Multi-Dimensional Validation of the Integration of Syntactic and Semantic Distance Measures for Clustering Fibromyalgia Patients in the Rheumatic Monitor Big Data Study

This study primarily aimed at developing a novel multi-dimensional methodology to discover and validate the optimal number of clusters. The secondary objective was to deploy it for the task of clustering fibromyalgia patients. We present a comprehensive methodology that includes the use of several different clustering algorithms, quality assessment using several syntactic distance measures (the Silhouette Index (SI), Calinski-Harabasz index (CHI), and Davies-Bouldin index (DBI)), stability assessment using the adjusted Rand index (ARI), and the validation of the internal semantic consistency of each clustering option via the performance of multiple clustering iterations after the repeated bagging of the data to select multiple partial data sets. Then, we perform a statistical analysis of the (clinical) semantics of the most stable clustering options using the full data set. Finally, the results are validated through a supervised machine learning (ML) model that classifies the patients back into the discovered clusters and is interpreted by calculating the Shapley additive explanations (SHAP) values of the model. Thus, we refer to our methodology as the clustering, distance measures and iterative statistical and semantic validation (CDI-SSV) methodology. We applied our method to the analysis of a comprehensive data set acquired from 1370 fibromyalgia patients. The results demonstrate that the K-means was highly robust in the syntactic and the internal consistent semantics analysis phases and was therefore followed by a semantic assessment to determine the optimal number of clusters (k), which suggested k = 3 as a more clinically meaningful solution, representing three distinct severity levels. the random forest model validated the results by classification into the discovered clusters with high accuracy (AUC: 0.994; accuracy: 0.946). SHAP analysis emphasized the clinical relevance of "functional problems" in distinguishing the most severe condition. In conclusion, the CDI-SSV methodology offers significant potential for improving the classification of complex patients. Our findings suggest a classification system for different profiles of fibromyalgia patients, which has the potential to improve clinical care, by providing clinical markers for the evidence-based personalized diagnosis, management, and prognosis of fibromyalgia patients.

A Cyber-Security Risk Assessment Methodology for Medical Imaging Devices: the Radiologists' Perspective

Medical imaging devices (MIDs) are exposed to cyber-security threats. Currently, a comprehensive, efficient methodology dedicated to MID cyber-security risk assessment is lacking. We propose the Threat identification, ontology-based Likelihood, severity Decomposition, and Risk assessment (TLDR) methodology and demonstrate its feasibility and consistency with existing methodologies, while being more efficient, providing details regarding the severity components, and supporting organizational prioritization and customization. Using our methodology, the impact of 23 MIDs attacks (that were previously identified) was decomposed into six severity aspects. Four Radiology Medical Experts (RMEs) were asked to assess these six aspects for each attack. The TLDR methodology's external consistency was demonstrated by calculating paired T-tests between TLDR severity assessments and those of existing methodologies (and between the respective overall risk assessments, using attack likelihood estimates by four healthcare cyber-security experts); the differences were insignificant, implying externally consistent risk assessment. The TLDR methodology's internal consistency was evaluated by calculating the pairwise Spearman rank correlations between the severity assessments of different groups of two to four RMEs and each of their individual group members, showing that the correlations between the severity rankings, using the TLDR methodology, were significant (P < 0.05), demonstrating that the severity rankings were internally consistent for all groups of RMEs. Using existing methodologies, however, the internal correlations were insignificant for groups of less than four RMEs. Furthermore, compared to standard risk assessment techniques, the TLDR methodology is also sensitive to local radiologists' preferences, supports a greater level of flexibility regarding risk prioritization, and produces more transparent risk assessments.

