User-friendly prognostic model for rectal neuroendocrine tumours: In the era of precision management

In this letter,we explore into the potential role of the recent study by Zeng et al.Rectal neuroendocrine tumours(rNETs)are rare,originate from peptidergic neurons and neuroendocrine cells,and express corresponding markers.Although most rNETs patients have a favourable prognosis,the median survival period significantly decreases when high-risk factors,such as larger tumours,poorer differentiation,and lymph node metastasis exist,are present.Clinical prediction models play a vital role in guiding diagnosis and prognosis in health care,but their complex calculation formulae limit clinical use.Moreover,the prognostic models that have been developed for rNETs to date still have several limitations,such as insufficient sample sizes and the lack of external validation.A high-quality prognostic model for rNETs would guide treatment and follow-up,enabling the precise formulation of individual patient treatment and follow-up plans.The future development of models for rNETs should involve closer collab-oration with statistical experts,which would allow the construction of clinical prediction models to be standardized and robust,accurate,and highly general-izable prediction models to be created,ultimately achieving the goal of precision medicine.

A Cascading Fault Path Prediction Method for Integrated Energy Distribution Networks Based on the Improved OPA Model under Typhoon Disasters

In recent times,the impact of typhoon disasters on integrated energy active distribution networks(IEADNs)has received increasing attention,particularly,in terms of effective cascading fault path prediction and enhanced fault recovery performance.In this study,we propose a modified ORNL-PSerc-Alaska(OPA)model based on optimal power flow(OPF)calculation to forecast IEADN cascading fault paths.We first established the topology and operational model of the IEADNs,and the typical fault scenario was chosen according to the component fault probability and information entropy.The modified OPA model consisted of two layers:An upper-layer model to determine the cascading fault location and a lower-layer model to calculate the OPF by using Yalmip and CPLEX and provide the data to update the upper-layer model.The approach was validated via the modified IEEE 33-node distribution system and two real IEADNs.Simulation results showed that the fault trend forecasted by the novel OPA model corresponded well with the development and movement of the typhoon above the IEADN.The proposed model also increased the load recovery rate by>24%compared to the traditional OPA model.

Establishment and evaluation of prediction model of recurrence after laparoscopic choledocholithotomy

BACKGROUND Choledocholithiasis is a common clinical bile duct disease,laparoscopic choledocholithotomy is the main clinical treatment method for choledocho-lithiasis.However,the recurrence of postoperative stones is a big challenge for patients and doctors.AIM To explore the related risk factors of gallstone recurrence after laparoscopic choledocholithotomy,establish and evaluate a clinical prediction model.METHODS A total of 254 patients who underwent laparoscopic choledocholithotomy in the First Affiliated Hospital of Ningbo University from December 2017 to December 2020 were selected as the research subjects.Clinical data of the patients were collected,and the recurrence of gallstones was recorded based on the postope-rative follow-up.The results were analyzed and a clinical prediction model was established.RESULTS Postoperative stone recurrence rate was 10.23%(26 patients).Multivariate Logistic regression analysis showed that cholangitis,the diameter of the common bile duct,the diameter of the stone,number of stones,lithotripsy,preoperative total bilirubin,and T tube were risk factors associated with postoperative recurrence(P<0.05).The clinical prediction model was ln(p/1-p)=-6.853+1.347×cholangitis+1.535×choledochal diameter+2.176×stone diameter+1.784×stone number+2.242×lithotripsy+0.021×preoperative total bilirubin+2.185×T tube.CONCLUSION Cholangitis,the diameter of the common bile duct,the diameter of the stone,number of stones,lithotripsy,preoperative total bilirubin,and T tube are the associated risk factors for postoperative recurrence of gallstone.The prediction model in this study has a good prediction effect,which has a certain reference value for recurrence of gallstone after laparoscopic choledocholi-thotomy.

