Diagnosis Rules Research Articles

Gout diagnoses uncertain when made by general practitioners without serum uric acid testing: an observational study.

General practitioners (GPs) diagnose the majority of all gout patients. They make their diagnosis clinically. Serum uric acid (SUA) level >0.35mmol/l is largely determinative for the clinical diagnosis of gout. We aimed to assess to what extent GPs test SUA when making a first gout diagnosis, and to consider consequences regarding diagnostic certainty of not testing it. We calculated proportions of patients from 87 Dutch general practices (1-1-2013 to 1-1-2022) with a first gout diagnosis and a recorded SUA test, evaluated if SUA testing was punctually timed with respect to the diagnosis date, whether SUA levels were >0.35mmol/L, and whether diagnoses corresponded with diagnoses according to the 'Acute Gout Diagnosis Rule' (AGD-rule). In 43.0% of 7782 patients (mean age 64.4 years; 68.8% male) no SUA test result was recorded, with substantial variation among practices: median 41.9% (IQR 20.2%). 'Gout very likely' according to the AGD-rule was seen in all males and in 83.1% of females when a punctually timed SUA >0.35mmol/L was present. When such SUA level was lacking, 'Questionable or indeterminate gout diagnosis' was seen in all males and in 67.1% of females, and 'Arthritis diagnosis other than gout' in 32.9% of females. GPs diagnosed gout without testing SUA in ~40% of cases. This implies avoidable diagnostic uncertainty with impact for the clinical care of many patients as well as for studies that include primary care patients with gout. Key Points • Serum uric acid (SUA) level > 0.35mmol is determinative when diagnosing gout: however, it is unknown how often SUA testing is applied as such. • More than 40% of patients with a first gout diagnosis according to general practitioners (GPs) had no SUA test result registered in their medical record. • Gout diagnosing by GPs without SUA testing appeared to lead to avoidable diagnostic uncertainty, as diagnoses are often questionable or sometimes even wrong. • Researchers investigating gout need to take into account diagnostic uncertainty if they include gout patients who are diagnosed in primary care in their studies without SUA information.

Functional neurological disorder: A rule-in diagnosis.

Functional neurological disorder: A rule-in diagnosis.

A real-world study of the differences in Traditional Chinese Medicine diagnosis and treatment rules for coronavirus disease 2019 between Northern and Southern China.

To explore the differences in Traditional Chinese Medicine (TCM) diagnosis and treatment rules for coronavirus disease 2019 (COVID-19) between Northern and Southern China based on the real-world data from 982 COVID-19 patients. All consecutive cases of COVID-19 admitted to the TCM department of designated COVID-19 hospitals in eight provinces and cities were retrospectively analyzed. Patients were divided into a Northern and a Southern group according to the location of the admitting hospital. The symptoms, syndrome elements, syndrome distribution and herbal treatments were analyzed. The core prescriptions were extracted using the multiscale backbone-based network comparison algorithm (msbNC). The distribution of syndrome elements showed that dampness was common in Northern and Southern China, wind and heat were more often present in the South, while fire toxin and spleen deficiency were more often encountered in the North. The distribution of syndromes showed that the South was dominated by heat dampness accumulating in the lung (55.69%), while the North was dominated by dampness-toxin stagnating in the lung (44.90%).The results of core prescription mining showed that dispelling dampness, dispersing wind, clearing heat and strengthening spleen were the common treatment methods in Northern and Southern China. For mild cases, Jinyinhua (Flos Lonicerae) and Lianqiao (Fructus Forsythiae Suspensae) were often used in the South to clear heat and relieve exterior symptoms, while Chaihu (Radix Bupleuri Chinensis) and Huangqin (Radix Scutellariae Baicalensis) were often used in the North to relieve muscles by expelling heat. For moderate cases, Chaihu (Radix Bupleuri Chinensis), Qinghao (Herba Artemisiae Annuae), and Shigao (Gypsum Fibrosum) were often used to clear heat of Tri-jiao Channel and stomach in the South, while Fuling (Poria), Chenpi (Pericarpium Citri Reticulatae), and Dangshen (Radix Codonopsis) were often used to invigorate spleen and remove dampness in the North. For severe cases, spleen invigoration and dampness removal as well as relaxing the bowels and discharging heat were often used in the North. There were certain North-South differences in terms of symptoms, syndrome elements and syndrome distribution of COVID-19, as well as differences in core prescriptions during different periods of the disease. The regional differences in the rules of TCM diagnosis and treatment for COVID-19 should be further considered in the process of optimization and revision of relevant treatment guidance.