A dual-layer context-based architecture for the detection of anomalous instructions sent to medical devices

Complex medical devices are controlled by instructions sent from a host personal computer (PC) to the device. Anomalous instructions can introduce many potentially harmful threats to patients (e.g., radiation overexposure), to physical device components (e.g., manipulation of device motors), or to functionality (e.g., manipulation of medical images). Threats can occur due to cyber-attacks, human error (e.g., using the wrong protocol, or misconfiguring the protocol's parameters by a technician), or host PC software bugs. Thus, anomalous instructions might represent an intentional threat to the patient or to the device, a human error, or simply a non-optimal operation of the device. To protect medical devices, we propose a new dual-layer architecture. The architecture analyzes the instructions sent from the host PC to the physical components of the device, to detect anomalous instructions using two detection layers: (1) an unsupervised context-free (CF) layer that detects anomalies based solely on the instruction's content and inter-correlations; and (2) a supervised context-sensitive (CS) layer that detects anomalies in both the clinical objective and patient contexts using a set of supervised classifiers pre-trained for each specific context. The proposed dual-layer architecture was evaluated in the computed tomography (CT) domain, using 4842 CT instructions that we recorded, including two types of CF anomalous instructions, four types of clinical objective context instructions and four types of patient context instructions. The CF layer was evaluated using 14 unsupervised anomaly detection algorithms. The CS layer was evaluated using six supervised classification algorithms applied to each context (i.e., clinical objective or patient). Adding the second CS supervised layer to the architecture improved the overall anomaly detection performance (by improving the detection of CS anomalous instructions [when they were not also CF anomalous]) from an F1 score baseline of 72.6%, to an improved F1 score of 79.1% to 99.5% (depending on the clinical objective or patient context used). Adding, the semantics-oriented CS layer enables the detection of CS anomalies using the semantics of the device's procedure, which is not possible when using just the purely syntactic CF layer. However, adding the CS layer also introduced a somewhat increased false positive rate (FPR), and thus reduced somewhat the specificity of the overall process. We conclude that by using both the CF and CS layers, a dual-layer architecture can better detect anomalous instructions to medical devices. The increased FPR might be reduced, in the future, through the use of stronger models, and by training them on more data. The improved accuracy, and the potential capability of adding explanations to both layers, might be useful for creating decision support systems for medical device technicians.

Patient-oriented Computerized Clinical Guidelines for Mobile Decision Support in Gestational Diabetes

The risks associated with gestational diabetes (GD) can be reduced with an active treatment able to improve glycemic control. Advances in mobile health can provide new patient-centric models for GD to create personalized health care services, increase patient independence and improve patients' self-management capabilities, and potentially improve their treatment compliance. In these models, decision-support functions play an essential role. The telemedicine system MobiGuide provides personalized medical decision support for GD patients that is based on computerized clinical guidelines and adapted to a mobile environment. The patient's access to the system is supported by a smartphone-based application that enhances the efficiency and ease of use of the system. We formalized the GD guideline into a computer-interpretable guideline (CIG). We identified several workflows that provide decision-support functionalities to patients and 4 types of personalized advice to be delivered through a mobile application at home, which is a preliminary step to providing decision-support tools in a telemedicine system: (1) therapy, to help patients to comply with medical prescriptions; (2) monitoring, to help patients to comply with monitoring instructions; (3) clinical assessment, to inform patients about their health conditions; and (4) upcoming events, to deal with patients' personal context or special events. The whole process to specify patient-oriented decision support functionalities ensures that it is based on the knowledge contained in the GD clinical guideline and thus follows evidence-based recommendations but at the same time is patient-oriented, which could enhance clinical outcomes and patients' acceptance of the whole system.

Developing nursing computer interpretable guidelines: a feasibility study of heart failure guidelines in homecare

Homecare is the fastest growing healthcare sector and evidence based information systems are critically needed. Nurses provide most of the care in homecare setting, yet there is a lack of knowledge on the feasibility of applying existing methodologies to generate computer interpretable nursing guidelines for home care. This study examined the feasibility of encoding homecare nursing heart failure guideline into a computer interpretable format. First, we achieved experts' consensus on the relevant guideline. Then, after training on the graphical tool for gradual knowledge specification (Gesher), we generated a comprehensive, hierarchical and time-oriented computer interpretable guideline using one of the guideline modeling languages (Asbru). The final guideline included 167 recommendations and experts' evaluation confirmed the adequacy of guideline knowledge representation. Future work should expand the applicability of our methodology and tools to nursing specialties other than heart failure and develop methods for comprehensive quality evaluation of the resulting guidelines.

