Multicentre, prospective observational study of the correlation between the Glasgow Admission Prediction Score and adverse outcomes

Objectives: To assess whether the Glasgow Admission Prediction Score (GAPS) is correlated with hospital length of stay, 6-month hospital readmission and 6-month all-cause mortality. This study represents a 6-month follow-up of patients who were included in an external validation of the GAPS’ ability to predict admission at the point of triage. Setting: Sampling was conducted between February and May 2016 at two separate emergency departments (EDs) in Sheffield and Glasgow. Participants: Data were collected prospectively at triage for consecutive adult patients who presented to the ED within sampling times. Any patients who avoided formal triage were excluded from the study. In total, 1420 patients were recruited. Primary outcomes: GAPS was calculated following triage and did not influence patient management. Length of hospital stay, hospital readmission and mortality against GAPS were modelled using survival analysis at 6 months. Results: Of the 1420 patients recruited, 39.6% of these patients were initially admitted to hospital. At 6 months, 30.6% of patients had been readmitted and 5.6% of patients had died. For those admitted at first presentation, the chance of being discharged fell by 4.3% (95% CI 3.2% to 5.3%) per GAPS point increase. Cox regression indicated a 9.2% (95% CI 7.3% to 11.1%) increase in the chance of 6-month hospital readmission per point increase in GAPS. An association between GAPS and 6-month mortality was demonstrated, with a hazard increase of 9.0% (95% CI 6.9% to 11.2%) for every point increase in GAPS. Conclusion: A higher GAPS is associated with increased hospital length of stay, 6-month hospital readmission and 6-month all-cause mortality. While GAPS’s primary application may be to predict admission and support clinical decision making, GAPS may provide valuable insight into inpatient resource allocation and bed planning

Deep Learning in the Prediction of Clinically Significant Outcomes in Stroke and General Medicine Patients

Background The need for novel strategies to improve outcome prediction and the categorisation of unstructured medical data will increase as the demands on hospitals, associated with the increasing age and complexity of admitted patients, continues to rise. Stroke is a highly specialised field, in which key performance indicators and discharge planning have an important role. General medicine is a field that encompasses a wide variety of multisystem and undifferentiated illnesses. It is possible that machine learning, in particular deep learning, may be able to assist with the prediction of clinically significant outcomes both in areas with highly specialised assessment and treatment considerations (such as stroke), as well as fields with a diverse mix of medical conditions and comorbidities (such as general medicine). Method This thesis comprised of studies using machine learning to predict clinically significant outcomes in stroke and general medicine inpatients. Initially a systematic review was conducted to evaluate the existing literature regarding the prediction of one such clinically significant outcome, length of stay, in medical inpatients. Derivation and validation studies were conducted to develop models for stroke inpatients to aid with the prediction of discharge independence, survival to discharge, discharge destination and length of stay. Stroke key performance indicator-automated extraction and clinical coding categorisation were undertaken in studies employing techniques including natural language processing. Natural language processing was applied to general medicine free-text data in pilot, derivation, and validation studies in the prediction of outcomes including discharge timing. Results The systematic review identified a particular lack of prospective validation studies for machine learning models developed to aid with length of stay prediction in medical inpatients. The stroke model derivation, prospective and external validation studies demonstrated the successful use of machine learning models in the prediction of outcomes relevant to discharge planning for stroke patients. For example, an area under the receiver operator curve (AUC) of 0.85 and 0.87 was achieved for the prediction of independence at the time of discharge in the prospective and external validation datasets respectively. The automated collection of stroke key performance indicators and the application of natural language processing to stroke clinical coding also demonstrated performance as high as an AUC of 0.95-1.00 in key performance indicator classification tasks. The general medicine pilot, derivation, prospective and external validation studies demonstrated the development and success of artificial neural networks in the prediction of discharge within the next 48 hours (AUC 0.78 and 0.74 in the prospective and external validation datasets respectively). Conclusions Machine learning models (including deep learning) can successfully predict clinically significant outcomes in stroke and general medicine patients.Thesis (Ph.D.) -- University of Adelaide, Adelaide Medical School, 202

