scholarly journals Balanced Event Prediction Through Sampled Survival Analysis

2019 ◽  
Vol 2 (1) ◽  
pp. 28-38 ◽  
Author(s):  
Gargi Datta ◽  
Leigh E. Alexander ◽  
Michael A. Hinterberg ◽  
Yolanda Hagar
Keyword(s):  
Survival Analysis ◽  
Event Prediction

scholarly journals Predicting Urban Dispersal Events: A Two-Stage Framework through Deep Survival Analysis on Mobility Data

2019 ◽  
Vol 33 ◽  
pp. 5199-5206 ◽  
Author(s):  
Amin Vahedian ◽  
Xun Zhou ◽  
Ling Tong ◽  
W. Nick Street ◽  
Yanhua Li
Keyword(s):  
Survival Analysis ◽  
Deep Learning ◽  
Learning Approaches ◽  
Two Stage ◽  
Demand Prediction ◽  
Mobility Data ◽  
Event Prediction ◽  
Safety Risks ◽  
Short Period ◽  
Dispersal Events

Urban dispersal events are processes where an unusually large number of people leave the same area in a short period. Early prediction of dispersal events is important in mitigating congestion and safety risks and making better dispatching decisions for taxi and ride-sharing fleets. Existing work mostly focuses on predicting taxi demand in the near future by learning patterns from historical data. However, they fail in case of abnormality because dispersal events with abnormally high demand are non-repetitive and violate common assumptions such as smoothness in demand change over time. Instead, in this paper we argue that dispersal events follow a complex pattern of trips and other related features in the past, which can be used to predict such events. Therefore, we formulate the dispersal event prediction problem as a survival analysis problem. We propose a two-stage framework (DILSA), where a deep learning model combined with survival analysis is developed to predict the probability of a dispersal event and its demand volume. We conduct extensive case studies and experiments on the NYC Yellow taxi dataset from 20142016. Results show that DILSA can predict events in the next 5 hours with F1-score of 0:7 and with average time error of 18 minutes. It is orders of magnitude better than the state-of-the-art deep learning approaches for taxi demand prediction.

Risk for psychotherapy drop-out in survival analysis: The influence of general change mechanisms and symptom severity.

2020 ◽  
Vol 67 (6) ◽  
pp. 712-722
Author(s):  
Sebastian Gmeinwieser ◽  
Kai Sebastian Schneider ◽  
Maximilian Bardo ◽  
Timo Brockmeyer ◽  
York Hagmayer
Keyword(s):  
Survival Analysis ◽  
Symptom Severity ◽  
Drop Out ◽  
General Change ◽  
Change Mechanisms

Five-year Survival Analysis of a Cohort of Hepatocellular Carcinoma Patients Who Treated at the National Cancer Center, Korea

2007 ◽  
Vol 13 (4) ◽  
pp. 530 ◽  
Author(s):  
Kyung Woo Park ◽  
Joong-Won Park ◽  
Tae Hyun Kim ◽  
Jun Il Choi ◽  
Seong Hoon Kim ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Survival Analysis ◽  
Cancer Center

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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