[Automatic application of clinical guidelines - from theory to practice]

ClinicaL guidelines (GLs) have been shown to be a powerful tool for enhancing the uniformity and quality of care, reducing its costs. However, since they are typically represented in free text, this leads to low rates of compliance. Therefore, physicians might benefit from GL automated decision support. It should be noted that not many studies evaluate the effect of providing support for the application of GLs over significant stretches of time on the quality of medical decisions. In this paper, we will describe the general architecture of medical decision support systems, review several known GL application frameworks, and focus on the research performed in the medicaL informatics research center at Ben-Gurion University [BGU] of the Negev which developed the Digital ELectronic Guideline Library, called DeGeL. In particular, we will describe a new GL application framework called PICARD that is intended for GL application over time, while ensuring that the GLs recommendations were followed. We will briefly introduce a technical evaluation of PICARD in the cardiology domain to manage patients according to a Coumadin [Warfarin] protocoL, and a functional evaluation in a complex pre-eclampsia/ eclampsia GL in the OB/GYN domain, which we performed with 36 physicians. The results showed that the PICARD creates independence in the quality of the decisions from any particular physician, level of expertise, clinicaL scenario, or decision type within the scenarios. CurrentLy, PICARD is a core component in the EU Mobiguide project, which focuses on remote monitoring and care of chronic patients, using mobile devices to send alerts and recommendations.

Patient-tailored workflow patterns from clinical practice guidelines recommendations

MobiGuide is a project devoted to the development of a patient-centric decision support system based on computerized clinical guidelines for chronic illnesses including Atrial Fibrillation (AF). In this paper we describe the process of (1) identifying guideline recommendations that will require patients to take actions (e.g., take measurement, take drug), thus impacting patients' daily-life behavior, (2) eliciting from the medical experts the corresponding set of personalized operationalized advices that are not explicitly written in the guideline (patient-tailored workflow patterns) and (3) delivering this advice to patients. The analysis of the AF guideline has resulted in four types of patient-tailored workflow patterns: therapy-related advisors, measurements advisors, suggestions for dealing with interventions that may require modulating patient therapy, and personalized packages for close monitoring of patients. We will show how these patterns can be generated using information stored in a patient health record that embeds clinical data and data about the patient's personal context and preferences.

A scalable architecture for incremental specification and maintenance of procedural and declarative clinical decision-support knowledge

Clinical guidelines have been shown to improve the quality of medical care and to reduce its costs. However, most guidelines exist in a free-text representation and, without automation, are not sufficiently accessible to clinicians at the point of care. A prerequisite for automated guideline application is a machine-comprehensible representation of the guidelines. In this study, we designed and implemented a scalable architecture to support medical experts and knowledge engineers in specifying and maintaining the procedural and declarative aspects of clinical guideline knowledge, resulting in a machine comprehensible representation. The new framework significantly extends our previous work on the Digital electronic Guidelines Library (DeGeL) The current study designed and implemented a graphical framework for specification of declarative and procedural clinical knowledge, Gesher. We performed three different experiments to evaluate the functionality and usability of the major aspects of the new framework: Specification of procedural clinical knowledge, specification of declarative clinical knowledge, and exploration of a given clinical guideline. The subjects included clinicians and knowledge engineers (overall, 27 participants). The evaluations indicated high levels of completeness and correctness of the guideline specification process by both the clinicians and the knowledge engineers, although the best results, in the case of declarative-knowledge specification, were achieved by teams including a clinician and a knowledge engineer. The usability scores were high as well, although the clinicians' assessment was significantly lower than the assessment of the knowledge engineers.

