An Ontology-Based Interpretable Fuzzy Decision Support System for Diabetes Diagnosis

Diabetes is a serious chronic disease. The importance of clinical decision support systems (CDSSs) to diagnose diabetes has led to extensive research efforts to improve the accuracy, applicability, interpretability, and interoperability of these systems. However, this problem continues to require optimization. Fuzzy rule-based systems are suitable for the medical domain, where interpretability is a main concern. The medical domain is data-intensive, and using electronic health record data to build the FRBS knowledge base and fuzzy sets is critical. Multiple variables are frequently required to determine a correct and personalized diagnosis, which usually makes it difficult to arrive at accurate and timely decisions. In this paper, we propose and implement a new semantically interpretable FRBS framework for diabetes diagnosis. The framework uses multiple aspects of knowledge-fuzzy inference, ontology reasoning, and a fuzzy analytical hierarchy process (FAHP) to provide a more intuitive and accurate design. First, we build a two-layered hierarchical and interpretable FRBS; then, we improve this by integrating an ontology reasoning process based on SNOMED CT standard ontology. We incorporate FAHP to determine the relative medical importance of each sub-FRBS. The proposed system offers numerous unique and critical improvements regarding the implementation of an accurate, dynamic, semantically intelligent, and interpretable CDSS. The designed system considers the ontology semantic similarity of diabetes complications and symptoms concepts in the fuzzy rules' evaluation process. The framework was tested using a real data set, and the results indicate how the proposed system helps physicians and patients to accurately diagnose diabetes mellitusThis work was supported by National Research Foundation of Korea-Grant funded by the Korean Government (Ministry of Science, ICT and Future Planning)-NRF-2017R1A2B2012337)S

IMPLEMENTASI REDUCED RULE BASED PADA BALITA GIZI BURUK DI KALIMANTAN BARAT

Gizi buruk merupakan bentuk terparah dari proses terjadinya kekurangan gizi menahun. Terdapat beberapa penyakit yang terjadi pada balita gizi buruk, yaitu Malaria, TB Paru, HIV/AIDS, Infeksi Saluran Pernapasan Akut (ISPA), Kusta, dan Pneumonia. Anak balita usia 12-59 bulan merupakan kelompok umur yang rawan terhadap gangguan kesehatan dan gizi. Sistem pakar merupakan salah satu cabang dari kecerdasan buatan yang berusaha mengadopsi pengetahuan manusia ke komputer untuk pengambilan keputusan dari satu atau lebih individu yang ahli dalam bidang tertentu. Aturan IF-Then mendefinisikan hubungan logis antara masalah yang ditetapkan. Penyederhanaan fungsi Boolean digunakan untuk mendapatkan reduced rule base. Metode penyederhanaan fungsi Boolean digunakan untuk memperoleh lebih sedikit situasi dengan menyederhanakan fungsi-fungsi logis. Pada penelitian ini penyederhanaan fungsi Boolean menggunakan metode peta karnaugh sehingga akan didapatkan beberapa kemungkinan gejala yang terjadi dengan nilai biner 1 dan 0. Data pasien yang digunakan sebanyak 30 pasien. Gejala yang digunakan pada penelitian ini adalah gejala TB Paru, ISPA, dan Pneumonia. Penyederhanaan fungsi Boolean menggunakan peta karnaugh menggunakan 12 gejala dengan perhitungan yaitu 212 = 4096, sehingga dibuatlah tabel penyederhanaan fungsi Boolean sebanyak 4096. Pengujian validitas nilai akurasi dilakukan dengan membandingkan data pasien dengan tabel kebenaran fungsi Boolean, keakurasian yang dihasilkan sebesar 73,34%

Sistem Pakar Berbasis Android untuk Diagnosis Diabetes Melitus dengan Metode Forward Chaining

