Traditional Medical Diagnosis Research Articles

Machine Learning and Natural Language Processing Algorithms in the Remote Mobile Medical Diagnosis System of Internet Hospitals

In order to alleviate the contradiction between supply and demand of professional pharmacists, integrate medical resources, and ensure the safety of patients’ medication, the telemedicine diagnosis system has played a great role. Today, in this “Internet +” era, all walks of life have begun to integrate with Internet technology. The purpose of this article was to discuss the practical utility of machine learning and natural language processing algorithms in the remote mobile medical diagnosis system of Internet hospitals, for which this article conducted in-depth discussion. This article first introduced the basic concepts, development, and characteristics of machine learning and natural language processing algorithms in detail, and carefully studied and analyzed the development and culture of traditional offline medical diagnosis models. Based on machine learning and natural language processing algorithms, a remote mobile medical diagnosis is designed. By combining with the medical diagnosis system of traditional hospitals, a new type of remote mobile medical diagnosis system for Internet hospitals was designed and developed, and the combination of traditional medical industry and Internet technology was deeply studied. According to each functional requirement, the image module, heart rate measurement module, and user setting module are designed, respectively. Compared to traditional medical diagnosis systems, the accuracy of the remote mobile medical diagnosis system based on machine learning applied in Internet hospital diagnosis in this article reached 80% or even higher. At the same time, it was found through experiments that when the evolution number was 3, the maximum fit value and average fit value were the same, both of which were 0.6. This indicates that the system can accommodate more than 10,000 people at the same time, and patients can receive good treatment plans, with a very broad application prospect

Efficacy of combination therapy with unkeito and boiogito for menopausal arthralgia: A retrospective observational study.

Arthralgia affects menopausal patients. Here, we report a retrospective observational study evaluating the therapeutic efficacy of a combination of unkeito and boiogito for menopausal arthralgia. Patients treated with a combination of unkeito and boiogito for menopausal arthralgia between April 2020 and October 2022 at three Japanese Kampo outpatient clinics were retrospectively examined. Treatment effectiveness was determined based on the patient's description of pain. Adverse events associated with this treatment regimen were also recorded. The study design was approved by the Tokai University Ethics Committee (Approval number: 22R196). This study was conducted by retrospectively analysing the medical records of patients who attended the three medical facilities. All patients received Kampo medicines based on traditional medical diagnoses. Outpatients with a diagnosis of menopausal arthralgia were selected, and information on those who were treated with a combination of unkeito and boiogito was collected. The age, height, and weight of all patients are represented as the mean ± standard deviation. Statistical analyses were not performed in this study as there was no comparison group. During the study period, nine patients with menopausal arthralgia received the unkeito and boiogito combination. Four patients showed a "significant" response to the combination treatment, four showed an "effective" response, and one patient showed an "ineffective" response. One patient reported headache as an adverse event. The combination of unkeito and boiogito may be effective in the treatment of menopausal arthralgia.

Physiological Big Data Mining Through Machine Learning and Wireless Sensor Networks

With the improvement of living standards, the requirements for medical care and daily healthcare quality have become higher and higher. However, the traditional medical diagnosis mode cannot provide patients with all-round, real-time, and accurate health status. With the aggravation of the aging population, the scale of physiological data will increase in a blowout manner. The traditional medical diagnosis model for monitoring, which is based at the central hospital, has been unable to meet the current real-time monitoring needs for families and individuals. In order to solve this issue, this paper establishes a wireless sensor network based medical platform, which implements sleep monitoring by mining electroencephalogram signals. The wireless sensor network-based medical platform adopts the end-edge-cloud architecture. The experiments and simulations show the effectiveness of the proposed end-edge-cloud architecture-based medical platform.

The Technology-Oriented Pathway for Auxiliary Diagnosis in the Digital Health Age: A Self-Adaptive Disease Prediction Model.