Ability of expert physicians to structure clinical guidelines: reality versus perception

RATIONALE, AIMS AND OBJECTIVES

Structuring Textual Clinical Guidelines (GLs) into a formal representation is a necessary prerequisite for supporting their automated application. We had developed a collaborative guideline-structuring methodology that involves expert physicians, clinical editors and knowledge engineers, to produce a machine-comprehensible representation for automated support of evidence-based, guideline-based care. Our goals in the current study were: (1) to investigate the perceptions of the expert physicians and clinical editors as to the relative importance, for the structuring process, of different aspects of the methodology; (2) to assess, for the clinical editors, the inter-correlations among (i) the reported level of understanding of the guideline structuring ontology's (knowledge scheme's) features, (ii) the reported ease of structuring each feature and (iii) the actual objective quality of structuring.

METHODS

A clinical consensus regarding the contents of three guidelines was prepared by an expert in the domain of each guideline. For each guideline, two clinical editors independently structured the guideline into a semi-formal representation, using the Asbru guideline ontology's features. The quality of the resulting structuring was assessed quantitatively. Each expert physician was asked which aspects were most useful for formation of the consensus. Each clinical editor filled questionnaires relating to: (1) the level of understanding of the ontology's features (before the structuring process); (2) the usefulness of various aspects in the structuring process (after the structuring process); (3) the ease of structuring each ontological feature (after the structuring process). Subjective reports were compared with objective quantitative measures of structuring correctness.

RESULTS

Expert physicians considered having medical expertise and understanding the ontological features as the aspects most useful for creation of a consensus. Clinical editors considered understanding the ontological features and the use of the structuring tools as the aspects most useful for structuring guidelines. There was a positive correlation (R = 0.87, P < 0.001) between the reported ease of understanding ontological features and the reported ease of structuring those features. However, there was no significant correlation between the reported level of understanding the features - or the reported ease of structuring by using those features - and the objective quality of the structuring of these features in actual guidelines.

CONCLUSIONS

Aspects considered important for formation of a clinical consensus differ from those for structuring of guidelines. Understanding the features of a structuring ontology is positively correlated with the reported ease of using these features, but neither of these subjective reports correlated with the actual objective quality of the structuring using these features.

A methodology for evaluation of a markup-based specification of clinical guidelines

We introduce a three-phase, nine-step methodology for specification of clinical guidelines (GLs) by expert physicians, clinical editors, and knowledge engineers, and for quantitative evaluation of the specification's quality. We applied this methodology to a particular framework for incremental GL structuring (mark-up) and to GLs in three clinical domains with encouraging results.

A quality assessment tool for markup-based clinical guidelines

We introduce a tool for quality assessment of procedural and declarative knowledge. We developed this tool for evaluating the specification of mark-up-based clinical GLs. Using this graphical tool, the expert physician and knowledge engineer collaborate to perform scoring, using pre-defined scoring scale, each of the knowledge roles of the mark-ups, comparing it to a gold standard. The tool enables scoring the mark-ups simultaneously at different sites by different users at different locations.

Intravitreal methotrexate for treating vitreoretinal lymphoma: 10 years of experience

AIM

To describe our experience in treating vitreoretinal involvement of primary central nervous system lymphoma, by intravitreal injections of methotrexate (MTX).

METHODS

Patients with suspected intraocular lymphoma underwent a diagnostic vitrectomy. Samples were sent for cytology, genetic evaluation and for interleukin level measurements. Treatment protocol included injection of 400 microg/0.1 ml MTX intravitreally twice weekly for 4 weeks, once weekly for 8 weeks, and then once monthly for 9 months, for a total of 25 injections. Data were collected from the patients' records and included, inter alia, response to intravitreal MTX measured by time to disappearance of vitreal cells and retinal infiltrates, changes in visual acuity, and clinical recurrence rate.