MBRB: Micro-belief rule Base model based on cautious conjunctive rule for interpretable fault diagnosis

—In aerospace engineering, implementing interpretable fault diagnosis technology is critical for improving the credibility of diagnostic results and ensuring the reliability of complex electromechanical systems. As a typical interpretable modeling method, the belief rule base (BRB) approach still faces two challenges that need to be addressed for its application in interpretable fault diagnosis: 1) The lengthy rule structures raise the cost of model interpretability, and 2) over-optimization of parameters diminishes the interpretability. To solve these problems, this paper proposes a modified micro-belief rule structure and develops a new micro-belief rule base (MBRB) model based on this structure. In addition, considering the independence of rules and the correlations between multiple BRBs, a cautious conjunctive rule-based reasoning process is established as the inference engine of MBRB. Moreover, an interpretable optimization method based on projection covariance matrix adaptive evolutionary strategy (PCMAES-I) is proposed for the MBRB model to balance model interpretability and diagnostic accuracy. Finally, the availability and effectiveness of the proposed MBRB is verified through an electromagnetic relay experiment.

Examining the impact of common faults on chiller performance through experimental investigation and parameter sensitivity analysis

Chillers are the important components across various applications, and understanding the impact of common faults on their performance is crucial for monitoring fault progression and predicting degradation. Consequently, an experimental investigation was conducted on a magnetic levitation-driven chiller across different severity levels. The quantitative influence of common faults on performance was then analyzed, with a specific focus on thermodynamic mechanism parameters. From a data variation perspective, a sensitivity analysis was implemented to identify the fault-sensitive parameters that exhibited noteworthy variations due to faults. From a classification performance perspective, the diagnosis-effective parameters were identified based on their significance in fault diagnosis. The results of this study led to several key conclusions: 1) Trends and magnitudes of changes in 38 parameters related to nine typical faults are identified in comparison to their normal levels; 2) Common faults have a significant influence on thermodynamic mechanism parameters, causing big changes in two to eight thermodynamic mechanism parameters for each fault; 3) Discrepancies between fault-sensitive parameters and diagnosis-effective parameters results in a difference of up to 64% in diagnostic performance; 4) Nine decoupling parameters are identified as pivotal in diagnosing concurrent faults, and an effective set of fault diagnosis rules is recommended. These findings provide valuable insights into the selection of sensitive parameters, identification of fault-decoupling parameters, and the development of fully interpretable and reliable fault diagnosis approaches.

D-Bot: Database Diagnosis System using Large Language Models

Database administrators (DBAs) play an important role in managing database systems. However, it is hard and tedious for DBAs to manage vast database instances and give timely response (waiting for hours is intolerable in many online cases). In addition, existing empirical methods only support limited diagnosis scenarios, which are also labor-intensive to update the diagnosis rules for database version updates. Recently large language models (LLMs) have shown great potential in various fields. Thus, we propose D-Bot , an LLM-based database diagnosis system that can automatically acquire knowledge from diagnosis documents, and generate reasonable and well-founded diagnosis report (i.e., identifying the root causes and solutions) within acceptable time (e.g., under 10 minutes compared to hours by a DBA). The techniques in D-Bot include ( i ) offline knowledge extraction from documents, ( ii ) automatic prompt generation (e.g., knowledge matching, tool retrieval), ( iii ) root cause analysis using tree search algorithm, and ( iv ) collaborative mechanism for complex anomalies with multiple root causes. We verify D-Bot on real benchmarks (including 539 anomalies of six typical applications), and the results show D-Bot can effectively identify root causes of unseen anomalies and significantly outperforms traditional methods and vanilla models like GPT-4.

