Deviation Detection in Clinical Pathways Based on Business Alignment

Algorithm 1

The noise filter algorithm.

	Input: diagnosis and treatment log L, precedence relationship matrix M of N1;
	Output: filtered log L′.
(1)	S = Ø;
(2)	L′ = L;
(3)	for all σ ∈ L′ do
(4)	S = S ∪ {σ (1)};
(5)	for all cur ∈ σ do
(6)	//terminate if current activity is the last one
(7)	if σ.indexOf (cur) == σ.size − 1 then
(8)	break;
(9)	end
(10)	post = σ (σ.indexOf (cur) + 1);
(11)	if (∃a ∈ S)  ⇒  (M [α^−1 (a)][α^−1(post)] == “#”) then
(12)	L′ = L′ – {σ};
(13)	//activity a can occur earlier than activity post, but the reverse is not true
(14)	else if (∃a ∈ S)  ⇒  (M [α^−1 (a)][α^−1(post)] == “⟶” && M [α^−1(post)][α^−1(a)] == “#”) then
(15)	for all (a ∈ S) ˄ (M [α^−1(a)][α^−1(post)] == “⟶” && M [α^−1(post)][α^−1(a)] == “#”) do
(16)	S = S – {a};
(17)	end
(18)	S = S ∪ {post};
(19)	else
(20)	S = S ∪ {post};
(21)	end
(22)	end
(23)	S = Ø;
(24)	end
(25)	return L′;