O9.7. INDIVIDUALIZED LONG-TERM OUTCOME PREDICTION OF PSYCHOSIS IN AN OBSERVATIONAL STUDY: A MACHINE LEARNING APPROACH

Abstract BACKGROUND Long-term outcome after subarachnoid hemorrhage, beyond the first few months, is difficult to predict, but has critical relevance to patients, their families, and carers. OBJECTIVE To assess the performance of the Subarachnoid Hemorrhage International Trialists (SAHIT) prediction models, which were initially designed to predict short-term (90 d) outcome, as predictors of long-term (2 yr) functional outcome after aneurysmal subarachnoid hemorrhage (aSAH). METHODS We included 1545 patients with angiographically-proven aSAH from the Genetic and Observational Subarachnoid Haemorrhage (GOSH) study recruited at 22 hospitals between 2011 and 2014. We collected data on age, WNFS grade on admission, history of hypertension, Fisher grade, aneurysm size and location, as well as treatment modality. Functional outcome was measured by the Glasgow Outcome Scale (GOS) with GOS 1 to 3 corresponding to unfavorable and 4 to 5 to favorable functional outcome, according to the SAHIT models. The SAHIT models were assessed for long-term outcome prediction by estimating measures of calibration (calibration slope) and discrimination (area under the receiver-operating characteristic curve [AUC]) in relation to poor clinical outcome. RESULTS Follow-up was standardized to 2 yr using imputation methods. All 3 SAHIT models demonstrated acceptable predictive performance for long-term functional outcome. The estimated AUC was 0.71 (95% CI: 0.65-0.76), 0.73 (95% CI: 0.68-0.77), and 0.74 (95% CI: 0.69-0.79) for the core, neuroimaging, and full models, respectively; the calibration slopes were 0.86, 0.84, and 0.89, indicating good calibration. CONCLUSION The SAHIT prediction models, incorporating simple factors available on hospital admission, show good predictive performance for long-term functional outcome after aSAH.

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Machine Learning Approach to Quantity Management for Long-Term Sustainable Development of Dockless Public Bike: Case of Shenzhen in China

Journal of Advanced Transportation ◽

10.1155/2020/8847752 ◽

2020 ◽

Vol 2020 ◽

pp. 1-13

Author(s):

Qingfeng Zhou ◽

Chun Janice Wong ◽

Xian Su

Keyword(s):

Machine Learning ◽

Sustainable Development ◽

Clustering Algorithms ◽

Utilization Efficiency ◽

Impact Factors ◽

Classification Algorithms ◽

Learning Approach ◽

High Efficient ◽

Machine Learning Approach

Since the number of bicycles is critical to the sustainable development of dockless PBS, this research practiced the introduction of a machine learning approach to quantity management using OFO bike operation data in Shenzhen. First, two clustering algorithms were used to identify the bicycle gathering area, and the available bike number and coefficient of available bike number variation were analyzed in each bicycle gathering area’s type. Second, five classification algorithms were compared in the accuracy of distinguishing the type of bicycle gathering areas using 25 impact factors. Finally, the application of the knowledge gained from the existing dockless bicycle operation data to guide the number planning and management of public bicycles was explored. We found the following. (1) There were 492 OFO bicycle gathering areas that can be divided into four types: high inefficient, normal inefficient, high efficient, and normal efficient. The high inefficient and normal inefficient areas gathered about 110,000 bicycles with low usage. (2) More types of bicycle gathering area will affect the accuracy of the classification algorithm. The random forest classification had the best performance in identifying bicycle gathering area types in five classification algorithms with an accuracy of more than 75%. (3) There were obvious differences in the characteristics of 25 impact factors in four types of bicycle gathering areas. It is feasible to use these factors to predict area type to optimize the number of available bicycles, reduce operating costs, and improve utilization efficiency. This work helps operators and government understand the characteristics of dockless PBS and contributes to promoting long-term sustainable development of the system through a machine learning approach.

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