Pritom Kumar Mondal

Childhood obesity is a public health concern in the United States. Consequences of childhood obesity include metabolic disease and heart, lung, kidney, and other health-related comorbidities. Therefore, the early determination of obesity risk is needed and predicting the trend of a child’s body mass index (BMI) at an early age is crucial. Early identification of obesity can lead to early prevention. Multiple methods have been tested and evaluated to assess obesity trends in children. Available growth charts help determine a child’s current obesity level but do not predict future obesity risk. The present methods of predicting obesity include regression analysis and machine learning-based classifications and risk factor (threshold)-based categorizations based on specific criteria. All the present techniques, especially current machine learning-based methods, require longitudinal data and information on a large number of variables related to a child’s growth (e.g., socioeconomic, family-related factors) in order to predict future obesity-risk. In this paper, we propose three different techniques for three different scenarios to predict childhood obesity based on machine learning approaches and apply them to real data. Our proposed methods predict obesity for children at five years of age using the following three data sets: (1) a single well-child visit, (2) multiple well-child visits under the age of two, and (3) multiple random well-child visits under the age of five. Our models are especially important for situations where only the current patient information is available rather than having multiple data points from regular spaced well-child visits. Our models predict obesity using basic information such as birth BMI, gestational age, BMI measures from well-child visits, and gender. Our models can predict a child’s obesity category (normal, overweight, or obese) at five years of age with an accuracy of 89%, 77%, and 89%, for the three application scenarios, respectively. Therefore, our proposed models can assist healthcare professionals by acting as a decision support tool to aid in predicting childhood obesity early in order to reduce obesity-related complications, and in turn, improve healthcare.

This study considers how patients with severe mental illness are referred to a psychiatric hospital from outpatient settings, community mental health centers, or general hospitals. Due to the complexities of many of the patients, the referral process has multiple steps and was experiencing significant delays that both hindered patient care and drove up system costs. The combination of a multistage patient assessment process and the need to observe patients prior to transfer create unique system constraints and objectives. An investigation of potential root causes of admissions process delay determined that insufficient capacity in the Mental Health Disorder Identification (MHDI) process was the primary cause. Discrete event simulation was used to analyze eight different capacity scenarios for MHDI resulting in a recommendation to increase daily MHDI capacity and to provide backup capacity to cover staff vacation days, the latter being one of the more important findings. The simulation model is also used to assess what capacity improvements are required to keep PM times low for up to a 25% increase in the patient arrival rate.