A Novel Hybrid Approach to the Diagnosis of Simultaneous Imbalance and Shaft Bowing Faults in a Jeffcott Rotor-Bearing System

Ensuring optimal performance and reliability in rotor-bearing systems is crucial for industrial applications. Imbalances and shaft bowing in these systems can lead to decreased efficiency and increased vibrations. The early detection and mitigation of a rotor’s faults are essential, and model-based fault identification has gained much attention in the manufacturing industry over the years. Over the past two decades, however, the development of fault diagnosis rules with data-driven and artificial intelligence (AI) methods has become a trend, and in the foreseeable future the combination of AI with big data will become mainstream. Nevertheless, the critical role of rotating machinery in manufacturing introduces a challenge, as often insufficient fault data are available. This limitation renders the establishment of diagnostic rules using data-driven methods and AI technologies impractical. In light of these challenges, this study proposes a novel hybrid approach that combines a physical model with machine learning (ML) techniques for the diagnosis of multi-faults (imbalances and shaft bowing are demonstrated) in a Jeffcott rotor. To overcome the lack of real-world labeled fault datasets, a physics-based Jeffcott rotor model is first derived and then used to generate abundant fault datasets for ML. Subsequently, simulated data are employed for the training of an artificial neural network (ANN), enabling the network to learn from and analyze the vast array of generated data. The results prove that a well-trained feed-forward neural network (FNN) can accurately isolate and diagnose imbalance and shaft bowing faults using the simulated and real data from the Jeffcott rotor experiment. These physics-based and ML approaches prove effective particularly for multi-faults, offering new possibilities for advanced rotor system monitoring and maintenance strategies in industrial applications.

A dynamic fuzzy rule-based inference system using fuzzy inference with semantic reasoning

The challenge of making flexible, standard, and early medical diagnoses is significant. However, some limitations are not fully overcome. First, the diagnosis rules established by medical experts or learned from a trained dataset prove static and too general. It leads to decisions that lack adaptive flexibility when finding new circumstances. Secondly, medical terminological interoperability is highly critical. It increases realism and medical progress and avoids isolated systems and the difficulty of data exchange, analysis, and interpretation. Third, criteria for diagnosis are often heterogeneous and changeable. It includes symptoms, patient history, demographic, treatment, genetics, biochemistry, and imaging. Symptoms represent a high-impact indicator for early detection. It is important that we deal with these symptoms differently, which have a great relationship with semantics, vary widely, and have linguistic information. This negatively affects early diagnosis decision-making. Depending on the circumstances, the diagnosis is made solo on imaging and some medical tests. In this case, although the accuracy of the diagnosis is very high, can these decisions be considered an early diagnosis or prove the condition is deteriorating? Our contribution in this paper is to present a real medical diagnostic system based on semantics, fuzzy, and dynamic decision rules. We attempt to integrate ontology semantics reasoning and fuzzy inference. It promotes fuzzy reasoning and handles knowledge representation problems. In complications and symptoms, ontological semantic reasoning improves the process of evaluating rules in terms of interpretability, dynamism, and intelligence. A real-world case study, ADNI, is presented involving the field of Alzheimer’s disease (AD). The proposed system has indicated the possibility of the system to diagnose AD with an accuracy of 97.2%, 95.4%, 94.8%, 93.1%, and 96.3% for AD, LMCI, EMCI, SMC, and CN respectively.

Development and application of Chinese medical ontology for diabetes mellitus

ObjectiveTo develop a Chinese Diabetes Mellitus Ontology (CDMO) and explore methods for constructing high-quality Chinese biomedical ontologies.Materials and methodsWe used various data sources, including Chinese clinical practice guidelines, expert consensus, literature, and hospital information system database schema, to build the CDMO. We combined top-down and bottom-up strategies and integrated text mining and cross-lingual ontology mapping. The ontology was validated by clinical experts and ontology development tools, and its application was validated through clinical decision support and Chinese natural language medical question answering.ResultsThe current CDMO consists of 3,752 classes, 182 fine-grained object properties with hierarchical relationships, 108 annotation properties, and over 12,000 mappings to other well-known medical ontologies in English. Based on the CDMO and clinical practice guidelines, we developed 200 rules for diabetes diagnosis, treatment, diet, and medication recommendations using the Semantic Web Rule Language. By injecting ontology knowledge, CDMO enhances the performance of the T5 model on a real-world Chinese medical question answering dataset related to diabetes.ConclusionCDMO has fine-grained semantic relationships and extensive annotation information, providing a foundation for medical artificial intelligence applications in Chinese contexts, including the construction of medical knowledge graphs, clinical decision support systems, and automated medical question answering. Furthermore, the development process incorporated natural language processing and cross-lingual ontology mapping to improve the quality of the ontology and improved development efficiency. This workflow offers a methodological reference for the efficient development of other high-quality Chinese as well as non-English medical ontologies.

