Artificial Intelligence in Clinical Decision Support: a Focused Literature Survey <s> Hybrid Approaches <s> Jaundice is a common and complex clinical symptom potentially occurring in hepatology, general surgery, pediatrics, infectious diseases, gynecology, and obstetrics, and it is fairly difficult to distinguish the cause of jaundice in clinical practice, especially for general practitioners in less developed regions. With collaboration between physicians and artificial intelligence engineers, a comprehensive knowledge base relevant to jaundice was created based on demographic information, symptoms, physical signs, laboratory tests, imaging diagnosis, medical histories, and risk factors. Then a diagnostic modeling and reasoning system using the dynamic uncertain causality graph was proposed. A modularized modeling scheme was presented to reduce the complexity of model construction, providing multiple perspectives and arbitrary granularity for disease causality representations. A “chaining” inference algorithm and weighted logic operation mechanism were employed to guarantee the exactness and efficiency of diagnostic reasoning under situations of incomplete and uncertain information. Moreover, the causal interactions among diseases and symptoms intuitively demonstrated the reasoning process in a graphical manner. Verification was performed using 203 randomly pooled clinical cases, and the accuracy was 99.01% and 84.73%, respectively, with or without laboratory tests in the model. The solutions were more explicable and convincing than common methods such as Bayesian Networks, further increasing the objectivity of clinical decision-making. The promising results indicated that our model could be potentially used in intelligent diagnosis and help decrease public health expenditure. <s> BIB001 </s> Artificial Intelligence in Clinical Decision Support: a Focused Literature Survey <s> Hybrid Approaches <s> Case-based reasoning and data interpretation is an artificial intelligence approach that capitalizes on past experience to solve current problems and this can be used as a method for practical intelligent systems. Case-based data reasoning is able to provide decision support for experts and clinicians in health systems as well as lifestyle systems. In this project we were focusing on developing a solution for healthy ageing considering daily activities, nutrition as well as cognitive activities. The data analysis of the reasoner followed state of the art guidelines from clinical practice. Guidelines provide a general framework to guide clinicians, and require consequent background knowledge to become operational, which is precisely the kind of information recorded in practice cases; cases complement guidelines very well and helps to interpret them. It is expected that the interest in case-based reasoning systems in the health. <s> BIB002 </s> Artificial Intelligence in Clinical Decision Support: a Focused Literature Survey <s> Hybrid Approaches <s> The AntiPhospholipid Syndrome (APS) is an acquired autoimmune disorder induced by high levels of antiphospholipid antibodies that cause arterial and veins thrombosis, as well as pregnancy-related complications and morbidity, as clinical manifestations. This autoimmune hypercoagulable state, usually known as Hughes syndrome, has severe consequences for the patients, being one of the main causes of thrombotic disorders and death. Therefore, it is required to be preventive; being aware of how probable is to have that kind of syndrome. Despite the updated of antiphospholipid syndrome classification, the diagnosis remains difficult to establish. Additional research on clinically relevant antibodies and standardization of their quantification are required in order to improve the antiphospholipid syndrome risk assessment. Thus, this work will focus on the development of a diagnosis decision support system in terms of a formal agenda built on a Logic Programming approach to knowledge representation and reasoning, complemented with a computational framework based on Artificial Neural Networks. The proposed model allows for improving the diagnosis, classifying properly the patients that really presented this pathology (sensitivity higher than 85%), as well as classifying the absence of APS (specificity close to 95%). <s> BIB003 </s> Artificial Intelligence in Clinical Decision Support: a Focused Literature Survey <s> Hybrid Approaches <s> Abstract Clinical narrative text includes information related to a patient’s medical history such as chronological progression of medical problems and clinical treatments. A chronological view of a patient’s history makes clinical audits easier and improves quality of care. In this paper, we propose a clinical Problem-Action relation extraction method, based on clinical semantic units and event causality patterns, to present a chronological view of a patient’s problem and a doctor’s action. Based on our observation that a clinical text describes a patient's medical problems and a doctor's treatments in chronological order, a clinical semantic unit is defined as a problem and/or an action relation. Since a clinical event is a basic unit of the problem and action relation, events are extracted from narrative texts, based on the external knowledge resources context features of the conditional random fields. A clinical semantic unit is extracted from each sentence based on time expressions and context structures of events. Then, a clinical semantic unit is classified into a problem and/or action relation based on the event causality patterns of the support vector machines. Experimental results on Korean discharge summaries show 78.8% performance in the F1-measure. This result shows that the proposed method is effectively classifies clinical Problem-Action relations. <s> BIB004 </s> Artificial Intelligence in Clinical Decision Support: a Focused Literature Survey <s> Hybrid Approaches <s> Artificial intelligence (AI), machine learning, and deep learning are terms now seen frequently, all of which refer to computer algorithms that change as they are exposed to more data. Many of these algorithms are