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Background: Breast cancer-related lymphedema (BCRL) could be one consequence of breast cancer (BC). Although several risk factors have been identified, a predictive algorithm still needs to be made available to determine the patient’s risk from an ensemble of clinical variables. Therefore, this study aimed to characterize the risk of BCRL by investigating the characteristics of autogenerated clusters of patients. Methods: The dataset under analysis was a multi-centric data collection of twenty-three clinical features from patients undergoing axillary dissection for BC and presenting BCRL or not. The patients’ variables were initially analyzed separately in two low-dimensional embeddings. Afterward, the two models were merged in a bi-dimensional prognostic map, with patients categorized into three clusters using a Gaussian mixture model. Results: The prognostic map represented the medical records of 294 women (mean age: 59.823±12.879 years) grouped into three clusters with a different proportion of subjects affected by BCRL (probability that a patient with BCRL belonged to Cluster A: 5.71%; Cluster B: 71.42%; Cluster C: 22.86%). The investigation evaluated intra- and inter-cluster factors and identified a subset of clinical variables meaningful in determining cluster membership and significantly associated with BCRL biological hazard. Conclusions: The results of this study provide potential insight for precise risk assessment of patients affected by BCRL, with implications in prevention strategies, for instance, focusing the resources on identifying patients at higher risk.

Background. The growing number of patients with prostate cancer (PCa) imposes additional requirements on the quality control system in healthcare, including ensuring the widespread availability of innovative algorithms for early diagnosis and treatment. One illustrative example of quality management initiatives is national PCa audit in the UK. Objective. Highlighting the approaches to quality assessments within audit of PCa care in the UK.Materials and methods. The relevant scientific data have been retrieved from Google and PubMed. The search horizon covered the last 10 years. The queries included such wording as: "prostate cancer" AND "audit" OR/AND "Great Britain" AND "quality assurance", etc.Results. At least four basic parameters were used as signal indicators to check the consistency and overall quality of the collected data on PCa patients in England and Wales. The fundamental arrangement of clinical quality indicators for PCa care comprised not less than fourteen measures. The outliers for some indicators were allocated into two groups using such criteria as: 1) more than three standard deviations from the national average (definition of an alarm); 2) more than two but below three standard deviations from the national average (definition of an alert). The outlier policy is usually applied for three treatment outcome performance indicators.Conclusion. The multidisciplinary teams must actively collaborate to provide the best standards of cancer care to the community. The introduction of multicriterial assessments to monitor the performance of highly specialized professional groups would bring a great benefit for cancer patients, particularly, through increasing the affordability of state-of-the-art medical algorithms across the counties.

Interdisciplinary approaches to creating high-tech computer systems for the diagnosis of melanoma using artificial intelligence are presented. A model is proposed for the architecture of an interactive expert system. This includes a set of features for a contemporary medical algorithm (the Kittler algorithm) along with a knowledge base and a diagnosis evaluation score for the case under study.

This is a type of manuscript that does not require an abstract

An "operational" definition of an exacerbation of asthma is a (sub) acute worsening of asthma symptoms that requires a change in treatment.To standardize definitions for the purpose of clinical research and to categorize the severity, ERS and ATS jointly (1) proposed that severe exacerbations are those that require treatment with systemic corticosteroids for at least 3 days or a hospitalization or emergency room visit for asthma that requires systemic corticosteroids.

Mastocytosis is a clonal disorder characterized by the proliferation and accumulation of mast cells (MCs) in various tissue types, preferentially skinand bone marrow (BM). Mastocytosis consists of cutaneous and systemic forms in both pediatric and adult patients. Both the excess and increased propensity of MCs to release mediators leads to a higher frequency and severity ofimmediate hypersensitivity reactions.1-4.

