Modelling Relevance towards Multiple Inclusion Criteria when Ranking Patients

In the medical domain, information retrieval systems can be used for identifying cohorts (i.e. patients) required for clinical studies. However, a challenge faced by such search systems is to retrieve the cohorts whose medical histories cover the inclusion criteria specified in a query, which are often complex and include multiple medical conditions. For example, a query may aim to find patients with both 'lupus nephritis' and 'thrombotic thrombocytopenic purpura'. In a typical best-match retrieval setting, any patient exhibiting all of the inclusion criteria should naturally be ranked higher than a patient that only exhibits a subset, or none, of the criteria. In this work, we extend the two main existing models for ranking patients to take into account the coverage of the inclusion criteria by adapting techniques from recent research into coverage-based diversification. We propose a novel approach for modelling the coverage of the query inclusion criteria within the records of a particular patient, and thereby rank highly those patients whose medical records are likely to cover all of the specified criteria. In particular, our proposed approach estimates the relevance of a patient, based on the mixture of the probability that the patient is retrieved by a patient ranking model for a given query, and the likelihood that the patient's records cover the query criteria. The latter is measured using the relevance towards each of the criteria stated in the query, represented in the form of sub-queries. We thoroughly evaluate our proposed approach using the test collection provided by the TREC 2011 and 2012 Medical Records track. Our results show significant improvements over existing strong baselines

Time-Series Embedded Feature Selection Using Deep Learning: Data Mining Electronic Health Records for Novel Biomarkers

As health information technologies continue to advance, routine collection and digitisation of patient health records in the form of electronic health records present as an ideal opportunity for data-mining and exploratory analysis of biomarkers and risk factors indicative of a potentially diverse domain of patient outcomes. Patient records have continually become more widely available through various initiatives enabling open access whilst maintaining critical patient privacy. In spite of such progress, health records remain not widely adopted within the current clinical statistical analysis domain due to challenging issues derived from such “big data”.Deep learning based temporal modelling approaches present an ideal solution to health record challenges through automated self-optimisation of representation learning, able to man-ageably compose the high-dimensional domain of patient records into data representations able to model complex data associations. Such representations can serve to condense and reduce dimensionality to emphasise feature sparsity and importance through novel embedded feature selection approaches. Accordingly, application towards patient records enable complex mod-elling and analysis of the full domain of clinical features to select biomarkers of predictive relevance.Firstly, we propose a novel entropy regularised neural network ensemble able to highlight risk factors associated with hospitalisation risk of individuals with dementia. The application of which, was able to reduce a large domain of unique medical events to a small set of relevant risk factors able to maintain hospitalisation discrimination.Following on, we continue our work on ensemble architecture approaches with a novel cas-cading LSTM ensembles to predict severe sepsis onset within critical patients in an ICU critical care centre. We demonstrate state-of-the-art performance capabilities able to outperform that of current related literature.Finally, we propose a novel embedded feature selection application dubbed 1D convolu-tion feature selection using sparsity regularisation. Said methodology was evaluated on both domains of dementia and sepsis prediction objectives to highlight model capability and generalisability. We further report a selection of potential biomarkers for the aforementioned case study objectives highlighting clinical relevance and potential novelty value for future clinical analysis.Accordingly, we demonstrate the effective capability of embedded feature selection ap-proaches through the application of temporal based deep learning architectures in the discovery of effective biomarkers across a variety of challenging clinical applications

Discovering which experiences physiotherapy students identify as learning facilitators in practical laboratories: An action research project