The advent of the digital age has accelerated the transformation and upgrading of the traditional medical diagnosis pattern. With the rise of the concept of digital health, the emerging information technologies, such as machine learning (ML) and data mining (DM), have been extensively applied in the medical and health field, where the construction of disease prediction models is an especially effective method to realize auxiliary medical diagnosis. However, the existing related studies mostly focus on the prediction analysis for a certain disease, using models with which it might be challenging to predict other diseases effectively. To address the issues existing in the aforementioned studies, this paper constructs four novel strategies to achieve a self-adaptive disease prediction process, i.e., the hunger-state foraging strategy of producers (PHFS), the parallel strategy for exploration and exploitation (EEPS), the perturbation–exploration strategy (PES), and the parameter self-adaptive strategy (PSAS), and eventually proposes a self-adaptive disease prediction model with applied universality, strong generalization ability, and strong robustness, i.e., multi-strategies optimization-based kernel extreme learning machine (MsO-KELM). Meanwhile, this paper selects six different real-world disease datasets as the experimental samples, which include the Breast Cancer dataset (cancer), the Parkinson dataset (Parkinson’s disease), the Autistic Spectrum Disorder Screening Data for Children dataset (Autism Spectrum Disorder), the Heart Disease dataset (heart disease), the Cleveland dataset (heart disease), and the Bupa dataset (liver disease). In terms of the prediction accuracy, the proposed MsO-KELM can obtain ACC values in analyzing these six diseases of 94.124%, 84.167%, 91.079%, 72.222%, 70.184%, and 70.476%, respectively. These ACC values have all been increased by nearly 2–7% compared with those obtained by the other models mentioned in this paper. This study deepens the connection between information technology and medical health by exploring the self-adaptive disease prediction model, which is an intuitive representation of digital health and could provide a scientific and reliable diagnostic basis for medical workers.

Data-driven intelligent decision for multimedia medical management

Since medical diagnosis runs through the whole journey of human health, the traditional medical diagnosis methods cannot ensure the diagnosis accurate due to the interference of multiple external factors. In response, this paper proposes a multimedia medical management method supported by the data-driven intelligent decision (MMD-DI). This method can be used to predict the survival time of cancer patients under the help of gradient boosting decision tree (GBDT) and hybrid neural network model. First, the feature factors were scanned to match the conditions by GBDT, according to the set value domain; Then the factors were inputted into the neural network. The hybrid neural network was employed to predict the survival time of cancer patients, and it was constructed by combining the convolutional neural network (CNN) and the long short-term memory (LSTM) model. Finally, the stability of the proposed MMD-DI was analyzed, and performance was compared with a series of commonly exploited baseline methods: the mean of cross-validated RMSE (Root Mean Squared Error) evaluation results is 0.183, the mean of cross-validated MAE (Mean Absolute Error) evaluation results is 0.147, the two indicators are both much lower than the commonly exploited baseline methods. A series of experiments proved that MMD-DI has excellent performance and can be used in the multimedia medical management systems.

Application of Human-Computer Interaction Technology in Ancillary Diagnosis of Nervous System Diseases: Current Situation and Prospect

With the development of human-computer interaction, how to use intelligent, natural and efficient methods to promote the development of the medical field has become a hot topic of research. Nervous system diseases have a great impact on the quality of people's daily life. Using the method of human-computer interaction for early warning and ancillary diagnosis of nervous system diseases can reduce discomfort of patients during exams and work intensity of doctors, which is of great significance to both doctors and patients. This paper discusses the application status, existing problems, future development of human-computer interaction in the ancillary diagnosis of nervous system diseases, and how to use computer technology to improve traditional medical diagnosis methods from the perspective of interaction.

Dermatoscopic image melanoma recognition based on CFLDnet fusion network

As a common skin disease, malignant melanoma has attracted great attention in dermatology with high morbidity and mortality. Traditional medical diagnoses rely on clinical experience and have a certain subjective bias. Aiming at the problem of similarity between classes of melanoma dermoscopic images and the imbalance of lesion data set, we propose a CFLDnet-based dermoscopic image lesion classification model, it mainly uses the improved convolutional block attention (CBA) DenseNet algorithm to enhance beneficial features. To better integrate the attention module into DenseNet without increasing too many parameters and wasting the computing resources, we discussed three types of variant networks derived from the CFLDnet. Moreover, to balance the different categories of the data set, we also use the Sample Focal Loss (SFL) to calculate the effective sample size of the data set and smooth the focal loss function. Large numbers of comparative experiments were done based on the ISIC2018 task3 dataset, the average recognition accuracy of the CFLDnet network proposed in this paper is 86.89%, which is much higher than other similar methods (VGG16, ResNet50, InceptionV3 and DenseNet121 with cross-entropy loss function).

