Theory of Dynamic Diagnosis Based on Integrated Maintenance Information

Based on the concept of an integrated maintenance information system and related information environment, this paper discusses the process of troubleshooting in modern maintenance in detail, and gives a model of dynamic diagnosis of faults, in which a reasoning program is designed through taking advantage of information fusion and time analysis. In the end, the authors present the logic process of dynamic diagnosis through a typical example, and proposes a dynamic diagnostic system based on information fusion.

Machine learning models for stroke detection by observing the eye-movement features under five-color visual stimuli in traditional Chinese medicine

Objective:To develop a novel diagnostic modality to identify and diagnose stroke in daily life scenarios for improving the therapeutic effects and prognoses of patients.Methods:In this study,16 stroke patients and 24 age-matched healthy participants as controls were recruited for comparative analysis.Leveraging a portable eye-tracking device and integrating traditional Chinese medicine theory with modern color psychology principles,we recorded the eye movement signals and calculated eye movement features.Meanwhile,the stroke recognition models based on eye movement features were further trained by using random forest(RF),k-nearest neighbors(KNN),decision tree(DT),gradient boosting classifier(GBC),XGBoost,and CatBoost.Results:The stroke group and the healthy group showed significant differences in some eye movement features(P<.05).The models trained based on eye movement characteristics had good performances in recognizing stroke individuals,with accuracies ranging from 77.40%to 88.45%.Under the red stimulus,the eye movement model trained by RF became the best machine learning model with a recall of 84.65%,a precision of 86.48%,a F1 score of 85.47%.Among the six algorithms,RF and CatBoost performed better in classification.Conclusion:This study pioneers the application of traditional Chinese medicine's five-color stimuli to visual observation tasks.On the basis of the combined design,the eye-movement models can accurately identify stroke,and the developed high-performance models may be used in daily life scenarios.

Pancreatic carcinoma coexisting with chronic pancreatitis versus tumor-forming pancreatitis:Diagnostic utility of the time-signal intensity curve from dynamic contrast-enhanced MR imaging

AIM: To evaluate the ability of the time-signal intensity curve (TIC) of the pancreas obtained from dynamic contrast-enhanced magnetic resonance imaging (MRI) for differentiation of focal pancreatic masses, especially pancreatic carcinoma coexisting with chronic pancreatitis and tumor-forming pancreatitis. METHODS: Forty-eight consecutive patients who underwent surgery for a focal pancreatic mass, including pancreatic ductal carcinoma (n = 33), tumor-forming pancreatitis (n = 8), and islet cell tumor (n = 7), were reviewed. Five pancreatic carcinomas coexisted with longstanding chronic pancreatitis. The pancreatic TICs were obtained from the pancreatic mass and the pancreatic parenchyma both proximal and distal to the mass lesion in each patient, prior to surgery, and were classified into 4 types according to the time to a peak: 25 s and 1, 2, and 3 min after the bolus injection of contrast material, namely, type-Ⅰ, Ⅱ, Ⅲ, and Ⅳ, respectively, and were then compared to the corresponding histological pancreatic conditions. RESULTS: Pancreatic carcinomas demonstrated type-Ⅲ (n = 13) or Ⅳ (n = 20) TIC. Tumor-forming pancreatitis showed type-Ⅱ (n = 5) or Ⅲ (n = 3) TIC. All islet cell tumors revealed type-Ⅰ. The type-Ⅳ TIC was only recognized in pancreatic carcinoma, and the TIC of carcinoma always depicted the slowest rise to a peak among the 3 pancreatic TICs measured in each patient, even in patients with chronic pancreatitis.CONCLUSION: Pancreatic TIC from dynamic MRI provides reliable information for distinguishing pancreatic carcinoma from other pancreatic masses, and may enable us to avoid unnecessary pancreatic surgery and delays in making a correct diagnosis of pancreatic carcinoma, especially, in patients with longstanding chronic pancreatitis.

