Development and implementation of clinical guidelines : an artificial intelligence perspective

Clinical practice guidelines in paper format are still the preferred form of delivery of medical knowledge and recommendations to healthcare professionals. Their current support and development process have well identified limitations to which the healthcare community has been continuously searching solutions. Artificial intelligence may create the conditions and provide the tools to address many, if not all, of these limitations.. This paper presents a comprehensive and up to date review of computer-interpretable guideline approaches, namely Arden Syntax, GLIF, PROforma, Asbru, GLARE and SAGE. It also provides an assessment of how well these approaches respond to the challenges posed by paper-based guidelines and addresses topics of Artificial intelligence that could provide a solution to the shortcomings of clinical guidelines. Among the topics addressed by this paper are expert systems, case-based reasoning, medical ontologies and reasoning under uncertainty, with a special focus on methodologies for assessing quality of information when managing incomplete information. Finally, an analysis is made of the fundamental requirements of a guideline model and the importance that standard terminologies and models for clinical data have in the semantic and syntactic interoperability between a guideline execution engine and the software tools used in clinical settings. It is also proposed a line of research that includes the development of an ontology for clinical practice guidelines and a decision model for a guideline-based expert system that manages non-compliance with clinical guidelines and uncertainty.This work is funded by national funds through the FCT – Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) within project PEst-OE/EEI/UI0752/2011"

Association of Gender with Clinical Expression, Quality of Life, Disability, and Depression and Anxiety in Patients with Systemic Sclerosis

OBJECTIVES: To assess the association of gender with clinical expression, health-related quality of life (HRQoL), disability, and self-reported symptoms of depression and anxiety in patients with systemic sclerosis (SSc). METHODS: SSc patients fulfilling the American College of Rheumatology and/or the Leroy and Medsger criteria were assessed for clinical symptoms, disability, HRQoL, self-reported symptoms of depression and anxiety by specific measurement scales. RESULTS: Overall, 381 SSc patients (62 males) were included. Mean age and disease duration at the time of evaluation were 55.9 (13.3) and 9.5 (7.8) years, respectively. One-hundred-and-forty-nine (40.4%) patients had diffuse cutaneous SSc (dcSSc). On bivariate analysis, differences were observed between males and females for clinical symptoms and self-reported symptoms of depression and anxiety, however without reaching statistical significance. Indeed, a trend was found for higher body mass index (BMI) (25.0 [4.1] vs 23.0 [4.5], p = 0.013), more frequent dcSSc, echocardiography systolic pulmonary artery pressure >35 mmHg and interstitial lung disease in males than females (54.8% vs 37.2%, p = 0.010; 24.2% vs 10.5%, p = 0.003; and 54.8% vs 41.2%, p = 0.048, respectively), whereas calcinosis and self-reported anxiety symptoms tended to be more frequent in females than males (36.0% vs 21.4%, p = 0.036, and 62.3% vs 43.5%, p = 0.006, respectively). On multivariate analysis, BMI, echocardiography PAP>35 mmHg, and anxiety were the variables most closely associated with gender. CONCLUSIONS: In SSc patients, male gender tends to be associated with diffuse disease and female gender with calcinosis and self-reported symptoms of anxiety. Disease-associated disability and HRQoL were similar in both groups

The basic probability assignment as a measure of diagnostic rules significance

Diagnostic rules are usually IF-THEN rules, but they should satisfy specific requirements of a diagnosis. Thus, not always the classical methods of rules determination are applicable. In the present paper it is suggested to find out the set of rules by an elimination of superfluous rules from the maximal rule set or adding rules that improve inference to the minimal set of rules. It is shown that the basic probability assignment determined in the Dempster-Shafer theory of evidence can be used as a measure indicating symptoms that are the most significant for a diagnosis and should create rules. A set of IF-THEN rules with fuzzy premises and crisp conclusions can be built in this way. The proposed method is illustrated by determining rules allowing for diagnostic inference for a database of thyroid gland diseases