surprisingly good at recognizing objects in images. The combination of large amounts of machine-consumable digital data, increased and cheaper computing power, and increasingly sophisticated statistical models combine to enable machines to find patterns in data in ways that are not only cost-effective but also potentially beyond humans' abilities. Building an AI algorithm can be surprisingly easy. Understanding the associated data structures and statistics, on the other hand, is often difficult and obscure. Converting the algorithm into a sophisticated product that works consistently in broad, general clinical use is complex and incompletely understood. To show how these AI products reduce costs and improve outcomes will require clinical translation and industrial-grade integration into routine workflow. Radiology has the chance to leverage AI to become a center of intelligently aggregated, quantitative, diagnostic information. Centaur radiologists, formed as a synergy of human plus computer, will provide interpretations using data extracted from images by humans and image-analysis computer algorithms, as well as the electronic health record, genomics, and other disparate sources. These interpretations will form the foundation of precision health care, or care customized to an individual patient. © RSNA, 2017. <s> BIB005 </s> Artificial Intelligence in Clinical Decision Support: a Focused Literature Survey <s> Hybrid Approaches <s> Use of heterogeneous data models in hospital information systems (HIS), obstructs the integration of clinical decision support system (CDSS) with clinical workflows. The diverse concepts diminish the interoperability level among the CDSS knowledge bases and data models of HIS. Standard terminology utilization in knowledge acquisition and its reconciliation with HIS data models are the candidate solution to overcome the interoperability barrier. We propose a reconciliation model to map concepts of diverse domain clinical models (DCM) with the standard terminology. In the proposed model, the implicit and explicit semantics are complemented to the word set of the targeted DCM concepts. The inclusion of semantics, mapped the DCM concepts to the SNOMED CT concepts with high accuracy. The results showed that the system correctly mapped 95% of concepts of DCM with standard terminology SNOMED CT concepts. <s> BIB006 </s> Artificial Intelligence in Clinical Decision Support: a Focused Literature Survey <s> Hybrid Approaches <s> Abstract Individuals with type 1 diabetes have to monitor their blood glucose levels, determine the quantity of insulin required to achieve optimal glycaemic control and administer it themselves subcutaneously, multiple times per day. To help with this process bolus calculators have been developed that suggest the appropriate dose. However these calculators do not automatically adapt to the specific circumstances of an individual and require fine-tuning of parameters, a process that often requires the input of an expert. To overcome the limitations of the traditional methods this paper proposes the use of an artificial intelligence technique, case-based reasoning, to personalise the bolus calculation. A novel aspect of our approach is the use of temporal sequences to take into account preceding events when recommending the bolus insulin doses rather than looking at events in isolation. The in silico results described in this paper show that given the initial conditions of the patient, the temporal retrieval algorithm identifies the most suitable case for reuse. Additionally through insulin-on-board adaptation and postprandial revision, the approach is able to learn and improve bolus predictions, reducing the blood glucose risk index by up to 27% after three revisions of a bolus solution. <s> BIB007 </s> Artificial Intelligence in Clinical Decision Support: a Focused Literature Survey <s> Hybrid Approaches <s> Objectives ::: In this study, a clinical decision support system was developed to help general practitioners assess the need for orthodontic treatment in patients with permanent dentition. ::: ::: ::: Methods ::: We chose a Bayesian network (BN) as the underlying model for assessing the need for orthodontic treatment. One thousand permanent dentition patient data sets chosen from a hospital record system were prepared in which one data element represented one participant with information for all variables and their stated need for orthodontic treatment. To evaluate the system, we compared the assessment results based on the judgements of two orthodontists to those recommended by the decision support system. ::: ::: ::: Results ::: In a BN decision support model, each variable is modelled as a node, and the causal relationship between two variables may be represented as a directed arc. For each node, a conditional probability table is supplied that represents the probabilities of each value of this node, given the conditions of its parents. There was a high degree of agreement between the two orthodontists (kappa value = 0.894) in their diagnoses and their judgements regarding the need for orthodontic treatment. Also, there was a high degree of agreement between the decision support system and orthodontists A (kappa value = 1.00) and B (kappa value = 0.894). ::: ::: ::: Conclusions ::: The study was the first testing phase in which the results generated by the proposed system were compared with those suggested by expert orthodontists. The system delivered promising results; it showed a high degree of accuracy in classifying patients into groups needing and not needing orthodontic treatment. <s> BIB008 </s> Artificial Intelligence in Clinical Decision Support: a Focused Literature Survey <s> Hybrid Approaches <s> Abstract Diabetic retinopathy is one of the most common comorbidities of diabetes. Unfortunately, the recommended annual screening of the eye fundus of diabetic patients is too resource-consuming. Therefore, it is necessary to develop tools that may help doctors to determine the risk of each patient to attain this condition, so that patients with a low risk may be screened less frequently and the use of