Application of SAFARI in Prediction of Heart Disease

Cardiovascular disease has been the major cause of mortality worldwide for last several decades. Diagnosis of heart disease through traditional approaches is a complex, time consuming and error prone process. To address this issue, several techniques have been proposed to automate the process of diagnosing the heart disease accurately in timely manner. However these techniques report limited accuracy of diagnosing the disease. In this paper SAFARI algorithm is used to diagnose the heart disease. Safari uses rule based approach i.e. it extracts rules from a dataset and uses the extracted rules for diagnosis. The various attribute values are first discretised into specific ranges, each range corresponds to a symbol. This results in a symbol table. Safari extracts rules from this symbol table. The approach has been thoroughly tested on the heart disease dataset publicly available on UCI machine learning repository. The results obtained using this approach are compared with the results of various techniques reported by other authors, a significant improvement was observed.

2022 revised European recommendations for the coding of the basis of diagnosis of cancer cases in population-based cancer registries.

The basis of diagnosis recommendations for population-based cancer registries aim to provide a standardized coding tool that reflects the certainty of cancer diagnosis, especially when pathological confirmation is lacking. The proportion of clinical diagnoses serves as an indicator of data quality. Given the evolving nature of diagnostic techniques, regular revision of the basis of diagnosis rules is crucial. To address this, a working group comprising representatives from the steering committee and member registries of the European Network of Cancer Registries was established. The original 1999 recommendations were comprehensively reviewed, resulting in the publication of an updated version. These new recommendations came into effect for incident cancer cases starting from January 1, 2023. The updated recommendations comprise an adapted code list for the basis of diagnosis, optional codes for histology cases, revisions related to flow cytometry, liquid biopsy, and cytogenetic/molecular testing, consolidation of histology codes 6 and 7, introduction of a new code 8 for cytogenetic/molecular confirmation, and establishment of new criteria for registering specific morphology codes in cancers lacking pathological confirmation.

A hybrid framework based on knowledge distillation for explainable disease diagnosis

Deep learning (DL) techniques provide highly accurate disease predictions by extracting implicit higher-order correlations between features. However, DL opinions are not advisable for direct clinical practice because they lack interpretability in their reasoning logic. Fuzzy rule-based systems (FRBSs) can provide transparent and effective diagnostic results, but the rule explosion problem and the long-tail distribution of rules weaken their performance. Therefore, DL and FRBS complement each other. This paper proposes a hybrid framework based on DL models and fuzzy rules for explainable disease diagnosis. This framework has three components: interpretable component, uninterpretable component, and knowledge distillation component. In the interpretable component, we propose a strong rule extraction method to avoid the rule explosion problem and the long-tail distribution of rules by discarding weak rules. In the uninterpretable component, we use DL models to capture the implicit higher-order interactions between features to improve the model's performance. The knowledge distillation component embeds the hidden knowledge extracted from DL models into SFRBSs to guide the parameter tuning. This framework can predict new instances and provide interpretable prediction results by extracting explicit knowledge (fuzzy rules) and feature weights. Experimental results based on three real-world medical datasets show that the framework achieves the highest AUC and accuracy, which is more accurate and interpretable than other diagnostic models.

