H2RM: A Hybrid Rough Set Reasoning Model for Prediction and Management of Diabetes Mellitus

Comparative Analysis of Aesthetic Emotion of Dance Movement: A Deep Learning Based Approach

Dance is a unique art with the human body movement as the main means, but dance is not limited to the human body movement itself. Like any art, dance is the product of human social behavior and a romantic behavior of human thoughts and emotions in the virtual world. Dances with different characteristics will also reflect different aesthetics, different cultural psychology, different living styles, and emotional trajectories of different times and different nationalities. People rely on the image of dance artists to develop and inherit the profound ideological connotation and philosophy of life. Viewers may form their own diversified and unique aesthetic characteristics. In the new era, in order to better promote the development, communication, and dissemination of dance art, it is very necessary to analyze and explore the connotation and aesthetic characteristics of dance art. Only through specific movements can the value and ideological connotation of works be expressed. Therefore, this paper comparatively analyzes dance movement aesthetic emotion based on deep learning. Experimentations are performed to systematically analyze the models from various perspectives. Findings of the evaluation show that CAP and CNN are effective models that can successfully extract high-level emotional features. The method proposes and effectively selects the best models among the five standard models based on key features and is, therefore, suitable in predicting the dancer's emotion and for the analysis of the dance movement in the future.

Evaluation of Application Effect of Self-Made Compression Cold Therapy in Postoperative Rehabilitation of Patients with Orthopedic Dyskinesia

With the accelerated aging of the population, orthopedic injuries have become more collective. Among them, the incidence of ankle fractures remains high. Surgery is an effective way to treat ankle fractures by utilizing special surgical site, complex anatomical structure, and specific surgical methods. With surgical approach, it is easy for basis postoperative blood loss, pain, swelling, and other problems. After surgery, most patients suffer from symptoms of fear, increased pain sensitivity, and excessive irrational concerns about physical movement or activity. Compression cold therapy combines cold therapy with air pressure therapy to ease local exudation, constrict blood vessels, improve circulation, relieve pain, and control inflammation through the effects of low temperature and pressure. Application during the rehabilitation period can prevent joint swelling, reduce muscle soreness, and promote the functional recovery of limbs, which provides an effective guarantee for postoperative rehabilitation of patients with orthopedic dyskinesia. Based on this, it is very important to evaluate the application and effect of self-made compression cold therapy in postoperative rehabilitation of patients with orthopedic dyskinesia. This work proposes a one-dimensional deep convolutional neural network-based method; DenseNet for analyzing the rehabilitation effect of patients with orthopedic dyskinesia after ankle fracture surgery. The approach is to evaluate the rehabilitation effect of self-made compression cold therapy from the perspectives of feature reuse, attention mechanism, and feature decoupling. Experiments on the dataset show that the proposed neural network has better efficacy evaluation performance. The proposed systematic assessment based on the emerging deep learning network has great significance in healthcare domain, particularly in assessing applicability, side effects, and noninvasiveness of treatment methods.

Analysis of the Effect of Applying Ultrasound-Guided Nerve Block Anesthesia to Fracture Patients in the Context of Internet-Based Blockchain

In the process of surgical treatment, the introduction of ultrasound technology to implement nerve block anesthesia can make the operations of patients with fractures under visualization and it can also significantly improve the anesthesia effect. With this technology, it is possible to minimize the anesthesia operation causing accidental injury and lay a good foundation for the smooth operation of surgical treatment. Blockchain technology is a new decentralized infrastructure and distributed computing paradigm. This technology has great development opportunities in the medical field and is expected to play an important role in the construction of Internet medical ecology. This study aims to investigate the effect of ultrasound-guided nerve block anesthesia on fracture treatment in the context of blockchain. This method has high application value and potential in medical data sharing, reducing treatment costs, improving the medical claims system, strengthening medical management, and optimizing medical decision-making using blockchain technology. This study also addresses the uniqueness and complexity of ultrasound-guided nerve block anesthesia itself and analyzes the effect of the proposed method. The analysis shows that using the internet-based blockchain ultrasound-guided subacromial nerve block anesthesia for fracture patients is effective, and the patient's vital signs are stable, and the block is effective.

