Computer-aided diagnosis of primary membranous nephropathy using expert system.

BACKGROUND: The diagnosis of primary membranous nephropathy (PMN) often depends on invasive renal biopsy, and the diagnosis based on clinical manifestations and target antigens may not be completely reliable as it could be affected by uncertain factors. Moreover, different experts could even have different diagnosis results due to their different experiences, which could further impact the reliability of the diagnosis. Therefore, how to properly integrate the knowledge of different experts to provide more reliable and comprehensive PMN diagnosis has become an urgent issue. METHODS: This paper develops a belief rule-based system for PMN diagnosis. The belief rule base is constructed based on the knowledge of the experts, with 9 biochemical indicators selected as the input variables. The belief rule-based system is developed of three layers: (1) input layer; (2) belief rule base layer; and (3) output layer, where 9 biochemical indicators are selected as the input variables and the diagnosis result is provided as the conclusion. The belief rule base layer is constructed based on the knowledge of the experts. The final validation was held with gold pattern clinical cases, i.e., with known and clinically confirmed diagnoses. RESULTS: 134 patients are used in this study, and the proposed method is defined by its sensitivity, specificity, accuracy and area under curve (AUC), which are 98.0%, 96.9%, 97.8% and 0.93, respectively. The results of this study present a novel and effective way for PMN diagnosis without the requirement of renal biopsy. CONCLUSIONS: Through analysis of the diagnosis results and comparisons with other methods, it can be concluded that the developed system could help diagnose PMN based on biochemical indicators with relatively high accuracy.

An overview of the implementation of SeDeM and SSCD in various formulation developments.

The Sediment Delivery Model explains experimental analysis and quantitative assessment of the powdered substance characterizing parameters, which offer pertinent data about the material's appropriateness for direct compression (DC) of tablet, which involves mathematical modeling and Semisolid Control Diagram using software like iTCM. The SeDeM diagram expert system (DES) determines the suitability of excipients and active ingredients for DC and the ratio of API to excipient is calculated. The DC is most suitable as it saves time and makes process easy, but these technique excipients compensate for their poor flow. Thus, a new system was required to help reduce number of experiments and time for making an optimized direct compression tablet. The SeDeM DES is based on quality by design (QbD) (ICH Q8) as it evaluates critical quality attributes that affect finished product's quality. This review mainly focuses on various dosage forms like Solid, Semisolid, Liquisolid, and Solidified liquid dosage forms. These techniques mainly characterize all substances using 12 parameters, resulting 12-sided regular polygon. However, parameters may increase or decrease according to the requirement of a particular dosage form like an Orodispersible tablet 15 and a Semisolid dosage form applying 5 parameters. The rationales behind limits for indexes are justified accordingly.

A novel optimization method for belief rule base expert system with activation rate.

Although the belief rule base (BRB) expert system has many advantages, such as the effective use of semi-quantitative information, objective description of uncertainty, and efficient nonlinear modeling capability, it is always limited by the problem of combinatorial explosion. The main reason is that the optimization of a BRB with many rules will consume many computing resources, which makes it unable to meet the real-time requirements in some complex systems. Another reason is that the optimization process will destroy the interpretability of those parameters that belong to the inadequately activated rules given by experts. To solve these problems, a novel optimization method for BRB is proposed in this paper. Through the activation rate, the rules that have never been activated or inadequately activated are pruned during the optimization process. Furthermore, even if there is a complete data set and all rules are activated, the activation rate can also be used in the parallel optimization process of the BRB expert system, where the training data set is divided into some subprocesses. The proposed method effectively solves the combinatorial explosion problem of BRB and can make full use of quantitative data without destroying the original interpretability provided by experts. Case studies prove the advantages and effectiveness of the proposed method, which greatly expands the application fields of the BRB expert system.

Performance of the Vitek 2 Advanced Expert System (AES) as a Rapid Tool for Reporting Antimicrobial Susceptibility Testing (AST) in Enterobacterales from North and Latin America.

