NLP-Driven Integration of Electrophysiology and Traditional Chinese Medicine for Enhanced Diagnostics and Management of Postpartum Pain

SLAS TECHNOLOGY, Journal Year: 2025, Volume and Issue: unknown, P. 100267 - 100267

Published: March 1, 2025

Postpartum pain encompasses a range of physical and emotional discomforts, often influenced by hormonal changes, recovery, individual psychological states. The complex interactions between the variables can make it difficult for traditional diagnostic techniques to fully capture, creating inadequacies inefficient management techniques. aims develop comprehensive framework postpartum integrating Natural Language Processing (NLP), electrophysiological data, Traditional Chinese Medicine (TCM) principles. seeks enhance accuracy diagnosis, uncover meaningful correlations TCM diagnoses physiological markers, optimize personalized treatment strategies. focuses on analyzing textual data from patient-reported symptoms, medical records, diagnosis notes. Data pre-processing involves text cleaning tokenization, followed feature extraction using Term Frequency-Inverse Document Frequency (TF-IDF) capture patterns. For diagnostics management, Refined Coyote Optimized Deep Recurrent Neural Network (RCO-DRNN) is employed analyze predict profiles, combining insights with markers. results highlight effectiveness RCO-DRNN in accurately diagnosing types offering holistic This approach represents significant advancement data-driven methodologies practices, providing more management. continuously beats other models after thorough evaluation metrics like MSE, MAE, R2, obtaining lowest MSE (0.005), smallest MAE (0.04), highest R2 (0.98).

Language: Английский

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A Convolutional Neural Network-based Automatic Identification and Intervention Model for Health Surveillance Data during Postpartum Recovery Periods

Applied Mathematics and Nonlinear Sciences, Journal Year: 2025, Volume and Issue: 10(1)

Published: Jan. 1, 2025

Abstract Women suffer great psychological pressure on the postpartum recovery period, which can cause certain diseases in long run if not paid attention to. Based research related to principle of health parameter detection and feature extraction method pulse wave data, study was conducted by extracting physiological signal features normal pulse, using improved support vector machine (OC-SVM) for abnormality detection, adding attention-based two-stage short-term memory network (DA-LSTM) AE, adaptively directs weights input sequences encoding/decoding stages, respectively allocation selecting hidden state encoder time step, respectively. Then, based experimental development monitoring system carried out from three major modules, namely, main control module, front-end acquisition processing auxiliary realize intervention recovery. Using this paper carry a three-month experiment women, it is found that group after each index value has decreased rate decrease large, somatization (1.26 ± 0.13) (1.09 0.58), compared with before significant difference (P < 0.05), help women recover their level more quickly childbirth.

Language: Английский

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A method for predicting postpartum depression via an ensemble neural network model

Frontiers in Public Health, Journal Year: 2025, Volume and Issue: 13

Published: April 14, 2025

Introduction Postpartum depression (PPD) has numerous adverse impacts on the families of new mothers and society at large. Early identification intervention are great significance. Although there many existing machine learning classifiers for PPD prediction, requirements high accuracy interpretability models present challenges. Methods This paper designs an ensemble neural network model predicting PPD, which combines a Fully Connected Neural Network (FCNN) with Dropout mechanism (DNN). The weights FCNN DNN in proposed determined by their accuracies training set respective values. structure is simple straightforward. connection pattern among neurons makes it easy to understand relationship between features target feature, endowing interpretability. Moreover, does not directly rely prevent overfitting. Its more stable than that DNN, weakens negative impact model. At same time, reduces overfitting risk enhances its generalization ability, enabling better adapt different clinical data. Results achieved following performance metrics dataset: 0.933, precision 0.958, recall 0.939, F1-score 0.948, Matthews Correlation Coefficient (MCC) 0.855, specificity 0.923, Negative Predictive Value (NPV) 0.889, False Positive Rate (FPR) 0.077, (FNR) 0.061. Compared 10 classic classifiers, under dataset split ratios, outperforms terms indicators such as accuracy, precision, recall, F1-score, also stability. Discussion research results show effectively improves prediction can provide guiding suggestions relevant medical staff postpartum women decision-making. In future, plans include collecting disease datasets, using predict these diseases, constructing online platform embed model, will help real-time prediction.

Language: Английский

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Exploration of Deep Learning and Transfer Learning Techniques in Bioinformatics

Advances in bioinformatics and biomedical engineering book series, Journal Year: 2024, Volume and Issue: unknown, P. 238 - 257

Published: March 22, 2024

The blend of profound learning and more techniques has brought about a worldview change in the creating subject bioinformatics. revolutionary environment that emerges when complexity biological data artificial intelligence meet is this book chapter. section explores models moves closer, as well imaginative applications, hardships, achievements have developed at crossing point these two powerful fields. opens with point-by-point assessment standards how they are applied to unique difficulties bioinformatics datasets. likewise digs into idea move learning—a strong utilizes information learned one space further develop execution another. It been shown movement powerful. dives involves mastery region

Language: Английский

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Ethical Considerations in Sharing Patient Data