Improving pre-bariatric surgery diagnosis of hiatal hernia using machine learning models

Background Bariatric patients have a high prevalence of hiatal hernia (HH). HH imposes various difficulties in performing laparoscopic bariatric surgery. Preoperative evaluation is generally inaccurate, establishing the need for better preoperative assessment. Objective To utilize machine learning ability to improve preoperative diagnosis of HH. Methods Machine learning (ML) prediction models were utilized to predict preoperative HH diagnosis using data from a prospectively maintained database of bariatric procedures performed in a high-volume bariatric surgical center between 2012 and 2015. We utilized three optional ML models to improve preoperative contrast swallow study (SS) prediction, automatic feature selection was performed using patients’ features. The prediction efficacy of the models was compared to SS. Results During the study period, 2482 patients underwent bariatric surgery. All underwent preoperative SS, considered the baseline diagnostic modality, which identified 236 (9.5%) patients with presumed HH. Achieving 38.5% sensitivity and 92.9% specificity. ML models increased sensitivity up to 60.2%, creating three optional models utilizing data and patient selection process for this purpose. Conclusion Implementing machine learning derived prediction models enabled an increase of up to 1.5 times of the baseline diagnostic sensitivity. By harnessing this ability, we can improve traditional medical diagnosis, increasing the sensitivity of preoperative diagnostic workout.

Defining the Roles and Research Priorities for Psychedelic-Assisted Therapies in Patients with Serious Illness: Expert Clinicians' and Investigators' Perspectives.

Background: Recent and preprohibition studies show that patients with serious illness might benefit from psychedelic-assisted therapies for a range of symptoms, physical, psychosocial, and existential. Objective: To explore the potential roles and research priorities of these therapies in patients with serious illness. Design, Setting, and Participants: Qualitative study based on semistructured interviews with 17 experts in serious illness care and/or psychedelic research from the United States and Canada. Measurements: The interview guide elicited participants' perspectives on (1) the potential roles of psychedelic-assisted therapies in this setting, (2) research priorities relevant to this population, and (3) the potential for integrating psychedelic-assisted therapies into existing delivery models of serious illness care. We used thematic analysis until thematic saturation. Results: Domain I: Participants had polar views on the therapeutic potential of psychedelic-assisted therapies, ranging from strong beliefs in their medical utility to reluctance about their use in this patient population. They shared concerns related to the risks of adverse effects, such as delirium or worsening of psychological distress. Domain II: Research priorities primarily concerned patients with clinically diagnosed psychosocial distress, such as depression, anxiety, or demoralization. Participants also articulated potential roles extending beyond traditional medical diagnosis. Domain III: Participants emphasized essential safety and efficacy guidelines relevant to the integration of these therapies into existing models of care. Conclusion: This qualitative study highlights issues and priorities for research on psychedelic-assisted therapies in patients with serious illness and proposes a conceptual framework for integrating these therapies into existing delivery models of serious illness care.

COMPUTER AIDED DIAGNOSIS FOR LIVER CANCER USING STATISTICAL MODEL

Liver Cancer is one of the most difficult cancer to cure and the number of deaths that it causes gener ally increasing. The signs and the symptoms of the liver cancer are not known, till th e cancer is in its advanced stage. So, early detect ion is the main problem. If it is detected earlier then it can be helpful for the Med ical treatment to limit the danger, but it is a challenging task due to the Cancer cell structure. Interpretation of Medical image is often difficult and time consuming, even for the experie nced Physicians. Most traditional medical diagnosis systems founded needs huge quantity of training data and takes long proc essing time. Focused on the solution to these problems, a Medical Diagnosis System based on Hidden Markov Model (HMM) is prese nted. This paperdescribes a computer aided diagnosis system for liver cancer that detects the liver tumor at an early stage from the chest CT images. This automation process reduces the time complexity and increases the diagnosis confidence.

A Novel Assessment of Various Bio-Imaging Methods for Lung Tumor Detection and Treatment by using 4-D and 2-D CT Images

Lung Cancer is known as one of the most difficult cancer to cure, and the number of deaths that it causes generally increasing. A detection of the Lung Cancer in its early stage can be helpful for Medical treatment to limit the danger, but it is a challenging problem due to Cancer cell structure. Interpretation of Medical image is often difficult and time consuming, even for the experienced Physicians. The aid of image analysis Based on machine learning can make this process easier. This paper describes fully Automatic Decision Support system for Lung Cancer diagnostic from CT Lung images. Most traditional medical diagnosis systems are founded on huge quantity of training data and takes long processing time. However, on the occasion that very little volume of data is available, the traditional diagnosis systems derive defects such as larger error, Time complexity. Focused on the solution to this problem, a Medical Diagnosis System based on Hidden Markov Model (HMM) is presented. In this paper we describe a pre-processing stage involving some Noise removal techniques help to solve this problem, we preprocess an images (by Mean Error Square Filtering and Histogram analysis)obtained after scanning the Lung CT images. Secondly separate the lung areas from an image by a segmentation process (by Thresholding and region growing techniques). Finally we developed HMM for the classification of Cancer Nodule. Results are checked for 2D and 4D CT images. This automation process reduces the time complexity and increases the diagnosis confidence.

