Observatorio de I+D+i UPM

Memorias de investigación
Tesis:
Design and evaluation of analytical tools for Emergency Department management based on machine learning techniques
Año:2016
Áreas de investigación
  • Tecnología electrónica y de las comunicaciones,
  • Ingeniería eléctrica, electrónica y automática
Datos
Descripción
The Spanish National Healthcare System (NHS) is mostly publicly funded and provided. It is considered highly cost-efficient according to international studies based on World Health Organization (WHO) data. However, the contention of healthcare costs increases while maintaining adequate levels of quality of care, is still a largely unsolved problem. In recent years, Emergency Departments (EDs) of specialized care hospitals have been subjected to budget restrictions, increased visits and increased clinical complexity of these visits. These circumstances require new approaches to ED management, which could benefit from decision support tools. Data for the development of these tools were provided by the Ramon y Cajal University Hospital of Madrid, a large specialized care referral center with all medical specialties excepting Obstetrics. In years 2011 and 2012 it had approximately 1,100 beds and approximately 553,000 patients assigned to its clinical area. The first topic of this Ph.D. thesis is the development of models for ED census forecasting (i.e. prediction of the number of patients present at the ED at a given time). One of the uses of ED census forecasting is nursing personnel allocation, based on national and international recommendations. In the case of maximum ED census forecasts, M5P was the best choice for the reduction of major and medium nursing personnel understaffing risks, for all forecast horizons. Compared to the usual staffing levels, personnel planning with M5P could reduce major understaffing (>9 nurse) risks more than 10-fold (a reduction to ~1% with M5P compared to ~13% obtained with the usual nurse staffing levels); and could reduce medium understaffing (7-9 nurses) risks approximately 3-fold (a reduction to ~3% with M5P compared to ~10% obtained with the usual staffing levels). The usage of M5P also implied 5% - 6.1% increases in estimated nursing personnel costs (compared to the usual staffing levels), which are acceptable given the large reductions in understaffing risks. In the case of average ED census forecasts, once again, M5P was the best choice for the reduction of major and medium underestimation risks, for all forecast horizons. Relative risk reductions were similar to those of maximum ED census forecasts, with M5P leading to cost reductions of more than 15% compared to the usual resource allocation policies. The second topic of this Ph.D. thesis is the development of models for real-time prediction of probabilities of inpatient admission from the ED. Our aim in this case was the development of classifiers with adequate performance in terms of both discrimination and calibration (goodness-of-fit), reliant on a small number of variables, available in most ED settings right after triage. In our setting, the Manchester Triage System (MTS) was used. Confidence intervals of average AUROCs for LR and ANN models slightly overlapped, although ANN models had higher AUROCs than LR models in all but one of the 12 test sets. Average H-L ?2 were, respectively, 35.15, 95% CI (32.57, 37.73) for LR models, 10.47, 95% CI (7.78, 13.17) for ANN models and 11.4, 95% CI (9.10, 13.75) for ad hoc ensemble classifier models. Both ANN and ad hoc ensemble classifier models possessed better calibration than LR models, with H-L p-values>0.05 in 10 of the 12 experiments. The third topic of this Ph.D thesis is the development and evaluation of software for the generation of logistic and Cox regression nomograms. We developed two programs (nomolog and nomocox) for these purposes, based on Stata. At the time of the writing of this Ph.D. thesis these programs are used by an international community of researchers in the fields of clinical medicine, epidemiology or biostatistics.
Internacional
No
ISBN
Tipo de Tesis
Doctoral
Calificación
Sobresaliente cum laude
Fecha
28/06/2016
Esta actividad pertenece a memorias de investigación
Participantes
  • Autor: Alexander Zlotnik Enaliev (UPM)
  • Director: Juan Manuel Montero Martinez (UPM)
  • Director: Ascensión Gallardo Antolín (Universidad Carlos III, Madrid)
Grupos de investigación, Departamentos, Centros e Institutos de I+D+i relacionados
  • Creador: Grupo de Investigación: Grupo de Tecnología del Habla
  • Centro o Instituto I+D+i: Centro de I+d+i en Procesado de la Información y Telecomunicaciones
  • Departamento: Ingeniería Electrónica
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