Diabetes Mellitus is the biggest cause of death disease. This is beause the lack of public knowledge about the symptoms of disease which is caused delayed in handling. This article presents the development of an expert system application used as a Diabetes Mellitus diagnosis tool on Android mobile. The purpose of application is to detect Diabetes Mellitus based on the type of Diabetes Mellitus symptoms and determine the possibility of the disease occurred. The method for developing an expert system is forward chaining. The implementation of forward chaining method is used to gather information then proceed by applying reasoning with the if-then rule as a result of the conclusion of a diagnosis according to symptoms. The stages in developing this expert system application use the Expert System Development Life Cycle (ESDLC). The result of its development is an expert system used for diagnosis of Diabetes Mellitus according to the symptoms experienced. The expert system application is implemented on the Android mobile. This expert system application displays specific results to displaying a diagnosis of Diabetes Mellitus and displays the percentage of the possibility of the disease. Keywords – Android; Diabetes Mellitus; Diagnose; Forward chaining; Expert system.Diabetes Melitus termasuk salah satu penyakit yang menyebabkan kematian terbesar. Hal tersebut disebabkan pengetahuan masyarakat yang kurang mengenai gejala-gejala penyakit yang ditimbulkan sehingga mengalami keterlambatan penanganan. Artikel ini menyajikan pengembangan aplikasi sistem pakar yang digunakan sebagai perangkat diagnosis Diabetes Melitus pada mobile Android. Tujuan aplikasi ini untuk mendeteksi Diabetes Melitus berdasakan gejala yang sedang dialami oleh seseorang sesuai dengan tipe penyakit Diabetes Melitus dan menentukan persentase kemungkinan terjadinya penyakit tersebut. Dalam pengembangkan aplikasi sistem pakar ini menggunakan metode forward chaining. Penerapan metode forward chaining digunakan untuk mengumpulkan informasi kemudian dilanjutkan dengan mengimplementasikan penalaran dengan aturan if-then sebagai hasil kesimpulan diagnosis sesuai dengan gejala. Tahapan dalam pengembangan aplikasi sistem pakar ini menggunakan Expert System Development Life Cycle (ESDLC). Hasil pengembangannya yaitu sistem pakar yang digunakan untuk diagnosis penyakit Diabetes Melitus sesuai gejala yang dialami. Aplikasi sistem pakar tersebut diimplementasikan pada mobile Android. Aplikasi sistem pakar ini menampilkan hasil yang spesifik yaitu selain menampilkan diagnosis Diabetes Melitus juga menampilkan persentase kemungkinan terjadinya penyakit tersebut pada seseorang. Kata Kunci – Android; Diabetes Melitus; Diagnosis; Forward chaining; Sistem pakar

DATABASE ACCESS REQUIREMENTS OF KNOWLEDGE-BASED SYSTEMS

Knowledge bases constitute the core of those Artificial Intelligence programs which have come to be known as Expert Systems. An examination of the most dominant knowledge representation schemes used in these systems reveals that a knowledge base can, and possibly should, be described at several levels using different schemes, including those traditionally used in operational databases. This chapter provides evidence that solutions to the organization and access problem for very large knowledge bases require the employment of appropriate database management methods, at least for the lowest level of description -- the facts or data. We identify the database access requirements of knowledge-based or expert systems and then present four general architectural strategies for the design of expert systems that interact with databases, together with specific recommendations for their suitability in particular situations. An implementation of the most advanced and ambitious of these strategies is then discussed in some detail.Information Systems Working Papers Serie

