The project is in the incubation stage and still under development. ForteHealth is a flexible and powerful ML workflow builder for biomedical and clinical scenarios. This is part of the CASL project: http://casl-project.ai/
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Create an example for MIMIC-III clinical note pipeline. #64
I had this idea because I wanted to have a pipeline that had the ability to cover all of our processors (in the NLP field) as much as possible. And I think the mimic-iii data satisfies that.
In this example, we should try to use all the processors we have, for example, if our sample data is selected from a patient's self-report or query or clinical diagnose records (maybe a covid-19 patient), which describes their physical condition, e.g., with A symptoms and without B symptoms (Negation Context Detect), and then give a diagnosis based on the symptom description (ICD Coding). The user description may have a more specific time, such as how it was last night, how it was last month, so that it can be extended to the Temporal domain. ( I know the temporal related processors may be not completed, we can just work on all the things we have currently).
But it may be hard to find a piece of data that covers all the processors, for this issue, maybe we can just concatenate them to achieve what we want.
Possible included components:
Sentence Segmenter
Tokenizer
Bio NER Tagger
Negation Context
ICD Coding
Temporal Mention Tagging
Temporal Relation Extraction
Deidentification
(Just ignore the processors we do not have currently)
I had this idea because I wanted to have a pipeline that had the ability to cover all of our processors (in the NLP field) as much as possible. And I think the mimic-iii data satisfies that.
In this example, we should try to use all the processors we have, for example, if our sample data is selected from a patient's self-report or query or clinical diagnose records (maybe a covid-19 patient), which describes their physical condition, e.g., with A symptoms and without B symptoms (Negation Context Detect), and then give a diagnosis based on the symptom description (ICD Coding). The user description may have a more specific time, such as how it was last night, how it was last month, so that it can be extended to the Temporal domain. ( I know the temporal related processors may be not completed, we can just work on all the things we have currently).
But it may be hard to find a piece of data that covers all the processors, for this issue, maybe we can just concatenate them to achieve what we want.
Possible included components:
(Just ignore the processors we do not have currently)