An AI-Based Heart Failure Treatment Adviser System

Chen, Zhuo; Salazar, Elmer; Marple, Kyle; Das, Sandeep R; Amin, Alpesh; Cheeran, Daniel; Tamil, Lakshman S.; Gupta, Gopal

doi:10.1109/jtehm.2018.2883069

Cited by 48 publications

(9 citation statements)

References 23 publications

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“…There are researches concerning synthetic data generation, while preserving privacy in the data. There are also many studies that address the complexities of healthcare data such as in Mandal et al 8 and Chen et al's works 9 . The objective of this section is to review previous work in these areas and to discover the lessons learned that can contribute to the proposed framework.…”

Section: Related Workmentioning

confidence: 99%

Generating and evaluating cross‐sectional synthetic electronic healthcare data: Preserving data utility and patient privacy

Wang¹,

Myles²,

Tucker

2021

Computational Intelligence

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Electronic healthcare record data have been used to study risk factors of disease, treatment effectiveness and safety, and to inform healthcare service planning. There has been increasing interest in utilizing these data for new purposes such as for machine learning to develop predictive algorithms to aid diagnostic and treatment decisions. Synthetic data could potentially be an alternative to real-world data for these purposes as well as reveal any biases in the data used for algorithm development. This article discusses the key requirements of synthetic data for multiple purposes and proposes an approach to generate and evaluate synthetic data focused on, but not limited to, cross-sectional healthcare data. To our knowledge, this is the first article to propose a framework to generate and evaluate synthetic healthcare data with the aim of simultaneously preserving the complexities of ground truth data in the synthetic data while also ensuring privacy. We include findings and new insights from synthetic datasets modeled on both the Indian liver patient dataset and UK primary care dataset to demonstrate the application of this framework under different scenarios.

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Section: Related Workmentioning

confidence: 99%

Generating and evaluating cross‐sectional synthetic electronic healthcare data: Preserving data utility and patient privacy

Wang¹,

Myles²,

Tucker

2021

Computational Intelligence

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“…31,32,79 Artificial intelligence has the capability to improve therapeutic recommendations although a positive impact on clinical outcomes is yet to be reliably demonstrated. 80…”

Section: Continuing Gdmt Optimisation In the Post-discharge Phasementioning

confidence: 99%

In-hospital Initiation and Up-titration of Guideline-directed Medical Therapies for Heart Failure with Reduced Ejection Fraction

et al. 2022

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Implementation of guideline-directed medical therapy for patients with heart failure is suboptimal. The use of guideline-directed medical therapy improves minimally after heart failure hospitalisation, despite this event clearly indicating increased risk of further hospitalisation and death. In-hospital initiation and titration of guideline-directed medical therapies is one potential strategy to fill these gaps in care, both in the acute vulnerable period after hospital discharge and in the long term. The purpose of this article is to review the knowledge gaps in best practices of in-hospital initiation and up-titration of guideline-directed medical therapies, the benefits and risks of in-hospital initiation and post-discharge focused titration of guideline-directed medical therapies, the recent literature evaluating these practices, and propose strategies to apply these principles to the care of patients with heart failure with reduced ejection fraction.

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“…Some studies have been conducted on treatment recommendations for multimorbidity [ 12 – 17 ], while others have focused on specific diseases, including sepsis [ 18 – 20 ], oncology [ 21 ], non-small-cell lung cancer [ 8 , 9 ], breast cancer [ 22 – 24 ], cerebral infarction disease [ 25 ], diabetes [ 26 , 27 ], hypertension [ 28 ], hypercholesterolemia [ 29 ], AIDS [ 30 ], adolescent depression [ 31 – 34 ], bipolar disorder [ 35 , 36 ], anxiety disorders [ 37 ], paediatric generalized schizophrenia [ 38 ], graft versus host disease [ 39 ], thrombosis [ 40 ], and paediatric cystic fibrosis [ 41 ]. Several works have modelled personalized treatment pathways [ 42 , 43 ], built automatic clinical guidelines [ 44 – 46 ], and developed optimized exercise prescription systems [ 47 ] for cardiovascular diseases. Few works have been conducted on intelligent learning of dynamic treatment strategies for CHD [ 48 ], especially dynamic drug recommendations according to the evolving health status of CHD patients.…”

Section: Introductionmentioning

confidence: 99%

Learning dynamic treatment strategies for coronary heart diseases by artificial intelligence: real-world data-driven study

Guo

Liu

et al. 2022

BMC Med Inform Decis Mak

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Background Coronary heart disease (CHD) has become the leading cause of death and one of the most serious epidemic diseases worldwide. CHD is characterized by urgency, danger and severity, and dynamic treatment strategies for CHD patients are needed. We aimed to build and validate an AI model for dynamic treatment recommendations for CHD patients with the goal of improving patient outcomes and learning best practices from clinicians to help clinical decision support for treating CHD patients. Methods We formed the treatment strategy as a sequential decision problem, and applied an AI supervised reinforcement learning-long short-term memory (SRL-LSTM) framework that combined supervised learning (SL) and reinforcement learning (RL) with an LSTM network to track patients’ states to learn a recommendation model that took a patient’s diagnosis and evolving health status as input and provided a treatment recommendation in the form of whether to take specific drugs. The experiments were conducted by leveraging a real-world intensive care unit (ICU) database with 13,762 admitted patients diagnosed with CHD. We compared the performance of the applied SRL-LSTM model and several state-of-the-art SL and RL models in reducing the estimated in-hospital mortality and the Jaccard similarity with clinicians’ decisions. We used a random forest algorithm to calculate the feature importance of both the clinician policy and the AI policy to illustrate the interpretability of the AI model. Results Our experimental study demonstrated that the AI model could help reduce the estimated in-hospital mortality through its RL function and learn the best practice from clinicians through its SL function. The similarity between the clinician policy and the AI policy regarding the surviving patients was high, while for the expired patients, it was much lower. The dynamic treatment strategies made by the AI model were clinically interpretable and relied on sensible clinical features extracted according to monitoring indexes and risk factors for CHD patients. Conclusions We proposed a pipeline for constructing an AI model to learn dynamic treatment strategies for CHD patients that could improve patient outcomes and mimic the best practices of clinicians. And a lot of further studies and efforts are needed to make it practical.

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An AI-Based Heart Failure Treatment Adviser System

Cited by 48 publications

References 23 publications

Generating and evaluating cross‐sectional synthetic electronic healthcare data: Preserving data utility and patient privacy

Generating and evaluating cross‐sectional synthetic electronic healthcare data: Preserving data utility and patient privacy

In-hospital Initiation and Up-titration of Guideline-directed Medical Therapies for Heart Failure with Reduced Ejection Fraction

Learning dynamic treatment strategies for coronary heart diseases by artificial intelligence: real-world data-driven study

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