Research on text structuralization in medical field

In the process of data integration, medical data is a huge obstacle to the development of medical information because of its complicated data type, large amount of data and semantic heterogeneity among different data sources. Integration of massive medical heterogeneous data is the urgent need to promote the process of medical information to be solved. According to the characteristics of medical data sources, an ontology-based medical heterogeneous data integration scheme is proposed. By introducing the medical similarity algorithm into the hybrid medical ontology, not only the multi-semantic problem is solved, but also the current heterogeneity of mass medical data is eliminated. Finally, experiments show that this method performs well in eliminating the semantic uncertainty of data in the medical field and can improve the integration efficiency of heterogeneous data in the medical data warehouse. It is practically feasible to solve the problem of medical heterogeneous data integration.

In recent days, many information and image evidence in the field of medicine is being designed and developed by the advancement of computer technology. Presently, sport medical data is an essential department for medical sector and it is responsible for assuring sports safety based on the recovery level after an injury due to sports activity. The approach to reliably interpretation and valuable data information using a vast number of medical sports data and events has become an important research path in the collection and analysis of medical data. This paper discusses the extraction, study, and lack of training and accuracy of complex algorithms for critical sporting medical data. This paper involves with an optimized convolutional neural network (OCNN) based on deep-learning model to ensure successful detection and risk assessments of sport-medicine diseases and adopts the Self-Adjustment Resizing algorithm (SAR) augmented by the self-coding method of the convolution (SCM). CNN model helps to evaluate sports medicine in multi-dimensional results and suggested OCNN classification constitutes two convolutional layers, two pool layers, a fully connected layer, and a SoftMax structure that can be used for the classification of sport-related medical data. The CNN facilitates multi-dimensional sports medicine data analysis and to conclude a cloud-based loop model to create an advanced medical data network for sports medicine. Experiments illustrate that this approach offers technical support and guide to deploying a specific cloud-based fusion system.

Medical data plays an essential role in several applications in the medical field [...]

In the latest progress in healthcare, the continuous creation of digital medical information is an important basis for the analysis of large medical data. This paper proposes a medical big data analysis algorithm based on access control system that provides reliable security protection to big medical data (BMD) by considering various quantitative parameters. The proposed algorithm calculates the reputation values of different users, which correspond to each parameter. Based on the calculated reputation values, the proposed algorithm grants access authority to each user. Simulation results are performed to verify the effectiveness of security system solving protection of the sensitive personal information of patients.

At present, how to make use of massive medical information resources to provide scientific decision-making for the diagnosis and treatment of diseases, summarize the curative effect of various treatment schemes, and better serve the decision-making management, medical treatment, and scientific research, has drawn more and more attention of researchers. Deep learning, as the focus of most concern by both academia and industry, has been effectively applied in many fields and has outperformed most of the machine learning methods. Under this background, deep learning based medical data analysis emerged. In this survey, we focus on reviewing and then categorizing the current development. Firstly, we fully discuss the scope, characteristic and structure of the heterogeneous medical data. Afterward and primarily, the main deep learning models involved in medical data analysis, including their variants and various hybrid models, as well as main tasks in medical data analysis are all analyzed and reviewed in a series of typical cases respectively. Finally, we provide a brief introduction to certain useful online resources of deep learning development tools.

Classification is a data processing technique of a great significance both for native eHealth systems and web telemedicine solutions. In this sense, artificial neural networks have been widely applied in telerehabilitation as powerful tools to process information and acquire a new medical knowledge. But effective analysis of multidimensional heterogeneous medical data, still poses considerable difficulties. It was shown that processing too many data features simultaneously is costly and has some adverse effects on the resulting models classification properties. Therefore, there is a strong need to develop new techniques for selecting features from the very large data sets that include many irrelevant, or redundant features. This work addresses the context-related feature selection problem from medical data by proposing utility of Sigma-if neural network being an effective model of neurology patients's low-level distributed selective attention mechanisms. Our experiments indicate that a context-aware technique can reduce the average cost of medical data acquisition and data processing as well as it can decrease classification error probability resulting in increasing the overall eHealth systems reliability.

