The Prediction Model of Warfarin Individual Maintenance Dose for Patients Undergoing Heart Valve Replacement, Based on the Back Propagation Neural Network

Li, Qian; Wang, Jing; Tao, Hongbing; Zhou, Qin; Chen, Jie; Fu, Bo; Qin, Wenzhe; Dong, Li; Hou, Jianglong; Chen, Jin; Zhang, Wei Hong

doi:10.1007/s40261-019-00850-0

Cited by 17 publications

(14 citation statements)

References 42 publications

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“…To date, no study has been speci cally designed to address this concern. Our previous studies found an extremely low prediction accuracy in the low-dose group (0.0% by BPNN [11] and 9.1% by ANFIS [12]) and high-dose group (0.0% by BPGA [10]). Considering the distribution of patients across different doses in the training set, the proportion in the medium-dose group was higher than that in the low-and high-dose groups (low-dose: 10.41%, medium-dose: 81.81%, high-dose: 7.78%).…”

Section: Reasons For Improved Prediction Property In Low-and High-dose Groupsmentioning

confidence: 84%

“…Thus, developing the optimal prediction model for warfarin dosing based on explicable clinical variables poses a challenging task. In previous studies, we established the Arti cial Neural Network (ANN) [9], the Back-Propagation neural network with Genetic Algorithm (BP-GA) [10], the Back Propagation Neural Network (BPNN) [11], and the Adapted Neural-Fuzzy Inference System (ANFIS) models [12], based on machine learning algorithms, to predict the maintenance dose of warfarin. As a result, we achieved the actual warfarin dose with 59-78% accuracy for patients who underwent AVR.…”

Section: Introductionmentioning

confidence: 99%

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An Adapted Neural-Fuzzy Inference System Model Using Preprocessed Balance Data to Improve the Predictive Accuracy of Warfarin Maintenance Dosing in Patients After Heart Valve Replacement

Huang

Dong

et al. 2021

Cardiovasc Drugs Ther

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BackgroundTailoring warfarin use poses a challenge for physicians and pharmacists due to its narrow therapeutic window and huge inter-individual variability. This study aimed to create an adapted neural-fuzzy inference system (ANFIS) model using preprocessed balance data to improve the predictive accuracy of warfarin maintenance dosing in Chinese patients undergoing heart valve replacement (HVR). MethodsThis retrospective study enrolled patients who underwent HVR between June 1, 2012 and June 1, 2016 from 35 centers in China. The primary outcomes were the mean difference between predicted warfarin dose by ANFIS models and actual dose, and the models' predictive accuracy, including the ideal predicted percentage, the mean absolute error (MAE), and the mean squared error (MSE). The eligible cases were divided into training, internal validation, and external validation groups. We explored input variables by univariate analysis of a general liner model and created two ANFIS models using imbalanced and balanced training sets. We nally compared the primary outcomes between the imbalanced and balanced ANFIS models in both internal and external validation sets. Strati ed analyses were conducted across warfarin doses (low, medium, and high doses). ResultsA total of 15,108 patients were included and grouped as follows: 12,086 in the imbalanced training set; 2,820 in the balanced training set; 1,511 in the internal validation set; and 1,511 in the external validation set. Eight variables were explored as predictors related to warfarin maintenance doses, and imbalanced and balanced ANFIS models with multi-fuzzy rules were developed. The results showed a low mean difference between predicted and actual doses (< 0.3 mg/d for each model) and an accurate prediction property in both the imbalanced model (ideal prediction percentage: 74.39-78.16%, MAE: 0.37 mg/daily, MSE: 0.39 mg/daily) and the balanced model (ideal prediction percentage: 73.46-75.31%, MAE: 0.42 mg/daily; MSE, 0.43 mg/daily). Compared to the imbalanced model, the balanced model had a signi cantly higher prediction accuracy in the low-dose (14.46% vs. 3.01%; P < 0.001) and the high-dose warfarin groups (34.71% vs. 23.14%; P = 0.047). The results from the external validation cohort con rmed this nding. ConclusionsThe ANFIS model can accurately predict the warfarin maintenance dose in patients after HVR. Through data preprocessing, the balanced model contributed to improved prediction ability in the low-and high-

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Section: Reasons For Improved Prediction Property In Low-and High-dose Groupsmentioning

confidence: 84%

Section: Introductionmentioning

confidence: 99%

An Adapted Neural-Fuzzy Inference System Model Using Preprocessed Balance Data to Improve the Predictive Accuracy of Warfarin Maintenance Dosing in Patients After Heart Valve Replacement

