We get the potential drugs , drug pairs and drug triplets which can result into Adverse Drug events which can be harmful, both to the hospital and the patients.

1. MAJOR PROJECT
ANALYZING ADVERSE DRUG EVENT USING DATA MINING APPROACH

2. What is Adverse Drug event
????
- Adverse drug event is the result of adverse drug reaction.
- Adverse drug reaction is defined as any undesirable experience associated
with the use of a medical product in a individual.
- In short Adverse drug event is defined as an unwanted or unintended
reaction that results from the normal use of one or more medications

3. Why Adverse Drug Events
happens ???
The adverse drug event have started taking place more frequently and is
increasing because of several reasons like development of new medications,
increase in the use of medications for disease prevention, increased
coverage prescription medications.

4. How can ADEs be prevented
????
If a database of adverse drug events which took place anywhere in the
world is maintained and can be accessed by the authorities ,hospitals ,etc. ,
any possible risks to patient's life can be avoided as we are already aware of
the outcomes.

5. So , How can we help here
????
In this project, we plan on creating an Adverse drug reaction response system
which refers to the analytical tool which will be helping to analyze the
adverse drug reaction triples from the database.

6. What is to be done ??
Firstly the database is maintained using the already existing data or the data
provided by the user via. Interfaces for which we use the client-server
connection. Then, we use Apriori algorithm for mining the data which gives us
the potential drugs or drug pairs or triplets which can result into Adverse Drug
Events which initially starts with a transaction table.

7. Data saved and managed
using client-server
connection.
5 interfaces for entry but one database from which the data is mined.

8. Apriori Algorithm used for
mining data
The Apriori algorithm is a classic algorithm for learning association rules.
Association rule learning is a popular method for discovering relations
between variables in large databases.

9. Some definitions to
be aware of

10. What is the support ??
The support of an item (or set of items) is the number of transactions in which
that item (or items) occur.

11. What is the support threshold
??
The support threshold is defined by the user and is a number for which the
support for each item (or items) has to be equal or above for the support
threshold to be fulfilled.
Lets say in this example ,the value is 40 %.

12. What is a frequent item set
??
A frequent item set is an item set whose number of occurrences in the
transactions are above the support threshold.

13. Firstly, start with the
transaction database.
Transaction database contains the relationship status of medicines to cases
already present in the database.

14. From where we create a
candidate item set – Pass 1
We basically find the support values for each item i.e. drug in our case.

15. Now we create the frequent
item set – Pass 1
Where we keep the items , i.e. drugs whose support >= threshold support (40%
in this case).

16. Now we find the support
values for drug pairs – Pass 2
Here the item set contains all the possible permutations between the given
items and their support values are found.

17. Then again, we find the
frequent item set for Pass 2
Support >= threshold or min. support , else the item i.e. drug pair is
removed ; which in turn means that the removed pairs are not so
frequent.

18. Now moving on to drug
triplets – Pass 3
- Here, first we combine the elements to get triplets using the frequent item
set from pass 2, where the first element is same.
- Their support value is the min. [support{1st element in the combination from
pass 2), support (2nd element in the combination from pass 3)]

19. For frequent item set ,the sets
are pruned similarly – Pass 3
Support >= threshold or min. support

20. So in this way we find the frequent item set in
each pass and the result can be displayed after
each pass, in accordance to the user
requirements. So the user can see the risk in taking
the drugs as their effect and rest of the related
information is displayed.

21. Pseudo Code :-
Pass 1
1.Generate the candidate itemsets in C1
2.Save the frequent itemsets in L1
3.Pass kGenerate the candidate itemsets in Ck from the frequent
itemsets in Lk-1
1. Join Lk-1 p with Lk-1q, as follows:
insert into Ck
select p.item1, p.item2, . . . , p.itemk-1, q.itemk-1
from Lk-1 p, Lk-1q
where p.item1 = q.item1, . . . p.itemk-2 = q.itemk-2, p.itemk-1 < q.itemk-1
2. Generate all (k-1)-subsets from the candidate itemsets in Ck
3. Prune all candidate itemsets from Ck where some (k-1)-subset of the candidate itemset is not in the frequent itemset Lk-1
4.Scan the transaction database to determine the support for each candidate itemset in Ck
5.Save the frequent itemsets in Lk

22. Pseudo Code (for our project) :-
In pass 1 , Candidate item sets are generated and then frequent item sets are
generated when the support values from candidate item sets are compared
to the specified min. support value which act as a threshold support value
and the items with support value less than this value is removed, which then
leads to pass 2 which uses the frequent item sets from pass 1 to create item
sets of entities pairs where only the elements with support value greater than
specified support value are kept giving us a new frequent item set . Then this
frequent item sets are used in pass 3 where the two elements with first
element same are combined to get triplets and the min. support value of the
taken two frequent set is taken and then compared to the min. specified
support value to get the frequent item sets for pass 3. So, here we can get the
output from all the passes depending upon the user requirements.

23. Supervisor – Ms. Megha Rathi
Submitted by – Rupal Bhardwaj (10503880)
Amisha Kathuria (10501832)
Batch – B11