A hybrid decision support model to discover informative knowledge in diagnosing acute appendicitis

BACKGROUND: The aim of this study is to develop a simple and reliable hybrid decision support model by combining statistical analysis and decision tree algorithms to ensure high accuracy of early diagnosis in patients with suspected acute appendicitis and to identify useful decision rules. METHODS: We enrolled 326 patients who attended an emergency medical center complaining mainly of acute abdominal pain. Statistical analysis approaches were used as a feature selection process in the design of decision support models, including the Chi-square test, Fisher's exact test, the Mann-Whitney U-test (p < 0.01), and Wald forward logistic regression (entry and removal criteria of 0.01 and 0.05, or 0.05 and 0.10, respectively). The final decision support models were constructed using the C5.0 decision tree algorithm of Clementine 12.0 after pre-processing. RESULTS: Of 55 variables, two subsets were found to be indispensable for early diagnostic knowledge discovery in acute appendicitis. The two subsets were as follows: (1) lymphocytes, urine glucose, total bilirubin, total amylase, chloride, red blood cell, neutrophils, eosinophils, white blood cell, complaints, basophils, glucose, monocytes, activated partial thromboplastin time, urine ketone, and direct bilirubin in the univariate analysis-based model; and (2) neutrophils, complaints, total bilirubin, urine glucose, and lipase in the multivariate analysis-based model. The experimental results showed that the model with univariate analysis (80.2%, 82.4%, 78.3%, 76.8%, 83.5%, and 80.3%) outperformed models using multivariate analysis (71.6%, 69.3%, 73.7%, 69.7%, 73.3%, and 71.5% with entry and removal criteria of 0.01 and 0.05; 73.5%, 66.0%, 80.0%, 74.3%, 72.9%, and 73.0% with entry and removal criteria of 0.05 and 0.10) in terms of accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and area under ROC curve, during a 10-fold cross validation. A statistically significant difference was detected in the pairwise comparison of ROC curves (p < 0.01, 95% CI, 3.13-14.5; p < 0.05, 95% CI, 1.54-13.1). The larger induced decision model was more effective for identifying acute appendicitis in patients with acute abdominal pain, whereas the smaller induced decision tree was less accurate with the test data. CONCLUSIONS: The decision model developed in this study can be applied as an aid in the initial decision making of clinicians to increase vigilance in cases of suspected acute appendicitis

Significance testing as perverse probabilistic reasoning

Truth claims in the medical literature rely heavily on statistical significance testing. Unfortunately, most physicians misunderstand the underlying probabilistic logic of significance tests and consequently often misinterpret their results. This near-universal misunderstanding is highlighted by means of a simple quiz which we administered to 246 physicians at two major academic hospitals, on which the proportion of incorrect responses exceeded 90%. A solid understanding of the fundamental concepts of probability theory is becoming essential to the rational interpretation of medical information. This essay provides a technically sound review of these concepts that is accessible to a medical audience. We also briefly review the debate in the cognitive sciences regarding physicians' aptitude for probabilistic inference

The evaluation and enhancement of case driven diagnostic advice systems: a study in three domains

Relevant literature has been reviewed regarding the performance, implementation and evaluation of computer based medical decision support systems. The diagnostic performance of five simple case driven acute chest pain advice systems, have been compared using a standardized set of clinical records. A Bayesian inference model demonstrated superiority over two derived by logistic regression. Small data set flow charts performed well but both relied upon the use of expert opinion. A Bayesian acute abdominal pain diagnostic advice system has been evaluated in a clinical trial. Standardized data collection improved the diagnostic performance of doctors. In practice, the computer system offered little additional user benefit. From further tests in primary care, it was concluded that, whereas general practitioners might enhance their performance by using data collection sheets, paramedics might benefit through direct use of the computer. DERMIS is a new dermatology primary care diagnostic advice system. Components include a database derived from 5203 prospectively collected clinical records, a user interface, and an enhanced Bayesian inference model incorporating combined frequency estimates, expert beliefs and rationalized end-point groups. On laboratory testing, the diagnostic accuracy of DERMIS was 83%. The correct diagnosis appeared in the top three, of a possible 42 disease list on 97% of occasions. In a semi-field trial of DERMIS involving 49 general practitioners, doctors did not always collect the same information as a dermatologist but were able to significantly increase their chance of making a correct diagnosis through use of the computer system. It has been concluded that although implementation of DERMIS might well increase general practitioner diagnostic accuracy and lead to improvements in the management of skin disease in primary care, rates of referral for specialist opinion might not be affected unless standard management plans are adopted

Do psychiatric diagnoses explain?:A philosophical investigation

This thesis is a philosophical examination of the explanatory roles of diagnoses in psychiatry. In medicine, diagnoses normally serve as causal explanations of patients’ symptoms. Given that psychiatry is a discipline whose practice is shaped by medical traditions, it is often implied that its diagnoses also serve such explanatory functions. This is evident in clinical texts that portray psychiatric diagnoses as referring to diseases that cause symptoms. However, there are problems which cast doubt on whether such portrayals are justified. I address these problems and examine whether psychiatric diagnoses provide explanations of symptoms. The first problem is conceptual. In diagnostic manuals, psychiatric diagnoses are defined by their symptoms. This suggests that invoking them as explanations of the symptoms amounts to circularity. I argue that this can be resolved with an appropriate conceptual framework that captures the complex semantic values of diagnostic terms and their different uses in clinical discourse. I put forward such a framework based on two-dimensional semantics. The second problem is ontological. Empirical research suggests that diagnostic categories in psychiatry do not correspond to invariant causal types, but are associated with variable combinations of diverse causes that interact across biological, psychological, and social levels. Given this heterogeneity, I argue that psychiatric diagnoses fall short of paradigmatic cases of causal explanation, but that some can still provide other sorts of useful causal explanatory information. The original contribution of this thesis is the illumination of the conceptual relations between diagnoses and symptoms. This philosophical work is important, because it can be brought to valuable application in modifying psychiatric practice

