In this task, you will be presented with a context from an academic paper and a question separated with a 
. You have to answer the question based on the context.
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Question: This paper used the real-time method to randomly collect 10% of publicly available English tweets using several pre-defined DDEO-related queries (Table TABREF6 ) within a specific time frame.  
 Question: Do they evaluate only on English data?

Answer: Yes


Question: 45 clinically interpretable features per admission were extracted as inputs to the readmission risk classifier. These features can be grouped into three categories (See Table TABREF5 for complete list of features):

Sociodemographics: gender, age, marital status, etc.

Past medical history: number of previous admissions, history of suicidality, average length of stay (up until that admission), etc.

Information from the current admission: length of stay (LOS), suicidal risk, number and length of notes, time of discharge, evaluation scores, etc.

The Current Admission feature group has the most number of features, with 29 features included in this group alone. These features can be further stratified into two groups: `structured' clinical features and `unstructured' clinical features.

Feature Extraction ::: Structured Features
Structure features are features that were identified on the EHR using regular expression matching and include rating scores that have been reported in the psychiatric literature as correlated with increased readmission risk, such as Global Assessment of Functioning, Insight and Compliance:

Global Assessment of Functioning (GAF): The psychosocial functioning of the patient ranging from 100 (extremely high functioning) to 1 (severely impaired) BIBREF13.

Insight: The degree to which the patient recognizes and accepts his/her illness (either Good, Fair or Poor).

Compliance: The ability of the patient to comply with medication and to follow medical advice (either Yes, Partial, or None).

These features are widely-used in clinical practice and evaluate the general state and prognosis of the patient during the patient's evaluation.

Feature Extraction ::: Unstructured Features
Unstructured features aim to capture the state of the patient in relation to seven risk factor domains (Appearance, Thought Process, Thought Content, Interpersonal, Substance Use, Occupation, and Mood) from the free-text narratives on the EHR. These seven domains have been identified as associated with readmission risk in prior work BIBREF14.

These unstructured features include: 1) the relative number of sentences in the admission notes that involve each risk factor domain (out of total number of sentences within the admission) and 2) clinical sentiment scores for each of these risk factor domains, i.e. sentiment scores that evaluate the patient’s psychosocial functioning level (positive, negative, or neutral) with respect to each of these risk factor domain. 
 Question: What features are used?

Answer: Sociodemographics: gender, age, marital status, etc. Past medical history: number of previous admissions, history of suicidality, average length of stay (up until that admission), etc. Information from the current admission: length of stay (LOS), suicidal risk, number and length of notes, time of discharge, evaluation scores, etc.


Question: The first step is to pre-train the model on one MCQA dataset referred to as the source task, which usually contains abundant training data. The second step is to fine-tune the same model on the other MCQA dataset, which is referred to as the target task, that we actually care about, but that usually contains much less training data.  
 Question: How different is the dataset size of source and target?

Answer:
the training dataset is large while the target dataset is usually much smaller