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The system logs learning-related features at multiple granularities (e.g., problem text, problem type, student actions, and timestamps) and provides researchers with a number of useful student, class, and school-level covariates.
Second, we searched for charts in which relevant terms ("Episodic Cluster Headache", "Chronic Cluster Headache" or "Cluster Headache") appeared in the free text problem list, as a diagnosis not associated with an ICD code, or elsewhere in the medical record.
After receiving approval from our institutional review board we obtained 1.5 million free text problem summary list diagnoses for approximately 327,000 patients in our clinical data repository.
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Patients who have problems reading Dutch text, problems with family communication or problems of psychological/psychiatric nature (including current use of related medication) will be excluded.
It was limited to practitioners at two affiliated, well-resourced, academic medical centers in Boston where the EHRs allow great freedom in what can be entered into the problem list (both coded and free text problems).
It is important to note that the respondents to these questions are allowed to add structured or free text problems to their patient's problem lists in the EHR and therefore have an active choice in their day to day practice of medicine to use personal preferences in listing terminology.
Coding of medical problems may be achieved by manually assigning a code when the problem is entered, or by using NLP techniques to map free-text problem list entries with an appropriate code, as described below.
Structure databases usually contain more than one structure, but problems against the databases with multiple structures can be reduced to the above single-text problem by just concatenating all the structures into a single long text structure and ignoring matches that cross over the boundaries of two concatenated structures.
Based on summarized key points in our pilot study, we first determine the following design criteria for solving the following potential texting problems.
"With an unprecedented amount of available text, our problem is not needing to write more of it; instead, we must learn to negotiate the vast quantity that exists".
Horvitz and his colleagues are already looking at a text classification problem very close to the spam one.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com