RESULTS

In the past 10 years we have treated 44 eyes of 26 patients; seven patients had monocular involvement, and 19 binocular. Six patients were initially diagnosed as having a non-responsive uveitis, and 16 with either CNS or systemic lymphoma with later involvement of the eye. Four patients had systemic lymphoma; one of them was found to have CNS lymphoma after the ocular involvement. Three patients had T cell lymphoma, and the rest had B cell lymphoma. Clinical remission was reached after 6.4 (3.4) (2-16) injections of MTX (mean (SD) (range)), with 95% of the eyes needing 13 injections or less to be cleared of malignant cells. None of the patients had an intraocular recurrence. Among the side effects, the most common was corneal epitheliopathy, which usually appeared after the third injection and began to subside when the intervals between injections increased.

CONCLUSIONS

Vitreoretinal involvement of lymphoma can be controlled effectively and without serious adverse reactions by intravitreal MTX injections. The treatment protocol described herein has resulted in no intraocular recurrence so far and has had bearable side effects. The accumulating clinical results bring us to propose the consideration of this protocol as a good first-line treatment option for intraocular lymphoma.

DeGeL: a clinical-guidelines library and automated guideline-support tools

Using machine-interpretable clinical guidelines to support evidence-based medicine promotes the quality of medical care. In this chapter, we present the Digital Electronic Guidelines Library (DeGeL), a comprehensive framework, including a Web-based guideline repository and a suite of tools, to support the use of automated guidelines for medical care, research, and quality assessment. Recently, we have developed a new version (DeGeL.NET) of the digital library and of its different tools. We intend to focus in our exposition on DeGeL's major tools, in particular for guideline specification in a Web-based and stand alone fashion (Uruz and Gesher), tools for search and retrieval (Vaidurya and DeGeLookFor) and for run time application (Spock); and to explain how these tools are combined within the typical lifecycle of a clinical guideline.

A framework for a distributed, hybrid, multiple-ontology clinical-guideline library, and automated guideline-support tools

Clinical guidelines are a major tool in improving the quality of medical care. However, most guidelines are in free text, not in a formal, executable format, and are not easily accessible to clinicians at the point of care. We introduce a Web-based, modular, distributed architecture, the Digital Electronic Guideline Library (DeGeL), which facilitates gradual conversion of clinical guidelines from text to a formal representation in chosen target guideline ontology. The architecture supports guideline classification, semantic markup, context-sensitive search, browsing, run-time application, and retrospective quality assessment. The DeGeL hybrid meta-ontology includes elements common to all guideline ontologies, such as semantic classification and domain knowledge; it also includes four content-representation formats: free text, semi-structured text, semi-formal representation, and a formal representation. These formats support increasingly sophisticated computational tasks. The DeGeL tools for support of guideline-based care operate, at some level, on all guideline ontologies. We have demonstrated the feasibility of the architecture and the tools for several guideline ontologies, including Asbru and GEM.

A graphical framework for specification of clinical guidelines at multiple representation levels

Formalization of a clinical guideline for purposes of automated application and quality assessment mainly involves conversion of its free-text representation into a machine comprehensible representation, i.e., a formal language, thus enabling auto-mated support. The main issues involved in this process are related to the collaboration between the expert physician and the knowledge engineer. We introduce GESHER - a graphical framework for specification of clinical guidelines at multiple representation levels. The GESHER architecture facilitates incremental specification through a set of views adapted to each representation level, enabling this process to proceed smoothly and in a transparent fashion, fostering extensive collaboration among the various types of users. The GESHER framework supports specification of guidelines at multiple representation levels, in more than one specification language, and uses the DeGeL digital guideline library architecture as its knowledge base. The GESHER architecture also uses a temporal abstraction knowledge base to store its declarative knowledge, and a standard medical-vocabularies server for generic specification of key terms, thus enabling reuse of the specification at multiple sites.

