171 research outputs found
A review of dynamic Bayesian network techniques with applications in healthcare risk modelling
Coping with an ageing population is a major concern for healthcare organisations around the world. The average cost of hospital care is higher than social care for older and terminally ill patients. Moreover, the average cost of social care increases with the age of the patient. Therefore, it is important to make efficient and fair capacity planning which also incorporates patient centred outcomes. Predictive models can provide predictions which their accuracy can be understood and quantified. Predictive modelling can help patients and carers to get the appropriate support services, and allow clinical decision-makers to improve care quality and reduce the cost of inappropriate hospital and Accident and Emergency admissions. The aim of this study is to provide a review of modelling techniques and frameworks for predictive risk modelling of patients in hospital, based on routinely collected data such as the Hospital Episode Statistics database. A number of sub-problems can be considered such as Length-of-Stay and End-of-Life predictive modelling. The methodologies in the literature are mainly focused on addressing the problems using regression methods and Markov models, and the majority lack generalisability. In some cases, the robustness, accuracy and re-usability of predictive risk models have been shown to be improved using Machine Learning methods. Dynamic Bayesian Network techniques can represent complex correlations models and include small probabilities into the solution. The main focus of this study is to provide a review of major time-varying Dynamic Bayesian Network techniques with applications in healthcare predictive risk modelling
Ensemble Risk Model of Emergency Admissions (ERMER)
Introduction
About half of hospital readmissions can be avoided with preventive interventions. Developing decision support tools for identification of patients’ emergency readmission risk is an important area of research. Because, it remains unclear how to design features and develop predictive models that can adjust continuously to a fast-changing healthcare system and population characteristics. The objective of this study was to develop a generic ensemble Bayesian risk model of emergency readmission.
Methods
We produced a decision support tool that predicts risk of emergency readmission using England's Hospital Episode Statistics inpatient database. Firstly, we used a framework to develop an optimal set of features. Then, a combination of Bayes Point Machine (BPM) models for different cohorts was considered to create an optimised ensemble model, which is stronger than the individual generative and non-linear classifications. The developed Ensemble Risk Model of Emergency Admissions (ERMER) was trained and tested using three time-frames: 1999-2004, 2000-05 and 2004-09, each of which includes about 20% of patients in England during the trigger year.
Results
Comparisons are made for different time-frames, sub-populations, risk cut-offs, risk bands and top risk segments. The precision was 71.6% to 73.9%, the specificity was 88.3% to 91.7% and the sensitivity was 42.1% to 49.2% across different time-frames. Moreover, the Area Under the Curve was 75.9% to 77.1%.
Conclusions
The decision support tool performed considerably better than the previous modelling approaches, and it was robust and stable with high precision. Moreover, the framework and the Bayesian model allow the model to continuously adjust it to new significant features, different population characteristics and changes in the system
Risk Modelling Framework for Emergency Hospital Readmission, Using Hospital Episode Statistics Inpatient Data
The objective of this study was to develop, test and benchmark a framework and a predictive risk model for hospital emergency readmission within 12 months. We performed the development using routinely collected Hospital Episode Statistics data covering inpatient hospital admissions in England. Three different timeframes were used for training, testing and benchmarking: 1999 to 2004, 2000 to 2005 and 2004 to 2009 financial years. Each timeframe includes 20% of all inpatients admitted within the trigger year.
The comparisons were made using positive predictive value, sensitivity and specificity for different risk cut-offs, risk bands and top risk segments, together with the receiver operating characteristic curve. The constructed Bayes Point Machine using this feature selection framework produces a risk probability for each admitted patient, and it was validated for different timeframes, sub-populations and cut-off points. At risk cut-off of 50%, the positive predictive value was 69.3% to 73.7%, the specificity was 88.0% to 88.9% and sensitivity was 44.5% to 46.3% across different timeframes. Also, the area under the receiver operating characteristic curve was 73.0% to 74.3%.
The developed framework and model performed considerably
better than existing modelling approaches with high precision and moderate sensitivity
Temporal Comorbidity-Adjusted Risk of Emergency Readmission (T-CARER): A Tool for Comorbidity Risk Assessment
Comorbidity in patients, along with attendant operations and complications, is associated with reduced long-term survival probability and an increased need for healthcare facilities. This study proposes a user-friendly toolkit to design an adjusted case-mix model of the risk of comorbidity for use by the public for its incremental development. The proposed model, Temporal Comorbidity-Adjusted Risk of Emergency Readmission (T-CARER), introduces a generic method for generating a pool of features from re-categorised and temporal features to create a customised comorbidity risk index.
Research on emergency admission has shown that demographics, temporal dimensions, length of stay, and time between admissions can noticeably improve statistical
measures related to comorbidities. The model proposed in this study, T-CARER, incorporates temporal aspects, medical procedures, demographics, admission details, and diagnoses. And, it tries to address four weakness areas in popular comorbidity risk indices: robustness, temporal adjustment, population stratication, and inclusion of major associated factors.
Three approaches to modelling, a logistic regression, a random forest, and a wide and deep neural network, are designed to predict the comorbidity risk index associated with 30- and 365-day emergency readmissions. The models were trained and tested using England's Hospital Episode Statistics inpatient database for two time-frames: 1999-2004 and 2004-2009, and various risk cut-os. Also, models are
compared against implementations of Charlson and Elixhauser's comorbidity indices from multiple aspects. Tests using k \u100000 fold cross-validation yielded stable and consistent results, with negative mean-squared error variance of -0.7 to -2.9. In terms of c-statistics, the wide and deep neural network and the random forest models outperformed Charlson's and Elixhauser's comorbidity indices. For the 30- and 365-day
emergency readmission models, the c-statistics ranged from 0.772 to 0.804 across the timeframes.