Application of Wearable Sensors in the Treatment of Cervical Spondylosis Radiculopathy with Acupuncture

Research shows that cervical spondylosis radiculopathy (CSR) is the most common type of cervical spondylosis in clinic, and Chinese medicine treatment has obvious advantages, among which acupuncture therapy has received increasing attention. CSR has the characteristics of high incidence, long treatment time, and easy recurrence after treatment. In order to meet the different needs of different patients, this paper uses wearable sensors to collect patient dynamic data, extracts the action features of cervical spondylosis to design a scoring system, analyzes the input feature scores through a convolutional neural network (CNN) model, and then outputs personalized acupuncture treatment plan. The development status of wearable sensors at home and abroad is introduced, and the modules and functions of the wearable sensors are designed. The CNN network is used as the network model for classification and recognition. The experimental results show that the CNN model used in this paper has a high classification accuracy, achieving an accuracy of up to 97%, and can help produce an effective treatment plan. In order to determine whether the treatment plan output by the model is effective, each group of data is handed over to two cervical spondylosis experts for scoring, and then the final treatment plan is determined from 10 acupuncture plans. In our experiments, 9 out of 10 plans generated by the CNN model were the same as generated by the experts, which shows the effectiveness of the model.

Clinical Application of Artificial Intelligence: Auto-Discerning the Effectiveness of Lidocaine Concentration Levels in Osteosarcoma Femoral Tumor Segment Resection

Adolescents and children worldwide are threatened by osteosarcoma, a tumor that predominantly affects the long bone epiphysis. Osteosarcoma is the most common and highly malignant bone tumor in youngsters. Early tumor detection is the key to effective treatment of this disease. The discovery of biomarkers and the growing understanding of molecules and their complex interactions have improved the outcome of clinical trials in osteosarcoma. This article describes biomarkers of osteosarcoma with the aim of positively influencing the progress of clinical treatment of osteosarcoma. Femoral bone tumor is a typical condition of osteosarcoma. Due to the wide range of femoral stem types, complexities in the distal femur, and tumors in the rotor part of femur, physicians following the traditional clinical approach face difficulties in removing the lesion and fixing the femur with resection of the tumor segment. In this paper, the effect of small doses of different concentrations of lidocaine anesthesia in patients undergoing lumpectomy for osteosarcoma femoral tumor segments is investigated. A computer-based artificial intelligence method for automated determination of different concentration levels of lidocaine anesthesia and amputation of osteosarcoma femoral tumor segment is proposed. Statistical analysis is carried on the empirical data including intraoperative bleeding, intraoperative and postoperative pain scores, surgical operation time, postoperative complications, patient satisfaction, and local anesthetic dose. The results showed that the patients in the study group had low intraoperative bleeding, short operation time, low postoperative hematoma formation rate, high patient satisfaction, higher dosage of anesthetic solution, and low dosage of lidocaine. Results revealed that mean arterial pressure and heart rate in extubating and intubating were significantly lower in the observation group than in the control group, and a significant difference (P < 0.05) was observed between the two groups. This proves that the proposed algorithm can adequately reduce bleeding, alleviate postoperative pain, shorten operation time, reduce complications, accelerate recovery, and ensure better treatment results.

Effect of Carbon Dioxide on Bispectral Index of EEG under Intravenous Target-Controlled Anesthesia Based on Intelligent Medical Treatment

Laparoscopic surgery has the advantages of less trauma and quick recovery, and it is more and more favored by surgeons and patients in clinical practice. However, the impact of carbon dioxide pneumoperitoneum on the body during laparoscopic surgery has attracted the attention of many scholars. Pneumoperitoneum can cause increased cerebral blood flow and increased intracranial pressure, cerebral metabolic rate is highly correlated with blood carbon dioxide partial pressure, and cerebral metabolism without cardiopulmonary bypass is linearly correlated with the depth of anesthesia. Electroencephalographic (EEG) bispectral index (BIS) is a signal analysis method, which can directly measure the effect of drugs on the cerebral cortex and reflect the depth of anesthesia. Based on this, this study takes smart medical treatment as the background and uses the improved BP neural network as a tool to explore the effect of carbon dioxide on EEG bispectral index under intravenous target-controlled anesthesia. The main purpose is to observe the correlation between arterial blood carbon dioxide partial pressure and EEG bispectral index under propofol target-controlled anesthesia during retroperitoneal laparoscopic surgery. The experimental results show that the model proposed in this study can efficiently and accurately obtain the size of the influencing factors, which provides a clinical basis for the anesthesia management and anesthesia depth regulation of carbon dioxide pneumoperitoneum laparoscopic surgery.

