1 Introduction

As defined by the Medical Waste Tracking Act of 1988, clinical solid waste consist of solid waste materials which are generated during diagnosis, treatment, vaccination, research or in the production or testing of biological products for humans and animals, including syringes, live vaccines, blood and other waste contaminated with bodily fluids and removed body organs, among others. This class of waste is now recognized as a potencially hazardous agent affecting both the environment and the human being, due to the labor intensive operations entailed in its collection, segregation and disposal, which involve many possibilities of direct contact with the waste and therefore increase the risk of infections for health care workers, the general public and waste handlers in particular [1, 2]. Focusing on the latter, poor management practices and improper precautions taken by clinical waste workers during these operations are quoted as being the main reason of the spread of infectious diseases among clinical waste handlers [3, 4]. Nevertheless, several studies [3–6] indicate that the clinical solid waste management at healthcare facilities is still inadequate in developed countries and that, in many situations, this class of waste is handled and disposed together with nonclinical waste. Given this scenario, the development of strategies for the definition of the best appropriate clinical waste management practice towards the minimization of occupational incidents and environmental contamination is of great importance. One of the main concerns still requiring addressing is the lack of awareness of both healthcare and clinical waste workers in what regards waste differential classification [1].

Taking into consideration the urgent need to bridge and amend these gaps, the present study aims at analysing and developing an artificial intelligence-driven approach for dealing with the judgement difficulties that arise from the waste classification process, especially in environments with defective information, based on the approach presented by Neves et. al in [7] and selectively focusing on the clinical waste management situation in Portugal.

2 Case Study

In Portugal, the management of clinical solid waste is regulated by law and its legal constraints are defined within the Portuguese Legislative Decree no. 178/2006 - September 5th [?]. Particularly, this document provides a classification system for clinical solid waste based on four distinct criteria: typology, danger, production site and treatment required [?], which allow for the clear assortment of waste samples based on their specific combination of criteria.

According to the classification criteria aforementioned, a database model was constructed (Figure 3), comprising a primary table and three secondary tables which refer to the analysed cases and each of the considered criteria, respectively.

So, in this legislative decree, clinical waste can be classificated in four groups [?]:

• Group I - equivalent to MSW (Municipal Solid Waste);

• Group II - non-biohazardous hospital waste;

• Group III - biohazardous hospital waste;

• Group IV - specific hospital waste.

Each group require a different treatment ... Intrinsic to this issue is risk management which compreends the cycle of assessment,

control, review and identification of risk and due to economic and environmental aspects, risk management is also an important tool in clinical solid waste management.

2.1 Knowledge Representation and Reasoning

2.1.1 Extended Logic Programs

Program 1: Extended logic program for the predicate waste. {

                   waste (ID, CL, SR, TY, CT)
                   not(waste (ID, CL, SR, TY, CT)),
               not(abducible_{waste} (ID, CL, SR, TY, CT)).

waste(1,C,6,1,1). waste(2,C,4,2,5). waste(3,C,6,4,5). waste(4,C,3,6,{2,3}).

2 CASE STUDY

Figure 1: Database Model. waste(5,C,{5,6},3,4). waste(6,C, ,1,5).

abducible_{waste} (4, C, 3, 6, 2).
abducible_{waste} (4, C, 3, 6, 3).
abducible_{waste} (5, C, 5, 3, 4).
abducible_{waste} (5, C, 6, 3, 4).
?((abducible_{waste} (ID, CL, SR_{1}, TY, CT)
abducible_{waste} (ID, CL, SR_{2}, TY, CT))

Figure 2: Database Model.

(abducible_{waste} (ID, CL, SR_{1}, TY, CT)
abducible_{waste} (ID, CL, SR_{2}, TY, CT)))
abducible_{waste} (6, C,  , 1, 5).
abducible_{waste} (ID, CL, SR, TY, CT)   waste (ID, CL,  , TY, CT).

} Program 2: Extended logic program for the predicate source. {

                  source (ID,GS,SS,WS,HS,LS,MS)
                 not(source (ID,GS,SS,WS,HS,LS,MS)),

not(abducible_{source} (ID,GS,SS,WS,HS,LS,MS)).

source(1,1,0,0,0,0,0). source(2,0,1,0,0,0,0). source(3,0,0,1,0,0,0). source(4,0,0,0,1,0,0). source(5,0,0,0,0,1,0). source(6,0,0,0,0,0,1).

