This work investigates the predictive potential of bipolar
electroencephalogram (EEG) recordings towards efficient prediction of poor
neurological outcomes. A retrospective design using a hybrid deep learning
approach is utilized to optimize an objective function aiming for high
specificity, i.e., true positive rate (TPR) with reduced false positives (<
0.05). A multi-channel EEG array of 18 bipolar channel pairs from a randomly
selected 5-minute segment in an hour is kept. In order to determine the outcome
prediction, a combination of a feature encoder with 1-D convolutional layers,
learnable position encoding, a context network with attention mechanisms, and
finally, a regressor and classifier blocks are used. The feature encoder
extricates local temporal and spatial features, while the following position
encoding and attention mechanisms attempt to capture global temporal
dependencies. Results: The proposed framework by our team, OUS IVS, when
validated on the challenge hidden validation data, exhibited a score of 0.57.

通过深度学习方法对双极脑电图记录进行预测，在高特异性、减少误报的目标函数优化下，利用多通道脑电图阵列对神经系统不良结局进行有效预测，并通过我们团队提出的 ONG IVS 框架，在挑战性验证数据上取得了 0.57 的得分。

昏迷患者自身和交叉通道关注机制下的多通道脑电图数据分析

A Multi-channel EEG Data Analysis for Poor Neuro-prognostication in  Comatose Patients with Self and Cross-channel Attention Mechanism

The preservation of soil health has been identified as one of the main
challenges of the XXI century given its vast (and potentially threatening)
ramifications in agriculture, human health and biodiversity. Here, we provide
the first deep investigation of the predictive potential of machine-learning
models to understand the connections between soil and biological phenotypes.
Indeed, we investigate an integrative framework performing accurate
machine-learning-based prediction of plant phenotypes from biological, chemical
and physical properties of the soil via two models: random forest and Bayesian
neural network. We show that prediction is improved, as evidenced by higher
weighted F1 scores, when incorporating into the models environmental features
like soil physicochemical properties and microbial population density in
addition to the microbiome information. Furthermore, by exploring multiple data
preprocessing strategies such as normalization, zero replacement, and data
augmentation, we confirm that human decisions have a huge impact on the
predictive performance. In particular, we show that the naive total sum scaling
normalization that is commonly used in microbiome research is not the optimal
strategy to maximize predictive power. In addition, we find that accurately
defined labels are more important than normalization, taxonomic level or model
characteristics. That is, if humans are unable to classify the samples and
provide accurate labels, the performance of machine-learning models will be
limited. Lastly, we present strategies for domain scientists via a full model
selection decision tree to identify the human choices that maximize the
prediction power of the models. Our work is accompanied by open source
reproducible scripts (this https URL) for
maximum outreach among the microbiome research community.

本研究使用机器学习的方法，通过分析土壤生物、化学和物理性质以及微生物群落密度等环境特征，提高了对植物表型的预测准确度，并对数据预处理策略进行探讨，强调合理的标签定义比正态化、分类水平或模型特征更为关键。