Curative Effect of Foraminal Endoscopic Surgery and Efficacy of the Wearable Lumbar Spine Protection Equipment in the Treatment of Lumbar Disc Herniation

Lumbar disc herniation is a common and frequently-occurring disease in pain clinics. The incidence rate of affliction is increasing with every passing year. Besides the aged, young people also suffer from long-term pain, which not only affects their daily routines but may also lead to serious impairment. The causes of chronic low back and leg pain caused by lumbar disc herniation are mainly related to mechanical compression, the adhesion of epidural space, intervertebral space, and aseptic inflammatory reaction. The treatment of lumbar disc herniation should follow the principle of step-by-step treatment. An appropriate treatment scheme needs to be adopted according to the patient's condition. About 80% of patients received nonsurgical treatment to get relief from the pain symptoms. However, 10% to 15% of patients still need traditional open surgery. Spinal foraminal surgery is a new method for the treatment of lumbar disc herniation, lumbar surgery failure syndrome, and lumbar spinal stenosis. However, there are only scattered clinical reports on the efficacy of spinal foraminal surgery. Based on it, this paper proposes a method to explore the efficacy of spinal foraminal mirror surgery in the treatment of lumbar disc herniation. Besides, postoperative wearable lumbar protective equipment is proposed to ensure a seamless rehabilitation effect on the patients. Statistical analysis performed using a t-test revealed that there was a significant difference between the visual analog scales (VAS) scores of the two groups after 3 and 6 months of treatment (P < 0.05). The paper analyzes and summarizes the cases with definite and poor curative effects, which not only provides the basis for clinical practice but also paves the way to multicenter clinical research.

Different strategies for detection of HbA1c emphasizing on biosensors and point-of-care analyzers

Measurement of glycosylated hemoglobin (HbA1c) is a gold standard procedure for assessing long term glycemic control in individuals with diabetes mellitus as it gives the stable and reliable value of blood glucose levels for a period of 90-120 days. HbA1c is formed by the non-enzymatic glycation of terminal valine of hemoglobin. The analysis of HbA1c tends to be complicated because there are more than 300 different assay methods for measuring HbA1c which leads to variations in reported values from same samples. Therefore, standardization of detection methods is recommended. The review outlines the current research activities on developing assays including biosensors for the detection of HbA1c. The pros and cons of different techniques for measuring HbA1c are outlined. The performance of current point-of-care HbA1c analyzers available on the market are also compared and discussed. The future perspectives for HbA1c detection and diabetes management are proposed.

Sysmex UF-1000i flow cytometer to screen urinary tract infections: the URISCAM multicentre study

The new Sysmex UF-1000i analyzer - which incorporates bacteria morphology distinction - allows to automatically screen samples to be cultured at microbiology laboratories. We have evaluated the feasibility and accuracy of Sysmex UF-1000i to screen urinary tract infections (UTIs). A total amount of 2468 urine samples from six Spanish hospitals were analysed. Demographic and clinical data such as age, gender, source and sample type, preserving conditions, cytometer parameters (bacteria, leucocytes and bacteria morphology) as well as urine culture results (gold standard) were recorded. After applying data mining techniques, the variables of age, bacteria count and rod morphology were defined as predictive variables of UTIs. By using the UF-1000i in combination with a predictive algorithm of three decision rules, we could identify 94·9 and 47·4% positive and negative urine samples, respectively, with a negative predictive value of 97 and only 1·17% diagnostic error. This error was reduced down to 0·4% when contaminated samples were excluded. Our results show that flow cytometry parameters together with age, by means of a predictive algorithm model, can be used to screen UTIs. Its implementation would avoid culturing 38% of urine samples, and therefore, would reduce time to diagnosis with a discrete false negative ratio.

SIGNIFICANCE AND IMPACT OF THE STUDY

Fluorescent flow cytometry performance has recently spread for urine screening. However, controversy about cytometer results can be drawn from medical literature. This study shows the diagnosis accuracy of Sysmex UF-1000i analyzer by means of a group of decision rules encompassing both demographic variables (age) and cytometer parameters (bacteria, leucocytes and bacteria morphology). After applying the predictive algorithm, the UF-1000i could optimally identify 95% urinary tract infections with high negative predictive value and low diagnostic error. Implementation of UF-1000i would avoid culturing almost 38% of urine samples, thus reducing time to diagnosis, unnecessary antibiotic treatments and consequently improving cost-effectiveness.

iCBLS: An interactive case-based learning system for medical education

Medical students should be able to actively apply clinical reasoning skills to further their interpretative, diagnostic, and treatment skills in a non-obtrusive and scalable way. Case-Based Learning (CBL) approach has been receiving attention in medical education as it is a student-centered teaching methodology that exposes students to real-world scenarios that need to be solved using their reasoning skills and existing theoretical knowledge. In this paper, we propose an interactive CBL System, called iCBLS, which supports the development of collaborative clinical reasoning skills for medical students in an online environment. The iCBLS consists of three modules: (i) system administration (SA), (ii) clinical case creation (CCC) with an innovative semi-automatic approach, and (iii) case formulation (CF) through intervention of medical students' and teachers' knowledge. Two evaluations under the umbrella of the context/input/process/product (CIPP) model have been performed with a Glycemia study. The first focused on the system satisfaction, evaluated by 54 students. The latter aimed to evaluate the system effectiveness, simulated by 155 students. The results show a high success rate of 70% for students' interaction, 76.4% for group learning, 72.8% for solo learning, and 74.6% for improved clinical skills.