Intelligent diagnosis of jaundice with dynamic uncertain causality graph model

Jaundice is a common and complex clinical symptom potentially occurring in hepatology, general surgery, pediatrics, infectious diseases, gynecology, and obstetrics, and it is faidy difficult to distinguish the cause of jaundice in clinical practice, especially for general practitioners in less developed regions. With collaboration between physicians and artificial intelligence engineers, a comprehensive knowledge base relevant to jaundice was created based on demographic information, symptoms, physical signs, laboratory tests, imaging diagnosis, medical histories, and risk factors. Then a diagnostic modeling and reasoning system using the dynamic uncertain causality graph was proposed. A modularized modeling scheme was presented to reduce the complexity of model construction, providing multiple perspectives and arbitrary granularity for disease causality representations. A ＂chaining＂ inference algorithm and weighted logic operation mechanism were employed to guarantee the exactness and efficiency of diagnostic rea- soning under situations of incomplete and uncertain information. Moreover, the causal interactions among diseases and symptoms intuitively demonstrated the reasoning process in a graphical manner. Verification was performed using 203 randomly pooled clinical cases, and the accuracy was 99.01% and 84.73%, respectively, with or without laboratory tests in the model. The solutions were more explicable and convincing than common methods such as Bayesian Networks, further increasing the objectivity of clinical decision-making. The promising results indicated that our model could be potentially used in intelligent diagnosis and help decrease public health expenditure.

Climate Characteristics of Abnormal Double-Blocking Activities over the Ural Mountains and Sea of Okhotsk

By using NCEP–NCAR daily reanalysis data for June–August of 1948–2009, the synoptic situation of the doubleblocking high pressure process over the Ural Mountains–Sea of Okhotsk region was identified objectively, and the climatic characteristics and dynamic mechanism during the double blockings were also investigated. The results suggest that the Urals–Okhotsk double-blocking high experienced obviously an abrupt change around 1977. After the change, the occurrence frequency and number of sustained days of the blocking high reduced significantly.Moreover, the zonal spacing between the Urals blocking and the Okhotsk blocking also decreased, while their meridional locations did not change much. The double blockings became strengthened in general, and the blocking high over the Sea of Okhotsk extended northward evidently. However, during the process without double-blocking activities, the intensity and location of the double-blocking high showed no noticeable variation before and after the abrupt change. In addition, dynamic diagnosis shows that after the abrupt change, the energy of the double-blocking high mainly propagated vertically from low to high levels, and transferred horizontally from low to high latitudes; whereas, previously, before the abrupt change, this occurred from high to low latitudes. Meanwhile, the development of a low pressure system was fueled between the double blockings, which meant that the double-blocking activity was beneficial to the maintenance and development of the Northeast China cold vortex. In short, diagnosis of the wave energy has provided us with a better explanation of the life cycle characteristics of this double-blocking phenomenon before and after 1977.

An innovative method to evaluate the severe level of general Qi deficiency

OBJECTIVE:The study aim was to use existing relevant indexes to establish an index series for the quantitative measurement of Qi deficiency pattern(QDP) in Traditional Chinese Medicine(TCM).METHODS:Seventeen indexes of arterial oxygen saturation(SaO_2),TCM pulse diagnosis,heart rate,and blood pressure were integrated into an index series,and 94 selected subjects were divided into a healthy control group,a mild QDP group,and a typical QDP group.All three groups were then synchronously given a breath-holding(BH) test and index detections using TCM apparatus for auxiliary diagnosis and treatment and a photoplethysmograph.The detections were carried out repeatedly until there was no monotonic change in the value of the minimal SaO_(2d)uring a BH measurement(SaO_(2 min)) or in the duration of BH during a BH measurement(TBH).RESULTS:BH measurement and index detection data from 380 eligible cases were analyzed and a significant intergroup difference was found for 12 indexes.CONCLUSION:An index series for quantitative QDP measurement could comprise the 12 effective indexes:RSaO_(2d)(rate of decline of SaO_(2d)uring a BH measurement),RSaO_(2r)(recovery rate of SaO_2 after a BH measurement),SaO_(2 min),BFV(blood flow velocity),TSaO_(2d)(the time at which SaO_2 started declining during a BH measurement),TBH,TSa_(O2 min)(the time of occurrence of the minimal SaO_(2d)uring a BH measurement),TSa O_(2 max)(the time of occurrence of the maximal SaO_2 after a BH measurement),PTP(pulse-touching pressure),SBP(systolic blood pressure),PWV(pulse wave velocity),and DBP(diastolic blood pressure).Of the indexes,RSaO_(2d)could play akey role in quantitative QDP measurement;RSaO_(2r) and Sa O_2 mincould be used to differentiate the QDP symptom of shortness of breath and as early-warning indexes for chronic obstructive pulmonary disease.BFV,TSaO_(2d),and TBHcould be used to quantitatively assess the QDP symptoms of spontaneous perspiration and shortness of breath.