The wide and deep neural network model generated predictions with high precision, and the random forest model performed better than the regression model, in terms of the micro-average of the F1-score. Our best models yielded precision values in the range of 0.582{0.639, and an average F1-score of 0.730{0.790.
The proposed temporal case-mix risk model T-CARER outperforms prevalent models, including Charlson's and Elixhauser's comorbidity indices, with superior precision, F1-score, and c-statistics. The proposed risk index can help monitor the temporal comorbidities of patients and reduce the cost of emergency admissions
Toward a Multiphase Local Approach in the Modeling of Flotation and Mass Transferin Gas-Liquid Contacting Systems
Predictive Risk Modelling for Integrated Care: a Structured Review
If patients at risk of admission or readmission
to hospital or other forms of care could be identified and offered suitable early interventions then their lives and long-term health may be improved by reducing the chances of future admission or readmission to care, and hopefully, their cost of care reduced.
Considerable work has been carried out in this subject area especially in the USA and the UK. This has led for instance to the development of tools such as PARR, PARR-30, and the Combined Predictive Model for prediction of emergency readmission or admission to acute care.
Here we perform a structured review the academic and grey
literature on predictive risk tools for social care utilisation, as well as admission and readmission to general hospitals and psychiatric hospitals. This is the first phase of a project in partnership with Docobo Ltd and funded by Innovate UK,in which we seek to develop novel predictive risk tools and dashboards to assist commissioners in Clinical Commissioning Groups with the triangulation of the intelligence available from routinely collected data to optimise integrated care and better understand the complex needs of individuals
Discovering Drug-Drug Interactions Using Association Rule Mining from Electronic Health Records
In this paper, we propose utilising Electronic Health Records (EHR) to discover previously unknown drug-drug interactions (DDI) that may result in high rates of hospital readmissions. We used association rule mining and categorised drug combinations as high or low risk based on the adverse events they caused. We demonstrate that the drug combinations in the high-risk group contain significantly more drug-drug interactions than those in the low-risk group. This approach is efficient for discovering potential drug interactions that lead to negative outcomes, thus should be given priority and evaluated in clinical trials. In fact, severe drug interactions can have life-threatening consequences and result in adverse clinical outcomes. Our findings were achieved using a new association rule metric, which better accounts for the adverse drug events caused by DDI
Les pseudo nodules thyroidiens
Buts : Rappeler les particularités cliniques et paracliniques des différentes étiologies des pseudonodules thyroïdiens et discuter les moyens de diagnostic positif et de prise en charge thérapeutique.Matériels et méthodes : il s'agit de 5 observations de patientes porteuses de pseudo-nodules thyroïdiens colligées au service d'oRL et de CCF du ChU Farhat hached de Sousse. L'âge variait entre 21 et 52 ans. dans tous les cas, le motif de consultation était une tuméfaction basicervicale associée à des signes de compression (dysphagie, dyspnée et/ou dysphonie). Une échographie et une scintigraphie thyroïdiennes ont été réalisées dans tous les cas. La cytoponction n'a été pratiquée que dans trois cas.Résultats : La tuméfaction basicervicale était mobile à la déglutition dans tous les cas. L'échographie et la scintigraphie ont évoqué une lésion thyroïdienne dans tous les cas. La cytoponction n'a été contributive dans aucun des trois cas. Après cervicotomie exploratrice et étude histologique, le diagnostic était, selon les cas, un kyste hydatique, un schwannome,un adénome parathyroïdien, un kyste du tractus thyréoglosse et un chondrosarcome cricoïdien.Conclusion: Les pseudo- nodules thyroïdiens sont des nodules cervicaux péri-thyroïdiens qui constituent des situations cliniques exceptionnelles voire anecdotiques que le chirurgien doit savoir évoquer. L'examen cytologique fait par une personne entraînée peut être contributif au diagnostic différentiel. Cependant, le diagnostic se fait souvent après cervicotomie exploratrice.Mots clés : pseudo- nodule, thyroïde, kyste hydatique, schwannome, adénome parathyroïdien, kyste du tractus thyréoglosse, chondrosarcom
La dysplasie fibreuse du rocher
La dysplasie fibreuse est une affection bénigne rare dont l’étiologie est inconnue. Elle représente une anomalie dans le développement normal de l’os. L’os temporal est rarement atteint, donnant des complications telles qu’une surdité et une paralysie faciale périphérique. L’imagerie, principalement la tomodensitométrie est capitale pour le diagnostic positif. On rapporte un cas de dysplasie fibreuse retrouvé chez une femme de 43 ans présentant une surdité brutale.Mots clés : os temporal, dysplasie, scanner.Fibrous dysplasia is an uncommon benign disorder of unknown etiology. It most likely represents a disorder of bone normal development. The temporal bone is rarely involved, giving complications such as hearing loss and facial nerve palsy. The imaging, mainly performed with computerized tomography, plays a major role in positive diagnosis. We report a case of fibrous dysplasia found on a 43 year-old women presenting an acute sensorineural hearing loss.Key words: temporal bone, dysplasia, CT
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