Real-time Online Probabilistic Medical Computation using Bayesian Networks

被引：2

作者：

Mclachlan, Scott ^{[1
]}

Paterson, Haydn ^{[2
]}

Dube, Kudakwashe ^{[3
]}

Kyrimi, Evangelia ^{[1
]}

Dementiev, Eugene ^{[1
]}

Neil, Martin ^{[1
]}

Daley, Bridget J. ^{[4
]}

Hitman, Graham A. ^{[4
]}

Fenton, Norman E. ^{[1
]}

机构：

[1] Queen Mary Univ London, Risk & Informat Management, London, England

[2] Acid Dev, Solut Dev, Auckland, New Zealand

[3] Massey Univ, Sch Fundamental Sci, Palmerston North, New Zealand

[4] Queen Mary Univ London, Blizard Inst, London, England

来源：

2020 8TH IEEE INTERNATIONAL CONFERENCE ON HEALTHCARE INFORMATICS (ICHI 2020) | 2020年

基金：

英国工程与自然科学研究理事会;

关键词：

Bayesian networks; Healthcare; mHealth; CLINICAL DECISION-SUPPORT; BARRIERS; MOTIVES;

D O I：

10.1109/ICHI48887.2020.9374378

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Advances in both computing power and novel Bayesian inference algorithms have enabled Bayesian Networks (BN) to be applied for decision-support in healthcare and other domains. This work presents CardiPro, a flexible, online application for interfacing with non-trivial causal BN models. Designed specifically to make BN use easy for less-technical users like patients and clinicians, CardiPro provides near realtime probabilistic computation. CardiPro was developed as part of the PamBayesian research project (www.pambayesian.org) and represents the first of a new generation of online BN-based applications that may benefit adoption of AI-based clinical decision-support.

引用

页码：355 / 362

页数：8

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