resources can be improved. This paper explores the use of two kinds of ensemble classifiers learned from data: fuzzy random forest and dominance-based rough set balanced rule ensemble. These classifiers use a small set of attributes which represent main risk factors to determine whether a patient is in risk of developing diabetic retinopathy. The levels of specificity and sensitivity obtained in the presented study are over 80%. This study is thus a first successful step towards the construction of a personalized decision support system that could help physicians in daily clinical practice. <s> BIB009 </s> Artificial Intelligence in Clinical Decision Support: a Focused Literature Survey <s> Hybrid Approaches <s> Abstract Although advances in speech processing have facilitated the prognostic assessment of patients with Parkinson's Disease (PD), there is no objective method towards its early detection in a clinical setting. This study investigated the application of a novel hybrid Artificial Intelligence-based classifier for aiding early diagnosis of PD. Data on dysphonic measures and clinical scores on 68 subjects were obtained from the University of California-Irvine (UCI) Machine Learning database. Weights derived from a Multi-Layer Perceptron (MLP) were applied for feature selection and their moduli used to rank the input features based on their relative importance towards discriminating between physiological and pathological data patterns. Thus, the initial 27 features were reduced to 20 selected diagnostic factors. This reduced feature set was then input to a Lagrangian Support Vector Machine (LSVM) for classification. The overall performance of this hybrid feature-driven algorithm (MLP-LSVM) was thus compared against commercially available software and classifiers from similar studies. Results indicate an overall classification accuracy and the area under the receiver operating characteristic curve of 100% for the proposed feature-driven algorithm (MLP-LSVM), with relatively faster convergence, thus demonstrating its potential for aiding early diagnosis of PD in a clinical setting. <s> BIB010
Rather interestingly, among the 26 knowledge-based approaches we analysed in section 3.2, 6 papers report on more properly hybrid approaches, since they are able to take advantage of both formalized knowledge and available data BIB007 BIB008 BIB001 BIB002 BIB003 BIB004 . This is a quite natural situation when adopting probabilistic graphical models such as Bayesian Networks, where domain knowledge is typically exploited to build the network structure and/or to identify the key variables (i.e., the qualitative network information), but conditional probability tables (i.e., the quantitative network information) are then learned/ refined from the available data BIB008 BIB001 BIB004 . CBR is also intrinsically "hybrid", since it conjugates the use of formalized knowledge, e.g., the case structure definition, with the exploitation of past operative examples, i.e., of data BIB007 BIB002 . However, we found also other less typical proposals, such as the work in BIB003 . This specific paper presents a DSS to be used in the domain of Anti-Phospho-we reviewed in this survey can be categorized in the data-driven methodological area. This finding demonstrates that knowledge-based methods are less represented than statistical and machine learning approaches in the very recent literature. Indeed, machine learning methodologies are particularly appropriate when dealing with high dimensional data, such as time series or medical images. As regards to medical image classification, in particular, deep learning tools are proving particularly suitable. More generally, taking advantage of the data accumulated across the entire care continuum in multiple data sources, medical professionals can use data-driven DSSs to improve patient care more successfully than knowledge-based techniques. As a matter of fact, the exploitation of all the available patient data is a key element to be considered in a DSS. In fact, in addition to the papers analysed in this survey, using the MeSH keywords described in the "Methods" section, we retrieved from PubMed other works (e.g., BIB005 BIB006 ) that dealt with interoperability, knowledge integration and knowledge management, as pre-requisites towards the development of a clinical DSS. While technically speaking these works could not be included in the survey, as they do not describe a DSS, they show the centrality of these themes. In particular, they suggest that the use of standard terminologies, such as SNOMED-CT, can help to actually implement interoperability. Moreover, developing user-friendly knowledge authoring tools and automatic knowledge acquisition facilities, implementing dimensionality reduction and missing value imputation as data preparation steps, can support the creation of sharable and interoperable knowledge as well. The paper in BIB006 also highlights another topic: data-driven AI can be cost-effective, but also potentially beyond human abilities. Building and adopting machine learning techniques can be relatively easy, but understanding the provided outcome is sometimes difficult and obscure, due to the typical black-box nature of machine learning. The need for transparency and explainability is nowadays being recognized as a central theme to be addressed by AI research. As an example, the DARPA research funding agency in the US highlights this issue lipid Syndrome diagnosis, which relies on a logic programming approach for knowledge representation and reasoning, complemented with a NN, where neuron connections are learned and tuned from the data, and do not mirror any explicit or easily explainable domain knowledge. Combinations of several machine learning methods are also frequent, as already mentioned in the "Data-driven decision support" section and can be provided in order to compare classification results obtained through different techniques (see e.g.,  ), to realize ensemble learning (see e.g., BIB009 ), or more generally to implement a more complex strategy able to overcome the results of simpler approaches (see e.g., BIB010 ).