Purpose: Students enrolled in courses that focus on patient contact participate in practical laboratories to learn clinical skills but this can be challenging in a pre-clinical environment. A simulated case based format using role play in small groups is commonly undertaken. Students may find it difficult to actively engage in learning and effective role playing without prior clinical experience. The aim of this study was to discover what type of experiences facilitated student learning in practical laboratory sessions. Method: Design: Action research study. Participants: Thirty two undergraduate second year physiotherapy students who were engaging in practical laboratories. Data collection and analysis: Teacher observations, minute papers and semi structured interviews were conducted over a nine week teaching period to gain the student perspective on what facilitated their learning. Data from these three sources were categorised and coded. A concept mapping technique was then used to represent the construct of learning facilitators identified, from which the final survey was developed. Results: Learning facilitators identified by students were categorised under three key units: those provided by the teacher, those initiated by the students themselves and material resources. Concept mapping revealed three emergent themes: provide multiple opportunities for learning that address all learning styles, formative learning support and resources to consolidate learning. Students rated timely feedback from the teacher while they practiced the required skills and behaviours as the highest valued learning facilitator (strongly agreed 78.6%, agreed 21.4%) followed by watching the teacher modelling the skill or behaviour required (strongly agreed 67.9%, agreed 25.0%). Students also reported that using a peer feedback checklist constructed by the teacher clarified their expectations of engaging in observation and feedback (strongly agreed 32.1%, agreed 50.0%) and guided their performance in the skills and behaviours expected (strongly agreed 35.7%, agreed 53.6%). Conclusions: Students at a pre-clinical level can identify which experiences facilitate their learning in practical laboratories, if given the opportunity. While these students place the highest value on teacher feedback they can actively engage in peer learning if given constructive guidance on the skills and behaviours required. Discovering what students identify as facilitating their learning in practical laboratories can guide successful evaluation of laboratory teaching plans to modify and create new learning opportunities and resources. This has the potential to improve student satisfaction and achievement of intended learning outcomes

Selecting the most suitable classification algorithm for supporting assistive technology adoption for people with dementia: A multicriteria framework

The number of people with dementia (PwD) is increasing dramatically. PwD exhibit impairments of reasoning, memory, and thought that require some form of self‐management intervention to support the completion of everyday activities while maintaining a level of independence. To address this need, efforts have been directed to the development of assistive technology solutions, which may provide an opportunity to alleviate the burden faced by the PwD and their carers. Nevertheless, uptake of such solutions has been limited. It is therefore necessary to use classifiers to discriminate between adopters and nonadopters of these technologies in order to avoid cost overruns and potential negative effects on quality of life. As multiple classification algorithms have been developed, choosing the most suitable classifier has become a critical step in technology adoption. To select the most appropriate classifier, a set of criteria from various domains need to be taken into account by decision makers. In addition, it is crucial to define the most appropriate multicriteria decision‐making approach for the modelling of technology adoption. Considering the above‐mentioned aspects, this paper presents the integration of a five‐phase methodology based on the Fuzzy Analytic Hierarchy Process and the Technique for Order of Preference by Similarity to Ideal Solution to determine the most suitable classifier for supporting assistive technology adoption studies. Fuzzy Analytic Hierarchy Process is used to determine the relative weights of criteria and subcriteria under uncertainty and Technique for Order of Preference by Similarity to Ideal Solution is applied to rank the classifier alternatives. A case study considering a mobile‐based self‐management and reminding solution for PwD is described to validate the proposed approach. The results revealed that the best classifier was k‐nearest‐neighbour with a closeness coefficient of 0.804, and the most important criterion when selecting classifiers is scalability. The paper also discusses the strengths and weaknesses of each algorithm that should be addressed in future research

A framework for enhancing the query and medical record representations for patient search