This study evaluated the performance of the Vitek 2 Advanced Expert System (AES) confidence level report as a rapid tool for reporting antimicrobial susceptibility testing (AST) results for a challenging set of Enterobacterales isolates from North and Latin America. Enterobacterales isolates (n = 513) were tested by CLSI broth microdilution (BMD) and Vitek 2 (N802 and XN15 AST cards). Wild-type isolates and isolates harboring acquired ß-lactamases by whole-genome sequencing were included. The AES assessment of confidence level (green, yellow, and red reports) was compared to BMD results and known genotypes and reviewed by a microbiologist for accuracy. Totals of 148 (28.8%) wild-type isolates and 365 (71.2%) Enterobacterales isolates harboring carbapenemase (211 [41.1%]), extended-spectrum ß-lactamase (ESBL) (122 [23.8%]), and/or transferrable AmpC (tAmpC) (32 [6.2%]) genes were evaluated. The AES confidence level was assessed for 488 isolates, and a phenotype was recognized for 447 (91.6%) isolates. Green, yellow, and red AES reports were noted for 382 (78.3%), 65 (13.3%), and 41 (8.4%) isolates, respectively. Compared to BMD, 96.3% of green AES reports could be confidently and rapidly auto-released, enabling rapid adjustments to antimicrobial therapy. In addition, 69.2% of yellow reports were acceptable, and recommendations to address current AES limitations were made. IMPORTANCE Antimicrobial susceptibility testing (AST) reports are one of the most important clinical microbiology laboratory tasks. AST reports are essential to drive antimicrobial therapy, provide information to monitor antimicrobial resistance rates, and trigger further tests to detect outbreaks or confirm new mechanisms of resistance. Commercial AST devices are frequently used to generate AST reports, and an advanced expert system (AES), such as the Vitek 2 AES, incorporates extensive knowledge to recognize certain susceptibility patterns as indicative of specific phenotypes. Moreover, the Vitek 2 AES also provides a level of confidence for auto-releasing the reports. In this study, the performance of the Vitek 2 AES was compared to state-of-the-art methodologies for AST, broth microdilution and ß-lactamase gene detection, whole-genome sequencing, against a collection of 513 Enterobacterales clinical isolates harboring various ß-lactamase genes, including carbapenemase, ESBL, and transferrable AmpC genes, from 73 medical centers in 7 countries in North and Latin America.

Medical Fuzzy-Expert System for Assessment of the Degree of Anatomical Lesion of Coronary Arteries.

BACKGROUND: Today, cardiovascular diseases cause 47% of all deaths among the European population, which is 4 million cases every year. In Ukraine, CAD accounts for 65% of the mortality rate from circulatory system diseases of the able-bodied population and is the main cause of disability. The aim of this study is to develop a medical expert system based on fuzzy sets for assessing the degree of coronary artery lesions in patients with coronary artery disease. METHODS: The method of using fuzzy sets for the implementation of an information expert system for solving the problems of medical diagnostics, in particular, when assessing the degree of anatomical lesion of the coronary arteries in patients with various forms of coronary artery disease, has been developed. RESULTS: The paper analyses the main areas of application of mathematical methods in medical diagnostics, and formulates the principles of diagnostics, based on fuzzy logic. The developed models and algorithms of medical diagnostics are based on the ideas and principles of artificial intelligence and knowledge engineering, the theory of experiment planning, the theory of fuzzy sets and linguistic variables. The expert system is tested on real data. Through research and comparison of the results of experts and the created medical expert system, the reliability of supporting the correct decision making of the medical expert system based on fuzzy sets for assessing the degree of anatomical lesion of the coronary arteries in patients with various forms of coronary artery disease with the assessment of experts was 95%, which shows the high efficiency of decision making. CONCLUSIONS: The practical value of the work lies in the possibility of using the automated expert system for the solution of the problems of medical diagnosis based on fuzzy logic for assessing the degree of anatomical lesion of the coronary arteries in patients with various forms of coronary artery disease. The proposed concept must be further validated for inter-rater consistency and reliability. Thus, it is promising to create expert medical systems based on fuzzy sets for assessing the degree of disease pathology.

Knowledge Engineering in Chemistry: From Expert Systems to Agents of Creation.