} Program 3: Extended logic program for the predicate type. {

type (ID, GP,FW, CP, OM, DP, IP, MW, NB, SW, IB, MD, CH, AC, CO)
not(type (ID, GP,OM, DP, FW, IP, CP, MW, IB, NB, SW, MD, CH, AC, CO)),
not(abducible_{type} (ID, GP,OM, DP, FW, IP, CP, MW, IB, NB, SW, MD, CH, AC,
      CO)).

type(1,1,0,0,0,0,0,0,0,0,0,0,0,0,0). type(2,0,1,0,0,0,0,0,0,0,0,0,0,0,0). type(3,0,0,1,0,0,0,0,0,0,0,0,0,0,0). type(4,0,0,0,1,0,0,0,0,0,0,0,0,0,0). type(5,0,0,0,0,1,0,0,0,0,0,0,0,0,0).

…

type(10,0,0,0,0,0,0,0,0,0,1,0,0,0,0). type(11,0,0,0,0,0,0,0,0,0,0,1,0,0,0). type(12,0,0,0,0,0,0,0,0,0,0,0,1,0,0). type(13,0,0,0,0,0,0,0,0,0,0,0,0,1,0). type(14,0,0,0,0,0,0,0,0,0,0,0,0,0,1).

} Program 4: Extended logic program for the predicate contamination. {

contamination (ID, BL, OF, IA, CA)
not(contamination (ID, BL, OF, IA, CA),
not(abducible_{contamination} (ID, BL, OF, IA, CA)).

contamination(1,1,0,0,0). contamination(2,0,1,0,0). contamination(3,0,0,1,0). contamination(4,0,0,0,1). contamination(5,0,0,0,0). … contamination(12,1,1,1,0). contamination(13,1,0,1,1). contamination(14,0,1,1,1). contamination(15,1,1,0,1). contamination(16,1,1,1,1).

}

2.1.2 Classification

clauses responsible for the waste classification. every clause has the following representation:

Condition
Action

As mencioned above, somewhere, there are four possible classes that will be now represented.

Class 1:

[waste (ID, Class, SR, TY, CT), SR 3, TY 3, CT 5]

[retract(waste(ID, Class, SR, TY, CT)), assert(waste(ID, C1, SR, TY, CT))].

Class 2:

[waste (ID, Class, SR, TY, CT), SR 6, 2 TY 7, CT 5]

[retract(waste(ID, Class, SR, TY, CT)), assert(waste(ID, C2, SR, TY, CT))].

Class 3:

[waste (ID, Class, SR, TY, CT), SR 2, 3 TY 10, CT 4]

[retract(waste(ID, Class, SR, TY, CT)), assert(waste(ID, C3, SR, TY, CT))].

2 CASE STUDY

Class 4:

[waste (ID, Class, SR, TY, CT), SR 4, TY 9, CT 4] [retract(waste(ID, Class, SR, TY, CT)), assert(waste(ID, C4, SR, TY, CT))].

Stopping Condition: [waste (ID, Class, SR, TY, CT)] [print(F),stop].

Figure 3: Analysed cases after waste classification.

3 References

[1] Md. Sohrab Hossain, Nik Norulaini Nik Ab Rahman, Venugopal Balakrishnan, Vignesh R. Puvanesuaran, Md. Zaidul Islam Sarker, and Mohd Omar Ab Kadir. Infectious Risk Assessment of Unsafe Handling Practices and Management of Clinical Solid Waste. International Journal of Environmental Research and Public Health, 10(2):556–567, 2013.

[2] Mahmut AKBOLAT, Cemile DEDE, Oguz ISIK, and Hakan SAGLAM. Medical Waste Management Practices in Turkey: A Case Study in Sakarya. Pakistan Journal of Medical Sciences, 27(4), 2011.

[3] Issam A Al-Khatib and Chikashi Sato. Solid Health Care Waste Management Status at Health Care Centers in the West Bank–Palestinian Territory. Waste Management, 29(8):2398–403, 2009.

[4] Akinwale Coker, Abimbola Sangodoyin, Mynepalli Sridhar, Colin Booth, Paul Olomolaiye, and Felix Hammond. Medical Waste Management in Ibadan, Nigeria: Obstacles and Prospects. Waste Manag, 29(2):804–11, 2009.

[5] Magda M. Abd El-Salam. Hospital Waste Management in El-Beheira Governorate, Egypt. Journal of Environmental Management, 91(3):618–629, 2010.

[6] Felicia Nemathaga, Sally Maringa, and Luke Chimuka. Hospital Solid Waste Management Practices in Limpopo Province, South Africa: a Case Study of Two Hospitals. Waste Management, 28(7):1236–1245, 2008.

[7] José Neves, Jorge Ribeiro, Paulo A. A. Pereira, Victor Alves, José Machado, António Abelha, Paulo Novais, César Analide, Manuel Santos, and Manuel Fernández-Delgado. Evolutionary Intelligence in Asphalt Pavement Modeling and Quality-of-Information. Progress in Artificial Intelligence, 1(1):119–135, 2012.