Data mining applied to the cognitive rehabilitation of patients with acquired brain injury

Acquired brain injury (ABI) is one of the leading causes of death and disability in the world and is associated with high health care costs as a result of the acute treatment and long term rehabilitation involved. Different algorithms and methods have been proposed to predict the effectiveness of rehabilitation programs. In general, research has focused on predicting the overall improvement of patients with ABI. The purpose of this study is the novel application of data mining (DM) techniques to predict the outcomes of cognitive rehabilitation in patients with ABI. We generate three predictive models that allow us to obtain new knowledge to evaluate and improve the effectiveness of the cognitive rehabilitation process. Decision tree (DT), multilayer perceptron (MLP) and general regression neural network (GRNN) have been used to construct the prediction models. 10-fold cross validation was carried out in order to test the algorithms, using the Institut Guttmann Neurorehabilitation Hospital (IG) patients database. Performance of the models was tested through specificity, sensitivity and accuracy analysis and confusion matrix analysis. The experimental results obtained by DT are clearly superior with a prediction average accuracy of 90.38%, while MLP and GRRN obtained a 78.7% and 75.96%, respectively. This study allows to increase the knowledge about the contributing factors of an ABI patient recovery and to estimate treatment efficacy in individual patients

Predictive modelling of hospital readmissions in diabetic patients clusters

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceDiabetes is a global public health problem with increasing incidence over the past 10 years. This disease's social and economic impacts are widely assessed worldwide, showing a direct and gradual decrease in the individual's ability to work, a gradual loss in the scale of quality of life and a burden on personal finances. The recurrence of hospitalisation is one of the most significant indexes in measuring the quality of care and the opportunity to optimise resources. Numerous techniques identify the patient who will need to be readmitted, such as LACE and HOSPITAL. The purpose of this study was to use a dataset related to the risk of hospital readmission in patients with Diabetes first to apply a clustering of subgroups by similarity. Then structures a predictive analysis with the main algorithms to identify the methodology of best performance. Numerous approaches were performed to prepare the dataset for these two interventions. The results found in the first phase were two clusters based on the total number of hospital recurrences and others on total administrative costs, with K=3. In the second phase, the best algorithm found was Neural Network 3, with a ROC of 0.68 and a misclassification rate of 0.37. When applied the same algorithm in the clusters, there were no gains in the confidence of the indexes, suggesting that there are no substantial gains in the division of subpopulations since the disease has the same behaviour and needs throughout its development

Improving operating room schedule in a portuguese hospital : a machine learning approach to predict operating room time

