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

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

Measuring and modelling occupancy time in NHS continuing healthcare

Background - Due to increasing demand and financial constraints, NHS continuing healthcare systems seek to find better ways of forecasting demand and budgeting for care. This paper investigates two areas of concern, namely, how long existing patients stay in service and the number of patients that are likely to be still in care after a period of time. Methods - An anonymised dataset containing information for all funded admissions to placement and home care in the NHS continuing healthcare system was provided by 26 (out of 31) London primary care trusts. The data related to 11289 patients staying in placement and home care between 1 April 2005 and 31 May 2008 were first analysed. Using a methodology based on length of stay (LoS) modelling, we captured the distribution of LoS of patients to estimate the probability of a patient staying in care over a period of time. Using the estimated probabilities we forecasted the number of patients that are likely to be still in care after a period of time (e.g. monthly). Results - We noticed that within the NHS continuing healthcare system there are three main categories of patients. Some patients are discharged after a short stay (few days), some others staying for few months and the third category of patients staying for a long period of time (years). Some variations in proportions of discharge and transition between types of care as well as between care groups (e.g. palliative, functional mental health) were observed. A close agreement of the observed and the expected numbers of patients suggests a good prediction model. Conclusions - The model was tested for care groups within the NHS continuing healthcare system in London to support Primary Care Trusts in budget planning and improve their responsiveness to meet the increasing demand under limited availability of resources. Its applicability can be extended to other types of care, such as hospital care and re-ablement. Further work will be geared towards updating the dataset and refining the results

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

La mucormycose nasosinusienne: Diagnostic et modalites therapeutiques

La mucormycose est une infection fongique rare qui touche essentiellement les sujets immunodéprimés et notamment diabétiques. La localisation de cette maladie est surtout nasosinusienne. Son pronostic reste mauvais malgré le développement des moyens de prise en charge. Nous rapportons deux cas de mucormycose nasosinusienne à travers lesquels nous discutons les aspects cliniques et radiologiques, ainsi que les moyens thérapeutiques de cette maladie. Il s’agit d’un homme et d’une femme âgés respectivement de 56 et 52 ans. Le premier était diabétique et la deuxième insuffisante rénale. L’évolution était lente dans le premier cas et très rapide dans le deuxième. Le diagnostic était dans les deux cas histologique. L’évolution était, dans le premier cas, favorable après traitement associant débridement chirurgical et amphotéricine B, et dans le second rapidement fatale. Conclusion : La mucormycose nasosinusienne est une affection grave dont le pronostic peut être mauvais malgré le traitement.Mots clés : Infection fongique, mucormycose rhinocérébrale, zygomycètes