Exploring the relationship between age and health conditions using electronic health records: from single diseases to multimorbidities

Background Two enormous challenges facing healthcare systems are ageing and multimorbidity. Clinicians, policymakers, healthcare providers and researchers need to know “who gets which diseases when” in order to effectively prevent, detect and manage multiple conditions. Identification of ageing-related diseases (ARDs) is a starting point for research into common biological pathways in ageing. Examining multimorbidity clusters can facilitate a shift from the single-disease paradigm that pervades medical research and practice to models which reflect the reality of the patient population. Aim To examine how age influences an individual’s likelihood of developing single and multiple health conditions over the lifecourse. Methods and Outputs I used primary care and hospital admission electronic health records (EHRs) of 3,872,451 individuals from the Clinical Practice Research Datalink (CPRD) linked to the Hospital Episode Statistics admitted patient care (HES-APC) dataset in England from 1 April 2010 to 31 March 2015. In collaboration with Professor Aroon Hingorani, Dr Osman Bhatti, Dr Shanaz Husain, Dr Shailen Sutaria, Professor Dorothea Nitsch, Mrs Melanie Hingorani, Dr Constantinos Parisinos, Dr Tom Lumbers and Dr Reecha Sofat, I derived the case definitions for 308 clinically important health conditions, by harmonising Read, ICD-10 and OPCS-4 codes across primary and secondary care records in England. I calculated the age-specific incidence rate, period prevalence and median age at first recorded diagnosis for these conditions and described the 50 most common diseases in each decade of life. I developed a protocol for identifying ARDs using machine-learning and actuarial techniques. Finally, I identified highly correlated multimorbidity clusters and created a tool to visualise comorbidity clusters using a network approach. Conclusions I have developed case definitions (with a panel of clinicians) and calculated disease frequency estimates for 308 clinically important health conditions in the NHS in England. I have described patterns of ageing and multimorbidity using these case definitions, and produced an online app for interrogating comorbidities for an index condition. This work facilitates future research into ageing pathways and multimorbidity

Machine learning for medical coding in health care surveys

This exploratory study evaluates the use of machine learning classifiers to perform automated medical coding for large statistical healthcare surveys

Enhance Representation Learning of Clinical Narrative with Neural Networks for Clinical Predictive Modeling

Medicine is undergoing a technological revolution. Understanding human health from clinical data has major challenges from technical and practical perspectives, thus prompting methods that understand large, complex, and noisy data. These methods are particularly necessary for natural language data from clinical narratives/notes, which contain some of the richest information on a patient. Meanwhile, deep neural networks have achieved superior performance in a wide variety of natural language processing (NLP) tasks because of their capacity to encode meaningful but abstract representations and learn the entire task end-to-end. In this thesis, I investigate representation learning of clinical narratives with deep neural networks through a number of tasks ranging from clinical concept extraction, clinical note modeling, and patient-level language representation. I present methods utilizing representation learning with neural networks to support understanding of clinical text documents. I first introduce the notion of representation learning from natural language processing and patient data modeling. Then, I investigate word-level representation learning to improve clinical concept extraction from clinical notes. I present two works on learning word representations and evaluate them to extract important concepts from clinical notes. The first study focuses on cancer-related information, and the second study evaluates shared-task data. The aims of these two studies are to automatically extract important entities from clinical notes. Next, I present a series of deep neural networks to encode hierarchical, longitudinal, and contextual information for modeling a series of clinical notes. I also evaluate the models by predicting clinical outcomes of interest, including mortality, length of stay, and phenotype predictions. Finally, I propose a novel representation learning architecture to develop a generalized and transferable language representation at the patient level. I also identify pre-training tasks appropriate for constructing a generalizable language representation. The main focus is to improve predictive performance of phenotypes with limited data, a challenging task due to a lack of data. Overall, this dissertation addresses issues in natural language processing for medicine, including clinical text classification and modeling. These studies show major barriers to understanding large-scale clinical notes. It is believed that developing deep representation learning methods for distilling enormous amounts of heterogeneous data into patient-level language representations will improve evidence-based clinical understanding. The approach to solving these issues by learning representations could be used across clinical applications despite noisy data. I conclude that considering different linguistic components in natural language and sequential information between clinical events is important. Such results have implications beyond the immediate context of predictions and further suggest future directions for clinical machine learning research to improve clinical outcomes. This could be a starting point for future phenotyping methods based on natural language processing that construct patient-level language representations to improve clinical predictions. While significant progress has been made, many open questions remain, so I will highlight a few works to demonstrate promising directions

Clinical decision-making in acute paediatrics : evaluation of the impact of an internet-delivered paediatric decision support system

Imperial Users onl

Recent Developments in Smart Healthcare

Medicine is undergoing a sector-wide transformation thanks to the advances in computing and networking technologies. Healthcare is changing from reactive and hospital-centered to preventive and personalized, from disease focused to well-being centered. In essence, the healthcare systems, as well as fundamental medicine research, are becoming smarter. We anticipate significant improvements in areas ranging from molecular genomics and proteomics to decision support for healthcare professionals through big data analytics, to support behavior changes through technology-enabled self-management, and social and motivational support. Furthermore, with smart technologies, healthcare delivery could also be made more efficient, higher quality, and lower cost. In this special issue, we received a total 45 submissions and accepted 19 outstanding papers that roughly span across several interesting topics on smart healthcare, including public health, health information technology (Health IT), and smart medicine

Fusion, 2022

https://hsrc.himmelfarb.gwu.edu/smhs_fusion/1014/thumbnail.jp