Tese de Mestrado, Engenharia Biomédica e Biofísica, 2022, Universidade de Lisboa, Faculdade de CiênciasFor most hospitals, the operating room (OR) is a significant source of expenses and income. A critical point of effective OR scheduling is the prediction of OR time for a patient procedure. An inefficient schedule results in two scenarios: underestimated or overestimated OR times. A solution reported in the literature is the implementation of machine learning (ML) models that include additional variables to improve the accuracy of these predictions. This project goal is to improve the OR schedule efficiency in a hospital center by achieving precise OR time predictions. This goal was accomplished by developing two ML models (Multiple Linear Regression (MLR) and Random Forest (RF)), through two different approaches. Firstly, for all the specialties on the dataset (All Specialties Model). Second, a specialty-specific model for each (Urology, General Surgery, and Orthopedics Models). This leads to eight models where the predictive features were identified based on the literature along with consultations with the professionals. The All Specialties Model presented a surgery median time of 115.0 minutes, with an R-squared surrounding 0.7. Urology had a median time of 70.0 minutes, with an R-squared of 0.822 and 0.831 and a MAE of 21.7 and 20.9 minutes for MLR and RF models, respectively. General Surgery had a median time of 110.0 minutes with an R-squared of 0.826 and 0.825 and a MAE of 26.2 and 26.1 minutes for MLR and RF, respectively. For Orthopedics, the RF was the only one able to model all the data with an R-squared of 0.683 and a MAE of 27.1 minutes. When compared with the current methods, considering a 10% threshold, the models achieved reductions in underestimation surgeries (41%), and an increase of within predictions (19%). However, with a 22% increase in overestimation predictions. We conclude that using ML approaches improve the accuracy of OR time predictions.O bloco operatório representa uma das unidades que gera maior despesas e receitas a nível hospitalar. Trata-se de um ambiente altamente complexo, onde é necessário alocar recursos materiais e humanos que são extremamente dispendiosos. Desta forma, o bloco operatório necessita de ser gerido de forma eficiente para garantir que o investimento inicialmente feito tem o seu retorno e é utilizado no seu máximo potencial. Paralelamente, os hospitais públicos, integrados no Serviço Nacional de Saúde, apresentam longas listas de espera às quais necessitam de dar resposta. Esta crescente demanda por serviços de saúde, que exige tratamento a nível de bloco operatório, é agravada pelo envelhecimento populacional, e leva a que todos os profissionais envolvidos neste ambiente coloquem os seus esforços no sentido de garantir que toda a população tem as suas necessidades asseguradas. Um ponto fulcral no problema descrito passa por, numa primeira instância, garantir um agendamento cirúrgico eficiente. Quando um paciente é eleito para uma cirurgia programável, cirurgia eletiva, é colocado em lista de espera e feito o seu agendamento, para mais tarde realizar o respetivo procedimento cirúrgico. No momento do agendamento é necessária a informação do tempo de sala de operação que o paciente irá requerer, para reservar o bloco de tempo de sala adequado ao seu procedimento cirúrgico. Um agendamento cirúrgico ineficiente pode gerar dois diferentes cenários que não são desejáveis. Por um lado, se existir uma subestimação do tempo de sala, situação em que o tempo previsto é inferior ao real, leva a que a cirurgia seja mais longa que o estimado e, consequentemente, atrase as operações seguintes. No pior dos cenários há operações que são canceladas. Por outro lado, se há uma sobrestimação, a cirurgia levou menos tempo que o estimado, não há um aproveitamento total dos recursos da sala de operação. Na maioria dos hospitais, esta previsão de tempo de sala é feita com base na experiência do cirurgião e a implementação de ferramentas de inteligência artificial para executar esta tarefa ainda é escassa. Este tipo de previsão leva a um elevado número de cirurgias subestimadas, pois o cirurgião, na sua maioria, não tem em consideração fatores do paciente e anestésicos que impactam o tempo de sala considerando, na maioria das vezes, somente o tempo necessário à cirurgia em si. Além disso, o cirurgião tende a alocar o maior número de cirurgias num curto bloco de tempo, o que leva a uma previsão irrealista. Uma solução apontada na literatura é a implementação de algoritmos de aprendizagem automática para o desenvolvimento de modelos que implementem variáveis associadas ao paciente, operacionais, anestésicas e relacionadas com o staff. Este tipo de abordagens mostrou melhorar a precisão na previsão do tempo de sala. O projeto apresentado foi baseado numa metodologia que, primeiramente, permitiu a compreensão dos métodos praticados no centro hospitalar abordado no projeto, o Centro Hospitalar Lisboa Central (CHULC), a validação da relevância do projeto e como objetivo principal, o aumento da eficiência do bloco operatório através da melhoria na precisão da predição do tempo de sala. Toda a metodologia foi desenvolvida tendo como fundamento a base de dados fornecida por esta instituição que contém todas as cirurgias relativas às especialidades de Urologia, Cirurgia Geral e Ortopedia realizadas nos últimos cinco anos (janeiro de 2017 a dezembro de 2021). Para alcançar o objetivo central de melhorar a predição do tempo de sala, foram propostos dois modelos de aprendizagem automática, cujo output é o tempo de sala, um modelo de regressão linear múltipla e de uma floresta aleatória (em inglês designado por Random Forest- RF) segundo duas abordagens. A primeira abordagem consistiu no desenvolvimento de um modelo único para todas as três especialidades apresentadas na base de dados e a segunda num modelo específico para cada especialidade individual. O que conduziu a um total de oito modelos, uma vez que em cada abordagem ambos os algoritmos de regressão linear múltipla e de RF foram implementados. As variáveis com potencial valor preditivo da base de dados do CHULC foram identificadas com base na revisão de literatura assim como em reuniões marcadas com os diretores de serviço das especialidades abordadas, administradores hospitalares e anestesiologistas. Uma vez abordada a metodologia atualmente implementada no CHULC para a previsão do tempo de sala, que é baseada na experiência do próprio cirurgião, foi avaliado o impacto do tempo controlado pelo cirurgião e relativo à anestesia no tempo de sala. O tempo controlado pelo cirurgião apresentou a maior correlação com o tempo de sala, com um coeficiente de Pearson de 0,966 seguido do tempo anestésico, com um coeficiente de 0,686. A elevada correlação do tempo controlado pelo cirurgião com o tempo de sala indica que, por um lado, a forma como a predição do tempo de sala é praticada atualmente não é totalmente errada, mas, por outro lado, não é tão realistas já que não considera todos os fatores que influenciam este tempo. Ao incluir as variáveis relativas ao paciente, hospital e anestesia nos oito modelos propostos, para uma mediana de tempo de sala de 115,0 minutos, o modelo de regressão linear relativo a todas as especialidades obteve um R-quadrado de 0,780 acompanhado por um erro médio absoluto de 26,9 minutos. Os modelos de Urologia apresentaram um R-quadrado de 0,822 e 0,831 e um erro médio de 21,7 e 20,9 minutos para o modelo de regressão linear e de RF, respetivamente, com uma mediana de cirurgia de 70,0 minutos. Para a Cirurgia Geral, a mediana de cirurgia é de 110,0 minutos com um R-quadrado de 0,826 e 0,825 e um erro médio de 26,2 e 26,1 minutos para os modelos de regressão linear e RF, respetivamente. No modelo de Ortopedia, o algoritmo de RF foi o único capaz de modelar todos os dados desta especialidade com um R-quadrado de 0,683 e um erro médio de 27,1 minutos, para uma mediana de cirurgia de 130,0 minutos. Nesta especialidade, a regressão linear conseguiu moldar todas as cirurgias com exceção das cirurgias relativas ao joelho e anca, com um R-quadrado de 0,685 e erro médio de 28,9 minutos. As possíveis causas foram levantadas e descritas em maior detalhe, a elevada variabilidade entre procedimentos e o perfil de doentes (polidiagnosticados e polimedicados) foram os pontos fulcrais apontados pelo diretor de cirurgia ortopédica do CHULC. Quando comparado com os métodos atuais do CHULC, todos os modelos alcançaram uma diminuição significativa no erro de predição do tempo de sala. Considerando uma margem de 10%, todos os modelos apresentaram uma redução na percentagem de cirurgias subestimadas, cerca de 41%, e um aumento nas percentagens das cirurgias estimadas corretamente, rondando os 19%. No entanto, os modelos registaram um aumento de 22% nas cirurgias sobrestimadas. Futuros estudos no sentido de traduzir o impacto de cirurgias subestimadas e sobrestimadas serão necessários para complementar estes resultados. A variável que apresentou um maior impacto em todos os modelos de RF foi a média do cirurgião com base no tipo de procedimento cirúrgico realizado. Dado o elevado grau de linearidade desta variável com o output do modelo, o tempo de sala, expresso por um coeficiente de Pearson de 0,865, levou a que o modelo de regressão linear conseguisse traduzir de forma precisa a relação entre estas variáveis, e, consequentemente, atingisse resultados semelhantes ao modelo de RF nas especialidades de Urologia e Cirurgia Geral. Conclui-se que a implementação de abordagens de aprendizagem automática melhora a precisão na predição do tempo de sala e podem servir como uma ferramenta de apoio à decisão clínica para o auxílio do agendamento cirúrgico. Para operacionalizar estes resultados a nível hospitalar é necessário trabalho futuro