Passing knowledge from human to human is a natural process that has continued since the beginning of humankind. Over the past few decades, we have witnessed that knowledge is no longer passed only between humans but also from humans to machines. The latter form of knowledge transfer represents a cornerstone in artificial intelligence (AI) and lays the foundation for knowledge engineering (KE). In order to pass knowledge to machines, humans need to structure, formalize, and make knowledge machine-readable. Subsequently, humans also need to develop software that emulates their decision-making process. In order to engineer chemical knowledge, chemists are often required to challenge their understanding of chemistry and thinking processes, which may help improve the structure of chemical knowledge.Knowledge engineering in chemistry dates from the development of expert systems that emulated the thinking process of analytical and organic chemists. Since then, many different expert systems employing rather limited knowledge bases have been developed, solving problems in retrosynthesis, analytical chemistry, chemical risk assessment, etc. However, toward the end of the 20th century, the AI winters slowed down the development of expert systems for chemistry. At the same time, the increasing complexity of chemical research, alongside the limitations of the available computing tools, made it difficult for many chemistry expert systems to keep pace.In the past two decades, the semantic web, the popularization of object-oriented programming, and the increase in computational power have revitalized knowledge engineering. Knowledge formalization through ontologies has become commonplace, triggering the subsequent development of knowledge graphs and cognitive software agents. These tools enable the possibility of interoperability, enabling the representation of more complex systems, inference capabilities, and the synthesis of new knowledge.This Account introduces the history, the core principles of KE, and its applications within the broad realm of chemical research and engineering. In this regard, we first discuss how chemical knowledge is formalized and how a chemist's cognition can be emulated with the help of reasoning algorithms. Following this, we discuss various applications of knowledge graph and agent technology used to solve problems in chemistry related to molecular engineering, chemical mechanisms, multiscale modeling, automation of calculations and experiments, and chemist-machine interactions. These developments are discussed in the context of a universal and dynamic knowledge ecosystem, referred to as The World Avatar (TWA).

Electronic Health Record-Integrated Clinical Decision Support for Clinicians Serving Populations Facing Health Care Disparities: Literature Review.

OBJECTIVES: To review current studies about designing and implementing clinician-facing clinical decision support (CDS) integrated or interoperable with an electronic health record (EHR) to improve health care for populations facing disparities. METHODS: We searched PubMed to identify studies published between January 1, 2011 and October 22, 2021 about clinician-facing CDS integrated or interoperable with an EHR. We screened abstracts and titles and extracted study data from articles using a protocol developed by team consensus. Extracted data included patient population characteristics, clinical specialty, setting, EHR, clinical problem, CDS type, reported user-centered design, implementation strategies, and outcomes. RESULTS: There were 28 studies (36 articles) included. Most studies were performed at safety net institutions (14 studies) or Indian Health Service sites (6 studies). CDS tools were implemented in primary care outpatient settings in 24 studies (86%) for screening or treatment. CDS included point-of-care alerts (93%), order facilitators (46%), workflow support (39%), relevant information display (36%), expert systems (11%), and medication dosing support (7%). Successful outcomes were reported in 19 of 26 studies that reported outcomes (73%). User-centered design was reported during CDS planning (39%), development (32%), and implementation phase (25%). Most frequent implementation strategies were education (89%) and consensus facilitation (50%). CONCLUSIONS: CDS tools may improve health equity and outcomes for patients who face disparities. The present review underscores the need for high-quality analyses of CDS-associated health outcomes, reporting of user-centered design and implementation strategies used in low-resource settings, and methods to disseminate CDS created to improve health equity.

The Nature-Based Solutions Case-Based System: A hybrid expert system.

Deriving knowledge and learning from past experiences is essential for the successful adoption of Nature-Based Solutions (NBS) as novel integrative solutions that involve many uncertainties. Past experiences in implementing NBS have been collected in a number of repositories; however, it is a major challenge to derive knowledge from the huge amount of information provided by these repositories. This calls for information systems that can facilitate the knowledge extraction process. This paper introduces the NBS Case-Based System (NBS-CBS), an expert system that uses a hybrid architecture to derive information and recommendations from an NBS experience repository. The NBS-CBS combines a 'black-box' artificial neural networks model with a 'white-box' case-based reasoning model to deliver an intelligent, adaptive, and explainable system. Experts have tested this system to assess its functionality and accuracy. Accordingly, the NBS-CBS appears to provide inspirational recommendations and information for the NBS planning and design process.

A Novel Expert System for Diagnosis of Iron Deficiency Anemia.

Diagnosis of a disease is one of the most important processes in the field of medicine. Thus, computer-aided detection systems are becoming increasingly important to assist physicians. The iron deficiency anemia (IDA) is a serious health problem that requires careful diagnosis. Diagnosis of IDA is a classification problem, and there are various studies conducted. Researchers also use feature selection approaches to detect significant variables. Studies so far investigate different classification problems such as outliers, class imbalance, presence of noise, and multicollinearity. However, datasets are usually affected by more than one of these problems. In this study, we aimed to create multiple systems that can separate diseased and healthy individuals and detect the variables that have a significant effect on these diseases considering influential classification problems. For this, we prepared different datasets based on the original dataset whose outliers were removed using different outlier detection methods. Then, a multistep classification algorithm was proposed for each dataset to see the results under irregular and regulated conditions. In each step, a different classification problem is handled. The results showed that it is important to consider each question together as it can and should change the outcome. Dataset and R codes used in the study are available as supplementary files online.

