P81. The impact of growing rod surgery on cervical alignment during the treatment of early onset scoliosis: A case-control study based on machine learning algorithms

The motion picture industry is one of the largest industries worldwide and has significant importance in the global economy. Considering the high stakes and high risks in the industry, forecast models and decision support systems are gaining importance. Several attempts have been made to estimate the theatrical performance of a movie before or at the early stages of its release. Nevertheless, these models are mostly used for predicting domestic performances and the industry still struggles to predict box office performances in overseas markets. In this study, the aim is to design a forecast model using different machine learning algorithms to estimate the theatrical success of US movies in Turkey. From various sources, a dataset of 1559 movies is constructed. Firstly, independent variables are grouped as pre-release, distributor type, and international distribution based on their characteristic. The number of attendances is discretized into three classes. Four popular machine learning algorithms, artificial neural networks, decision tree regression and gradient boosting tree and random forest are employed, and the impact of each group is observed by compared by the performance models. Then the number of target classes is increased into five and eight and results are compared with the previously developed models in the literature.

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Maternal-Fetal Management in Trombophilia Related and Placenta-Mediated Pregnancy Complications

Revista de Chimie ◽

10.37358/rc.18.9.6541 ◽

2018 ◽

Vol 69 (9) ◽

pp. 2396-2401

Author(s):

Costin Berceanu ◽

Elena Loredana Ciurea ◽

Monica Mihaela Cirstoiu ◽

Sabina Berceanu ◽

Anca Maria Ofiteru ◽

...

Keyword(s):

Pregnant Women ◽

Pregnancy Complications ◽

Case Control Study ◽

Case Control ◽

Placental Insufficiency ◽

Management Options ◽

Specific Therapy ◽

Pregnancy Morbidity ◽

The Impact ◽

Control Study

It is widely accepted that thrombophilia in pregnancy greatly increases the risk of venous thromboembolism. Pregnancy complications arise, at least partly, from placental insufficiency. Any change in the functioning of the gestational transient biological system, such as inherited or acquired thrombophilia, might lead to placental insufficiency. In this research we included 64 pregnant women with trombophilia and 70 cases non-trombophilic pregnant women, with or without PMPC, over a two-year period. The purpose of this multicenter case-control study is to analyze the maternal-fetal management options in obstetric thrombophilia, the impact of this pathology on the placental structure and possible correlations with placenta-mediated pregnancy complications. Maternal-fetal management in obstetric thrombophilia means preconceptional or early diagnosis, prevention of pregnancy morbidity, specific therapy as quickly as possible and fetal systematic surveilance to identify the possible occurrence of placenta-mediated pregnancy complications.

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Predicting dementia diagnosis from cognitive footprints in electronic health records: a case–control study protocol

BMJ Open ◽

10.1136/bmjopen-2020-043487 ◽

2020 ◽

Vol 10 (11) ◽

pp. e043487

Author(s):

Hao Luo ◽

Kui Kai Lau ◽

Gloria H Y Wong ◽

Wai-Chi Chan ◽

Henry K F Mak ◽

...

Keyword(s):

Machine Learning ◽

Risk Factors ◽

Hong Kong ◽

Electronic Health Records ◽

Case Control Study ◽

Case Control ◽

Dementia Diagnosis ◽

Health Records ◽

Electronic Health ◽

Control Study

IntroductionDementia is a group of disabling disorders that can be devastating for persons living with it and for their families. Data-informed decision-making strategies to identify individuals at high risk of dementia are essential to facilitate large-scale prevention and early intervention. This population-based case–control study aims to develop and validate a clinical algorithm for predicting dementia diagnosis, based on the cognitive footprint in personal and medical history.Methods and analysisWe will use territory-wide electronic health records from the Clinical Data Analysis and Reporting System (CDARS) in Hong Kong between 1 January 2001 and 31 December 2018. All individuals who were at least 65 years old by the end of 2018 will be identified from CDARS. A random sample of control individuals who did not receive any diagnosis of dementia will be matched with those who did receive such a diagnosis by age, gender and index date with 1:1 ratio. Exposure to potential protective/risk factors will be included in both conventional logistic regression and machine-learning models. Established risk factors of interest will include diabetes mellitus, midlife hypertension, midlife obesity, depression, head injuries and low education. Exploratory risk factors will include vascular disease, infectious disease and medication. The prediction accuracy of several state-of-the-art machine-learning algorithms will be compared.Ethics and disseminationThis study was approved by Institutional Review Board of The University of Hong Kong/Hospital Authority Hong Kong West Cluster (UW 18-225). Patients’ records are anonymised to protect privacy. Study results will be disseminated through peer-reviewed publications. Codes of the resulted dementia risk prediction algorithm will be made publicly available at the website of the Tools to Inform Policy: Chinese Communities’ Action in Response to Dementia project (https://www.tip-card.hku.hk/).

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