Service Selection using Predictive Models and Monte-Carlo Tree Search

This article proposes a method for automated service selection to improve treatment efficacy and reduce re-hospitalization costs. A predictive model is developed using the National Home and Hospice Care Survey (NHHCS) dataset to quantify the effect of care services on the risk of re-hospitalization. By taking the patient's characteristics and other selected services into account, the model is able to indicate the overall effectiveness of a combination of services for a specific NHHCS patient. The developed model is incorporated in Monte-Carlo Tree Search (MCTS) to determine optimal combinations of services that minimize the risk of emergency re-hospitalization. MCTS serves as a risk minimization algorithm in this case, using the predictive model for guidance during the search. Using this method on the NHHCS dataset, a significant reduction in risk of re-hospitalization is observed compared to the original selections made by clinicians. An 11.89 percentage points risk reduction is achieved on average. Higher reductions of roughly 40 percentage points on average are observed for NHHCS patients in the highest risk categories. These results seem to indicate that there is enormous potential for improving service selection in the near future

La isla de calor urbana de superficie y sus factores condicionantes: El caso del área metropolitana de Santiago

Se ha sintetizado la información de las temperaturas de emisión superficial obtenidas mediante 53 imágenes del satélite Terra MODIS, aplicando para ello promedios anuales y estacionales de las temperaturas estandarizadas, y reducción de las imágenes a factores complejos de los patrones espaciales usando Análisis de Componentes Principales (ACP); finalmente, se han realizado mapas de diferencias térmicas para conocer la intensidad de la isla de calor urbana de superficie (ICUs) estacional y anual. Los resultados muestran que la isla de calor tiende a localizar el máximo térmico en las comunas de Santiago, Providencia, Las Condes, Ñuñoa y Vitacura, conformando un núcleo cálido asociado a la mayor densidad construida; además, las comunas de Huechuraba y Quilicura conforman otro núcleo cálido, el que está asociado a viviendas de alto nivel de ingresos en el primer caso e industrias en el caso de Quilicura. El ACP revela cuatro patrones típicos, que explican el 90,6% de las situaciones, a saber: ICUs consolidada (44,5%), ICUs del piedmont y cuña de altos ingresos (22,3%), un tipo sin isla de calor urbana (20,2%) e ICUs más intensa al sur (3,6%). Finalmente, la intensidad de la isla de calor urbana de superficie es de mayor magnitud durante el otoño (7,4ºC), seguida de verano (5,9ºC), primavera (5,4ºC) e invierno (5,0ºC); incluso en verano y otoño supera los 7ºC en la zona oriente de la ciudad. De las situaciones sin isla de calor, se ha sugerido la hipótesis de efecto sumidero de calor o «urban heat sink», asociado a fuerte brisa de la Cordillera de Los Andes, que barre la ICUs y la desplaza al poniente de la ciudad, lo que en días de contaminación por material particulado (PM10) tendría efectos nocivos sobre la salud de la población de esa parte de la ciudad. Además, se modela la isla de calor urbana de superficie (ICUs) en función de una serie de variables geográficas y urbanas, con el propósito de plantear alternativas para la mitigación de los efectos negativos que las ICUs provocan a los habitantes de la ciudad de Santiago. Además, se entregan algunas directrices para una planificación territorial más sostenible. En total se utilizaron 42 mapas de isla de calor del año 2010, los cuales se generaron con imágenes satelitales Terra MODIS. Ellos fueron sintetizados en cinco cartografías, un mapa anual y cuatro correspondientes a las estaciones del año. Las variables explicativas para predecir la isla de calor fueron nueve: densidad de población y construida, elevación topográfica, NDVI, albedo, radiación solar y las distancias euclidianas al centro, la costa y los ríos y esteros. Los resultados de los modelos de regresión lineal múltiple por pasos indican coeficientes de determinación entre 47,39% y 80,08% (invierno y verano respectivamente). Las variables explicativas más influyentes son el albedo y el NDVI (relación negativa), y la densidad construida (relación positiva). Al modificar las variables que explican la ICUs se podría variar la intensidad de ella, por ejemplo: al aumentar de 10.000 m2 a 20.000 m2 construidos por hectárea la temperatura deberá aumentar entre 1ºC y 2ºC; implementando de áreas verdes un espacio sin vegetación, la isla de calor se verá reducida entre 1,2ºC y 5,5ºC; y al modificar el albedo de la ciudad desde un 10% a un 20% se consigue una reducción de la ICUs entre 1,1ºC y 2,7ºC

Fiber tracking in newborns with perinatal hypoxic-ischemia at birth and at 3 months