The literature and the Internet provide different sources, in which medical community as well as patients can browse through medical algorithms. These algorithms are dispersed and use different formats of presentation. We present visualized diagnosis (ViDis), a web platform aimed to construction and sharing of graphical representations of medical algorithms in a single place and in a unified format. ViDis is accessible as a web application, which can run on an arbitrary platform with a modern web browser. The platform's user friendly interfaces allow the users with different backgrounds to construct, share, and browse through medical algorithms. Visualization of the algorithms can be created using a flowchart diagram notation that is commonly applied in the design of computer software and is very intuitive to use and understand. Algorithms can be viewed in two different formats, that is, in the format of flowchart diagrams or in the format of sequential steps that guide the user from the beginning to the end of a medical procedure in dependence on his or her decisions made in each step of the process. ViDis enables registered users to create, edit, and share visualized medical algorithms and guest users to view these visualizations. To the best of our knowledge, this is the first platform for efficient sharing of medical algorithms with the community. We believe that ViDis provides an excellent platform for sharing medical knowledge and information among diagnosticians, clinicians, researchers, and patients.

Medical students mast understand the concept of algorithm. There are three main ways for algorithm writing and representation. The main and most convenient is the well-known flowcharts. Therefore, we need special software for creating flowcharts of medical algorithms of diagnosis and treatments.The most common recommendations are about using Microsoft solutions: “insert figures” button/command in Microsoft Word, or Microsoft Visio for professional cases. Also there are large number software utilities for building UML diagrams. Our goal is selecting the standard for flowcharts and finding good software. There are 4 alternative flowchart rules: standard ISO 5807:1985, Dragon-schemas, and two versions of Unified Modeling Language (ISO/IEC standards). The merits of the new solutions are actual only for large and complex algorithms and it takes more time for learn and use. Unfortunately, we have only two practical classes by course syllabus. So we select simplified version of old standard ISO 5807: 1985.The requirements mast be defined before making choice about software. There are several basic requirements. Application launch on the workstations instead of server or cloud. By this way we will not dependent of quality of internet or LAN services. Compatibility with most popular versions of Windows. Availability as freeware and we need rights for installing multiple copies. Regular updating software versions. Functionality and usability, the "intuitive interface."By this set of requirements, we select “yEd Graph Editor” software, because it is easy to learn and use, have all of diagram shapes, can save and export results, and it is freeware. Students understand how to make the base actions in this environment in a few minutes - after the first demonstration. Now there is enough time for drawing a complex algorithm flowchart during the lesson. And student have time for making corrections and update flowchart after teacher checking.We used this software over the last two years and we can recommend to use "yEd Graph Editor" in the case of limited time for practical classes. Key words: algorithm, computer science, flowcharts, software.

A systematic approach to both diagnosis and investigations is essential, when investigating a patient with a suspected perioperative hypersensitivity reaction. The perioperative setting is extremely complex with documented and undocumented exposures to many different drugs and substances. In addition, the effect of anaesthetic drugs and surgical procedure may mimic hypersensitivity. To ensure that all these complexities are addressed, collaboration between allergist and anaesthetist is essential. Also, the current recommendation is, that investigation of these patients should take place in highly specialized centres or in centres investigating a minimum of 20 patients/year, and where close collaboration between allergists and anaesthetists is established.1 Such collaborations have been endorsed in the recent 6th British National Audit Project (NAP6),2 and in recent publications from European and international working groups making recommendations on the management and investigation of perioperative hypersensitivity reactions.1, 3 In the following, two algorithms are presented based on recent recommendations1-7: Algorithm 1 shows an approach to gathering the complete and correct information, deciding on whether perioperative hypersensitivity is likely and identifying the relevant potential culprits to investigate. Algorithm 2 presents an approach to which investigations should be performed, how to assess causality for individual drugs and how to reach final conclusions.