Development and application of hepatocellular carcinoma risk prediction model based on clinical characteristics and liver related indexes

BACKGROUND Hepatocellular carcinoma(HCC)is difficult to diagnose with poor therapeutic effect,high recurrence rate and has a low survival rate.The survival of patients with HCC is closely related to the stage of diagnosis.At present,no specific serolo-gical indicator or method to predict HCC,early diagnosis of HCC remains a challenge,especially in China,where the situation is more severe.AIM To identify risk factors associated with HCC and establish a risk prediction model based on clinical characteristics and liver-related indicators.METHODS The clinical data of patients in the Affiliated Hospital of North Sichuan Medical College from 2016 to 2020 were collected,using a retrospective study method.The results of needle biopsy or surgical pathology were used as the grouping criteria for the experimental group and the control group in this study.Based on the time of admission,the cases were divided into training cohort(n=1739)and validation cohort(n=467).Using HCC as a dependent variable,the research indicators were incorporated into logistic univariate and multivariate analysis.An HCC risk prediction model,which was called NSMC-HCC model,was then established in training cohort and verified in validation cohort.RESULTS Logistic univariate analysis showed that,gender,age,alpha-fetoprotein,and protein induced by vitamin K absence or antagonist-II,gamma-glutamyl transferase,aspartate aminotransferase and hepatitis B surface antigen were risk factors for HCC,alanine aminotransferase,total bilirubin and total bile acid were protective factors for HCC.When the cut-off value of the NSMC-HCC model joint prediction was 0.22,the area under receiver operating characteristic curve(AUC)of NSMC-HCC model in HCC diagnosis was 0.960,with sensitivity 94.40%and specificity 95.35%in training cohort,and AUC was 0.966,with sensitivity 90.00%and specificity 94.20%in validation cohort.In early-stage HCC diagnosis,the AUC of NSMC-HCC model was 0.946,with sensitivity 85.93%and specificity 93.62%in training cohort,and AUC was 0.947,with sensitivity 89.10%and specificity 98.49%in validation cohort.CONCLUSION The newly NSMC-HCC model was an effective risk prediction model in HCC and early-stage HCC diagnosis.

Preliminary Exploration of the Initial Diagnostic Prediction Model of Moderate Coronavirus Disease 2019 (2019-nCoV) Based on Clinical Data

Objective: To explore those differences and relationships of the initial diagnostic clinical data between confirmed cases of 2019-nCoV and suspected cases of COVID-19, and then to establish prediction models for predicting the probability of the first diagnosis of 2019-nCoV. Methods: A total of 81 suspected cases and 87 confirmed cases of moderate 2019-nCoV diagnosed initially in the isolation wards of the First People’s Hospital of Wuhu and the People’s Hospital of Wuwei and Wuhan Caidian Module Hospital with the help of our hospital doctors were gathered, and retrospectively analyzed. Results: The most common symptoms were fever (76.79%) and cough (64.29%) in the total of 168 cases. The median age was 45 (35 - 56) years old in the 87 confirmed cases of moderate 2019-nCoV, older than the median age 36 (29 - 50) in the 81 suspected cases. There were significant more in the former than in the latter in the incidence of myalgia, ground-glass opacity (GGO), invasions of lesion in the peripheral lobes, vascular thickening and bronchial wall thickening, interlobular septal thicking, and small pulmonary nodules. On the contrary, there were less in the former than in the latter in the total number of leukocytes and neutrophils in blood routine examination and the levels of procalcitonin (PCT). Two groups were statistically significantly different (P < 0.05). Multivariate logistic regression analysis showed that age, fever, myalgia, GGO, vascular thickening and bronchial wall thickening, invasions of lesion in the peripheral lobes were independent factors for identification of 2019-nCoV, and the total number of leukocytes, cough, pharyngalgia and headache were negatively related. The established mathematical equation for predicting model for predicting the probability of the first diagnosis of 2019-nCoV is: P = ex/(1 + ex), x = −6.226 + (0.071 × ages) + (1.720 × fever) + (2.858 × myalgia) + (2.131 × GGO) + (3.000 × vascular thickening and bron-chial wall thickening) + (3.438 × invasions of lesion in the peripheral lobes) + (−0.304 × the number of leukocytes) + (−1.478 × cough) + (−1.830 × pharyngalgia) + (−2.413 × headache), where e is a natural logarithm. The area under the ROC curve (AUC) of this model was calculated to be 0.945 (0.915 - 0.976). The sensitivity is 0.920 and the specificity is 0.827 when the appropriate critical point is 0.360. Conclusions: A mathematical equation prediction model for predicting the probability of the first diagnosis of 2019-nCoV can be established based on the initial diagnostic clinical data of moderate 2019-nCoV. The prediction model is a good assistant diagnostic method for its high accurateness.

