Automatic Modeling for Clinical Event Prediction (200 Patients)

2014 ◽  
pp. 119-127
Author(s):  
Ton J. Cleophas ◽  
Aeilko H. Zwinderman
Keyword(s):  
Clinical Event ◽  
Automatic Modeling ◽  
Event Prediction

scholarly journals Comparison of machine learning algorithms for clinical event prediction (risk of coronary heart disease)

2019 ◽  
Vol 97 ◽  
pp. 103257 ◽  
Author(s):  
Juan-Jose Beunza ◽  
Enrique Puertas ◽  
Ester García-Ovejero ◽  
Gema Villalba ◽  
Emilia Condes ◽  
...  
Keyword(s):  
Machine Learning ◽  
Coronary Heart Disease ◽  
Heart Disease ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Clinical Event ◽  
Event Prediction ◽  
Prediction Risk

scholarly journals Pairwise-Ranking based Collaborative Recurrent Neural Networks for Clinical Event Prediction

2018 ◽  
Author(s):  
Zhi Qiao ◽  
Shiwan Zhao ◽  
Cao Xiao ◽  
Xiang Li ◽  
Yong Qin ◽  
...  
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
High Rate ◽  
Clinical Event ◽  
Patient Specific ◽  
Human Errors ◽  
Event Prediction ◽  
Clinical Events ◽  
Classification Framework ◽  
Simultaneous Prediction

Patient Electronic Health Records (EHR) data consist of sequences of patient visits over time. Sequential prediction of patients' future clinical events (e.g., diagnoses) from their historical EHR data is a core research task and motives a series of predictive models including deep learning. The existing research mainly adopts a classification framework, which treats the observed and unobserved events as positive and negative classes. However, this may not be true in real clinical setting considering the high rate of missed diagnoses and human errors. In this paper, we propose to formulate the clinical event prediction problem as an events recommendation problem. An end-to-end pairwise-ranking based collaborative recurrent neural networks (PacRNN) is proposed to solve it, which firstly embeds patient clinical contexts with attention RNN, then uses Bayesian Personalized Ranking (BPR) regularized by disease co-occurrence to rank probabilities of patient-specific diseases, as well as use point process to provide simultaneous prediction of the occurring time of these diagnoses. Experimental results on two real world EHR datasets demonstrate the robust performance, interpretability, and efficacy of PacRNN.

A Research Study on Event Prediction in IoT Based Upon WBAN

2018 ◽  
Vol 8 (5) ◽  
pp. 116
Author(s):  
Krittika Singh
Keyword(s):  
Research Study ◽  
Network Connectivity ◽  
Physical World ◽  
Flight Schedules ◽  
Event Prediction ◽  
Software Sensors ◽  
Medical Emergencies ◽  
Computer Based ◽  
The Internet Of Things ◽  
Exchange Data

The Internet of things is the internetworking of physical devices, vehicles, buildings, and other items—embedded with electronics, software, sensors, actuators, and network connectivity that enable these objects to collect and exchange data. The IoT allows objects to be sensed and/or controlled remotely across existing network infrastructure, creating opportunities for more direct integration of the physical world into computer-based systems, and resulting in improved efficiency, accuracy and economic benefit in addition to reduced human intervention. In this research an expert system based upon the IOT is developed in which the next event in the flight schedules due to any kind of medical emergencies is to be predicted. For this the medical data of all the patients are to be collected through WBAN.

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