基于标准光盘/光驱的数字化分子检测技术研究进展

Traditional medical diagnosis is typically time-consuming and labor-intensive, which is currently limited to well-equipped laboratories and hospitals where the required special instrumentation and highly trained personnel are available. Since patients cannot obtain their test results on time, the consequence of delayed treatment can be disastrous. Therefore, affordable and simple point-of-care (POC) biosensor devices are needed to perform diagnoses in the clinic or even at home. Compact disc (CD) technology is promising for the development of inexpensive materials and tools for POC molecular screening; while the discs may offer versatile biochip fabrication materials, a standard computer optical drive can be adapted as a precise signal readout device. In this mini-review, we briefly describe our recent progress in the development of computer-readable disc-based bioassays, including the UV/ozone activation of the polycarbonate surface, the preparation of DNA hybridization/antibody-antigen binding bioassays, and the establishment of a digital protocol for signal reading.

“Can It Read My Mind?” – What Do the Public and Experts Think of the Current (Mis)Uses of Neuroimaging?

Emerging applications of neuroimaging outside medicine and science have received intense public exposure through the media. Media misrepresentations can create a gulf between public and scientific understanding of the capabilities of neuroimaging and raise false expectations. To determine the extent of this effect and determine public opinions on acceptable uses and the need for regulation, we designed an electronic survey to obtain anonymous opinions from as wide a range of members of the public and neuroimaging experts as possible. The surveys ran from 1st June to 30 September 2010, asked 10 and 21 questions, respectively, about uses of neuroimaging outside traditional medical diagnosis, data storage, science communication and potential methods of regulation. We analysed the responses using descriptive statistics; 660 individuals responded to the public and 303 individuals responded to the expert survey. We found evidence of public skepticism about the use of neuroimaging for applications such as lie detection or to determine consumer preferences and considerable disquiet about use by employers or government and about how their data would be stored and used. While also somewhat skeptical about new applications of neuroimaging, experts grossly underestimated how often neuroimaging had been used as evidence in court. Although both the public and the experts rated highly the importance of a better informed public in limiting the inappropriate uses to which neuroimaging might be put, opinions differed on the need for, and mechanism of, actual regulation. Neuroscientists recognized the risks of inaccurate reporting of neuroimaging capabilities in the media but showed little motivation to engage with the public. The present study also emphasizes the need for better frameworks for scientific engagement with media and public education.

The trivialization of diagnosis

Although it is widely recognized that diagnosis plays a central role in clinical medicine, in recent years the primacy of diagnosis has come under attack from several sources. 1. "Billable terms" are replacing traditional medical diagnoses. The former are based on International Classification of Diseases lists, which include many non-diagnoses such as symptoms and signs. 2. Diagnosis often gets short shrift because of the perceived urgency of discharge. 3. The problem oriented record, in practice, has frequently led to a shift in emphasis from synthesis of findings to fragmentation of problems. 4. Presumptive diagnoses frequently metamorphose into established diagnoses in medical records, even if incorrect. 5. A number of authors have apparently disparaged the importance of diagnosis. Nonetheless, it is clear that diagnosis must continue to play a central role in clinical medicine. We propose several ways by which we can resist these forces and assure that diagnosis retains its appropriate position of primacy.

Investigation of Genetic Evidence for Sasang Constitution Types in South Korea

In Sasang constitutional medicine, both disease susceptibility and drug response are considered to be related to the characteristics of an individual’s physiology and psychology: a theory which is central to traditional Korean medicine. Based on such observable characteristics, Sasang constitutional medicine classifies people into four constitutional types. Genetic studies of Sasang constitution would help reveal the inheritance patterns and models of the typological traits and, moreover, help with traditional medical diagnosis and treatment. To investigate the heritable aspect of Sasang constitution, we collected various pedigrees from South Korea. The study population has 101 pedigrees composed of 593 individuals. The determination of the Sasang constitution type of each individual was performed by doctors who diagnose the Sasang constitutional type of individuals as part of their professional practice. We calculated estimates of familial correlation and heritability. Parent-Offspring pairs showed the strongest familial correlation of Sasang constitutional type, with the correlation values of 0.21 and 0.28, followed by sibling pairs with the value ranging between 0.14 and 0.25. From the heritability analysis conducted with the Variance-Component method, the heritability of TE (Tae-Eum) type, SY (So-Yang) type, and SE (So-Eum) type were 55%, 41%, and 47%, respectively. This pattern of heritability was consistent with different set of analyses, which suggest the robustness of our result. Our result clearly shows that the Sasang constitution type is heritable, and further genetic analysis based on our result will shed light on the biological mechanism of Sasang constitution.

