Mining unexpected patterns using decision trees and interestingness measures: a case study of endometriosis

[[abstract]]Because clinical research is carried out in complex environments, prior domain knowledge, constraints, and expert knowledge can enhance the capabilities and performance of data mining. In this paper we propose an unexpected pattern mining model that uses decision trees to compare recovery rates of two different treatments, and to find patterns that contrast with the prior knowledge of domain users. In the proposed model we define interestingness measures to determine whether the patterns found are interesting to the domain. By applying the concept of domain-driven data mining, we repeatedly utilize decision trees and interestingness measures in a closed-loop, in-depth mining process to find unexpected and interesting patterns. We use retrospective data from transvaginal ultrasound-guided aspirations to show that the proposed model can successfully compare different treatments using a decision tree, which is a new usage of that tool. We believe that unexpected, interesting patterns may provide clinical researchers with different perspectives for future research.[[incitationindex]]SCI[[incitationindex]]EI[[booktype]]紙本[[booktype]]電子

PREDICTIVE DIAGNOSIS THROUGH DATA MINING FOR CARDIOVASCULAR DISEASES

Abstract Cardiovascular diseases (CVDs) are a leading cause of mortality worldwide, and early detection and accurate diagnosis are critical for effective treatment and prevention. Data mining techniques have emerged as powerful tools for analyzing large datasets to extract meaningful patterns and make predictions. This research paper aims to explore the application of data mining in predictive diagnosis for cardiovascular diseases. The study will start by collecting a comprehensive dataset comprising patient information, including demographics, medical history, lifestyle factors, and diagnostic test results. Various data mining techniques, such as classification, clustering, and association rule mining, will be applied to uncover hidden patterns and relationships within the data. Feature selection methods will be employed to identify the most relevant attributes for accurate prediction. The research will investigate different predictive models, including decision trees, support vector machines, and neural networks, to develop a reliable diagnostic system. Model performance will be evaluated using metrics such as accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC-ROC). Additionally, the study will employ cross-validation techniques to ensure the generalizability and robustness of the developed models. The research will explore the integration of advanced techniques, such as deep learning and ensemble methods, to enhance the predictive accuracy of the diagnosis. The use of explainable AI techniques will also be considered to provide interpretable insights into the predictive models' decision-making process. The findings of this research will contribute to the advancement of predictive diagnosis for cardiovascular diseases by leveraging data mining techniques. The developed diagnostic models will assist healthcare professionals in making accurate and timely predictions, leading to improved patient outcomes, personalized treatment plans, and effective preventive measures

Interpretable Subgroup Discovery in Treatment Effect Estimation with Application to Opioid Prescribing Guidelines

The dearth of prescribing guidelines for physicians is one key driver of the current opioid epidemic in the United States. In this work, we analyze medical and pharmaceutical claims data to draw insights on characteristics of patients who are more prone to adverse outcomes after an initial synthetic opioid prescription. Toward this end, we propose a generative model that allows discovery from observational data of subgroups that demonstrate an enhanced or diminished causal effect due to treatment. Our approach models these sub-populations as a mixture distribution, using sparsity to enhance interpretability, while jointly learning nonlinear predictors of the potential outcomes to better adjust for confounding. The approach leads to human-interpretable insights on discovered subgroups, improving the practical utility for decision suppor

Applying Machine Learning Algorithms to Predict Endometriosis Onset

Endometriosis is a commonly occurring progressive gynecological disorder, in which tissues similar to the lining of the uterus grow on other parts of the female body, including ovaries, fallopian tubes, and bowel. It is one of the primary causes of pelvic discomfort and fertility challenges in women. The actual cause of the endometriosis is still undetermined. As a result, the objective of the chapter is to identify the drivers of endometriosis’ diagnoses via leveraging selected advanced machine learning (ML) algorithms. The primary risks of infertility and other health complications can be minimized to a greater extent if a likelihood of endometriosis could be predicted well in advance. Logistic regression (LR) and eXtreme Gradient Boosting (XGB) algorithms leveraged 36 months of medical history data to demonstrate the feasibility. Several direct and indirect features were identified as important to an accurate prediction of the condition onset, including selected diagnosis and procedure codes. Creating analytical tools based on the model results that could be integrated into the Electronic Health Records (EHR) systems and easily accessed by healthcare providers might aid the objective of improving the diagnostic processes and result in a timely and precise diagnosis, ultimately increasing patient care and quality of life