Practical diagnosis of cirrhosis in non-alcoholic fatty liver disease using currently available non-invasive fibrosis tests

Unlike for advanced liver fibrosis, the practical rules for the early non-invasive diagnosis of cirrhosis in NAFLD remain not well defined. Here, we report the derivation and validation of a stepwise diagnostic algorithm in 1568 patients with NAFLD and liver biopsy coming from four independent cohorts. The study algorithm, using first the elastography-based tests Agile3+ and Agile4 and then the specialized blood tests FibroMeterV3G and CirrhoMeterV3G, provides stratification in four groups, the last of which is enriched in cirrhosis (71% prevalence in the validation set). A risk prediction chart is also derived to allow estimation of the individual probability of cirrhosis. The predicted risk shows excellent calibration in the validation set, and mean difference with perfect prediction is only −2.9%. These tools improve the personalized non-invasive diagnosis of cirrhosis in NAFLD.

Gout Staging Diagnosis Method Based on Deep Reinforcement Learning

In clinical practice, diseases with a prolonged course and disease characteristics at the time of diagnosis are often classified into specific stages. The precision of disease staging significantly impacts the therapeutic and curative outcomes for patients, and the diagnosis of multi-clinical-stage diseases based on electronic medical records is a problem that needs further research. Gout is a multi-stage disease. This paper focuses on the research of gout and proposes a staging diagnosis method for gout based on deep reinforcement learning. This method firstly uses the candidate binary classification model library for accurate diagnosis of gout, and then corrects the results of the binary classification through the set medical rules for diagnosis of gout, and then uses the machine learning model to diagnose different stages of corrected accurate data. In the course of the experiment, deep reinforcement learning was introduced to solve the hyperparameter tuning problem of the staging model. Through experiments conducted on 24,872 electronic medical records, the accuracy rate of gout diagnosis was found to be 90.03%, while the accuracy rate for diagnosing different stages of gout disease reached 86.85%. These findings serve as a valuable tool in assisting clinicians with accurate staging and diagnosis of gout. The application of deep reinforcement learning in gout staging diagnosis demonstrates a significant enhancement in diagnostic accuracy, thereby validating the effectiveness and feasibility of this method.

Diagnostic Accuracy of Biomarkers and International Ovarian Tumor Analysis Simple Rules in Diagnosis of Ovarian Cancer

This study investigated whether combining International Ovarian Tumor Analysis (IOTA) Simple Rules with tumor biomarkers would improve the diagnostic accuracy for early detection of adnexal malignancies. Receiver operating characteristic curve analysis of suspected adnexal tumors was performed in 226 women admitted for surgery at the University Clinical Center of Kosovo. Primary outcome was the diagnostic accuracy of the combination of adnexal mass biomarkers and IOTA Simple Rules. IOTA Simple Rules combined with biomarker indications increased the diagnostic accuracy of classifying adnexal masses. Data analysis of individual measures showed that ferritin had the lowest rate of sensitivity.

A discrete equilibrium optimization algorithm for breast cancer diagnosis

Illness diagnosis is the essential step in designating a treatment. Nowadays, Technological advancements in medical equipment can produce many features to describe breast cancer disease with more comprehensive and discriminant data. Based on the patient’s medical data, several data-driven models are proposed for breast cancer diagnosis using learning techniques such as naive Bayes, neural networks, and SVM. However, the models generated are hardly understandable, so doctors cannot interpret them. This work aims to study breast cancer diagnosis using the associative classification technique. It generates interpretable diagnosis models. In this work, an associative classification approach for breast cancer diagnosis based on the Discrete Equilibrium Optimization Algorithm (DEOA) named Discrete Equilibrium Optimization Algorithm for Associative Classification (DEOA-AC) is proposed. DEOA-AC aims to generate accurate and interpretable diagnosis rules directly from datasets. Firstly, all features in the dataset that contains continuous values are discretized. Secondly, for each class, a new dataset is created from the original dataset and contains only the chosen class’s instances. Finally, the new proposed DEOA is called for each new dataset to generate an optimal rule set. The DEOA-AC approach is evaluated on five well-known and recently used breast cancer datasets and compared with two recently proposed and three classical breast cancer diagnosis algorithms. The comparison results show that the proposed approach can generate more accurate and interpretable diagnosis models for breast cancer than other algorithms.