The purpose of our study was to retrospectively investigate the feasibility of using fiber tracking to detect disturbances in white matter development in newborns suffering from hypoxic-ischemia, at birth and at the age of 3 months. Diffusion Tensor Images were obtained from full-term newborns, 7 with normal MRI and 10 with pathology related to perinatal hypoxic-ischemia. All parents gave informed consent. Visualization of white matter tracts was investigated using a volume tracing and quantification technique. Fiber tracking was useful to study the neonatal brain. Pathology resulted in different fiber patterns, compared to normal. Frequently affected were the corona radiata fibers

Predictive analytics and tailored interventions improve clinical outcomes in older adults: a randomized controlled trial

Abstract This study explored the potential to improve clinical outcomes in patients at risk of moving to the top segment of the cost acuity pyramid. This randomized controlled trial evaluated the impact of a Stepped-Care approach (predictive analytics + tailored nurse-driven interventions) on healthcare utilization among 370 older adult patients enrolled in a homecare management program and using a Personal Emergency Response System. The Control group (CG) received care as usual, while the Intervention group (IG) received Stepped-Care during a 180-day intervention period. The primary outcome, decrease in emergency encounters, was not statistically significant (15%, p = 0.291). However, compared to the CG, the IG had significant reductions in total 90-day readmissions (68%, p = 0.007), patients with 90-day readmissions (76%, p = 0.011), total 180-day readmissions (53%, p = 0.020), and EMS encounters (49%, p = 0.006). Predictive analytics combined with tailored interventions could potentially improve clinical outcomes in older adults, supporting population health management in home or community settings

Solute Transport in Cyclically Deformed Porous Tissue Scaffolds with Controlled Pore Cross-Sectional Geometries

The objective of this study was to investigate the influence of pore geometry on the transport rate and depth after repetitive mechanical deformation of porous scaffolds for tissue engineering applications. Flexible cubic imaging phantoms with pores in the shape of a circular cylinder, elliptic cylinder, and spheroid were fabricated from a biodegradable polymer blend using a combined 3D printing and injection molding technique. The specimens were immersed in fluid and loaded with a solution of a radiopaque solute. The solute distribution was quantified by recording 20 μm pixel-resolution images in an X-ray microimaging scanner at selected time points after intervals of dynamic straining with a mean strain of 8.6 ± 1.6% at 1.0 Hz. The results show that application of cyclic strain significantly increases the rate and depth of solute transport, as compared to diffusive transport alone, for all pore shapes. In addition, pore shape, pore size, and the orientation of the pore cross-sectional asymmetry with respect to the direction of strain greatly influence solute transport. Thus, pore geometry can be tailored to increase transport rates and depths in cyclically deformed scaffolds, which is of utmost importance when thick, metabolically functional tissues are to be engineered

Healthcare utilization in older patients using personal emergency response systems: an analysis of electronic health records and medical alert data: Brief Description: A Longitudinal Retrospective Analyses of healthcare utilization rates in older patients using Personal Emergency Response Systems from 2011 to 2015

Background: Personal Emergency Response Systems (PERS) are traditionally used as fall alert systems for older adults, a population that contributes an overwhelming proportion of healthcare costs in the United States. Previous studies focused mainly on qualitative evaluations of PERS without a longitudinal quantitative evaluation of healthcare utilization in users. To address this gap and better understand the needs of older patients on PERS, we analyzed longitudinal healthcare utilization trends in patients using PERS through the home care management service of a large healthcare organization. Methods: Retrospective, longitudinal analyses of healthcare and PERS utilization records of older patients over a 5-years period from 2011–2015. The primary outcome was to characterize the healthcare utilization of PERS patients. This outcome was assessed by 30-, 90-, and 180-day readmission rates, frequency of principal admitting diagnoses, and prevalence of conditions leading to potentially avoidable admissions based on Centers for Medicare and Medicaid Services classification criteria. Results: The overall 30-day readmission rate was 14.2%, 90-days readmission rate was 34.4%, and 180-days readmission rate was 42.2%. While 30-day readmission rates did not increase significantly (p = 0.16) over the study period, 90-days (p = 0.03) and 180-days (p = 0.04) readmission rates did increase significantly. The top 5 most frequent principal diagnoses for inpatient admissions included congestive heart failure (5.7%), chronic obstructive pulmonary disease (4.6%), dysrhythmias (4.3%), septicemia (4.1%), and pneumonia (4.1%). Additionally, 21% of all admissions were due to conditions leading to potentially avoidable admissions in either institutional or non-institutional settings (16% in institutional settings only). Conclusions: Chronic medical conditions account for the majority of healthcare utilization in older patients using PERS. Results suggest that PERS data combined with electronic medical records data can provide useful insights that can be used to improve health outcomes in older patients