Abstract 

The dataset analyzed in this study is the result of a systematic literature review and a crowdsourced mini-project that aimed to identify and validate metrics relevant to maternal and neonatal healthcare examinations. The study involved a diverse group of participants, including 193 registered medical personnel from reputable institutions and 161 non-medical individuals who were active on various social media platforms related to maternal and neonatal healthcare. The study identified multiple metrics that were evaluated during healthcare examinations, and the findings were validated through the participation of highly trained maternal health medical personnel and non-medical individuals. The dataset is comprehensive and includes a wide range of experiences and perspectives, which allowed for a detailed analysis of the most relevant metrics in maternal and neonatal healthcare examination. The metrics identified in this study can be used to improve the quality of maternal and neonatal healthcare and to inform policies and practices in the field. The dataset is a valuable resource for researchers and practitioners who are interested in improving maternal and neonatal healthcare outcomes.

Instructions: 

Here are instructions on how to use the dataset described above for NLP, machine learning, data science, and AI:

 

Obtain the dataset: The first step is to obtain the dataset. The dataset may be available for download on a public repository or may need to be requested from the authors. Once the dataset is obtained, it should be stored in a secure and organized manner.

 

Data preprocessing: Before using the dataset for NLP, machine learning, data science, and AI, it is important to preprocess the data. This may involve tasks such as cleaning, filtering, tokenizing, stemming, and lemmatizing the text. The preprocessing steps will depend on the specific research questions and goals.

 

Exploratory data analysis (EDA): EDA is an important step in understanding the dataset and identifying patterns and relationships within the data. EDA may involve tasks such as visualizing the data, analyzing the frequency of terms, and identifying correlations between variables.

 

Feature engineering: Feature engineering is the process of selecting and transforming the data to create features that can be used in machine learning models. This may involve tasks such as creating word embeddings, extracting linguistic features, and identifying relevant metrics.

 

Model training: After the data has been preprocessed and features have been engineered, machine learning models can be trained on the data. The choice of model will depend on the research questions and goals. Popular models for NLP include convolutional neural networks, recurrent neural networks, and transformer models.

 

Model evaluation: Once the models have been trained, they should be evaluated on a held-out test set. This can involve tasks such as measuring accuracy, precision, recall, and F1-score. The evaluation metrics will depend on the specific research questions and goals.

 

Interpretation: After the models have been trained and evaluated, the results should be interpreted in the context of the research questions and goals. This may involve tasks such as identifying important features, understanding the impact of different variables, and drawing conclusions from the results.

 

In summary, using the dataset described above for NLP, machine learning, data science, and AI involves obtaining the dataset, preprocessing the data, performing exploratory data analysis, feature engineering, model training, model evaluation, and interpretation. These steps are essential for using the dataset to answer research questions and inform policies and practices in the field of maternal and neonatal healthcare.