Mining a Small Medical Data Set by Integrating the Decision Tree and t-test

[[abstract]]Although several researchers have used statistical methods to prove that aspiration followed by the injection of 95% ethanol left in situ (retention) is an effective treatment for ovarian endometriomas, very few discuss the different conditions that could generate different recovery rates for the patients. Therefore, this study adopts the statistical method and decision tree techniques together to analyze the postoperative status of ovarian endometriosis patients under different conditions. Since our collected data set is small, containing only 212 records, we use all of these data as the training data. Therefore, instead of using a resultant tree to generate rules directly, we use the value of each node as a cut point to generate all possible rules from the tree first. Then, using t-test, we verify the rules to discover some useful description rules after all possible rules from the tree have been generated. Experimental results show that our approach can find some new interesting knowledge about recurrent ovarian endometriomas under different conditions.[[journaltype]]國外[[incitationindex]]EI[[booktype]]紙本[[countrycodes]]FI

The incommensurability of the archaic perceptions of the maxim res ipsa loquitur in medical negligence litigation

This thesis assesses the legal doctrine res ipsa loquitur ('the thing speaks for itself) in the context of delictual claims for compensation for medical negligence in South African law. The thesis accepts that the doctrine is defensible in principle: a civil court may justifiably draw an inference that a defendant's negligent conduct was a factual cause of the harm suffered in simple cases where there is uncontradicted evidence sufficient to establish a prima facie case. However, it is argued that the South Africa Appellate Division's rejection of the doctrine in the context of medical negligence in 1924 remains justified. It is sometimes thought that the doctrine would assist plaintiffs in complex medical cases by easing the difficulty of establishing a cause of action on a balance of probabilities. However, the thesis argues to the contrary that applying the doctrine in the context of medical negligence claims in South Africa is potentially unjust to claimants and defendants alike. Judgments of medical negligence cannot be made soundly without a proper appreciation of the relevant medical facts. The availability of the doctrine, in the South African context, provides a motivation for plaintiffs to advance insufficiently-prepared evidence, sometimes without the views of experts. This results in the oversimplification of complex medical realities, which increases the risk that courts may reach conclusions regarding negligence and factual causation for reasons that are unjustifiable from a medical perspective. Insufficiently-prepared evidence is also vulnerable to rebuttal by defendant-doctors on 'exotic' or inadequate grounds from a medical perspective, resulting in the unjust rejection of negligence claims. By enabling a superficial approach to deciding questions of medical negligence in the South African context, the doctrine may promote the erroneous assumption that bad medical outcomes typically result from medical wrongdoing. To make its case, the thesis draws on case studies of a variety of medical procedures and contrasts the operation of res ipsa loquitur in South Africa against English legal experience. Differences between the two systems of medical negligence cast doubt on the notion that the English approach should be transplanted to South Africa. Rather than relying on the res ipsa loquitur doctrine to bolster claims made without medical expert evidence, the South African plaintiff should instead rely on constitutional arguments, appealing to basic rights to bodily integrity and dignity, to justify the injection of a degree of flexibility into the common-law elements of a delictual claim

Integrative Multi-Omics in Biomedical Research

Genomics technologies revolutionised biomedicine research, but the genome alone is not sufficient to capture biological complexity. Postgenomic methods, typically based on mass spectrometry, comprise the analysis of metabolites, lipids, and proteins and are an essential complement to genomics and transcriptomics. Multidimensional omics is becoming established to provide accurate and comprehensive state descriptions. This book covers the latest methodological developments for, and applications of integrative multi-omics in biomedical research