Predictive analytics framework for electronic health records with machine learning advancements : optimising hospital resources utilisation with predictive and epidemiological models

The primary aim of this thesis was to investigate the feasibility and robustness of predictive machine-learning models in the context of improving hospital resources’ utilisation with data- driven approaches and predicting hospitalisation with hospital quality assessment metrics such as length of stay. The length of stay predictions includes the validity of the proposed methodological predictive framework on each hospital’s electronic health records data source. In this thesis, we relied on electronic health records (EHRs) to drive a data-driven predictive inpatient length of stay (LOS) research framework that suits the most demanding hospital facilities for hospital resources’ utilisation context. The thesis focused on the viability of the methodological predictive length of stay approaches on dynamic and demanding healthcare facilities and hospital settings such as the intensive care units and the emergency departments. While the hospital length of stay predictions are (internal) healthcare inpatients outcomes assessment at the time of admission to discharge, the thesis also considered (external) factors outside hospital control, such as forecasting future hospitalisations from the spread of infectious communicable disease during pandemics. The internal and external splits are the thesis’ main contributions. Therefore, the thesis evaluated the public health measures during events of uncertainty (e.g. pandemics) and measured the effect of non-pharmaceutical intervention during outbreaks on future hospitalised cases. This approach is the first contribution in the literature to examine the epidemiological curves’ effect using simulation models to project the future hospitalisations on their strong potential to impact hospital beds’ availability and stress hospital workflow and workers, to the best of our knowledge. The main research commonalities between chapters are the usefulness of ensembles learning models in the context of LOS for hospital resources utilisation. The ensembles learning models anticipate better predictive performance by combining several base models to produce an optimal predictive model. These predictive models explored the internal LOS for various chronic and acute conditions using data-driven approaches to determine the most accurate and powerful predicted outcomes. This eventually helps to achieve desired outcomes for hospital professionals who are working in hospital settings

Advancing the Understanding of Clinical Sepsis Using Gene Expression-Driven Machine Learning to Improve Patient Outcomes