3D reconstruction of a femoral shape using a parametric model and two 2D fluoroscopic images

In medical diagnostic imaging, the X-ray CT scanner and the MRI system have been widely used to examine 3D shapes and internal structures of living organisms and bones. However, these apparatuses are generally large and very expensive. Since an appointment is also required before examination, these systems are not suitable for urgent fracture diagnosis in emergency treatment. However, X-ray/fluoroscopy has been widely used as traditional medical diagnosis. Therefore, the realization of the reconstruction of precise 3D shapes of living organisms or bones from a few conventional 2D fluoroscopic images might be very useful in practice, in terms of cost, labor, and radiation exposure. The present paper proposes a method by which to estimate a patient-specific 3D shape of a femur from only two fluoroscopic images using a parametric femoral model. First, we develop a parametric femoral model by the statistical analysis of 3D femoral shapes created from CT images of 56 patients. Then, the position and shape parameters of the parametric model are estimated from two 2D fluoroscopic images using a distance map constructed by the Level Set Method. Experiments using synthesized images, fluoroscopic images of a phantom femur, and in vivo images for hip prosthesis patients are successfully carried out, and it is verified that the proposed system has practical applications.

めまい患者における東洋医学的気血水の検討

Japanese traditional medical diagnosis using Ki-Ketsu-Sui in vertiginous patients was examined. Among all parameters, the highest positive rate was demonstrated for Suitai. Kiutsu and Kikyo also showed high positive rates, suggesting that psychogenic factors play important roles in the occurrence of vertigo. Multiple parameters were shown to be positive in young and elderly patients, suggesting that multiple factors may contribute to the occurrence of vertigo. A high positive rate was present for Suitai but not for Oketsu with peripheral vestibular disorder. On the other hand, vertebrobasilar insufficiency and idiopathic vertigo demonstrated a high positivity rate for Suitai, Oketsu, and Ketsukyo, suggesting the contribution of vascular disorders to the occurrence of vertigo.

東洋医学的診断と平衡機能検査との関連性についての検討

The relationship between Japanese traditional medical diagnosis and examination of equilibrium was studied. Spontaneous and positional nystagmus, and the results of sinusoidal rotatory test and Shellong test, which might be strongly related to the active stage in vertigo, were not relevant to Japanese traditional medical diagnosis. However, it is likely that the abnormal findings in caloric stimulation test or optokinetic nystagmus test which suggest the malfunction of the peripheral or central vestibular system are partly related to Japanese traditional medical diagnosis.

A statistically rule-based decision support system for the management of patients with suspected liver disease.

The paper describes how a decision support system in liver diseases, mostly oriented to prediction of the necessity for liver biopsy, has been developed. The system designed is a hybrid one and consists of two parts: logical and statistical. The logical part contains rules, formulated on the basis of current medical knowledge, which enables recognition of clear cases; diseased or non-diseased. The unclear cases are classified on the basis of rules statistically extracted from databases. These rules have been reached after a comprehensive exploratory analysis of the sample of 165 patients with slightly to moderately raised levels of routine liver tests but without signs or symptoms of liver diseases. The extracted decision diagrams which simulate traditional medical diagnosis conduct have been found to be superior to discriminant analysis and probabilistic inductive learning. They use only a limited number of laboratory tests to detect the necessity for biopsy.

Teaching psychological medicine to family practice residents.

The authors describe the development of a curriculum focused on teaching residents in family practice how to recognize and deal with the psychological issues presented by patients and by their own reactions to patients. They describe how teachers in a medical school's divisions of family practice and psychosomatic and liaison psychiatry collaborated to develop an integrated training program and the changes this program effected in the attitudes and understanding of residents. They found that residents in this program shifted their emphasis from a narrow view of traditional medical diagnosis and treatment to a broader psycholgical-biological understanding of patients.