Automation of Banff rules for precision diagnosis

Automation of Banff rules for precision diagnosis

Present situation and development prospects of the diagnosis and treatment of rotator cuff tears.

Rotator cuff tears are an important cause of shoulder pain and are caused by degeneration or trauma of the shoulder tendon at the anatomical neck of the humeral head. The understanding and research of rotator cuff tears have a history of hundreds of years, and their etiology, diagnosis, and treatment have a complete system, but some detailed rules of diagnosis and treatment still have room for development. This research paper briefly introduces the diagnosis and treatment of rotator cuff tears. The current situation and its valuable research direction are described.

Reinforcing Regional Emergency and Critical Care Networks: The Implementation of Telemedicine Systems in Remote Areas

<p>隨著全國每萬人醫師數逐年上升，醫療資源仍集中於都會區域，偏遠地區緊急醫療資源仍相對不足。衛生福利部於2018年「通訊診療治療辦法」放寬遠距醫療之照護對象與模式，並在2021年正式將急診納入「全民健康保險遠距醫療給付計畫」給付範圍。遠距醫療在急重症病人照護已有實證應用，基於遠距醫療專科門診系統建置經驗，結合醫療與資訊科技發展，建置急診遠距醫療會診系統。在面臨緊急醫療搶救，急診遠距醫療須具備好的工具進行資訊傳遞，包含友善操作介面、即時且重要的醫療資訊交換、紀錄留存及綠色通道團隊預先啟動，並可提供大量傷患迅速應變支援，縮短急重症病人醫療處置等待時間。急診遠距醫療會診系統之建置已開啟創新之急重症就醫模式，惟系統功能仍須透過滾動式修正及持續教育訓練，使醫護人員熟悉操作並列入常規系統使用，以作為偏遠地區醫院急診病人第二意見諮詢及轉診評估，降低病人不必要之轉診，提升病人醫療品質。</p> <p> </p><p>Although the number of medical doctors per 10,000 residents in Taiwan has been increasing, most medical resources remain concentrated in metropolitan areas, and emergency medical resources in remote areas remain insufficient. In 2018, the Ministry of Health and Welfare passed the Rules of Medical Diagnosis and Treatment by Telecommunications to expand the coverage of people and models eligible for telemedicine services. In 2021, emergency services were included in the coverage of the National Health Insurance Telemedicine Benefits Plan. Telemedicine has been applied in emergency and critical care with empirical evidence. Based on the experience in establishing a telemedicine specialist outpatient system and the incorporation of medical and information technologies, a remote emergency consultation system was established. To ensure effective emergency care, an emergency telemedicine model must incorporate favorable tools for information transmission, including a user-friendly interface that facilitates real-time and critical medical information exchange and record keeping. The model should also allow for early preparation of the green channel team, ensure that support is at hand for rapid response to a large number of patients, and shorten the waiting time of emergency and critically ill patients for medical treatment. The establishment of the remote emergency consultation system has opened the door to an innovative emergency and critical care model. However, the system functions should be improved on a rolling basis, and continuous staff training is required to familiarize staff with the operation of the system as a routine system. Thus, the system can be used for second opinion consultation and referral assessment for emergency patients in remote hospitals, thereby reducing unnecessary referrals and improving the quality of medical care.</p> <p> </p>

Developing TCM clinical practice guidelines: A comparison between traditional Chinese medicine and western medicine

Clinical practice guidelines in Traditional Chinese Medicine (CPG-TCM) is the recommendation that aims to provide the best service to users by identifying and summarizing the rules of prevention, diagnosis, treatment, rehabilitation, and regression of diseases based on systematic reviews of evidence and balancing the advantages and disadvantages of different interventions for clinical questions of Traditional Chinese Medicine. Over the past 30 years, the concept and methods of evidence-based medicine have had a significant impact on the development of clinical practice guidelines in Western Medicine (CPG-WM), and their standardized guideline development methods are being adapted and used in the development of CPG-TCM. However, the quality of CPG-TCM is far from CPG-WM, and the methodological system for developing CPG-TCM is not yet fully established. Therefore, this study aims to explore the methodological differences between CPG-TCM and CPG-WM and to inform the development of high-quality CPGTCM.