The Digital electronic Guideline Library (DeGeL): a hybrid framework for representation and use of clinical guidelines

We propose to present a poster (and potentially also a demonstration of the implemented system) summarizing the current state of our work on a hybrid, multiple-format representation of clinical guidelines that facilitates conversion of guidelines from free text to a formal representation. We describe a distributed Web-based architecture (DeGeL) and a set of tools using the hybrid representation. The tools enable performing tasks such as guideline specification, semantic markup, search, retrieval, visualization, eligibility determination, runtime application and retrospective quality assessment. The representation includes four parallel formats: Free text (one or more original sources); semistructured text (labeled by the target guideline-ontology semantic labels); semiformal text (which includes some control specification); and a formal, machine-executable representation. The specification, indexing, search, retrieval, and browsing tools are essentially independent of the ontology chosen for guideline representation, but editing the semi-formal and formal formats requires ontology-specific tools, which we have developed in the case of the Asbru guideline-specification language. The four formats support increasingly sophisticated computational tasks. The hybrid guidelines are stored in a Web-based library. All tools, such as for runtime guideline application or retrospective quality assessment, are designed to operate on all representations. We demonstrate the hybrid framework by providing examples from the semantic markup and search tools.

A distributed, collaborative, structuring model for a clinical-guideline digital-library

The Digital Electronic Guideline Library (DeGeL) is a Web-based framework and a set of distributed tools that facilitate gradual conversion of clinical guidelines from free text, through semi-structured text, to a fully structured, executable representation. Thus, guidelines exist in a hybrid, multiple-format representation The three formats support increasingly sophisticated computational tasks. The tools perform semantic markup, classification, search, and browsing, and support computational modules that we are developing, for run-time application and retrospective quality assessment. We describe the DeGeL architecture and its collaborative-authoring authorization model, which is based on (1) multiple medical-specialty authoring groups, each including a group manager who controls group authorizations, and (2) a hierarchical authorization model based on the different functions involved in the hybrid guideline-specification process. We have implemented the core modules of the DeGeL architecture and demonstrated distributed markup and retrieval using the knowledge roles of two guidelines ontologies (Asbru and GEM). We are currently evaluating several of the DeGeL tools.

In vivo assessment of the window of barrier opening after osmotic blood-brain barrier disruption in humans

OBJECT

Osmotic blood-brain barrier (BBB) disruption induced by intraarterial infusion of mannitol is used in conjunction with chemotherapy to treat human brain tumors. The time course to barrier closure, or the so-called therapeutic window, has been examined in animals but little information is available in humans. The authors, therefore assessed the time course to barrier closure after osmotic BBB disruption in humans.

METHODS

Disruption of the BBB was demonstrated using 99mTc-glucoheptonate (TcGH) single-photon emission computerized tomography (SPECT) scanning in 12 patients who were treated monthly with combination chemotherapy in conjunction with BBB disruption. The primary diagnosis was primary central nervous system lymphoma in seven patients and primitive neuroectodermal tumors in five. The TcGH (20 mCi) was injected at 1- to 480-minute intervals after osmotic BBB disruption, and patients underwent SPECT scanning after 4 hours. A total of 38 studies was performed. Good-to-excellent BBB disruption was obtained in 29 procedures and poor-to-moderate disruption was seen in the other nine studies. The TcGH indices correlated with the degree of BBB disruption as measured postprocedure on contrast-enhanced CT scans (r = 0.852). Mean baseline TcGH indices were 1.02+/-0.07. For the group of patients with good-to-excellent disruptions the mean indices at 1 minute postdisruption measured 2.19+/-0.18. After 40 minutes no significant change was noted (mean index 2.13+/-0.2). Then the indices declined more steeply and at 120 minutes after the disruption the index was 1.36+/-0.02. A very slow decline was noted between 120 and 240 minutes after mannitol infusion. At 240 minutes the barrier was still open for all good-to-excellent disruptions (index 1.33+/-0.08) but at 480 minutes the mean indices had returned to the baseline level.

CONCLUSIONS

Results of these in vivo human studies indicate that the time course to closure of the disrupted BBB for low-molecular-weight complexes is longer than previously estimated. The barrier is widely open during the first 40 minutes after osmotic BBB disruption and returns to baseline levels only after 6 to 8 hours following the induction of good or excellent disruption. These findings have important clinical implications for the design of therapeutic protocols.