Impact of Terminology Mapping on Population Health Cohorts IMPaCt

Background and Objectives: The population health care delivery model uses phenotype algorithms in the electronic health record (EHR) system to identify patient cohorts targeted for clinical interventions such as laboratory tests, and procedures. The standard terminology used to identify disease cohorts may contribute to significant variation in error rates for patient inclusion or exclusion. The United States requires EHR systems to support two diagnosis terminologies, the International Classification of Disease (ICD) and the Systematized Nomenclature of Medicine (SNOMED). Terminology mapping enables the retrieval of diagnosis data using either terminology. There are no standards of practice by which to evaluate and report the operational characteristics of ICD and SNOMED value sets used to select patient groups for population health interventions. Establishing a best practice for terminology selection is a step forward in ensuring that the right patients receive the right intervention at the right time. The research question is, “How does the diagnosis retrieval terminology (ICD vs SNOMED) and terminology map maintenance impact population health cohorts?” Aim 1 and 2 explore this question, and Aim 3 informs practice and policy for population health programs. Methods Aim 1: Quantify impact of terminology choice (ICD vs SNOMED) ICD and SNOMED phenotype algorithms for diabetes, chronic kidney disease (CKD), and heart failure were developed using matched sets of codes from the Value Set Authority Center. The performance of the diagnosis-only phenotypes was compared to published reference standard that included diagnosis codes, laboratory results, procedures, and medications. Aim 2: Measure terminology maintenance impact on SNOMED cohorts For each disease state, the performance of a single SNOMED algorithm before and after terminology updates was evaluated in comparison to a reference standard to identify and quantify cohort changes introduced by terminology maintenance. Aim 3: Recommend methods for improving population health interventions The socio-technical model for studying health information technology was used to inform best practice for the use of population health interventions. Results Aim 1: ICD-10 value sets had better sensitivity than SNOMED for diabetes (.829, .662) and CKD (.242, .225) (N=201,713, p Aim 2: Following terminology maintenance the SNOMED algorithm for diabetes increased in sensitivity from (.662 to .683 (p Aim 3: Based on observed social and technical challenges to population health programs, including and in addition to the development and measurement of phenotypes, a practical method was proposed for population health intervention development and reporting