50 Years of Achievements and Persistent Challenges for Biomedical and Health Informatics and John Mantas' Educational and Nursing Informatics Contributions.

Biomedical and Health Informatics (BMHI) have been essential catalysts for achievements in medical research and healthcare applications over the past 50 years. These include increasingly sophisticated information systems and data bases for documentation and processing, standardization of biomedical data, nomenclatures, and vocabularies to assist with large scale literature indexing and text analysis for information retrieval, and methods for computationally modeling and analyzing research and clinical data. Statistical and AI techniques for decision support, instrumentation integration, and workflow aids with improved data/information management tools are critical for scientific discoveries in the - omics revolutions with their related drug and vaccine breakthroughs and their translation to clinical and preventive healthcare. Early work on biomedical image and pattern recognition, knowledge-based expert systems, innovative database, software and simulation techniques, natural language processing and computational ontologies have all been invaluable for basic research and education. However, these methods are still in their infancy and many fundamental open scientific problems abound. Scientifically this is due to persistent limitations in understanding biological processes within complex living environments and ecologies. In clinical practice the modeling of fluid practitioner roles and methods as they adjust to novel cybernetic technologies present great opportunities but also the potential of unintended e-iatrogenic harms which must be constrained in order to adhere to ethical Hippocratic norms of responsible behavior. Balancing the art, science, and technologies of BMHI has been a hallmark of debates about the field's historical evolution. The present article reviews selected milestones, achievements, and challenges in BMHI education mainly, from a historical perspective, including some commentaries from leaders and pioneers in the field, a selection of which have been published online recently by the International Medical Informatics Association (IMIA) as the first volume of an IMIA History WG eBook. The focus of this chapter is primarily on the development of BMHI in terms of those of its educational activities which have been most significant during the first half century of IMIA, and it concentrates mainly on the leadership and contributions of John Mantas who is being honored on his retirement by the Symposia in Athens for which this chapter has been written.

My Journey Through the Field of Medical Informatics.

In this contribution some achievements and milestones in the field of medical informatics, especially concerning decision support, as perceived by the author, are presented. The author focuses on those topics with respect to decision support that during his career in medical informatics impressed him and triggered him to convince his PhD students to start research on related topics. Both some of these achievements and the related research of some of his PhD students will be presented. The contribution starts with signal classification. Both ECG classification and sleep EEG classification are discussed. Then the use of Bayes' theorem for diagnostic purposes is discussed and some early applications pass review, among which the AAPHelp system developed by de Dombal and colleagues. Attention is subsequently paid to the advent of expert systems and other knowledge-based systems such as MYCIN and INTERNIST and to guideline-based decision support systems. Finally, the author presents his ideas about challenges for the field.

Processability evaluation of multiparticulate units prepared by selective laser sintering using the SeDeM Expert System approach.

3D printing in dosage forms fabrication is in the focus of researchers, however, the attempts in multiparticulate units (MPUs) preparation are scarce. The aim of this study was to fabricate different size MPUs by selective laser sintering (SLS), using different polymers, and investigate their processability based on the SeDeM Expert System approach. MPUs (1- or 2-mm size) were prepared with model drug (ibuprofen or caffeine), polymer (poly(ethylene)oxide (PEO), ethyl cellulose (EC) or methacrylic acid-ethyl acrylate copolymer (MA-EA)) and printing aid. Comprehensive sample characterization was performed and experimentally obtained parameters were mathematically transformed and evaluated using the SeDeM Expert System framework. The obtained samples exhibited irregular shape, despite the spherical printing object design. Polymer incorporated notably affected MPUs properties. The obtained samples exhibited low bulk density, good flowability-, as well as stability-related parameters, which indicated their suitability for filling into capsules or sachets. Low density values implied that compressibility enhancing excipients may be required for MPUs incorporation in tablets. Samples containing EC and MA-EA were found suitable for compression, due to high compacts tensile strength. The obtained results indicate that SeDeM Expert System may extended from powder compressibility evaluation tool to framework facilitating powders/multiparticulate units processing.

Developing an AI-assisted clinical decision support system to enhance in-patient holistic health care.