Obstructive Sleep Apnea (OSA) is a chronic sleep-related pathology characterized by recurrent episodes of total or partial obstruction of the upper airways during sleep. It entails a high impact on the health and quality of life of patients, affecting more than one thousand million people worldwide, which has resulted in an important public health concern in recent years. The usual diagnosis involves performing a sleep test, cardiorespiratory polygraphy, or polysomnography, which allows characterizing the pathology and assessing its severity. However, this procedure cannot be used on a massive scale in general screening studies of the population because of its execution and implementation costs; therefore, causing an increase in waiting lists which would negatively affect the health of the affected patients. Additionally, the symptoms shown by these patients are often unspecific, as well as appealing to the general population (excessive somnolence, snoring, etc.), causing many potential cases to be referred for a sleep study when in reality are not suffering from OSA. This paper proposes a novel intelligent clinical decision support system to be applied to the diagnosis of OSA that can be used in early outpatient stages, quickly, easily, and safely, when a suspicious OSA patient attends the consultation. Starting from information related to the patient’s health profile (anthropometric data, habits, comorbidities, or medications taken), the system is capable of determining different alert levels of suffering from sleep apnea associated with different apnea-hypopnea index (AHI) levels to be studied. To that end, a series of automatic learning algorithms are deployed that, working concurrently, together with a corrective approach based on the use of an Adaptive Neuro-Based Fuzzy Inference System (ANFIS) and a specific heuristic algorithm, allow the calculation of a series of labels associated with the different levels of AHI previously indicated. For the initial software implementation, a data set with 4600 patients from the Álvaro Cunqueiro Hospital in Vigo was used. The results obtained after performing the proof tests determined ROC curves with AUC values in the range 0.8–0.9, and Matthews correlation coefficient values close to 0.6, with high success rates. This points to its potential use as a support tool for the diagnostic process, not only from the point of view of improving the quality of the services provided, but also from the best use of hospital resources and the consequent savings in terms of costs and time. Xunta de Galicia | Ref. ED481A-2020/038

Acute kidney injury (AKI) occurs in approximately 20% of hospitalized patients and is associated with increased morbidity and mortality. The care of hospitalized patients with AKI has been shown to be variable in clinical practices. Systematic tools including checklists, care bundles and medical algorithms have been developed and implemented to improve the care and outcomes of AKI patients. However, whether these systematic tools can improve the quality of care and outcomes of AKI patients is still unknown. The committee of the 19th Acute Disease Quality Initiative (ADQI) conference dedicated a workgroup with the task of developing a study protocol to investigate this question. A comprehensive literature search was performed using PubMed and Embase. Key questions and feasibility of potential study proposals were discussed during the conference. Then a two-step Delphi process was used to reach consensus regarding several aspects of the study protocol. The group suggested that patient risk assessment be included in the study protocol and the choice of systematic tool be depending on different clinical contexts. The group also proposed a two-phase study with the use of oliguria and systematic tool to investigate the quality of care and outcomes of AKI patients. Consensus was reached on a study protocol regarding the efficacy of using systematic tools to improve clinical management and outcomes of AKI patients.

(1) Background: Clinical decision support (CDS) is a vitally important adjunct to the implementation of pharmacogenomic-guided prescribing in clinical practice. A novel CDS was sought for the APOL1, NAT2, and YEATS4 genes to guide optimal selection of antihypertensive medications among the African American population cared for at multiple participating institutions in a clinical trial. (2) Methods: The CDS committee, made up of clinical content and CDS experts, developed a framework and contributed to the creation of the CDS using the following guiding principles: 1. medical algorithm consensus, 2. actionability, 3. context-sensitive triggers, 4. workflow integration, 5. feasibility, 6. interpretability, 7. portability, and 8. discrete reporting of lab results. (3) Results: Utilizing the principle of discrete patient laboratory and vital information, a novel CDS for APOL1, NAT2, and YEATS4 was created for use in a multi-institutional trial based on a medical algorithm consensus. The alerts are actionable and easily interpretable, clearly displaying the purpose and recommendations with pertinent laboratory results, vitals and links to ordersets with suggested antihypertensive dosages. Alerts were either triggered immediately once a provider starts to order relevant antihypertensive agents or strategically placed in workflow-appropriate general CDS sections in the electronic health record (EHR). Detailed implementation instructions were shared across institutions to achieve maximum portability. (4) Conclusions: Using sound principles, the created genetic algorithms were applied across multiple institutions. The framework outlined in this study should apply to other disease-gene and pharmacogenomic projects employing CDS.

