cobre32: Data for Differential Diagnosis for Schizophrenia
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cobre32 R Documentation
Data for Differential Diagnosis for Schizophrenia
Description
A subset of the COBRE dataset has been retrieved, by querying SchizConnect for 105 patients with neurological and clinical symptoms, collecting also their corresponding diagnosis.
Usage
cobre32
Format
A matrix with 105 rows and 32 columns. Column names are related to different scales for depression and Schizophrenia:
- COSAS_n
The Simpson-Angus Scale, 7 items to evaluate Parkinsonism-like alterations, related to schizophrenia, in an individual.
- FICAL_n
The Calgary Depression Scale for Schizophrenia, 9 items (attributes) assessing the level of depression in schizophrenia, differentiating between positive and negative aspects of the disease.
- SCIDII_n
The Structured Clinical Interview for DSM-III-R Personality Disorders, with 14 variables related to the presence of signs affecting personality.
- dx_ss
if TRUE, the diagnosis is strict schizophrenia.
- dx_other
it TRUE, the diagnosis is other than schizophrenia, including schizoaffective, bipolar disorder and major depression.
In summary, the dataset consists in the previous 30 attributes related to signs or symptoms, and 2 attributes related to diagnosis (these diagnoses are mutually exclusive, thus only one of them is assigned to each patient). This makes a dataset with 105 objects (patients) and 32 attributes to explore. The symptom attributes are multi-valued.
Thus, according to the specific scales used, all attributes are fuzzy and graded. For a given attribute (symptom), the available grades range from absent to extreme, with minimal, mild, moderate, moderate severe and severe in between.
These fuzzy attributes are mapped to values in the interval [0, 1].
Source
Aine, C. J., Bockholt, H. J., Bustillo, J. R., Cañive, J. M., Caprihan, A., Gasparovic, C., ... & Liu, J. (2017). Multimodal neuroimaging in schizophrenia: description and dissemination. Neuroinformatics, 15(4), 343-364.
https://pubmed.ncbi.nlm.nih.gov/26142271/
fcaR documentation built on May 29, 2024, 2:05 a.m.
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| cobre32 | R Documentation |
Data for Differential Diagnosis for Schizophrenia
Description
A subset of the COBRE dataset has been retrieved, by querying SchizConnect for 105 patients with neurological and clinical symptoms, collecting also their corresponding diagnosis.
Usage
cobre32
Format
A matrix with 105 rows and 32 columns. Column names are related to different scales for depression and Schizophrenia:
- COSAS_n
The Simpson-Angus Scale, 7 items to evaluate Parkinsonism-like alterations, related to schizophrenia, in an individual.
- FICAL_n
The Calgary Depression Scale for Schizophrenia, 9 items (attributes) assessing the level of depression in schizophrenia, differentiating between positive and negative aspects of the disease.
- SCIDII_n
The Structured Clinical Interview for DSM-III-R Personality Disorders, with 14 variables related to the presence of signs affecting personality.
- dx_ss
if
TRUE, the diagnosis is strict schizophrenia.- dx_other
it
TRUE, the diagnosis is other than schizophrenia, including schizoaffective, bipolar disorder and major depression.
In summary, the dataset consists in the previous 30 attributes related to signs or symptoms, and 2 attributes related to diagnosis (these diagnoses are mutually exclusive, thus only one of them is assigned to each patient). This makes a dataset with 105 objects (patients) and 32 attributes to explore. The symptom attributes are multi-valued.
Thus, according to the specific scales used, all attributes are fuzzy and graded. For a given attribute (symptom), the available grades range from absent to extreme, with minimal, mild, moderate, moderate severe and severe in between.
These fuzzy attributes are mapped to values in the interval [0, 1].
Source
Aine, C. J., Bockholt, H. J., Bustillo, J. R., Cañive, J. M., Caprihan, A., Gasparovic, C., ... & Liu, J. (2017). Multimodal neuroimaging in schizophrenia: description and dissemination. Neuroinformatics, 15(4), 343-364. https://pubmed.ncbi.nlm.nih.gov/26142271/
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