Sepsis remains a major challenge that necessitates improved approaches to enhance patient outcomes. This study explored the potential of Machine Learning (ML) techniques to bridge the gap between clinical data and gene expression information to better predict and understand sepsis. We discuss the application of ML algorithms, including neural networks, deep learning, and ensemble methods, to address key evidence gaps and overcome the challenges in sepsis research. The lack of a clear definition of sepsis is highlighted as a major hurdle, but ML models offer a workaround by focusing on endpoint prediction. We emphasize the significance of gene transcript information and its use in ML models to provide insights into sepsis pathophysiology and biomarker identification. Temporal analysis and integration of gene expression data further enhance the accuracy and predictive capabilities of ML models for sepsis. Although challenges such as interpretability and bias exist, ML research offers exciting prospects for addressing critical clinical problems, improving sepsis management, and advancing precision medicine approaches. Collaborative efforts between clinicians and data scientists are essential for the successful implementation and translation of ML models into clinical practice. ML has the potential to revolutionize our understanding of sepsis and significantly improve patient outcomes. Further research and collaboration between clinicians and data scientists are needed to fully understand the potential of ML in sepsis management

Personalized Clinical Treatment Selection Using Genetic Algorithm and Analytic Hierarchy Process

The development of Machine Learning methods and approaches offers enormous growth opportunities in the Healthcare field. One of the most exciting challenges in this field is the automation of clinical treatment selection for patient state optimization. Using necessary medical data and the application of Machine Learning methods (like the Genetic Algorithm and the Analytic Hierarchy Process) provides a solution to such a challenge. Research presented in this paper gives the general approach to solve the clinical treatment selection task, which can be used for any type of disease. The distinguishing feature of this approach is that clinical treatment is tailored to the patient's initial state, thus making treatment personalized. The article also presents a comparison of the different classification methods used to model patient indicators after treatment. Additionally, special attention was paid to the possibilities and potential of using the developed approach in real Healthcare challenges and tasks

Psychological factors in chronic low back pain: an investigation of the role of physical and psychological factors in the severity of illness

Summary available: p. [xvi]

Utilizing Consumer Health Posts for Pharmacovigilance: Identifying Underlying Factors Associated with Patients’ Attitudes Towards Antidepressants

Non-adherence to antidepressants is a major obstacle to antidepressants therapeutic benefits, resulting in increased risk of relapse, emergency visits, and significant burden on individuals and the healthcare system. Several studies showed that non-adherence is weakly associated with personal and clinical variables, but strongly associated with patients’ beliefs and attitudes towards medications. The traditional methods for identifying the key dimensions of patients’ attitudes towards antidepressants are associated with some methodological limitations, such as concern about confidentiality of personal information. In this study, attempts have been made to address the limitations by utilizing patients’ self report experiences in online healthcare forums to identify underlying factors affecting patients attitudes towards antidepressants. The data source of the study was a healthcare forum called “askapatients.com”. 892 patients’ reviews were randomly collected from the forum for the four most commonly prescribed antidepressants including Sertraline (Zoloft) and Escitalopram (Lexapro) from SSRI class, and Venlafaxine (Effexor) and duloxetine (Cymbalta) from SNRI class. Methodology of this study is composed of two main phases: I) generating structured data from unstructured patients’ drug reviews and testing hypotheses concerning attitude, II) identification and normalization of Adverse Drug Reactions (ADRs), Withdrawal Symptoms (WDs) and Drug Indications (DIs) from the posts, and mapping them to both The UMLS and SNOMED CT concepts. Phase II also includes testing the association between ADRs and attitude. The result of the first phase of this study showed that “experience of adverse drug reactions”, “perceived distress received from ADRs”, “lack of knowledge about medication’s mechanism”, “withdrawal experience”, “duration of usage”, and “drug effectiveness” are strongly associated with patients attitudes. However, demographic variables including “age” and “gender” are not associated with attitude. Analysis of the data in second phase of the study showed that from 6,534 identified entities, 73% are ADRs, 12% are WDs, and 15 % are drug indications. In addition, psychological and cognitive expressions have higher variability than physiological expressions. All three types of entities were mapped to 811 UMLS and SNOMED CT concepts. Testing the association between ADRs and attitude showed that from twenty-one physiological ADRs specified in the ASEC questionnaire, “dry mouth”, “increased appetite”, “disorientation”, “yawning”, “weight gain”, and “problem with sexual dysfunction” are associated with attitude. A set of psychological and cognitive ADRs, such as “emotional indifference” and “memory problem were also tested that showed significance association between these types of ADRs and attitude. The findings of this study have important implications for designing clinical interventions aiming to improve patients\u27 adherence towards antidepressants. In addition, the dataset generated in this study has significant implications for improving performance of text-mining algorithms aiming to identify health related information from consumer health posts. Moreover, the dataset can be used for generating and testing hypotheses related to ADRs associated with psychiatric mediations, and identifying factors associated with discontinuation of antidepressants. The dataset and guidelines of this study are available at https://sites.google.com/view/pharmacovigilanceinpsychiatry/hom