Development of a machine learning-based model for predicting risk of early postoperative recurrence of hepatocellular carcinoma

BACKGROUND Surgical resection is the primary treatment for hepatocellular carcinoma(HCC).However,studies indicate that nearly 70%of patients experience HCC recurrence within five years following hepatectomy.The earlier the recurrence,the worse the prognosis.Current studies on postoperative recurrence primarily rely on postoperative pathology and patient clinical data,which are lagging.Hence,developing a new pre-operative prediction model for postoperative recurrence is crucial for guiding individualized treatment of HCC patients and enhancing their prognosis.AIM To identify key variables in pre-operative clinical and imaging data using machine learning algorithms to construct multiple risk prediction models for early postoperative recurrence of HCC.METHODS The demographic and clinical data of 371 HCC patients were collected for this retrospective study.These data were randomly divided into training and test sets at a ratio of 8:2.The training set was analyzed,and key feature variables with predictive value for early HCC recurrence were selected to construct six different machine learning prediction models.Each model was evaluated,and the bestperforming model was selected for interpreting the importance of each variable.Finally,an online calculator based on the model was generated for daily clinical practice.RESULTS Following machine learning analysis,eight key feature variables(age,intratumoral arteries,alpha-fetoprotein,preoperative blood glucose,number of tumors,glucose-to-lymphocyte ratio,liver cirrhosis,and pre-operative platelets)were selected to construct six different prediction models.The XGBoost model outperformed other models,with the area under the receiver operating characteristic curve in the training,validation,and test datasets being 0.993(95%confidence interval:0.982-1.000),0.734(0.601-0.867),and 0.706(0.585-0.827),respectively.Calibration curve and decision curve analysis indicated that the XGBoost model also had good predictive performance and clinical application value.CONCLUSION The XGBoost model exhibits superior performance and is a reliable tool for predicting early postoperative HCC recurrence.This model may guide surgical strategies and postoperative individualized medicine.

Probability Prediction Model for Landslide Occurrences in Xi'an, Shaanxi Province, China

Landslides are increasing since the 1980s in Xi'an, Shaanxi Province, China. This is due to the increase of the frequency and intensity of precipitation caused by complex geological structures, the presence of steep landforms, seasonal heavy rainfall, and the intensifcation of human activities. In this study, we propose a landslide prediction model based on the analysis of intraday rainfall(IR) and antecedent effective rainfall(AER). Primarily, the number of days and degressive index of the antecedent effective rainfall which affected landslide occurrences in the areas around Qin Mountains, Li Mountains and Loess Tableland was established. Secondly, the antecedent effective rainfall and intraday rainfall were calculated from weather data which were used to construct critical thresholds for the 10%, 50% and 90% probabilities for future landslide occurrences in Qin Mountain, Li Mountain and Loess Tableland. Finally, the regions corresponding to different warning levels were identified based on the relationship between precipitation and the threshold, that is; "A" region is safe, "B" region is on watch alert, "C" region is on warning alert and "D" region is on severe warning alert. Using this model, a warning program is proposed which can predict rainfall-induced landslides by means of real-time rain gauge data and real-time geo-hazard alert and disaster response programs. Sixteen rain gauges were installed in the Xi'an region by keeping in accordance with the regional geology and landslide risks. Based on the data from gauges, this model accurately achieves the objectives of conducting real-time monitoring as well as providing early warnings of landslides in the Xi'an region.

