Descripción de la base de datos

Tomado de aquí.

Domain

The database was collected at Chiba University hospital. Each patient came to the outpatient clinic of the hospital on collagen diseases, as recommended by a home doctor or a general physician in the local hospital.

Collagen diseases are auto-immune diseases. Patients generate antibodies attacking their own bodies. For example, if a patient generates antibodies in lungs, he/she will chronically lose the respiratory function and finally lose life. The disease mechanisms are only partially known and their classification is still fuzzy. Some patients may generate many kinds of antibodies and their manifestations may include all the characteristics of collagen diseases.

In collagen diseases, thrombosis is one of the most important and severe complications, one of the major causes of death. Thrombosis is an increased coagulation of blood, that cloggs blood vessels. Usually it will last several hours and can repeat over time. Thrombosis can arise from different collagen diseases. It has been found that this complication is closely related to anti-cardiolipin antibodies. This was discovered by physicians, one of whom donated the datasets for discovery challenge.

Thrombosis must be treated as an emergency. It is important to detect and predict the possibilities of its occurence. However, such database analysis has not been made by any experts on immunology. Domain experts are very much interested in discovering regularities behind patients' observations.

Goals

1. Search for patterns which detect and predict thrombosis.
2. Search for temporal patterns specific/sensitive to thrombosis. (Examination date is very close to the date on thrombosis. If we can find specific/sensitive patterns before/after the thrombosis, they are very useful.)
3. Search for features which classifies collagen diseases correctly.
4. Search for temporal patterns specific/sensitive to each collagen disease.

 

TSUM_A.CSV

Basic information about patients (input by doctors). This dataset includes all patients (about 1000 records).

item	meaning	remark
ID	identification of the patient
Sex
Birthday		YYYY/M/D
Description date	the first date when a patient data was recorded	YY.MM.DD
First date	the date when a patient came to the hospital	YY.MM.DD
Admission	patient was admitted to the hospital (+) or followed at the outpatient clinic (-)
Diagnosis	disease names	multivalued attribute

 

TSUM_B.CSV

Special laboratory examinations (input by doctors) (measured by the Laboratory on Collagen Diseases). This dataset does not include all the patients, but includes the patients with these special tests.

item	meaning	remark
ID	identification of the patient
Examination Date	date of the test	YYYY/MM/DD
aCL IgG	anti-Cardiolipin antibody (IgG) concentration
aCL IgM	anti-Cardiolipin antibody (IgM) concentration
ANA	anti-nucleus antibody concentration
ANA Pattern	pattern observed in the sheet of ANA examination
aCL IgA	anti-Cardiolipin antibody (IgA) concentration
Diagnosis	disease names	multivalued attribute
KCT	meassure of degree of coagulation
RVVT	meassure of degree of coagulation
LAC	meassure of degree of coagulation
Symptoms	other symptoms observed	multivalued attribute
Thrombosis	degree of thrombosis	0: negative (no thrombosis) 1: positive (the most severe one) 2: positive (severe) 3: positive (mild)

Examination date is very close to the date on thrombosis. In negative examples, these tests are examined when thrombosis is suspected.

 

TSUM_C.CSV

Laboratory Examinations stored in Hospital Information Systems (Stored from 1980 to March 1999) All the data include ordinary laboratory examinations and have temporal stamps. The tests are not necessarily connected to thrombosis.

item	meaning	normal range
ID	identification of the patient
Date	Date of the laboratory tests (YYMMDD)
GOT	AST glutamic oxaloacetic transaminase	N < 60
GPT	ALT glutamic pylvic transaminase	N < 60
LDH	lactate dehydrogenase	N < 500
ALP	alkaliphophatase	N < 300
TP	total protein	6.0 < N < 8.5
ALB	albumin	3.5 < N < 5.5
UA	uric acid	N > 8.0 (Male) N > 6.5 (Female)
UN	urea nitrogen	N < 30
CRE	creatinine	N < 1.5
T-BIL	total bilirubin	N < 2.0
T-CHO	total cholesterol	N < 250
TG	triglyceride	N < 200
CPK	creatinine phosphokinase	N < 250
GLU	blood glucose	N < 180
WBC	White blood cell	3.5 < N < 9.0
RBC	Red blood cell	3.5 < N < 6.0
HGB	Hemoglobin	10 < N < 17
HCT	Hematoclit	29 < N < 52
PLT	platelet	100 < N < 400
PT	prothrombin time	N < 14
Note	comment for the test PT
APTT	activated partial prothrombin time	N < 45
FG	fibrinogen	150 < N < 450
AT3	marker of DIC, one of the most important complications of collagen diseases	70 < N < 130
A2PI	marker of DIC	70 < N < 130
U-PRO	proteinuria	0 < N < 30
IGG	Ig G	900 < N < 2000
IGA	Ig A	80 < N < 500
IGM	Ig M	40 < N < 400
CRP	C-reactive protein	N= -, +-, or N < 1.0
RA	Rhuematoid Factor	N= -, +-
RF	RAHA	N < 20
C3	complement 3	N > 35
C4	complement 4	N > 10
RNP	anti-ribonuclear protein	N= -, +-
SM	anti-SM	N= -, +-
SCl70	anti-scl70	N= -, +-
SSA	anti-SSA	N= -, +-
SSB	anti-SSB	N= -, +-
CENTROMEA	anti-centromere	N= -, +-
DNA	anti-DNA	N < 8
DNA-II	anti-DNA	N < 8

Ejercicio

Con lo aprendido en la introducción, realiza lo siguiente

1. Lee los archivos y colocalos en diferentes RDD
2. Limpialos los datasets (crea nuevas variables, recodifica, etc)
3. Revisa las tablas de arriba ¿Puedes imaginar tener una sola fuente de datos?
4. Coloca cada RDD en una tabla temporal
5. Realiza queries para ver que todo esté bien cargado.
6. Une las tablas en una nueva usando SQL ¿Se te ocurre otra manera de hacerlo?
7. Lluvia de ideas para realizar análisis
8. Lluvia de ideas para visualización