Explainable Deep Learning

Il grande successo che il Deep Learning ha ottenuto in ambiti strategici per la nostra società quali l'industria, la difesa, la medicina etc., ha portanto sempre più realtà a investire ed esplorare l'utilizzo di questa tecnologia. Ormai si possono trovare algoritmi di Machine Learning e Deep Learning quasi in ogni ambito della nostra vita. Dai telefoni, agli elettrodomestici intelligenti fino ai veicoli che guidiamo. Quindi si può dire che questa tecnologia pervarsiva è ormai a contatto con le nostre vite e quindi dobbiamo confrontarci con essa. Da questo nasce l’eXplainable Artificial Intelligence o XAI, uno degli ambiti di ricerca che vanno per la maggiore al giorno d'oggi in ambito di Deep Learning e di Intelligenza Artificiale. Il concetto alla base di questo filone di ricerca è quello di rendere e/o progettare i nuovi algoritmi di Deep Learning in modo che siano affidabili, interpretabili e comprensibili all'uomo. Questa necessità è dovuta proprio al fatto che le reti neurali, modello matematico che sta alla base del Deep Learning, agiscono come una scatola nera, rendendo incomprensibile all'uomo il ragionamento interno che compiono per giungere ad una decisione. Dato che stiamo delegando a questi modelli matematici decisioni sempre più importanti, integrandole nei processi più delicati della nostra società quali, ad esempio, la diagnosi medica, la guida autonoma o i processi di legge, è molto importante riuscire a comprendere le motivazioni che portano questi modelli a produrre determinati risultati. Il lavoro presentato in questa tesi consiste proprio nello studio e nella sperimentazione di algoritmi di Deep Learning integrati con tecniche di Intelligenza Artificiale simbolica. Questa integrazione ha un duplice scopo: rendere i modelli più potenti, consentendogli di compiere ragionamenti o vincolandone il comportamento in situazioni complesse, e renderli interpretabili. La tesi affronta due macro argomenti: le spiegazioni ottenute grazie all'integrazione neuro-simbolica e lo sfruttamento delle spiegazione per rendere gli algoritmi di Deep Learning più capaci o intelligenti. Il primo macro argomento si concentra maggiormente sui lavori svolti nello sperimentare l'integrazione di algoritmi simbolici con le reti neurali. Un approccio è stato quelli di creare un sistema per guidare gli addestramenti delle reti stesse in modo da trovare la migliore combinazione di iper-parametri per automatizzare la progettazione stessa di queste reti. Questo è fatto tramite l'integrazione di reti neurali con la Programmazione Logica Probabilistica (PLP) che consente di sfruttare delle regole probabilistiche indotte dal comportamento delle reti durante la fase di addestramento o ereditate dall'esperienza maturata dagli esperti del settore. Queste regole si innescano allo scatenarsi di un problema che il sistema rileva durate l'addestramento della rete. Questo ci consente di ottenere una spiegazione di cosa è stato fatto per migliorare l'addestramento una volta identificato un determinato problema. Un secondo approccio è stato quello di far cooperare sistemi logico-probabilistici con reti neurali per la diagnosi medica da fonti di dati eterogenee. La seconda tematica affrontata in questa tesi tratta lo sfruttamento delle spiegazioni che possiamo ottenere dalle rete neurali. In particolare, queste spiegazioni sono usate per creare moduli di attenzione che aiutano a vincolare o a guidare le reti neurali portandone ad avere prestazioni migliorate. Tutti i lavori sviluppati durante il dottorato e descritti in questa tesi hanno portato alle pubblicazioni elencate nel Capitolo 14.2.The great success that Machine and Deep Learning has achieved in areas that are strategic for our society such as industry, defence, medicine, etc., has led more and more realities to invest and explore the use of this technology. Machine Learning and Deep Learning algorithms and learned models can now be found in almost every area of our lives. From phones to smart home appliances, to the cars we drive. So it can be said that this pervasive technology is now in touch with our lives, and therefore we have to deal with it. This is why eXplainable Artificial Intelligence or XAI was born, one of the research trends that are currently in vogue in the field of Deep Learning and Artificial Intelligence. The idea behind this line of research is to make and/or design the new Deep Learning algorithms so that they are interpretable and comprehensible to humans. This necessity is due precisely to the fact that neural networks, the mathematical model underlying Deep Learning, act like a black box, making the internal reasoning they carry out to reach a decision incomprehensible and untrustable to humans. As we are delegating more and more important decisions to these mathematical models, it is very important to be able to understand the motivations that lead these models to make certain decisions. This is because we have integrated them into the most delicate processes of our society, such as medical diagnosis, autonomous driving or legal processes. The work presented in this thesis consists in studying and testing Deep Learning algorithms integrated with symbolic Artificial Intelligence techniques. This integration has a twofold purpose: to make the models more powerful, enabling them to carry out reasoning or constraining their behaviour in complex situations, and to make them interpretable. The thesis focuses on two macro topics: the explanations obtained through neuro-symbolic integration and the exploitation of explanations to make the Deep Learning algorithms more capable or intelligent. The neuro-symbolic integration was addressed twice, by experimenting with the integration of symbolic algorithms with neural networks. A first approach was to create a system to guide the training of the networks themselves in order to find the best combination of hyper-parameters to automate the design of these networks. This is done by integrating neural networks with Probabilistic Logic Programming (PLP). This integration makes it possible to exploit probabilistic rules tuned by the behaviour of the networks during the training phase or inherited from the experience of experts in the field. These rules are triggered when a problem occurs during network training. This generates an explanation of what was done to improve the training once a particular issue was identified. A second approach was to make probabilistic logic systems cooperate with neural networks for medical diagnosis on heterogeneous data sources. The second topic addressed in this thesis concerns the exploitation of explanations. In particular, the explanations one can obtain from neural networks are used in order to create attention modules that help in constraining and improving the performance of neural networks. All works developed during the PhD and described in this thesis have led to the publications listed in Chapter 14.2