The learning health system is a conceptual model for continuous learning and knowledge generation rooted in the daily practice of medicine. While companies such as Google and Amazon use dynamic learning systems that learn iteratively through every customer interaction, this efficiency has not materialized on a comparable scale in health systems. An ideal learning health system would learn from every patient interaction to benefit the care for the next patient. Notable advances include the greater use of data generated in the course of clinical care, Common Data Models, and advanced analytics. However, many remaining barriers limit the most effective use of large and growing health care data assets. In this review, we explore the accomplishments, opportunities, and barriers to realizing the learning health system.

Cotard delusion (CD) is a rare psychiatric disorder in which the patient believes to be dead,To do so, an extensive literature research was performed using several bibliographic databases. Since data on this topic is scarce, references in every article were cross-checked, aiming to obtain all available peer-reviewed works on CD.Research resulted in 328 cases. Several treatment modalities were reported to improve the symptoms of CD, from pharmacotherapy - mainly consisting of antipsychotics and antidepressants - to electroconvulsive therapy.Despite its challenging diagnosis, the delusion can be treated with readily available care. Hopefully, this work can be a useful tool to doctors when encountering this odd affliction.

Clinical pathways (CPs) are structured healthcare plans designed to implement evidence-based clinical guidelines, medical algorithms, and protocols. In recent years, a community called BPM+ Health has worked to establish a shareable and computer-consumable representation of CP, leveraging standard notations. These notations, collectively referred to as BPM+, include the Business Process Management and Notation (BPMN), Case Management Model and Notation (CMMN), and Decision Model and Notation (DMN), which aim to support clinical management and standardized communication between different stakeholders. However, the adaptation of these notations for the existing guidelines has largely been left unexplored. This paper introduces procedural steps and criteria considerations to apply components of BPM+ notations to reconstruct a guideline for Stable Ischemic Heart Disease. This paper describes how each of the three different notations is mapped to a medical guideline and discusses the advantages and limitations of representing CPs with BPM+ as compared with paper-based medical guidelines.

A medical prescription is a set of health care instructions that govern the plan of care for an individual patient, which may include orders for drug therapy, diet, clinical assessment, and laboratory testing. Clinicians have long used algorithmic thinking to describe and implement prescriptions but without the benefit of a formal programming language. Instead, medical algorithms are expressed using a natural language patois, flowcharts, or as structured data in an electronic medical record system. The lack of a prescription programming language inhibits expressiveness; results in prescriptions that are difficult to understand, hard to debug, and awkward to reuse; and increases the risk of fatal medical error. This article reports on the design and evaluation of Patient-Oriented Prescription Programming Language (POP-PL), a domain-specific programming language designed for expressing prescriptions. The language is based around the idea that programs and humans have complementary strengths that, when combined properly, can make for safer, more accurate performance of prescriptions. Use of POP-PL facilitates automation of certain low-level vigilance tasks, freeing up human cognition for abstract thinking, compassion, and human communication. We implemented this language and evaluated its design attempting to write prescriptions in the new language and evaluated its usability by assessing whether clinicians can understand and modify prescriptions written in the language. We found that some medical prescriptions can be expressed in a formal domain-specific programming language, and we determined that medical professionals can understand and correctly modify programs written in POP-PL. We also discuss opportunities for refining and further developing POP-PL.

