Generating and Verifying Risk Prediction Models using Data Mining

Risk prediction model estimates event occurrence based on related data. Conventional statistical metrics that utilized primary data generates simple descriptive analysis that often provide insufficient knowledge for decision making. In contrast, data mining techniques that have the capability to find hidden pattern from the secondary data in large databases and create prediction for de- sired output has become a popular approach to develop any risk prediction model. In healthcare particularly, data mining techniques can be applied in disease risk prediction model to provide reliable prediction on the possibility of acquiring the disease based on individual’s clinical and non-clinical data. Due to the increased use of data mining in healthcare, this study aims at identifying the data mining techniques and algorithms that are commonly implemented in studies related to various disease risk prediction model as well as finding the accuracy of the algorithms. The accuracy evaluation consists of various method, but this paper is focusing on overall accuracy which is measured by the total number of correctly predicted output over the total number of prediction. A systematic literature review approach that search across five databases found 170 articles, of which 7 articles were selected in the final process. This review found that most prediction model used classification technique, with a focus on decision tree, neural network, support vector machines, and Naive Bayes algorithms where heart-related disease is commonly studied. Further research can apply similar algorithms to develop risk prediction model for other types of diseases, such as infectious disease prediction.

Assessing opioid overdose risk: a review of clinical prediction models utilizing patient-level data

Identifying high-risk individuals using a risk prediction model could be a crucial first stage of screening pathways to improve the early detection of pancreatic cancer. A systematic review was conducted to critically evaluate the published primary literature on the development or validation of clinical risk prediction models for pancreatic cancer risk. MEDLINE, Embase, and Web of Science were searched for relevant articles from the inception of each database up to November 2021. Study selection and data extraction were conducted by 2 independent reviewers. The Prediction model Risk Of Bias Assessment Tool (PROBAST) was applied to assess risk of bias. In total, 33 studies were included, describing 38 risk prediction models. Excluding studies with an overlapping population, this study consist of 15,848,100 participants, of which 58,313 were diagnosed with pancreatic cancer. Eight studies externally validated their model, and 13 performed internal validation. The studies described risk prediction models for pancreatic cancer in the general population (n = 14), patients with diabetes (n = 8), and individuals with gastrointestinal (and other) symptoms (symptoms included abdominal pain, unexplained weight loss, jaundice, and change in bowel habits and indigestion; n = 11). The commonly used clinical risk factors in the model were cigarette smoking (n = 27), age (n = 25), diabetes history (n = 22), chronic pancreatitis (n = 18), and body mass index (n = 14). In the 25 studies that assessed model performance, C-statistics ranged from 0.61 to 0.98. Of the 33 studies included, 6 were rated as being at a low risk of bias based on PROBAST. Many clinical risk prediction models for pancreatic cancer had been developed for different target populations. Although low risk-of-bias studies were identified, these require external validation and implementation studies to ensure that these will benefit clinical decision making.

Prediction models: stepwise development and simultaneous validation is a step back

A risk prediction model was developed to predict the risk of major amputation in patients with diabetic foot ulcer (DFU) on admission, and instruct patients to prevent and control early, and guide doctors to make clinical decisions. We used data from the Electronic Medical Record (EMR) database of the First Teaching Hospital of Tianjin University of Traditional Chinese Medicine from February 2014 to July 2020. DFU patients were divided into major amputation group and non-major amputation group, and nested case-control study method was used to identify case group and control group. The results of the first laboratory tests, imaging examinations, complications and other information of DFU patients at admission were collected, and initial predictive variables were selected. Logistic regression and LASSO regression in R software were used to develop a clinical prediction model for DFU patients with major amputation, which was displayed in the form of nomographs, and the model was evaluated by internal validation. A total of 3654 patients were diagnosed as DFU, 695 patients were included in the study on the development of risk prediction model of DFU major amputation, 139 patients in the case group and 556 patients in the control group. 9 variables (WBC, Hb, ALB, Wagner grade, amputation history, smoking, ABI < 0.4, ulcer duration > 1 month, HbA1c) screened by logistic regression and LASSO regression were used as predictors of major amputation in DFU patients. The internal validation showed that the C index adjusted by Bootstrap method was 0.91 (95% CI, 0.894–0.943), the average absolute error of the prediction model for drawing the calibration curve was 0.01, and the brier score was 0.08. The clinical risk prediction model of major amputation in DFU patients developed in this study has good discrimination and calibration, can accurately predict the outcome events, can be used as an effective tool to guide doctors to make clinical decisions, and enrich and improve the content of DFU prevention and control work, but the promotion and use of the model still needs further verification of external data. Not applicable.