Huang

Dong

et al. 2021

Cardiovasc Drugs Ther

Self Cite

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“…CVD Setting: Mathur et al (2020) surveyed techniques to predict CVD outcomes, risk factors, early rule-based symptoms limited the use of computational technology for clinical decision-making. Thus, following the advent of deep learning, a wide range of CVD applications, including prediction of drug therapy, pharmacogenomics, heart failure management, cardiovascular imaging, and diagnostics, has been developed (Kalinin et al, 2018;Li et al, 2020;Powles and Hodson, 2017). Social media analysis on prediction of CVD-related symptoms, risk factors, behaviors and outcomes, to our knowledge has not been studied before.…”

Section: Related Workmentioning

confidence: 99%

A Computational Approach to Understand Mental Health from Reddit: Knowledge-aware Multitask Learning Framework

Lokala¹,

Srivastava²,

Dastidar³

et al. 2022

Preprint

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Analyzing gender is critical to study mental health (MH) support in CVD (cardiovascular disease). The existing studies on using social media for extracting MH symptoms consider symptom detection and tend to ignore user context, disease, or gender. The current study aims to design and evaluate a system to capture how MH symptoms associated with CVD are expressed differently with the gender on social media. We observe that the reliable detection of MH symptoms expressed by persons with heart disease in user posts is challenging because of the co-existence of (dis)similar MH symptoms in one post and due to variation in the description of symptoms based on gender. We collect a corpus of 150k items (posts and comments) annotated using the subreddit labels and transfer learning approaches. We propose GeM, a novel taskadaptive multi-task learning approach to identify the MH symptoms in CVD patients based on gender. Specifically, we adapt a knowledgeassisted RoBERTa based bi-encoder model to capture CVD-related MH symptoms. Moreover, it enhances the reliability for differentiating the gender language in MH symptoms when compared to the state-of-art language models 1 . Our model achieves high (statistically significant) performance and predicts four labels of MH issues and two gender labels, which outperforms RoBERTa, improving the recall by 2.14% on the symptom identification task and by 2.55% on the gender identification task.

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“…Similar results were found by Syn et al 24 in Asian patient population on warfarin therapy. Li et al 25 have also utilized back propagation neural network model for predicting the warfarin maintenance dose after heart valve replacement. Furthermore, deep learning-based AI systems have potential groundbreaking applications in drug discovery, personalized drug therapy and precision medicine.…”

Section: Ai Clinical Applications and Cardiovascular Drug Therapymentioning

confidence: 99%

“…Overall, we can conclude that AI has already started to make a considerable impact on the way we treat several cardiovascular conditions and drug therapy. The studies by Li et al 25 for warfarin dosing, applications in heart failure by Shah et al, 26,27 and Przewlocka-Kosmala et al 31 have shown that AI and precision medicine is here to stay. AI has, therefore, opened new avenues in cardiovascular therapeutics and drug therapeutics ( Figure 4).…”

Section: Ai Clinical Applications and Cardiovascular Drug Therapymentioning

confidence: 99%

Artificial Intelligence, Machine Learning, and Cardiovascular Disease

Mathur

Srivastava

et al. 2020

Clin Med Insights Cardiol

112

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Artificial intelligence (AI)-based applications have found widespread applications in many fields of science, technology, and medicine. The use of enhanced computing power of machines in clinical medicine and diagnostics has been under exploration since the 1960s. More recently, with the advent of advances in computing, algorithms enabling machine learning, especially deep learning networks that mimic the human brain in function, there has been renewed interest to use them in clinical medicine. In cardiovascular medicine, AI-based systems have found new applications in cardiovascular imaging, cardiovascular risk prediction, and newer drug targets. This article aims to describe different AI applications including machine learning and deep learning and their applications in cardiovascular medicine. AI-based applications have enhanced our understanding of different phenotypes of heart failure and congenital heart disease. These applications have led to newer treatment strategies for different types of cardiovascular diseases, newer approach to cardiovascular drug therapy and postmarketing survey of prescription drugs. However, there are several challenges in the clinical use of AI-based applications and interpretation of the results including data privacy, poorly selected/outdated data, selection bias, and unintentional continuance of historical biases/stereotypes in the data which can lead to erroneous conclusions. Still, AI is a transformative technology and has immense potential in health care.

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The Prediction Model of Warfarin Individual Maintenance Dose for Patients Undergoing Heart Valve Replacement, Based on the Back Propagation Neural Network

Cited by 17 publications

References 42 publications

An Adapted Neural-Fuzzy Inference System Model Using Preprocessed Balance Data to Improve the Predictive Accuracy of Warfarin Maintenance Dosing in Patients After Heart Valve Replacement

An Adapted Neural-Fuzzy Inference System Model Using Preprocessed Balance Data to Improve the Predictive Accuracy of Warfarin Maintenance Dosing in Patients After Heart Valve Replacement

A Computational Approach to Understand Mental Health from Reddit: Knowledge-aware Multitask Learning Framework

Artificial Intelligence, Machine Learning, and Cardiovascular Disease

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