Electronic medical records (EMRs) are digital documents stored by medical institutions that detail the observed symptoms, the conducted diagnostic tests, the identified diagnoses and the prescribed treatments. These EMRs are being increasingly used worldwide to improve healthcare services. For example, when a doctor compiles the possible treatments for a patient showing some particular symptoms, it is advantageous to consult the information about patients who were previously treated for those same symptoms. However, finding patients with particular medical conditions is challenging, due to the implicit knowledge inherent within the patients' medical records and queries - such knowledge may be known by medical practitioners, but may be hidden from an information retrieval (IR) system. For instance, the mention of a treatment such as a drug may indicate to a practitioner that a particular diagnosis has been made for the patient, but this diagnosis may not be explicitly mentioned in the patient's medical records. Moreover, the use of negated language (e.g.\ `without', `no') to describe a medical condition of a patient (e.g.\ the patient has no fever) may cause a search system to erroneously retrieve that patient for a query when searching for patients with that medical condition (e.g.\ find patients with fever). This thesis focuses on enhancing the search of EMRs, with the aim of identifying patients with medical histories relevant to the medical conditions stated in a text query. During retrieval, a healthcare practitioner indicates a number of inclusion criteria describing the medical conditions of the patients of interest. To attain effective retrieval performance, we hypothesise that, in a patient search system, both the information needs and patients' histories should be represented based upon \emph{the medical decision process}. In particular, this thesis argues that since the medical decision process typically encompasses four aspects (symptom, diagnostic test, diagnosis and treatment), a patient search system should take into account these aspects and apply inferences to recover the possible implicit knowledge. We postulate that considering these aspects and their derived implicit knowledge at three different levels of the retrieval process (namely, sentence, medical record and inter-record levels) enhances the retrieval performance. Indeed, we propose a novel framework that can gain insights from EMRs and queries, by modelling and reasoning upon information during retrieval in terms of the four aforementioned aspects at the three levels of the retrieval process, and can use these insights to enhance patient search. Firstly, at the sentence level, we extract the medical conditions in the medical records and queries. In particular, we propose to represent only the medical conditions related to the four medical aspects in order to improve the accuracy of our search system. In addition, we identify the context (negative/positive) of terms, which leads to an accurate representation of the medical conditions both in the EMRs and queries. In particular, we aim to prevent patients whose EMRs state the medical conditions in the contexts different from the query from being ranked highly. For example, preventing patients whose EMRs state ``no history of dementia'' from being retrieved for a query searching for patients with dementia. Secondly, at the medical record level, using external knowledge-based resources (e.g.\ ontologies and health-related websites), we leverage the relationships between medical terms to infer the wider medical history of the patient in terms of the four medical aspects. In particular, we estimate the relevance of a patient to the query by exploiting association rules that we extract from the semantic relationships between medical terms using the four aspects of the medical process. For example, patients with a medical history involving a \emph{CABG surgery} (treatment) can be inferred as relevant to a query searching for a patient suffering from \emph{heart disease} (diagnosis), since a CABG surgery is a treatment of heart disease. Thirdly, at the inter-record level, we enhance the retrieval of patients in two different manners. First, we exploit knowledge about how the four medical aspects are handled by different hospital departments to gain a better understanding about the appropriateness of EMRs created by different departments for a given query. We propose to aggregate EMRs at the department level (i.e.\ inter-record level) to extract implicit knowledge (i.e.\ the expertise of each department) and model this department's expertise, while ranking patients. For instance, patients having EMRs from the cardiology department are likely to be relevant to a query searching for patients who suffered from a heart attack. Second, as a medical query typically contains several medical conditions that the relevant patients should satisfy, we propose to explicitly model the relevance towards multiple query medical conditions in the EMRs related to a particular patient during retrieval. In particular, we rank highly those patients that match all the stated medical conditions in the query by adapting coverage-based diversification approaches originally proposed for the web search domain. Finally, we examine the combination of our aforementioned approaches that exploit the implicit knowledge at the three levels of the retrieval process to further improve the retrieval performance by adapting techniques from the fields of data fusion and machine learning. In particular, data fusion techniques, such as CombSUM and CombMNZ, are used to combine the relevance scores computed by the different approaches of the proposed framework. On the other hand, we deploy state-of-the-art learning to rank approaches (e.g.\ LambdaMART and AdaRank) to learn from a set of training data an effective combination of the relevance scores computed by the approaches of the framework. In addition, we introduce a novel selective ranking approach that uses a classifier to effectively apply one of the approaches of the framework on a per-query basis. This thesis draws insights from a thorough evaluation and analysis of the proposed framework using a standard test collection provided by the TREC Medical Records track. The experimental results show the effectiveness of the framework. In particular, the results demonstrate the importance of dealing with the implicit knowledge in patient search by focusing on the medical decision criteria aspects at the three levels of the retrieval process