Comprehensible knowledge model creation for cancer treatment decision making

BACKGROUND

A wealth of clinical data exists in clinical documents in the form of electronic health records (EHRs). This data can be used for developing knowledge-based recommendation systems that can assist clinicians in clinical decision making and education. One of the big hurdles in developing such systems is the lack of automated mechanisms for knowledge acquisition to enable and educate clinicians in informed decision making.

MATERIALS AND METHODS

An automated knowledge acquisition methodology with a comprehensible knowledge model for cancer treatment (CKM-CT) is proposed. With the CKM-CT, clinical data are acquired automatically from documents. Quality of data is ensured by correcting errors and transforming various formats into a standard data format. Data preprocessing involves dimensionality reduction and missing value imputation. Predictive algorithm selection is performed on the basis of the ranking score of the weighted sum model. The knowledge builder prepares knowledge for knowledge-based services: clinical decisions and education support.

RESULTS

Data is acquired from 13,788 head and neck cancer (HNC) documents for 3447 patients, including 1526 patients of the oral cavity site. In the data quality task, 160 staging values are corrected. In the preprocessing task, 20 attributes and 106 records are eliminated from the dataset. The Classification and Regression Trees (CRT) algorithm is selected and provides 69.0% classification accuracy in predicting HNC treatment plans, consisting of 11 decision paths that yield 11 decision rules.

CONCLUSION

Our proposed methodology, CKM-CT, is helpful to find hidden knowledge in clinical documents. In CKM-CT, the prediction models are developed to assist and educate clinicians for informed decision making. The proposed methodology is generalizable to apply to data of other domains such as breast cancer with a similar objective to assist clinicians in decision making and education.

Mechanistic modeling of hemoglobin glycation and red blood cell kinetics enables personalized diabetes monitoring

The amount of glycated hemoglobin (HbA1c) in diabetic patients' blood provides the best estimate of the average blood glucose concentration over the preceding 2 to 3 months. It is therefore essential for disease management and is the best predictor of disease complications. Nevertheless, substantial unexplained glucose-independent variation in HbA1c makes its reflection of average glucose inaccurate and limits the precision of medical care for diabetics. The true average glucose concentration of a nondiabetic and a poorly controlled diabetic may differ by less than 15 mg/dl, but patients with identical HbA1c values may have true average glucose concentrations that differ by more than 60 mg/dl. We combined a mechanistic mathematical model of hemoglobin glycation and red blood cell kinetics with large sets of within-patient glucose measurements to derive patient-specific estimates of nonglycemic determinants of HbA1c, including mean red blood cell age. We found that between-patient variation in derived mean red blood cell age explains all glucose-independent variation in HbA1c. We then used our model to personalize prospective estimates of average glucose and reduced errors by more than 50% in four independent groups of greater than 200 patients. The current standard of care provided average glucose estimates with errors >15 mg/dl for one in three patients. Our patient-specific method reduced this error rate to 1 in 10. Our personalized approach should improve medical care for diabetes using existing clinical measurements.

Application of Rough Set Based Models in Medical Diagnosis

Modeling intelligent system in the field of medical diagnosis is still a challenging work. Intelligent systems in medical diagnosis can be utilized as a supporting tool to the medical practitioner, mainly country like India with vast rural areas and absolute shortage of physicians. Intelligent systems in the field of medical diagnosis can also able to reduce cost and problems for the diagnosis like dynamic perturbations, shortage of physicians, etc. An intelligent system may be considered as an information system that provides answer to queries relating to the information stored in the Knowledge Base (KB), which is a repository of human knowledge. Rough set theory is an efficient model to capture uncertainty in data and the processing of data using rough set techniques is easy and convincing. Rule generation is an inherent component in rough set analysis. So, medical systems which have uncertainty inherent can be handled in a better way using rough sets and its variants. The objective of this chapter is to discuss on several such applications of rough set theory in medical diagnosis.