Clinical prediction models can enhance timely clinical decision-making when appropriately developed and integrated within clinical workflows. A risk prediction model is typically a regression equation that uses patient risk factor data to estimate the probability of the presence of disease (diagnostic) or its future occurrence (prognostic). Risk prediction models are widely studied in the surgical literature and commonly developed using logistic regression. For a risk prediction model to be useful, it must balance statistical performance and clinical usefulness. This article provides a brief overview of the various methodologic issues to consider when developing or validating a risk prediction model and common pitfalls.

Prognosis is a venerable component of medical knowledge introduced by Hippocrates (460-377 BC). This educational review presents a contemporary evidence-based approach for how to incorporate clinical risk prediction models in modern psychiatry. The article is organized around key methodological themes most relevant for the science of prognosis in psychiatry. Within each theme, the article highlights key challenges and makes pragmatic recommendations to improve scientific understanding of prognosis in psychiatry. The initial step to building clinical risk prediction models that can affect psychiatric care involves designing the model: preparation of the protocol and definition of the outcomes and of the statistical methods (theme 1). Further initial steps involve carefully selecting the predictors, preparing the data, and developing the model in these data. A subsequent step is the validation of the model to accurately test its generalizability (theme 2). The next consideration is that the accuracy of the clinical prediction model is affected by the incidence of the psychiatric condition under investigation (theme 3). Eventually, clinical prediction models need to be implemented in real-world clinical routine, and this is usually the most challenging step (theme 4). Advanced methods such as machine learning approaches can overcome some problems that undermine the previous steps (theme 5). The relevance of each of these themes to current clinical risk prediction modeling in psychiatry is discussed and recommendations are given. Together, these perspectives intend to contribute to an integrative, evidence-based science of prognosis in psychiatry. By focusing on the outcome of the individuals, rather than on the disease, clinical risk prediction modeling can become the cornerstone for a scientific and personalized psychiatry.

Identification of potential injury risk factors and construction of prediction models for athletes using data mining algorithms involve analyzing various factors such as biomechanical, physiological, and environmental variables to determine their correlation with injury occurrences. Data mining techniques, such as decision trees, logistic regression, or neural networks, are then applied to identify patterns and relationships within the data. By integrating this information, predictive models can be developed to forecast the likelihood of athletes sustaining injuries based on their individual characteristics and training conditions. This paper introduces a novel approach for athlete injury risk prediction using the Noninvasive Data Mining Model with Multi-relational and Multi-dimensional Clustering (Ni-DMMMC) algorithm. By leveraging advanced data mining techniques, Ni-DMMMC aims to identify hidden relationships between various athlete characteristics, including biomechanical risk, training load, physiological markers, and environmental factors, to predict injury susceptibility accurately. Through multi-relational and multi-dimensional clustering, the algorithm effectively categorizes athletes into distinct clusters based on their unique risk profiles. These clusters provide valuable insights into the diverse injury risk profiles present within athlete populations, enabling targeted intervention strategies and personalized athlete management approaches. The proposed approach holds significant promise for enhancing injury prevention efforts, optimizing athlete performance, and improving overall well-being in sports medicine practice. athletes in Cluster 1 exhibit high biomechanical risk (0.85), moderate training load (0.60), low physiological markers (0.25), and moderate environmental risk (0.45), while athletes in Cluster 2 display moderate biomechanical risk (0.70), high training load (0.90), high physiological markers (0.80), and low environmental risk (0.30). These numerical values serve as indicators for assessing an athlete's injury risk profile comprehensively.

TNM-classification inadequately estimates patient-specific overall survival (OS). We aimed to improve this by developing a risk-prediction model for patients with advanced larynx cancer. Cohort study. We developed a risk prediction model to estimate the 5-year OS rate based on a cohort of 3,442 patients with T3T4N0N+M0 larynx cancer. The model was internally validated using bootstrapping samples and externally validated on patient data from five external centers (n = 770). The main outcome was performance of the model as tested by discrimination, calibration, and the ability to distinguish risk groups based on tertiles from the derivation dataset. The model performance was compared to a model based on T and N classification only. We included age, gender, T and N classification, and subsite as prognostic variables in the standard model. After external validation, the standard model had a significantly better fit than a model based on T and N classification alone (C statistic, 0.59 vs. 0.55, P < .001). The model was able to distinguish well among three risk groups based on tertiles of the risk score. Adding treatment modality to the model did not decrease the predictive power. As a post hoc analysis, we tested the added value of comorbidity as scored by American Society of Anesthesiologists score in a subsample, which increased the C statistic to 0.68. A risk prediction model for patients with advanced larynx cancer, consisting of readily available clinical variables, gives more accurate estimations of the estimated 5-year survival rate when compared to a model based on T and N classification alone. 2c. Laryngoscope, 128:1140-1145, 2018.