A large number of patient healthcare data have been collected in the process of diagnosis and treatment of intensive care medicine, which provides major benefits for patient safety and quality. Unfortunately, the application of medical data is greatly limited. Key barriers to the use of the data include difficulties in data extraction and cleaning, and the construction of high-quality data sets promotes the research of medical big data analysis. In China, there is few intensive care data set built by clinicians has been used for clinical outcome prediction. This study developed and evaluated an Intensive Care Medical (ICM) data set for critically care patients that can be used for deep learning. The ICM data set contained four types of data collected routinely in Chinese hospitals, including all-cause characteristics of administrative information, vital signs, laboratory tests, and intravenous medication records. A total of 17,291 ICU admissions involving 12,815 patients aged 14 years and older were extracted from the data set. Deep learning model achieved high accuracy for tasks in hospital mortality predicting (AUROC[area under the receiver operator curve] reach 0.8941). We believe that the ICM data set can be used to create accurate predictions for a variety of clinical scenarios.

Medical data is increasing rapidly. The rapid fusion of medical record text data, medical laboratory data and image data in many regional hospitals have brought light to the screening, diagnosis and treatment of diseases. Medical text data records the patient's detailed condition and treatment process, with abundant information. As a branch of artificial intelligence, deep learning is gradually penetrating the medical field. The application of deep learning in the medical field shows great application prospects. Therefore, mining the knowledge contained in medical texts can provide better medical services for the majority of patients, which has good theoretical and practical significance. In this paper, the content of medical texts was analyzed, and the test questions and standard answers of the USMLE Step 2 Clinical Skills Exam were used as data sets to explore the core content of the medical chief texts. After comparing and analyzing different methods, such as BERT and DeBERTa, an intelligent follow-up system based on DeBERTa was finally proposed, hoping to help clinicians read medical data more effectively and weaken the problem of poor diagnosis results caused by personal academic ability differences.

It is of great significance to establish a scientific and perfect application model of medical and health big data analysis for promoting the application of medical and health big data. This paper mainly studies the computer-aided medical and health data analysis of Inner Mongolia Autonomous Region based on big data. This paper adopts literature research method, field research method and data science method. Literature research is used to understand big data platforms and their technologies, and to analyze the limitations of data analysis methods in traditional data warehouses. The application process of data analysis based on big data platform is studied by using data science method. The established medical and health data analysis application process based on the big data platform and the described medical and health big data application theme were consulted by using the expert consultation method, and they were modified and improved according to the expert opinions.KeywordsBig data platformComputer aidedHealthcare dataData analysis

BackgroundImplementing accurate data management systems ensure safe and efficient transfer of confidential health care data. However, health care professionals overlooked their important tasks of medical data processing. Hence, using high-quality electronic health record (EHR) applications in health care is important to minimize medical errors. Therefore, this review tries to indicate the roles of EHR in advancing quality health care service provisions.MethodsThe keywords identified were EHR, EMR, medical data processing, medical data retention, medical data destruction, health care, and patient care, and a few related terms with different combinations. PubMed (National Library of Medicine), Google Scholar, and Google search engine were used to search for articles from those databases. Searching was done using boolean words “AND”, “OR”, and “NOT” using all [All fields] and [MeSH Terms] searching strategies.ResultsArticles were screened using the title, checked by their abstract, and the remaining related full-text materials were included or excluded by two individuals deciding its eligibility. Finally, 73 materials issued from 2013–2018 were used for qualitatively synthesizing and reconciling the idea to produce this review article.ConclusionPoor medical data processing systems are the key reasons for medical errors. Employing standardized data management systems reduce errors and associated sufferings. Therefore, using electronic tools in the health care institution ensures safe and efficient data management. Therefore, it is important to establish appropriate medical data management systems for efficient health care delivery.

This study investigates the potential of large language models (LLMs) in standardizing Chinese medical examination reports, which are crucial for health assessment but often suffer from non-standardized terminologies across different hospitals. We utilized a dataset of over 900,000 Chinese reports and explored the effectiveness of prompt engineering and LoRA fine-tuning techniques on various LLMs. Our results demonstrate that fine-tuned LLMs, particularly Qwen2.5-14B, achieved high accuracy in predicting standardized department and item detail names, significantly outperforming traditional methods like BERT. Notably, the model exhibits strong generalization capabilities, achieving high accuracy on unseen hospital data with an average accuracy of 98.34% in cross-validation experiments. This research highlights the potential of LLMs in medical data standardization, laying the foundation for automated processing and analysis of large-scale medical data.