Development of a prediction model for enteral feeding intolerance in intensive care unit patients:A prospective cohort study

BACKGROUND Enteral nutrition(EN)is essential for critically ill patients.However,some patients will have enteral feeding intolerance(EFI)in the process of EN.AIM To develop a clinical prediction model to predict the risk of EFI in patients receiving EN in the intensive care unit.METHODS A prospective cohort study was performed.The enrolled patients’basic information,medical status,nutritional support,and gastrointestinal(GI)symptoms were recorded.The baseline data and influencing factors were compared.Logistic regression analysis was used to establish the model,and the bootstrap resampling method was used to conduct internal validation.RESULTS The sample cohort included 203 patients,and 37.93%of the patients were diagnosed with EFI.After the final regression analysis,age,GI disease,early feeding,mechanical ventilation before EN started,and abnormal serum sodium were identified.In the internal validation,500 bootstrap resample samples were performed,and the area under the curve was 0.70(95%CI:0.63-0.77).CONCLUSION This clinical prediction model can be applied to predict the risk of EFI.

Application of the predictable model ofregional time-magnitude to North and Southwest China region

In this paper, the method which can combine different seismic data with the different precision and completeness, even the palaeo-earthquake data, has been applied to estimate the yearly seismic moment rate in the seismic region. Based on this, the predictable model of regional time-magnitude has been used in North China and Southwest China. The normal correlation between the time interval of the events and the magnitude of the last strong earthquake shows that the model is suitable. The value of the parameter c is less than the average value of 0.33 that is obtained from the events occurred in the plate boundary in the world. It is explained that the correlativity between the recurrence interval of the earthquake and the magnitude of the last strong event is not obvious. It is shown that the continental earthquakes in China are different from that occurred in the plate boundary and the recurrence model for the continental events are different from the one for the plate boundary events. Finally the seismic risk analysis based on this model for North China and Southwest China is given in this paper.

Improving Multi-model Ensemble Probabilistic Prediction of Yangtze River Valley Summer Rainfall

Seasonal prediction of summer rainfall over the Yangtze River valley（YRV） is valuable for agricultural and industrial production and freshwater resource management in China, but remains a major challenge. Earlier multi-model ensemble（MME） prediction schemes for summer rainfall over China focus on single-value prediction, which cannot provide the necessary uncertainty information, while commonly-used ensemble schemes for probability density function（PDF） prediction are not adapted to YRV summer rainfall prediction. In the present study, an MME PDF prediction scheme is proposed based on the ENSEMBLES hindcasts. It is similar to the earlier Bayesian ensemble prediction scheme, but with optimization of ensemble members and a revision of the variance modeling of the likelihood function. The optimized ensemble members are regressed YRV summer rainfall with factors selected from model outputs of synchronous 500-h Pa geopotential height as predictors. The revised variance modeling of the likelihood function is a simple linear regression with ensemble spread as the predictor. The cross-validation skill of 1960–2002 YRV summer rainfall prediction shows that the new scheme produces a skillful PDF prediction, and is much better-calibrated, sharper, and more accurate than the earlier Bayesian ensemble and raw ensemble.

