PredictMed: A logistic regression–based model to predict health conditions in cerebral palsy

Abstract

Logistic regression–based predictive models are widely used in the healthcare field but just recently are used to predict comorbidities in children with cerebral palsy. This article presents a logistic regression approach to predict health conditions in children with cerebral palsy and a few examples from recent research. The model named PredictMed was trained, tested, and validated for predicting the development of scoliosis, intellectual disabilities, autistic features, and in the present study, feeding disorders needing gastrostomy. This was a multinational, cross-sectional descriptive study. Data of 130 children (aged 12–18 years) with cerebral palsy were collected between June 2005 and June 2015. The logistic regression–based model uses an algorithm implemented in R programming language. After splitting the patients in training and testing sets, logistic regressions are performed on every possible subset (tuple) of independent variables. The tuple that shows the best predictive performance in terms of accuracy, sensitivity, and specificity is chosen as a set of independent variables in another logistic regression to calculate the probability to develop the specific health condition (e.g. the need for gastrostomy). The average of accuracy, sensitivity, and specificity score was 90%. Our model represents a novelty in the field of some cerebral palsy–related health outcomes treatment, and it should significantly help doctors’ decision-making process regarding patient prognosis.

Prediction Model for Identifying Factors Associated with Epilepsy in Children with Cerebral Palsy

“…Narrative notes on the CP diagnosis, etiology, topography of the spasticity, epilepsy, functional assessments, and medical history were coded and entered into an electronic database [ 8 ]. The data verification and collection for the development of the Epi-PredictMed model has been performed from June to December 2017, while the data analysis began at the end of 2018 and lasted for 2 years.…”

“…There are few studies evaluating related issues despite epilepsy being common in children with CP [ 7 ]; there is no accurate data on the factors associated with epilepsy in children with CP using a prediction model. A machine-learning model, PredictMed, has been implemented and validated to predict, in children with CP, the onset of neuromuscular scoliosis and feeding disorders requiring gastrostomy [ 8 ], the factors associated with intellectual disabilities [ 9 ], autism spectrum disorder [ 10 ], and neuromuscular hip dysplasia [ 11 ]. Thus, we aimed to adapt and test Epi-PredictMed to identify the factors associated with epilepsy in children with CP.…”

Machine learning for monitoring and evaluating physical activity in cerebral palsy

“…The present study reminds us of the undeniable need for collaboration between clinicians and machine learning experts 3,4 . To establish personalized diagnoses and treatments, doctors need to analyse a large amount of data that can be acquired from movement sensors, 1,3 images, assessment tools, and medical and biological records 2,4 .…”

“…3,4 To establish personalized diagnoses and treatments, doctors need to analyse a large amount of data that can be acquired from movement sensors, 1,3 images, assessment tools, and medical and biological records. 2,4 A machine learning model could be the appropriate support in analysing these heterogeneous clinical data collected from patient's electronic health records. 4 However, machine learning-based movement assessment studies are at the early stage of research.…”

“…2,4 A machine learning model could be the appropriate support in analysing these heterogeneous clinical data collected from patient's electronic health records. 4 However, machine learning-based movement assessment studies are at the early stage of research. Wearable sensors (such as internal sensors) are increasingly being used in rehabilitation studies, exploring the possibility of inertial data to assess spasticity and physical activity in patients with neurological disorders.…”