Breast cancer is currently one of the main causes of death and tumoral diseases in women. Even if early diagnosis processes have evolved in the last years thanks to the popularization of mammogram tests, nowadays, it is still a challenge to have available reliable diagnosis systems that are exempt of variability in their interpretation. To this end, in this work, the design and development of an intelligent clinical decision support system to be used in the preventive diagnosis of breast cancer is presented, aiming both to improve the accuracy in the evaluation and to reduce its uncertainty. Through the integration of expert systems (based on Mamdani-type fuzzy-logic inference engines) deployed in cascade, exploratory factorial analysis, data augmentation approaches, and classification algorithms such as k-neighbors and bagged trees, the system is able to learn and to interpret the patient’s medical-healthcare data, generating an alert level associated to the danger she has of suffering from cancer. For the system’s initial performance tests, a software implementation of it has been built that was used in the diagnosis of a series of patients contained into a 130-cases database provided by the School of Medicine and Public Health of the University of Wisconsin-Madison, which has been also used to create the knowledge base. The obtained results, characterized as areas under the ROC curves of 0.95–0.97 and high success rates, highlight the huge diagnosis and preventive potential of the developed system, and they allow forecasting, even when a detailed and contrasted validation is still pending, its relevance and applicability within the clinical field. Xunta de Galicia | Ref. ED481A-2020/038

Answering medical questions related to complex medical cases, as required in modern Clinical Decision Support (CDS) systems, imposes (1) access to vast medical knowledge and (2) sophisticated inference techniques. In this article, we examine the representation and role of combining medical knowledge automatically derived from (a) clinical practice and (b) research findings for inferring answers to medical questions. Knowledge from medical practice was distilled from a vast Electronic Medical Record (EMR) system, while research knowledge was processed from biomedical articles available in PubMed Central. The knowledge automatically acquired from the EMR system took into account the clinical picture and therapy recognized from each medical record to generate a probabilistic Markov network denoted as a Clinical Picture and Therapy Graph (CPTG). Moreover, we represented the background of medical questions available from the description of each complex medical case as a medical knowledge sketch. We considered three possible representations of medical knowledge sketches that were used by four different probabilistic inference methods to pinpoint the answers from the CPTG. In addition, several answer-informed relevance models were developed to provide a ranked list of biomedical articles containing the answers. Evaluations on the TREC-CDS data show which of the medical knowledge representations and inference methods perform optimally. The experiments indicate an improvement of biomedical article ranking by 49% over state-of-the-art results.

Medical artificial intelligence is cost-effective, scalable, and often outperforms human providers. One important barrier to its adoption is the perception that algorithms are a “black box”—people do not subjectively understand how algorithms make medical decisions, and we find this impairs their utilization. We argue a second barrier is that people also overestimate their objective understanding of medical decisions made by human healthcare providers. In five pre-registered experiments with convenience and nationally representative samples (N = 2,699), we find that people exhibit such an illusory understanding of human medical decision making (Study 1). This leads people to claim greater understanding of decisions made by human than algorithmic healthcare providers (Studies 2A-B), which makes people more reluctant to utilize algorithmic providers (Studies 3A-B). Fortunately, we find that asking people to explain the mechanisms underlying medical decision making reduces this illusory gap in subjective understanding (Study 1). Moreover, we test brief interventions that, by increasing subjective understanding of algorithmic decision processes, increase willingness to utilize algorithmic healthcare providers without undermining utilization of human providers (Studies 3A-B). Corroborating these results, a study on Google testing ads for an algorithmic skin cancer detection app shows that interventions that increase subjective understanding of algorithmic decision processes lead to a higher ad click-through rate (Study 4). Our findings show how reluctance to utilize medical algorithms is driven both by the difficulty of understanding algorithms, and an illusory understanding of human decision making.

Quantitative and qualitative evaluation of blood cells is an important parameter used to detect and suspect pathologies, whether hematological or infectious. Considering that laboratory medical exams often present costs that are inaccessible to populations of underdeveloped and developing countries, it is of great importance to create tools that facilitate the obtaining of medical reports with low cost and high reliability. The present study aims to develop an algorithm that is able to perform the quantification of red blood cells (RBCs) and leukocytes through personal computers, making the methodology more accessible to diverse populations. For this, an algorithm based on the Hough transform and object detection by coloration (HT-DC) was developed in Matlab software version 8.3 of 64 bits (2014a). Thus, 10 images in digital format of blood smear fields involving erythrocytes and leukocytes in a nonpathological state were submitted to the HT-DC algorithm. As a result, accuracy, sensitivity, and specificity of 85%, 70%, and 99% respectively, were obtained, demonstrating the high reliability of the methodology. The processing time of the algorithm was evaluated through the CPU time analyses, showing a mean time of 5.20–7.46 s depending on the processor used (Intel Dual Core, Intel Quad, Intel i3, or Intel i5). In this way, the HT-DC methodology can be seen as the initial step for the development of a blood count from simple usual everyday tools, such as a computer.