Epidemiology of canine atopic dermatitis

Canine atopic dermatitis (CAD) is a chronic, allergic skin disease associated with IgE-mediated reactions to environmental allergens. Atopic dermatitis/eczema in humans shares many similarities with CAD and is an increasing problem in industrialized countries. This increase has been attributed to lifestyle and environment factors. The current knowledge about the epidemiology of CAD is limited. The aim of this thesis was therefore to investigate the incidence of and potential risk factors for the development of CAD. Three of the included studies involve the use of a large animal-insurance database. The database includes information about a large number of healthy and diseased individuals over time, but it was not collected for research purposes and data-quality issues needed to be addressed. A validation of the diagnosis CAD in the insurance database showed that although the vast majority of the recorded cases could be considered allergic, the important differential diagnosis cutaneous adverse food reactions had not been ruled out for many patients. The overall incidence rate of CAD was 1.7 cases per 1000 dog years at risk. Several factors were found to be associated with an increased risk of CAD in the insured population; living in an urban area or in the south of Sweden, being born in the autumn and belonging to a high-risk breed. Furthermore, a spatial analysis showed that the incidence of CAD increased by increasing human population density and increasing annual rainfall, and was decreased in the north of Sweden and if there was no veterinary dermatologist present in the county. Finally, a case-control study was performed where 12 veterinarians collected CAD cases from the three identified high-risk breeds; boxer, bullterrier and West Highland white terrier. The main finding was that feeding a diet containing home-made/ non-commercial ingredients to the bitch during lactation protected her offspring from developing CAD. In conclusion, a strong breed predisposition for CAD was seen. Evidence of an increased incidence of CAD in densely populated areas exists but might be biased by the locations of veterinary dermatologists. The potential of using diet for primary prevention of CAD is interesting but randomized controlled clinical trials are required to support this finding

Predictive Modelling Approach to Data-Driven Computational Preventive Medicine

This thesis contributes novel predictive modelling approaches to data-driven computational preventive medicine and offers an alternative framework to statistical analysis in preventive medicine research. In the early parts of this research, this thesis presents research by proposing a synergy of machine learning methods for detecting patterns and developing inexpensive predictive models from healthcare data to classify the potential occurrence of adverse health events. In particular, the data-driven methodology is founded upon a heuristic-systematic assessment of several machine-learning methods, data preprocessing techniques, models’ training estimation and optimisation, and performance evaluation, yielding a novel computational data-driven framework, Octopus. Midway through this research, this thesis advances research in preventive medicine and data mining by proposing several new extensions in data preparation and preprocessing. It offers new recommendations for data quality assessment checks, a novel multimethod imputation (MMI) process for missing data mitigation, a novel imbalanced resampling approach, and minority pattern reconstruction (MPR) led by information theory. This thesis also extends the area of model performance evaluation with a novel classification performance ranking metric called XDistance. In particular, the experimental results show that building predictive models with the methods guided by our new framework (Octopus) yields domain experts' approval of the new reliable models’ performance. Also, performing the data quality checks and applying the MMI process led healthcare practitioners to outweigh predictive reliability over interpretability. The application of MPR and its hybrid resampling strategies led to better performances in line with experts' success criteria than the traditional imbalanced data resampling techniques. Finally, the use of the XDistance performance ranking metric was found to be more effective in ranking several classifiers' performances while offering an indication of class bias, unlike existing performance metrics The overall contributions of this thesis can be summarised as follow. First, several data mining techniques were thoroughly assessed to formulate the new Octopus framework to produce new reliable classifiers. In addition, we offer a further understanding of the impact of newly engineered features, the physical activity index (PAI) and biological effective dose (BED). Second, the newly developed methods within the new framework. Finally, the newly accepted developed predictive models help detect adverse health events, namely, visceral fat-associated diseases and advanced breast cancer radiotherapy toxicity side effects. These contributions could be used to guide future theories, experiments and healthcare interventions in preventive medicine and data mining