Advances in bioinformatics and biomedical engineering book series, Journal Year: 2024, Volume and Issue: unknown, P. 201 - 216

Published: March 22, 2024

In the contemporary digital landscape, exchange of patient data plays a pivotal role in enhancing healthcare outcomes, fostering research advancements, and promoting public health benefits. However, this practice raises significant ethical considerations that warrant careful examination. This systematic review delves into moral implications associated with sharing data, shedding light on intricate interplay between security, consent, privacy, societal welfare. Within industry, holds immense potential to propel clinical care forward, drive medical progress, shape policy. The analysis provides practical insights navigating delicate balance benefits concerns related bias, discrimination, access. “Ethical Considerations Sharing Patient Data” not only furnishes advice, but also presents comprehensive framework for terrain inherent sharing. Policymakers, professionals, researchers, patients stand gain valuable recommendations from study.

Language: Английский

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Federated Learning for Privacy Preservation in Healthcare

Advances in healthcare information systems and administration book series, Journal Year: 2024, Volume and Issue: unknown, P. 121 - 136

Published: April 19, 2024

This chapter delves into fundamental concepts of privacy preservation and federated learning (FL) in healthcare. Emphasizing the importance healthcare data, it explores ethical regulatory considerations surrounding sensitive patient information. The history significance FL, distinct from traditional centralized machine learning, are discussed, highlighting its relevance addressing concerns. limitations ML contrasted with FL's advantages, particularly preserving privacy. Techniques such as FL averaging, aggregation, secure multi-party computation (SMPC) for privacy-preserving model updates examined. Real-world examples illustrate their application scenarios. concludes by technical challenges linked to healthcare, emphasizing potential balance data protection AI advancements. Privacy concerns persist AI, making a promising solution. discussion extends emerging trends breakthroughs this dynamic field.

Language: Английский

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Overview of Federated Learning and Its Advantages

Advances in healthcare information systems and administration book series, Journal Year: 2024, Volume and Issue: unknown, P. 257 - 273

Published: April 19, 2024

Federated learning (FL) is closely linked to decentralized education. A system primarily targets expediting the operation phase, whereas federated concentrates on constructing a cooperative prototype devoid of privacy disclosure. Some most notable and frequently utilized FL-driven applications include Android's Keyboard for smart typing assistance Google Virtual Assistant. FL can address data distributed across rows based specimens spread columns features in training environment. This chapter explores fundamental principles FL, elucidating its foundational technologies structures. In this chapter, categorization utilization market scenarios fields analytics, medical care, learning, business are examined. also pinpoints research forefronts tackle contribute progressing our comprehension Learning forthcoming enhancement.

Language: Английский

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An Overview and Analysis of Machine Learning Classification Algorithms in Healthcare

Advances in bioinformatics and biomedical engineering book series, Journal Year: 2024, Volume and Issue: unknown, P. 39 - 61

Published: March 22, 2024

To decode a wide range of complex and challenging problems around us, we must utilize the data that already exists in our surroundings as effectively possible. This will be functional diverse fields everyday life, plays most crucial role healthcare medicine, finance banking, information technology. The first, foremost, prime reason for forming or generating this big is increasing complexity real-world problems, which takes considerable work to implement. For example, extensive are needed detect deadly diseases like cancer, cardiovascular diseases, HIV/AIDS effectively. Classification algorithms essential substantial machine learning used numerous real-life industry. Implementing algorithm encounters fewer regarding time space comparatively better interpretability scalability. Through study, authors have demonstrated how different classification perform system.

Language: Английский

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A Comprehensive Analysis of the Health Effects of 5G Radiation

Advances in bioinformatics and biomedical engineering book series, Journal Year: 2024, Volume and Issue: unknown, P. 75 - 93

Published: March 22, 2024

The human race has acquired the peak of sensation today in augmenting development technological domain. With gratitude to state-of-the-art technology, humans are now enjoying heavenly delights, and 5G technology with high-wave transmission capability can be considered a universally acknowledged example. However, radiation from which employs high-frequency waves, puts humankind at unimaginable health risks, weakens us further radiation. Seemingly invisible high-power radiations function like dormant volcanoes body. extremes have become so widespread that miscellaneous physical problems provoked by easily termed manufactured disorders progressively construct. In addition causing chronic diseases, this plays distinct role suppressing regular immune system This chapter tries clarify shed light on dangers arriving technology-supported devices various types incurable diseases

Language: Английский

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Learning From Scarcity

Advances in bioinformatics and biomedical engineering book series, Journal Year: 2024, Volume and Issue: unknown, P. 333 - 350

Published: March 22, 2024

This chapter delves into the transformative synergy of few-shot learning and healthcare, elucidating its impact on medical procedures. Anchored in machine fundamentals, it establishes a core framework through review algorithms. Addressing challenges small healthcare datasets, highlights pivotal role learning. Innovative methods like multimodal integration federated enhance model robustness, offering insights complex scenarios. Formal mathematical explanations categorize challenges, opening avenues for deeper understanding implementation imaging.

Language: Английский

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