Validation of prognostic scores for predicting acute liver failure and in-hospital death in patients with dengue-induced severe hepatitis

BACKGROUND Acute liver failure(ALF)in dengue is rare but fatal.Early identification of patients who are at risk of ALF is the key strategy to improve survival.AIM To validate prognostic scores for predicting ALF and in-hospital mortality in dengue-induced severe hepatitis(DISH).METHODS We retrospectively reviewed 2532 dengue patients over a period of 16 years(2007-2022).Patients with DISH,defined as transaminases>10 times the normal reference level and DISH with subsequent ALF,were included.Univariate regre-ssion analysis was used to identify factors associated with outcomes.Youden’s index in conjunction with receiver operating characteristic(ROC)analysis was used to determine optimal cut-off values for prognostic scores in predicting ALF and in-hospital death.Area under the ROC(AUROC)curve values were compared using paired data nonparametric ROC curve estimation.RESULTS Of 193 DISH patients,20 developed ALF(0.79%),with a mortality rate of 60.0%.International normalized ratio,bilirubin,albumin,and creatinine were indepen-dent predictors associated with ALF and death.Prognostic scores showed excel-lent performance:Model for end-stage liver disease(MELD)score≥15 predicted ALF(AUROC 0.917,sensitivity 90.0%,specificity 88.4%)and≥18 predicted death(AUROC 0.823,sensitivity 86.9%,specificity 89.1%);easy albumin-bilirubin(ALBI)score≥-30 predicted ALF and death(ALF:AUROC 0.835,sensitivity80.0%,specificity 72.2%;death:AUROC 0.808,sensitivity 76.9%,specificity 69.3%);ALBI score≥-2 predicted ALF and death(ALF:AUROC 0.806,sensitivity 80.0%,specificity 77.4%;death:AUROC 0.799,sensitivity 76.9%,specificity 74.3%).Platelet-ALBI score also showed good performance in predicting ALF and death(AUROC=0.786 and 0.699,respectively).MELD and EZ-ALBI scores had similar performance in predicting ALF(Z=1.688,P=0.091)and death(Z=0.322,P=0.747).CONCLUSION MELD score is the best predictor of ALF and death in DISH patients.EZ-ALBI score,a simpler yet effective score,shows promise as an alternative prognostic tool in dengue patients.

From prediction to prevention:Machine learning revolutionizes hepatocellular carcinoma recurrence monitoring

In this editorial,we comment on the article by Zhang et al entitled Development of a machine learning-based model for predicting the risk of early postoperative recurrence of hepatocellular carcinoma.Hepatocellular carcinoma(HCC),which is characterized by high incidence and mortality rates,remains a major global health challenge primarily due to the critical issue of postoperative recurrence.Early recurrence,defined as recurrence that occurs within 2 years posttreatment,is linked to the hidden spread of the primary tumor and significantly impacts patient survival.Traditional predictive factors,including both patient-and treatment-related factors,have limited predictive ability with respect to HCC recurrence.The integration of machine learning algorithms is fueled by the exponential growth of computational power and has revolutionized HCC research.The study by Zhang et al demonstrated the use of a groundbreaking preoperative prediction model for early postoperative HCC recurrence.Challenges persist,including sample size constraints,issues with handling data,and the need for further validation and interpretability.This study emphasizes the need for collaborative efforts,multicenter studies and comparative analyses to validate and refine the model.Overcoming these challenges and exploring innovative approaches,such as multi-omics integration,will enhance personalized oncology care.This study marks a significant stride toward precise,efficient,and personalized oncology practices,thus offering hope for improved patient outcomes in the field of HCC treatment.

A Fuzzy Probability-based Markov Chain Model for Electric Power Demand Forecasting of Beijing, China

In this study, a fuzzy probability-based Markov chain model is developed for forecasting regional long-term electric power demand. The model can deal with the uncertainties in electric power system and reflect the vague and ambiguous during the process of power load forecasting through allowing uncertainties expressed as fuzzy parameters and discrete intervals. The developed model is applied to predict the electric power demand of Beijing from 2011 to 2019. Different satisfaction degrees of fuzzy parameters are considered as different levels of detail of the statistic data. The results indicate that the model can reflect the high uncertainty of long term power demand, which could support the programming and management of power system. The fuzzy probability Markov chain model is helpful for regional electricity power system managers in not only predicting a long term power load under uncertainty but also providing a basis for making multi-scenarios power generation/development plans.

