at the conclusion of each round thanks to the value function.…”

Section: Models and Evaluation Methodsmentioning

confidence: 99%

Design and Application of Artificial Intelligence Technology-Driven Education and Teaching System in Universities

Zhang

2022

Computational and Mathematical Methods in Medicine

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In recent years, many colleges and universities have been experimenting and exploring the evaluation of education and teaching system and have achieved certain results. In order to understand the quality of education and teaching system in colleges and universities, to improve the school conditions, and to promote the reform of teaching management, methods and means of evaluating the quality of education and teaching system in general higher education institutions are needed. Modern university education and teaching system should realize the combination of classroom teaching and practice teaching, and education and teaching system adopts the mode of the combination of on-campus practice and off-campus practice, so the design of teaching system is the key to the quality of teaching. Aiming at the current problem that talents cultivated by colleges and universities can hardly meet social demands in terms of engineering practice ability, innovation ability, and international competitiveness, this paper proposes the evaluation and adjustment of college education and teaching system driven by algorithms based on artificial intelligence (AI). By designing the teaching system of talent cultivation, and then establishing a quantitative and controllable quality assurance system for practical teaching, a new mechanism for the design of university education system is further explored. Specifically, the framework of the instructional system is built with the aid of an actor-critic algorithm in reinforcement learning, which assists in the design of the university education system, allowing students to truly understand, master and flex their knowledge, and strengthening the correct understanding of the students’ internal learning mechanisms. The practical teaching effect shows that the AI-driven instructional designs are more popular with contemporary students and have higher evaluation scores. The numerical experiment results also show the stability of the instructional design, overcoming the drawbacks of traditional manual subjectivity in the design. AI-driven college education and teaching system is conducive to cultivating students’ solid technical theoretical foundation. Therefore, through the AI-driven teaching system to strengthen the training of practical ability, so as to comprehensively improve students' comprehensive quality and innovation ability.

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Section: Models and Evaluation Methodsmentioning

confidence: 99%

Design and Application of Artificial Intelligence Technology-Driven Education and Teaching System in Universities

Zhang

2022

Computational and Mathematical Methods in Medicine

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“…Shi et al presented an offline q-learning approach trained on the OhioT1DM dataset for selecting discrete doses of basal insulin at hourly intervals [37]. Similarly, Li et al used a model-based RL algorithm to learn the blood glucose dynamics of patients recovering from diabetic ketoacidosis [26]. This learned model was then used to train an online q-learning algorithm to select the optimal basal dose at three hour intervals over a 24 hour period.…”

Section: Related Workmentioning

confidence: 99%

Offline Reinforcement Learning for Safer Blood Glucose Control in People with Type 1 Diabetes

Emerson¹,

Guy²,

McConville³

2022

Preprint

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Hybrid closed loop systems represent the future of care for people with type 1 diabetes (T1D). These devices usually utilise simple control algorithms to select the optimal insulin dose for maintaining blood glucose levels within a healthy range. Online reinforcement learning (RL) has been utilised as a method for further enhancing glucose control in these devices. Previous approaches have been shown to reduce patient risk and improve time spent in the target range when compared to classical control algorithms, but are prone to instability in the learning process, often resulting in the selection of unsafe actions. This work presents an evaluation of offline RL as a means for developing clinically effective dosing policies without the need for patient interaction. This paper examines the utility of BCQ, CQL and TD3-BC in managing the blood glucose of nine virtual patients within the UVA/Padova glucose dynamics simulator. When trained on less than a tenth of the data required by online RL approaches, this work shows that offline RL can significantly increase time in the healthy blood glucose range when compared to the strongest state-of-art baseline. This is achieved without any associated increase in low blood glucose events. Offline RL is also shown to be able to correct for common and challenging scenarios such as incorrect bolus dosing, irregular meal timings and sub-optimal training data. The code for this work is available at: https://github.com/hemerson1/offline-glucose.

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“…Li et al [12] utilized an optimization approach for reinforcement learning on the Electronic Health Records (EHR) for the treatment. Reinforcement learning provided an efficient path for providing a decision sequentially.…”

Section: The Contribution Of the Research Work Is As Followsmentioning

confidence: 99%

Medical Big Data Analysis using Binary Moth-Flame with Whale Optimization Approach

Rao¹,

Rao²,

Pvgd³

2022

IJACSA

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The accurate analysis of medical data is dependent on early disease detection and the value of accuracy is reduced when the medical data quality is poor. However, existing techniques have lower efficiency in handling heterogeneous medical data and the complexity of the features was not enhanced using an optimal feature selection model. The present research work has used the machine learning algorithm effectively for chronic disease prediction such as heart disease, cancer, diabetes, stroke, and arthritis for the frequent communities. The detailed information about the attributes is required to be known as it is significant in analyzing the medical data. The process of selecting the attributes plays an important role in decision-making for medical disease analysis. This research proposes Binary Moth-Flame Optimization (B-MFO) for effective feature selection to achieve higher performance in small and medium datasets. Additionally, the Whale Optimization Algorithm (WOA) is used that showed better performances for LSTM that suited well for the process of classification to predict the time series. The present research work utilizes Spark Streaming layers for data streaming to diagnose using Long Short Term Memory (LSTM) with whale optimization approach which is from the heterogeneous medical data. The proposed B-MFO-WOA method results showed that the proposed method obtained 97.45% accuracy better compared to the existing Modified adaptive neuro-fuzzy inference system of 95.91% of accuracy and B-MFO of 92.43 % accuracy for the models.

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Electronic health records based reinforcement learning for treatment optimizing

Cited by 37 publications

References 32 publications

Design and Application of Artificial Intelligence Technology-Driven Education and Teaching System in Universities

Design and Application of Artificial Intelligence Technology-Driven Education and Teaching System in Universities

Offline Reinforcement Learning for Safer Blood Glucose Control in People with Type 1 Diabetes

Medical Big Data Analysis using Binary Moth-Flame with Whale Optimization Approach

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