SBMI Datathon 2021 for Stroke Prediction

About the Datathon

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In 2017, 7.8 million adults in the U.S. reported having survived a stroke. While deaths attributable to stroke have declined, stroke remains a leading cause of morbidity and disability. By 2030, stroke-related costs are expected to reach $183 billion. Despite early treatment, stroke survivors often have a severe long-term disability including both physical and cognitive issues that require constant monitoring and care from the community. Rehabilitation is essential to recovery and begins soon after the injury when the brain is especially receptive to processes that can enhance repair . The appropriate quantity, quality, and timing of rehab therapy is unknown to optimize outcomes and remedy disabilities effectively. An accurate prediction of the functional and cognitive outcome at the acute stage of stroke is important for a personalized rehabilitation plan and improving communication among patient, family, and clinicians regarding possible outcomes and expectations.

THEME

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The theme of this Datathon is to ask participants to compete on the development of algorithms to predict changes in cognitive and Functional Independence Measure (FIM) scores (18 subcategories) during inpatient rehabilitation (difference between admission FIM score and discharge for each subcategory). FIM score is extensively used across North America to measure disabilities. It includes eighteen subcategories of assessment items, grouped in six sections. The FIM assesses both motor and cognitive functions, and an increasing FIM score implies functional improvement while a decreasing score implies a decline in the patient's functional status.

FIM score for each category range from 1 to 7 where:

7	6	5	4	3	2	1
完全独立	Modified Independence	Supervision	最小的帮助	Moderate Assistance	最大帮助	Total Assistance or not Testable

Objective

The participants are expected to develop algorithms to jointly predict changes in FIM score during inpatient rehabilitation in each subcategory from admission to discharge.

Predictive variables

The predictive variables consist of both continuous and categorical variables. While a great deal of effort has been invested in organizing and cleaning the dataset, participants are expected to be able to use novel strategies to deal with missing values in predictive variables.

EVALUATION

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In this machine learning challenge, we ask the participants to build models (in a justifiable manner) and evaluate final performance, based on L1 (Manhattan) distance表示FIM分数的实际和预测变化（即P子类别）。如果表现有联系，则应将参与者的绩效绑定，将对模型的解释性和预测变量重要性的识别进行额外考虑。

Example of final output:

ID	Eating-Change	Bathing-Change				Memory-Change
100	5	7	1	...	3	2
101	2	7	3		2	5
102	4	3	1		1	2

Data Description

火车数据以下格式位于单个CSV文件（Train.csv）中：

该标签包含18个FIM子类别，并有望预测参与者的矢量（18），其中矢量中的每个值代表每个子类别的入学FIM评分和放电的差异。

COMPETITION RULES

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• Participants are required to submit source codes (e.g., Jupyter Notebook) in a self-contained manner.
• 严格禁止从我们的服务器下载数据并在竞争后在本地保存这些数据以供当地使用。
• 不允许在竞争期间私下共享我们提供的环境之外的数据。
• 参与者必须使用算法方法进行预测。对方法的任何更改都必须以自动化的方式进行，以便可以将方法推广到新主题。
• Use of external data is permitted.
• All participants are asked to prepare summary slides that will describe their models.
• The top three participants will be asked to give a short presentation and the top ten participants on the leaderboard may have an opportunity to publish their results in the special issue of a journal (under negotiation).
• 参与者最多可以提交10个条目。出于最终判断的目的，具有最佳性能的条目。

prizes

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A total of $1,500 sponsored by UTHealth

• 第一名：$ 1000
• Second place: $300
• 第三名：$ 200

FAQS

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