Synthetic Model for Prediction Seismic Risk in About 10 Years

In this paper, the relation that strong earthquakes and seismo-geological conditions bear with precursory phenomena has been analyzed. The information content concerning the long-term (i.e., a decade) seismic risk that various precursors can provide has been estimated by using the quantitative judgment method. On such a basis, a synthetic probability model for predicting the strong earthquake risk in about 10 years has been suggested. With the northern part of North China used as the test region, the feasibility of the model which is used for medium-term to long-term prediction has been proved.

Model of Probabilistic Prediction for Life Loss

Based on discussion of the reasonableness of the seismic destruction described by using the death degree,the probabilistic curves of the different death degrees with different future times in several regions on the Chinese mainland have been obtained by applying the probabilistic model,which is in accord with present seismic destruction data and related data.The historical data of the life loss have been processed with the dynamic system model.Finally,the results from two kinds of data have been analyzed and discussed.

Medical imaging for pancreatic diseases:Prediction of severe acute pancreatitis complicated with acute respiratory distress syndrome

In this editorial we comment on the article published in the recent issue of the World Journal of Gastroenterology[2022;28(19):2123-2136].We pay attention to how to construct a simpler and more reliable new clinical predictive model to early identify patients at high risk of acute respiratory distress syndrome(ARDS)associated with severe acute pancreatitis(SAP),and to early predict the severity of organ failure from chest computed tomography(CT)findings in SAP patients.As we all know,SAP has a sudden onset,is a rapidly changing condition,and can be complicated with ARDS and even multiple organ dysfunction syndrome,and its mortality rate has remained high.At present,there are many clinical scoring systems for AP,including the bedside index for severity in AP,acute physiology and chronic health evaluation II,systemic inflammatory response syndrome,Japanese severe score,quick sepsis-related organ failure assessment,etc.However,some of these scoring systems are complex and require multiple and difficult clinical parameters for risk stratification.Although the aforementioned biomarkers are readily available,their ability to predict ARDS varies.Accordingly,it is extremely necessary to establish a simple and valuable novel model to predict the development of ARDS in AP.In addition,the extra-pancreatic manifestations of AP patients often involve the chest,among which pleural effusion and pulmonary consolidation are the more common complications.Therefore,by measuring the semi-quantitative indexes of chest CT in AP patients,such as the amount of pleural effusion and the number of lobes involved as pulmonary consolidation,it has important reference value for the early diagnosis of SAP complicated with ARDS and is expected to provide a basis for the early treatment of ARDS.

A nomogram predicting clinical pregnancy in the first fresh embryo transfer for women undergoing in vitro fertilization and intracytoplasmic sperm injection(IVF/ICSI) treatments

The extent to which factors affect the probability of clinical pregnancy in the first fresh embryo transfer after assisted conception is unknown.In order to examine the predictors of clinical pregnancy,a retrospective cohort study was launched between January 1,2013 and December 31,2016 in four infertility clinics including 19837 in vitro fertilization and intracytoplasmic sperm injection(IVF/ICSI)fresh cycles with known outcomes and relevant records.A multivariable logistic regression was used to select the most significant predictors in the final nomogram for predicting clinical pregnancy.Furthermore,the model was validated by an independent validation set and the performance of the model was evaluated by the receiver operating characteristic(ROC)curves along with the area under the ROC curve(AUC)and calibration plots.In a training set including 17854 participants,we identified that female age,tubal factor,number of embryos transferred,endometrial thickness and number of good-quality embryos were independent predictors for clinical pregnancy.We developed a nomogram using these five factors and the predictive ability was 0.66 for AUC(95%CI=0.64−0.68),which was independently validated in the validation set(AUC=0.66,95%CI=0.65−0.68).Our results show that some specific factors can be used to provide infertile couples with an accurate assessment of clinical pregnancy following assisted conception and facilitate to guide couples and clinicians.

