Artif Intell Med - Clinical time series prediction: Toward a hierarchical dynamical system framework.

Tópicos

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Resumo

JECTIVE: Developing machine learning and data mining algorithms for building temporal models of clinical time series is important for understanding of the patient condition, the dynamics of a disease, effect of various patient management interventions and clinical decision making. In this work, we propose and develop a novel hierarchical framework for modeling clinical time series data of varied length and with irregularly sampled observations.MATERIALS AND METHODS: Our hierarchical dynamical system framework for modeling clinical time series combines advantages of the two temporal modeling approaches: the linear dynamical system and the Gaussian process. We model the irregularly sampled clinical time series by using multiple Gaussian process sequences in the lower level of our hierarchical framework and capture the transitions between Gaussian processes by utilizing the linear dynamical system. The experiments are conducted on the complete blood count (CBC) panel data of 1000 post-surgical cardiac patients during their hospitalization. Our framework is evaluated and compared to multiple baseline approaches in terms of the mean absolute prediction error and the absolute percentage error.RESULTS: We tested our framework by first learning the time series model from data for the patients in the training set, and then using it to predict future time series values for the patients in the test set. We show that our model outperforms multiple existing models in terms of its predictive accuracy. Our method achieved a 3.13% average prediction accuracy improvement on ten CBC lab time series when it was compared against the best performing baseline. A 5.25% average accuracy improvement was observed when only short-term predictions were considered.CONCLUSION: A new hierarchical dynamical system framework that lets us model irregularly sampled time series data is a promising new direction for modeling clinical time series and for improving their predictive performance.

Resumo Limpo

jectiv develop machin learn data mine algorithm build tempor model clinic time seri import understand patient condit dynam diseas effect various patient manag intervent clinic decis make work propos develop novel hierarch framework model clinic time seri data vari length irregular sampl observationsmateri method hierarch dynam system framework model clinic time seri combin advantag two tempor model approach linear dynam system gaussian process model irregular sampl clinic time seri use multipl gaussian process sequenc lower level hierarch framework captur transit gaussian process util linear dynam system experi conduct complet blood count cbc panel data postsurg cardiac patient hospit framework evalu compar multipl baselin approach term mean absolut predict error absolut percentag errorresult test framework first learn time seri model data patient train set use predict futur time seri valu patient test set show model outperform multipl exist model term predict accuraci method achiev averag predict accuraci improv ten cbc lab time seri compar best perform baselin averag accuraci improv observ shortterm predict consideredconclus new hierarch dynam system framework let us model irregular sampl time seri data promis new direct model clinic time seri improv predict perform

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