Data-based creation of diagnostic rules

W pracy przedstawiono metodę tworzenia reguł diagnostycznych o rozmytych przesłankach reprezentujących objawy i nierozmytej konkluzji odpowiadającej diagnozie. Reguły tworzy się na podstawie danych uczących, lecz są one zrozumiałe dla ekspertów i mogą być przez nich weryfikowane. Zbiór reguł dla każdej z diagnoz jest ustalany odrębnie, z zastosowaniem oryginalnego algorytmu eliminacji reguł. Obliczenia dla dwóch benchmarkowych baz danych potwierdzają efektywność proponowanych metod.A method of diagnostic rule creation is presented in the paper. The rules have fuzzy premises that represent symptoms and a crisp conclusion relevant to the diagnosis. Each rule has an assigned weight that is determined as a value of the basic probability assignment defined in the Dempster-Shafer theory. Having created the rules, there is performed the diagnostic reasoning for a consulted case whose outcomes are values of the Bel belief measure (of the Dempster-Shafer theory) for all diagnostic hypotheses. The hypothesis of the maximal belief is the ultimate conclusion. Membership functions of symptoms and the basic probability assignment are found from the training data. Although the rules are created by means of data, they are understandable for human experts who can interpret and verified them. An individual set of rules is provided for each diagnosis. It results from an original elimination algorithm that is proposed in the paper. The elimination process starts from the complete set of rules and the algorithm indicates rule(s) of the lowest diagnostic significance, which are next deleted. Numerical experiments for two benchmark databases show the properties of the proposed method

Uncertainty and imprecision in medical diagnosis support

The paper concerns methods of representation of uncertainty and imprecision in successful medical support applications. Advantages of the methods are pointed out and some of their drawbacks are explained. A method of simultaneous representation of imprecision of symptoms and uncertainty of diagnostic rules is proposed. The method suggests an extension of the Dempster-Sahfer theory for fuzzy focal elements. An example of the method is given and their links as well differences from previous approaches are discussed. Conclusions about uncertainty and imprecision representation in medical diagnosis support are provided

Simultaneous estimation of quantity and quality information in medical diagnosis support

Diagnoza medyczna bazuje na niepełnej i nieprecyzyjnej informacji, dlatego algorytmy wspomagania wnioskowania medycznego muszą spełniać specyficzne wymagania. Praca koncentruje się na jednoczesnej i równoważnej ocenie parametrów medycznych rozmaitej natury: mierzalnych (np. testy laboratoryjne), formułowanych ściśle (ciąża), określanych nieprecyzyjnie (przyrost wagi), a czasem definiowanych w umownej skali (ból). Proponuje się modelowanie wnioskowania medycznego z zastosowaniem teorii Dempstera-Shafera rozszerzonej poprzez zdefiniowanie rozmytych elementów ogniskowych. Pozwala to na reprezentację wiedzy w postaci reguł. W przesłankach tych reguł mogą występować zarówno zmienne ilościowe, jak i jakościowe. Każdej regule jest przypisana wartość bazowego prawdopodobiestwa zdefiniowanego zgodnie z teorią Dempstera-Shafera. Funkcje przynależności charakteryzujące zmienne w przesłankach reguł oraz rozkładu bazowego prawdopodobieństwa można wyznaczyć na podstawie danych uczących. Wniosek diagnostyczny jest wynikiem porównania wartości miar przekonania (Bel) dla kilku hipotez. Przedstawiony model wnioskowania został zweryfikowany się dla 3 niezależnych baz danych dotyczących chorób tarczycy.Medical diagnosis is based on uncertain and imprecise information. Therefore, algorithms that support medical inference comply with specific requirements. This paper is focused on simultaneous and equal estimation of medical parameters of different nature: measurable (like laboratory tests), precisely formulated (pregnancy), described in an imprecise way (putting on weight), or defined on an assumed scale (pain). It is suggested to model a medical inference in the framework of the Dempster-Shafer theory extended for fuzzy focal elements. By means of the proposed algorithm, diagnostic rules can be formulated. Premises of the rules may include both quantity and quality variables. Each rule is assigned with a value of the basic probability assignment that is defined according to the Dempster-Shafer theory. Membership functions of rule predicates as well as the basic probability assignment are found from training data. The diagnostic conclusion is formulated after a comparison of belief values for several hypotheses. The model of inference is verified for 3 independent data bases of thyroid gland diseases

Membership Functions for Fuzzy Focal Elements

The paper presents a study on data-driven diagnostic rules, which are easy to interpret by human experts. To this end, the Dempster-Shafer theory extended for fuzzy focal elements is used. Premises of the rules (fuzzy focal elements) are provided by membership functions which shapes are changing according to input symptoms. The main aim of the present study is to evaluate common membership function shapes and to introduce a rule elimination algorithm. Proposed methods are first illustrated with the popular Iris data set. Next experiments with five medical benchmark databases are performed. Results of the experiments show that various membership function shapes provide different inference efficiency but the extracted rule sets are close to each other. Thus indications for determining rules with possible heuristic interpretation can be formulated