Development of a validated model to predict 30-day stroke and 1-year survival after carotid endarterectomy for asymptomatic stenosis using the Vascular Quality Initiative

This study aimed to identify patients with the highest risk of periodontal disease (PD), and to provide recommendations for the effective use and application of data mining (DM) techniques when establishing evidence-based dental-care policies for vulnerable groups at a high risk of PD. This study used the SEMMA (Sample, Explore, Modify, Model, and Assess) methodology to construct DM models based on data acquired from the fifth and sixth Korea National Health and Nutrition Examination Surveys (2000-2015). We analyzed the sociodemographic and comorbidity variables that influence PD by applying the popular DM techniques of decision-tree, neural-network, and regression models, and also attempted to improve the predictive power and reliability by comparing the results obtained by these three models. Our comparisons of the three DM algorithms confirmed that the average squared error, misclassification rate, receiver operating characteristic index, Gini coefficient, and Kolmogorov-Smirnov test results were the most appropriate for the decision-tree model. The analysis of the decision-tree model revealed that age and smoking status exert major effects on the risk of PD, and that stress and education level exert effects in rural areas, whereas education level, sex, hyperlipidemia, and alcohol intake exert effects in urban areas. We demonstrated that the decision-tree model is an effective DM technique for identifying the complex risk factors for PD. These results are expected to be helpful in improving the equality and efficacy of dental-care policies for vulnerable groups at a high risk of PD.

Clinical prediction models for hospital-acquired infection of multidrug-resistant organism in intensive care units: a systematic review.

Prognostic relevance of baseline pro- and anti-inflammatory markers in STEMI: An APEX AMI substudy

Education can be utilized as a tool to face many problems, overcome many hurdles in life. The knowledge obtained from education helps to enhance opportunities in one's employment development. To extract useful information from the knowledge obtained, Educational Data Mining is widely used. Educational data mining provides the process of applying different data mining tools and techniques to analyze and visualize the data of an institution (school) and can be used to discover a unique pattern of students' academic performance and behavior. The present work intends to enhance students' academic performance in secondary school using data mining techniques. Real data was collected using school reports and questionnaire method by the Portugal school which has been used in this paper. Naive Bayesian algorithm can be easily implemented to predict the students' academic performance and behavior. Classification of students into two classes, pass and fail, involves training phase and testing phase. In training phase, Naive Bayes classifier is built and in the testing phase, Naive Bayes classifier is used to make the prediction. The accuracy of the classifier is calculated using WEKA tool in which confusion matrix is generated. The accuracy of the classifier obtained is 87% which can be further improved by the selection of appropriate attributes. Developing the classification algorithms in this way helps to obtain a more efficient student performance predictor tool using other data mining algorithms and it also helps to improve the quality of education in an educational institution.

This study aimed to analyze the clinical characteristics of patients undergoing endocervical curettage (ECC), identify factors influencing ECC positivity, and develop a predictive model to assess the risk of positive ECC results. The goal was to assist clinicians in making ECC decisions and reduce missed diagnoses of cervical lesions. A retrospective analysis was performed on 953 patients who underwent colposcopically directed biopsy and ECC at the gynecology clinic of the First Affiliated Hospital of Xi'an Jiaotong University between October 2021 and September 2023 due to abnormal screening results. Univariate and multivariate logistic regression analyses were used to identify predictive factors for ECC positivity. An individualized prediction model for ECC positivity risk was developed using R Studio, and the model was subsequently evaluated and validated. Among the 953 women, the ECC positive rate was 31.48% (300/953). Logistic regression analysis identified age (P<0.001), human papillomavirus (HPV) status (P<0.01), cytology results (P<0.05), acetowhite changes (P<0.01), Lugol staining (P<0.01), and colposcopic impression (P<0.01) as independent predictors of ECC positivity. These factors were incorporated into the prediction model for ECC positivity risk. The area under the receiver operating characteristic curve (AUC) of the model was 0.792 (95% CI:0.760-0.824). The Hosmer-Lemeshow test yielded a χ2 value of 10.489 (P=0.2324), and the calibration and clinical decision curves demonstrated that the model exhibited satisfactory calibration and clinical utility. The clinical prediction model developed in this study demonstrated good discrimination, calibration, and clinical utility. It can be used to evaluate the risk of ECC positivity in patients undergoing colposcopy, reduce missed diagnoses of cervical lesions, and aid clinicians in making ECC decisions.