Comput. Biol. Med. - A general model for continuous noninvasive pulmonary artery pressure estimation.

Tópicos

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Resumo

Elevated pulmonary artery pressure (PAP) is a significant healthcare risk. Continuous monitoring for patients with elevated PAP is crucial for effective treatment, yet the most accurate method is invasive and expensive, and cannot be performed repeatedly. Noninvasive methods exist but are somewhat inaccurate, expensive, and cannot be used for continuous monitoring. We present a machine learning model based on heart sounds that estimates pulmonary artery pressure with enough accuracy to exclude an invasive diagnostic operation, allowing for consistent monitoring of heart condition in suspect patients without the cost and risk of invasive monitoring. We conduct a greedy search through 38 possible features using a 109-patient cross-validation to find the most predictive features. Our best general model has a standard estimate of error (SEE) of 8.3mmHg, which outperforms the previous best performance in the literature on a general set of unseen patient data.

Resumo Limpo

elev pulmonari arteri pressur pap signific healthcar risk continu monitor patient elev pap crucial effect treatment yet accur method invas expens perform repeat noninvas method exist somewhat inaccur expens use continu monitor present machin learn model base heart sound estim pulmonari arteri pressur enough accuraci exclud invas diagnost oper allow consist monitor heart condit suspect patient without cost risk invas monitor conduct greedi search possibl featur use patient crossvalid find predict featur best general model standard estim error see mmhg outperform previous best perform literatur general set unseen patient data

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