Purpose: The purpose of this article is to offer an overview of the recent applications of cloud computing in medical imaging and their advances in the field. It reviews existing scientific and academic literature on cloud computing-based platforms and solutions for medical image analysis and storage of medical imaging data. Materials and methods: This article uses available scientific literature on the applications of cloud computing in medical imaging from PubMed, Google Scholar and ScienceDirect. Results: The review shows that interest and research in cloud computing applications in medical imaging has increased in recent years. This has led to new and more effective platforms and solutions for medical image analysis and storage of medical imaging data. Innovative applications of cloud computing in medical imaging try to address ethical and security concerns using authentication, encryption, anonymization and access controls. Conclusions: Cloud computing offers promising applications in medical imaging. Notwithstanding, further research is necessary to demonstrate their effectiveness, safety and security in a medical setting.

Recent years have witnessed the advent of technologies such as the Internet of Things, cloud computing, and big data. Also, the analysis and research of data have attracted more and more attention from researchers. For example, the analysis of medical data can help government agencies do right decisions in public health services, or assist medical research institutes in conducting medical research. But it is followed by the privacy preserving of patient medical data in a cloud environment. Our paper proposes a problem-solving scheme that is a k-anonymous privacy preserving scheme based on bilinear pairings (KPSBP) which combines k-anonymity and secure searchable encryption to ensure the patient data privacy is not compromised and the medical data is well shared.

The Internet of Things (IoT) and cloud technologies have significantly facilitated healthcare. In such a context, medical data are collected by the terminals from the patients, manipulated, and stored on the cloud by hospitals (doctors). This brings asymmetry problems in medical data access control, processing, and storage between doctors and patients, which results in medical data sharing face many challenges such as privacy leakage and malicious feedback from cloud servers on queries. To solve these asymmetry problems, this paper proposes a medical data sharing scheme with cloud-chain cooperation and policy fusion in the IoT. Regarding asymmetrical access control rights, a conflict resolution and fusion algorithm that enables co-authorization of medical data by the doctor and the patient is introduced. To balance the symmetry of medical data storage and processing, a cloud-chain cooperation ciphertext retrieval method is proposed by means of two-stage joint searching from cloud servers and the blockchain, which can not only detect malicious medical data feedback from cloud servers, but also improve the data search efficiency. The security analysis showed that this scheme satisfies the confidentiality and verifiability of the retrieved information, and the feasibility of the proposed scheme was demonstrated through experiments.

BackgroundObsessive-compulsive disorder (OCD) is characterized by recurrent intrusive thoughts, urges, or images (obsessions) and repetitive physical or mental behaviors (compulsions). Previous factor analytic and clustering studies suggest the presence of three or four subtypes of OCD symptoms. However, these studies have relied on predefined symptom checklists, which are limited in breadth and may be biased toward researchers’ previous conceptualizations of OCD.ObjectiveIn this study, we examine a large data set of freely reported obsession symptoms obtained from an OCD mobile app as an alternative to uncovering potential OCD subtypes. From this, we examine data-driven clusters of obsessions based on their latent semantic relationships in the English language using word embeddings.MethodsWe extracted free-text entry words describing obsessions in a large sample of users of a mobile app, NOCD. Semantic vector space modeling was applied using the Global Vectors for Word Representation algorithm. A domain-specific extension, Mittens, was also applied to enhance the corpus with OCD-specific words. The resulting representations provided linear substructures of the word vector in a 100-dimensional space. We applied principal component analysis to the 100-dimensional vector representation of the most frequent words, followed by k-means clustering to obtain clusters of related words.ResultsWe obtained 7001 unique words representing obsessions from 25,369 individuals. Heuristics for determining the optimal number of clusters pointed to a three-cluster solution for grouping subtypes of OCD. The first had themes relating to relationship and just-right; the second had themes relating to doubt and checking; and the third had themes relating to contamination, somatic, physical harm, and sexual harm. All three clusters showed close semantic relationships with each other in the central area of convergence, with themes relating to harm. An equal-sized split-sample analysis across individuals and a split-sample analysis over time both showed overall stable cluster solutions. Words in the third cluster were the most frequently occurring words, followed by words in the first cluster.ConclusionsThe clustering of naturally acquired obsessional words resulted in three major groupings of semantic themes, which partially overlapped with predefined checklists from previous studies. Furthermore, the closeness of the overall embedded relationships across clusters and their central convergence on harm suggests that, at least at the level of self-reported obsessional thoughts, most obsessions have close semantic relationships. Harm to self or others may be an underlying organizing theme across many obsessions. Notably, relationship-themed words, not previously included in factor-analytic studies, clustered with just-right words. These novel insights have potential implications for understanding how an apparent multitude of obsessional symptoms are connected by underlying themes. This observation could aid exposure-based treatment approaches and could be used as a conceptual framework for future research.