Does the National Institute for Health and Clinical Excellence take account of factors such as uncertainty and equity as well as incremental cost-effectiveness in commissioning health care services? A binary choice experiment

Background: NICE is an independent organisation responsible for providing national guidance on the promotion of good health and the prevention and treatment of ill health in England and Wales. One of NICE’s main roles is to produce national guidance on the use of health technologies within the NHS. Despite the Institute’s recent efforts to clarify the way in which its Appraisal Committees reach their recommendations concerning the use of health technologies, there remains ambiguity about how cost-effectiveness evidence is interpreted alongside other considerations such as the degree of clinical need within the patient population, and the degree of uncertainty surrounding cost-effectiveness estimates. Objective: To explore whether the NICE takes account of factors such as uncertainty and equity as well as incremental cost-effectiveness in commissioning health care services. Methods: A binary choice experiment was undertaken using NICE’s three Appraisal Committees. The experiment included five attributes: (1) Incremental cost-effectiveness (2) Degree of economic uncertainty (3) Age of the target population (4) Baseline health-related quality of life (5) Availability of other therapies A choice questionnaire detailing 18 scenarios was administered to NICE’s Appraisal Committees. For each scenario, respondents were asked to indicate whether they would recommend the intervention under consideration or not. The stated preference data obtained from respondents were analysed using a random effects logit regression model. Results: A response rate of 46% was obtained from the Appraisal Committees. The regression model suggests that increases in cost-effectiveness, economic uncertainty, and the availability of other therapies are associated with statistically significant reductions in the odds of adoption (p<0.05). The transition from a very low to a comparatively high level of health-related quality of life is also associated with a statistically significant reduction in the odds of a positive recommendation. Smaller changes in health-related quality of life, and the age of the target population are not associated with a statistically significant reduction in the odds of a positive recommendation. Analysis of revealed preference data indicates that the model is capable of distinguishing between those technologies which the Appraisal Committees would be highly likely to recommend, and those technologies which appear to be less attractive, although further external validation is warranted. Conclusion: The modelling suggests that cost-effectiveness, uncertainty and certain equity concerns influence the NICE Appraisal Committees’ recommendations on the use of health technologies. The modelling results appear to support Rawlins and Culyer’s notion of a probabilistic cost-effectiveness threshold approach; the "mythical" £30,000 per QALY gained threshold assumed within the literature is not supported by this stated preference modelling analysis

Does the National Institute for Health and Clinical Excellence take account of factors such as uncertainty and equity as well as incremental cost-effectiveness in commissioning health care services? A binary choice experiment

Background NICE is an independent organisation responsible for providing national guidance on the promotion of good health and the prevention and treatment of ill health in England and Wales. One of NICE’s main roles is to produce national guidance on the use of health technologies within the NHS. Despite the Institute’s recent efforts to clarify the way in which its Appraisal Committees reach their recommendations concerning the use of health technologies, there remains ambiguity about how cost-effectiveness evidence is interpreted alongside other considerations such as the degree of clinical need within the patient population, and the degree of uncertainty surrounding cost-effectiveness estimates. Objective To explore whether the NICE takes account of factors such as uncertainty and equity as well as incremental cost-effectiveness in commissioning health care services. Methods A binary choice experiment was undertaken using NICE’s three Appraisal Committees. The experiment included five attributes: (1) Incremental cost-effectiveness (2) Degree of economic uncertainty (3) Age of the target population (4) Baseline health-related quality of life (5) Availability of other therapies A choice questionnaire detailing 18 scenarios was administered to NICE’s Appraisal Committees. For each scenario, respondents were asked to indicate whether they would recommend the intervention under consideration or not. The stated preference data obtained from respondents were analysed using a random effects logit regression model. Results A response rate of 46% was obtained from the Appraisal Committees. The regression model suggests that increases in cost-effectiveness, economic uncertainty, and the availability of other therapies are associated with statistically significant reductions in the odds of adoption (puncertainty; equity; cost-effectiveness; public health