Holistic health care (HHC) is a synonym for complete patient care, and as such an efficient clinical decision support system (CDSS) for HHC is critical to support the judgement of physician's decision in response of patient's physical, emotional, social, economic, and spiritual needs. The field of artificial intelligence (AI) has evolved considerably in the past decades and many AI applications have been deployed in various contexts. Therefore, this study aims to propose an AI-assisted CDSS model that predicts patients in need of HHC and applies an improved recurrent neural network (RNN) model, long short-term memory (LSTM) for the prediction. The data sources include in-patient's comorbidity status and daily vital sign attributes such as blood pressure, heart rate, oxygen prescription, etc. A two-year dataset consisting of 121 thousand anonymized patient cases with 890 thousand physiological medical records was obtained from a medical center in Taiwan for system evaluation. Comparing with the rule-based expert system, the proposed AI-assisted CDSS improves sensitivity from 26.44% to 80.84% and specificity from 99.23% to 99.95%. The experimental results demonstrate that an AI-assisted CDSS could efficiently predict HHC patients.

Fuzzy-Based Expert Supervision System for Feedback Controlled Oxygenation.

Supervision of mechanical ventilation is currently still performed by clinical staff. With the increasing level of automation in the intensive care unit, automatic supervision is becoming necessary. We present a fuzzy-based expert supervision system applicable to automatic feedback control of oxygenation. An adaptive fuzzy limit checking and trend detection algorithm was implemented. A knowledge-based fuzzy logic system combines these outputs into a final score, which subsequently triggers alarms if a critical event is registered. The system was evaluated against annotated experimental data. An accuracy of 83 percent and a precision of 95 percent were achieved. The automatic detection of critical events during feedback control of oxygenation provides an additional layer of safety and assists in alerting clinicians in the case of abnormal behavior of the system. Clinical relevance - Automatic supervision is a necessary feature of physiological feedback systems to make them safer and more reliable in the future.

The next generation of soil and water bodies heavy metals prediction and detection: New expert system based Edge Cloud Server and Federated Learning technology.

Heavy metals (HMs) in soil and water bodies greatly threaten human health. The wide separation of HMs urges the necessity to develop an expert system for HMs prediction and detection. In the current perspective, several propositions are discussed to design an innovative intelligence system for HMs prediction and detection in soil and water bodies. The intelligence system incorporates the Edge Cloud Server (ECS) data center, an innovative deep learning predictive model and the Federated Learning (FL) technology. The ECS data center is based on satellite sensing sources under human expertise ruling and HMs in-situ measurement. The FL system comprises a machine learning (ML) technique that trains an algorithm across multiple decentralized edge servers holding local data samples without exchanging them or breaching data privacy. The expected outcomes of the intelligence system are to quantify the soil and water bodies' HMs, develop new modified HMs pollution contamination indices and provide decision-makers and environmental experts with an appropriate vision of soil, surface water, and crop health.

Fuzzy-clustering and fuzzy network based interpretable fuzzy model for prediction.

Interpretability is the dominant feature of a fuzzy model in security-oriented fields. Traditionally fuzzy models based on expert knowledge have obtained well interpretation innately but imprecisely. Numerical data based fuzzy models perform well in precision but not necessarily in interpretation. To utilize the expert knowledge and numerical data in a fuzzy model synchronously, this paper proposed a hybrid fuzzy c-means (FCM) clustering algorithm and Fuzzy Network (FN) method-based model for prediction. The Mamdani rule-based structure of the proposed model is identified based on FCM algorithm from data and by expert-system method from expert knowledge, both of which are combined by FN method. Particle swarm optimization (PSO) algorithm is utilized to optimize the fuzzy set parameters. We tested the proposed model on 6 real datasets comparing the results with the ones obtained by using FCM algorithm. The results showed that our model performed best in interpretability, transparency, and accuracy.

[Intelligent fault diagnosis expert system for multi-parameter monitor based on fault tree].

Aiming at the dilemma of expensive and difficult maintenance, lack of technical data and insufficient maintenance force for modern medical equipment, an intelligent fault diagnosis expert system of multi-parameter monitor based on fault tree was proposed in this study. Firstly, the fault tree of multi-parameter monitor was established and analyzed qualitatively and quantitatively, then based on the analysis results of fault tree, the expert system knowledge base and inference engine were constructed and the overall framework of the system was determined, finally the intelligent fault diagnosis expert system for multi-parameter monitor was developed by using the page hypertext preprocessor (PHP) language, with an accuracy rate of 80% in fault diagnosis. The results showed that technology fusion on the basis of fault tree and expert system can effectively realize intelligent fault diagnosis of multi-parameter monitors and provide troubleshooting suggestions, which can not only provide experience accumulation for fault diagnosis of multi-parameter monitors, but also provide a new idea and technical support for fault diagnosis of medical equipment.