Predicting length of stay (LOS) in a hospital post-sugery

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceThe amount of time a patient stays in the hospital after a surgery has been an issue that hospital management faces, a longer stay in the recovery room involves a high cost to the hospital and consumes a lot of hospital resources, manpower and equipment. The amount of time is difficult to predict precisely since there are many external and internal factors that account for a longer or shorter stay and it is difficult for a team to consider all these factors and make this estimation manually. With the advancement of machine learning methods and models this prediction can be made automatically. The aim of this study was to create a predicting model that look at the patient data and the procedure data and predicts the amount of time the patient will stay after the surgery to make the current prediction of the length of stay by the hospital more accurate and compliment the current surgery scheduling and discharge system. To achieve the objective, a data mining approach was implemented. Python Language was used, with particular emphasis on Scikit-Learn, pandas and Seaborn packages. Tables from a relational database were processed and extracted to build a dataset. Exploratory data analysis was performed, and several model configurations were tested. The main differences that separate the models are outlier treatment, sampling techniques, feature scalers, feature engineering and type of algorithm – Linear Regression, Decision Trees Regressor, Multilayer Perceptron Regressor, Random Forest Regressor, Light Gradient Boosting Machine Regressor and Gradient Boosting Regressor. A total of 32993 hospital episodes were observed on this study. Out of these, 2006 were eliminated due to some data anomalies, namely, values that were wrong or impossible. The data was split in training and test data. Several model configurations were tested. The main differences that separate the models are outlier treatment, feature scalers, feature engineering and the type of algorithm. The best performing model had a score of 0.73 R2 which was obtained by using the Light Gradient Boosting Machine Regressor Algorithm using outlier removal, Robust Scaling and using all the features in the dataset

Contribution à la conception d'un système d'aide à la décision pour la gestion de situations de tension au sein des systèmes hospitaliers. Application à un service d'urgence.

The management of patient flow, especially the flow resulting from health (flu, heat waves and exceptional circumstances) is one of the most important problems to manage in the emergency department (ED). To handle the influx of patients, emergency departments require significant human and material resources, and a high degree of coordination between these resources. Under these conditions, the medical and the paramedical staffs are often confronted with strain situations which greatly complicate their task. The main purpose of this thesis is to contribute to improving the management of situations of tension occurring in the emergency department by providing a decision support system, SAGEST. This DSS allows i) a proactive control of the ED: predicting at short and/or medium-term the occurrence of potential strain situations and proposing corrective actions to prevent the occurrence of these situations, ii) a reactive control in the case of no-detection of the strain situation occurrence. A functional architecture of the SAGEST system, based on the manager’s decision making process is proposed. Used methodologies and models embedded in the main functions and the knowledge base of the SAGEST system are described. Finally, experiments and results of different models of SAGEST system applied to the paediatric emergency department (PED) of the Regional University Hospital of Lille are presented and discussed.La prise en charge des flux des patients, en particulier les flux récurrents et consécutifs à des crises sanitaires (grippes, canicules, situations exceptionnelles) est l'un des problèmes les plus importants auquel les services des urgences (SU) doivent faire face. Pour gérer cet afflux de patients, les services des urgences nécessitent des ressources humaines et matérielles importantes, ainsi qu'un degré élevé de coordination entre ces ressources. Dans ces conditions, le personnel médical se voit confronté très fréquemment à des situations de tension qui compliquent très fortement sa tâche. L‘objet de cette thèse est de contribuer à l’amélioration de la gestion des situations de tension se produisant dans un service d’urgence en proposant un système d’aide à la décision, SAGEST (Système d’Aide à la décision pour la GEstion des Situations de Tensions), permettant i) le pilotage proactif du SU : prévision à court et/ou moyen terme de l'apparition de situations de tension et l'évolution du flux patients et la proposition d'actions de correction afin d'éviter l’occurrence de ces situations et ii) le pilotage réactif dans le cas où l'occurrence de la situation de tension n'a pas été détectée. Une architecture fonctionnelle du système SAGEST, s'appuyant sur le processus décisionnel du responsable du service d'urgence, est proposée. Les méthodologies et les modèles utilisés dans la construction des principales fonctions et de la base de connaissances sont décrits. Enfin, les résultats d’application des différents modèles du système SAGEST pour le service d’urgence pédiatrique (SUP) du centre hospitalier régional universitaire du Lille sont présentés et discutés