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- Title
PredictMed: A logistic regression–based model to predict health conditions in cerebral palsy.
- Authors
Bertoncelli, Carlo M; Altamura, Paola; Vieira, Edgar Ramos; Iyengar, Sundaraja Sitharama; Solla, Federico; Bertoncelli, Domenico
- 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.
- Subjects
AUTISM risk factors; ALGORITHMS; CEREBRAL palsy; CONFIDENCE intervals; DECISION support systems; EPILEPSY; FISHER exact test; GASTROSTOMY; MEDICAL databases; INFORMATION storage &; retrieval systems; MACHINE learning; RESEARCH methodology; PEOPLE with intellectual disabilities; MULTIVARIATE analysis; NUTRITIONAL assessment; PROGRAMMING languages; REGRESSION analysis; RISK assessment; SCOLIOSIS; SEX distribution; DATA mining; DECISION making in clinical medicine; LOGISTIC regression analysis; PREDICTION models; CROSS-sectional method; DESCRIPTIVE statistics; NUTRITIONAL status; ODDS ratio; DISEASE complications; DISEASE risk factors; CHILDREN
- Publication
Health Informatics Journal, 2020, Vol 26, Issue 3, p2105
- ISSN
1460-4582
- Publication type
Article
- DOI
10.1177/1460458219898568