Preliminary Study on Probabilistic Prediction of Seismic Hazard in a Period of 10 Years

Many uncertainty factors need be dealt with in the prediction of seismic hazard for a 10-year period.Restricted by these uncertainties,the result of prediction is also uncertain to a certain extent,so the probabilistic analysis method of seismic hazard should be adopted.In consideration of the inhomogeneity of the time,location,and magnitude of future earthquakes and the probabilistic combination of the background of long-term seismic hazard(geology,geophysical field,etc.)and the precursors of earthquake occurrence,a model of probabilistic prediction of seismic hazard in a period of 10 years s proposed.Considering the inhomogeneity of data and earthquake precursors for different regions in China,a simplified model is also proposed in order to satisfy the needs of different regions around the country.A trial in North China is used to discuss the application of the model.The method proposed in this paper can be used in the probabilistic prediction of seismic hazard in a period of 10 years.According to

Prediction of Smear Positive TB Cases at Different Types of Designated Microscopy Centres, Karnataka, India

Background: Under the Revised National Tuberculosis control Programme (RNTCP) in India, the designated microscopy centres (DMCs) form the basic unit of smear positive TB case detection in a district. There is a need by the programme managers to estimate the mean and range of smear positive tuberculosis (TB) cases that can be detected at DMCs located in different type of health facilities to channelize their resources. Methods: It is a cross-sectional study conducted in the state of Karnataka, India during January 2014 to December 2014 based on the compiled reports from past five years received from all the 30 districts of the state. The prediction was made based on the performance of these DMCs in the last five years using a modeling technique. Results: The proportions of the DMCs located at health facilities are Primary Health Institutions/Centres (PHIs)—73%, Tuberculosis Units (TUs)—15%, Medical colleges (MC)—7%, District TB centres (DTC)—3% and Private Practitioners (PP)—2%. The maximum number of cases that can be detected at DTC is 3621 (SD 54), TU is 9224 (SD 90), PHI is 20,412 (SD 135), PP is 859 (SD 26) and MC is 8322 (SD 84). Conclusion: The predicted values will essentially serve as a tool for the programme managers of Karnataka to plan, strategize and monitor the performance of DMCs in the state.

Test of Strain Behavior Model with Radon Anomaly in Seismogenic Area: A Bayesian Melding Approach

Mathematical models in seismo-geochemical monitoring offer powerful tools for the study and exploration of complex dynamics associated with discharge of radon as the indicator of change of intense-deformed conditions of seismogenic layers or blocks within the lithosphere. Seismic precursory model of radon gas emanation in the process of earthquake prediction research aims to find out the distinct anomaly variation necessary to correlate radon gas with processes of preparation and realization of tectonic earthquakes in long-term and short-term forecasts tectonic earthquakes. The study involves a radon gas volume analytic model to find the correlation of radon fluctuations to stress drop under compression and dilatation strain condition. Here, we present a mathematical inference by observing radon gas emanation prior to the occurrence of earthquake that may reduce the uncertainties in models and updating their probability distributions in a Bayesian deterministic model. Using Bayesian melding theorem, we implement an inferential framework to understand the process of preparation of tectonic earthquake and concurrent occurrence of radon discharge during a tectonic earthquake phenomena. Bayesian melding for deterministic simulation models was augmented to make use of prior knowledge on correlations between model inputs. The background porosity is used as a priori information for analyzing the block subjected to inelastic strain. It can be inferred that use of probabilistic framework involving exhalation of radon may provide a scenario of earthquake occurrences on recession of the curve that represents a qualitative pattern of radon activity concentration drop, indicating associated stress change within the causative seismogenic fault. Using evidence analysis, we propose a joint conditional probability framework model simulation to understand how a single fracture may be affected in response to an external load and radon anomaly change that can be used to detect the slip, a predictable nature of the causative fault in the subsurface rock.