Updating the Dermal Sensitisation Thresholds using an expanded dataset and an in silico expert system.

The Dermal Sensitisation Thresholds (DST) are Thresholds of Toxicological Concern, which can be used to justify exposure-based waiving when conducting a skin sensitisation risk assessment. This study aimed to update the published DST values by expanding the size of the Local Lymph Node Assay dataset upon which they are based, whilst assigning chemical reactivity using an in silico expert system (Derek Nexus). The potency values within the expanded dataset fitted a similar gamma distribution to that observed for the original dataset. Derek Nexus was used to classify the sensitisation activity of the 1152 chemicals in the expanded dataset and to predict which chemicals belonged to a High Potency Category (HPC). This two-step classification led to three updated thresholds: a non-reactive DST of 710 µg/cm2 (based on 79 sensitisers), a reactive (non-HPC) DST of 73 µg/cm2 (based on 331 sensitisers) and an HPC DST of 1.0 µg/cm2 (based on 146 sensitisers). Despite the dataset containing twice as many sensitisers, these values are similar to the previously published thresholds, highlighting their robustness and increasing confidence in their use. By classifying reactivity in silico the updated DSTs can be applied within a skin sensitisation risk assessment in a reproducible, scalable and accessible manner.

A Hybrid Expert System for Individualized Quantification of Electrical Status Epilepticus During Sleep Using Biogeography-Based Optimization.

Electrical status epilepticus during sleep (ESES) is an epileptic encephalopathy in children with complex clinical manifestations. It is accompanied by specific electroencephalography (EEG) patterns of continuous spike and slow-waves. Quantifying such EEG patterns is critical to the diagnosis of ESES. While most of the existing automatic ESES quantification systems ignore the morphological variations of the signal as well as the individual variability among subjects. To address these issues, this paper presents a hybrid expert system that dedicates to mimicking the decision-making process of clinicians in ESES quantification by taking the morphological variations, individual variability, and medical knowledge into consideration. The proposed hybrid system not only offers a general scheme that could propel a semi-auto morphology analysis-based expert decision model to a fully automated ESES quantification with biogeography-based optimization (BBO), but also proposes a more precise individualized quantification system to involve the personalized characteristics by adopting an individualized parameters-selection framework. The feasibility and reliability of the proposed method are evaluated on a clinical dataset collected from twenty subjects at Children's Hospital of Fudan University, Shanghai, China. The estimation error for the individualized quantitative descriptor ESES is 0-4.32% and the average estimation error is 0.95% for all subjects. Experimental results show the presented system outperforms existing works and the individualized system significantly improves the performance of ESES quantification. The favorable results indicate that the proposed hybrid expert system for automatic ESES quantification is promising to support the diagnosis of ESES.

Pharmacotechnical Evaluation by SeDeM Expert System to Develop Orodispersible Tablets.

Sediment delivery model (SeDeM) system is innovative tool to correlate micromeritic properties of powders with compressibility. It involves computation of indices which facilitate direct compressibility of solids and enable corrective measures through particle engineering. Study had multiple objectives, viz, (i) to enhance solubility of BCS class II, nevirapine using solid dispersions; (ii) SeDeM analyses of excipients and solid dispersions to analyze direct compressibility; and (iii) prepare orodispersible tablets (ODT). Solid dispersions were prepared by solvent evaporation. Superdisintegrants and solid dispersions were analyzed for primary indices of dimension, compressibility, flowability, stability, and disgregability derived from micromeritic properties. Radar diagrams were constructed to provide visual clues to deficient properties for direct compressibility. ODTs were prepared using excipients which passed criteria for direct compressibility and evaluated for tablet properties. Solid dispersions with Eudragit S100 revealed 6 to 10 fold increase in solubility in various dissolution media including biorelevant media in comparison with plain drug. Solubility was found to be pH dependent. SeDeM analyses facilitated identification of superdisintegrants and excipients with unfavorable compressibility. Radar diagrams provided a clear pictorial evidence of lacunae in powder properties. Based on SeDeM results, tablets were formulated by direct compression using crosspovidone, croscarmellose sodium, and mannitol. All batches showed 40% release in first minute in simulated salivary fluid.