Respiratory diseases are currently considered to be amongst the most frequent causes of death and disability worldwide, and even more so during the year 2020 because of the COVID-19 global pandemic. Aiming to reduce the impact of these diseases, in this work a methodology is developed that allows the early detection and prevention of potential hypoxemic clinical cases in patients vulnerable to respiratory diseases. Starting from the methodology proposed by the authors in a previous work and grounded in the definition of a set of expert systems, the methodology can generate alerts about the patient&rsquo, s hypoxemic status by means of the interpretation and combination of data coming both from physical measurements and from the considerations of health professionals. A concurrent set of Mamdani-type fuzzy-logic inference systems allows the collecting and processing of information, thus determining a final alert associated with the measurement of the global hypoxemic risk. This new methodology has been tested experimentally, producing positive results so far from the viewpoint of time reduction in the detection of a blood oxygen saturation deficit condition, thus implicitly improving the consequent treatment options and reducing the potential adverse effects on the patient&rsquo, s health.

Atopic dermatitis (AD, atopic eczema) is a chronic, relapsing, pruritic, non-communicable inflammatory skin disease that affects children and adults1 . It almost always has its debut in early life2,3 , therefore this medical algorithm focuses on diagnosis (part I) and therapy (part II4 ) of AD in early childhood, which is defined by UNESCO as 'the period from birth to eight years old'.

Present-day society shows keen interest in the field of medical treatment, and the diagnostic mode is now developing toward doctor–patient shared decision-making. Therefore, a patient׳s source of medical information is quite important, with that source needing to be reliable, accurate, and easily accessible. Ensuring that informational sources meet these requirements becomes a challenge with the development of the informational network, which causes the amount of material available online to steadily increase and the general public to become more aware of health- and medical-treatment-related information. Therefore, focusing on the medical information seeker, this paper will discuss two user identities: patients and healthcare professionals. For patients, online medical articles are a major source of medical information; patients with concerns about diseases often search for their symptoms on the Internet and look for related medical information. However, online medical articles are usually long, so patients sometimes self-diagnose their disease or determine the severity of their condition based on only part of an article or on limited, incomplete, or even inaccurate information in several articles related to the symptoms searched out. Consequently, patients may misdiagnose their condition or underestimate the severity or seriousness of the condition and delay treatment. In addition, present medical technology advances rapidly, so physicians and other healthcare professionals must obtain the latest medical information from the Internet. However, searching for and reading professional in-depth medical articles to find required, critical information online is time-consuming, creating a time-management challenge. To address these aforementioned problems, this paper develops an Automatic Key Medical Information Generating model, uses medical articles as the basis of analysis, and develops and designs a medical article key-information-generating methodology applicable to medical article retrieval and reading. The word segmentation is implemented for the articles according to the Chinese Knowledge and Information Processing (CKIP) of Academia Sinica, and the medical articles are then distributed to various clusters by the clustering technology of this model, so that the medical information seeker can conduct a rapid search for the required medical article information. When the medical information seeker finds the target medical article, the article׳s key statements are screened out by the keywords rule base created in this paper, and the key statement scores are calculated. The medical article key information is sequenced according to the key statements so as to generate the medical article key information table. In addition, a web-based key-medical-information-generating system will be built based on the proposed model, and the effectiveness and feasibility of the model and technology will be evaluated using a real-world case. In summary, this paper presents a model to analyze the keywords and key statements of medical articles to generate a medical article key information table. This model can help the medical information seeker look for the required health information rapidly and accurately on the Internet, shortening the time for screening medical information and increasing the probability of obtaining the required information.