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Monitoring heart electrical activity is an effective way of detecting existing and developing conditions. This is usually performed as a non-invasive test using a network of up to 12 sensors (electrodes) on the chest and limbs to create an electrocardiogram (ECG). By visually observing these readings, experienced professionals can make accurate diagnoses and, if needed, request further testing. However, the training and experience needed to make accurate diagnoses are significant. This work explores the potential of recurrent neural networks for anomaly detection in ECG readings. Furthermore, to attain the best possible performance for these networks, training parameters, and network architectures are optimized using a modified version of the well-established particle swarm optimization algorithm. The performance of the optimized models is compared to models created by other contemporary optimizers, and the results show significant potential for real-world applications. Further analyses are carried out on the best-performing models to determine feature importance.

Background: Congenital Heart Disease represents the most frequent fetal malformation. The lack of prenatal identification of congenital heart defects can have adverse consequences for the neonate, while a correct prenatal diagnosis of specific cardiac anomalies improves neonatal care neurologic and surgery outcomes. Sonographers perform prenatal diagnosis manually during the first or second-trimester scan, but the reported detection rates are low. This project's primary objective is to develop an Intelligent Decision Support System that uses two-dimensional video files of cardiac sweeps obtained during the standard first-trimester fetal echocardiography (FE) to signal the presence/absence of previously learned key features., Methods: The cross-sectional study will be divided into a training part of the machine learning approaches and the testing phase on previously unseen frames and eventually on actual video scans. Pregnant women in their 12-13 + 6 weeks of gestation admitted for routine first-trimester anomaly scan will be consecutively included in a two-year study, depending on the availability of the experienced sonographers in early fetal cardiac imaging involved in this research. The Data Science / IT department (DSIT) will process the key planes identified by the sonographers in the two- dimensional heart cine loop sweeps: four-chamber view, left and right ventricular outflow tracts, three vessels, and trachea view. The frames will be grouped into the classes representing the plane views, and then different state-of-the- art deep-learning (DL) pre-trained algorithms will be tested on the data set. The sonographers will validate all the intermediary findings at the frame level and the meaningfulness of the video labeling., Discussion: FE is feasible and efficient during the first trimester. Still, the continuous training process is impaired by the lack of specialists or their limited availability. Therefore, in our study design, the sonographer benefits from a second opinion provided by the developed software, which may be very helpful, especially if a more experienced colleague is unavailable. In addition, the software may be implemented on the ultrasound device so that the process could take place during the live examination., Trial Registration: The study is registered under the name "Learning deep architectures for the Interpretation of Fetal Echocardiography (LIFE)", project number 408PED/2020, project code PN-III-P2-2.1-PED-2019., Trial Registration: ClinicalTrials.gov , unique identifying number NCT05090306, date of registration 30.10.2020., (© 2023. The Author(s).)

There are many machine learning approaches available and commonly used today, however, the extreme learning machine is appraised as one of the fastest and, additionally, relatively efficient models. Its main benefit is that it is very fast, which makes it suitable for integration within products that require models taking rapid decisions. Nevertheless, despite their large potential, they have not yet been exploited enough, according to the recent literature. Extreme learning machines still face several challenges that need to be addressed. The most significant downside is that the performance of the model heavily depends on the allocated weights and biases within the hidden layer. Finding its appropriate values for practical tasks represents an NP-hard continuous optimization challenge. Research proposed in this study focuses on determining optimal or near optimal weights and biases in the hidden layer for specific tasks. To address this task, a multi-swarm hybrid optimization approach has been proposed, based on three swarm intelligence meta-heuristics, namely the artificial bee colony, the firefly algorithm and the sine-cosine algorithm. The proposed method has been thoroughly validated on seven well-known classification benchmark datasets, and obtained results are compared to other already existing similar cutting-edge approaches from the recent literature. The simulation results point out that the suggested multi-swarm technique is capable to obtain better generalization performance than the rest of the approaches included in the comparative analysis in terms of accuracy, precision, recall, and f1-score indicators. Moreover, to prove that combining two algorithms is not as effective as joining three approaches, additional hybrids generated by pairing, each, two methods employed in the proposed multi-swarm approach, were also implemented and validated against four challenging datasets. The findings from these experiments also prove superior performance of the proposed multi-swarm algorithm. Sample code from devised ELM tuning framework is available on the GitHub.

We live in a period when smart devices gather a large amount of data from a variety of sensors and it is often the case that decisions are taken based on them in a more or less autonomous manner. Still, many of the inputs do not prove to be essential in the decision-making process; hence, it is of utmost importance to find the means of eliminating the noise and concentrating on the most influential attributes. In this sense, we put forward a method based on the swarm intelligence paradigm for extracting the most important features from several datasets. The thematic of this paper is a novel implementation of an algorithm from the swarm intelligence branch of the machine learning domain for improving feature selection. The combination of machine learning with the metaheuristic approaches has recently created a new branch of artificial intelligence called learnheuristics. This approach benefits both from the capability of feature selection to find the solutions that most impact on accuracy and performance, as well as the well known characteristic of swarm intelligence algorithms to efficiently comb through a large search space of solutions. The latter is used as a wrapper method in feature selection and the improvements are significant. In this paper, a modified version of the salp swarm algorithm for feature selection is proposed. This solution is verified by 21 datasets with the classification model of K-nearest neighborhoods. Furthermore, the performance of the algorithm is compared to the best algorithms with the same test setup resulting in better number of features and classification accuracy for the proposed solution. Therefore, the proposed method tackles feature selection and demonstrates its success with many benchmark datasets.

The study presented here considers the analysis of a medical dataset for the identification of the stage of onset of COVID-19 coronavirus. These data, presented in previous work by the authors, have been subjected to extensive analysis and additional calculations. The data were obtained by analyzing blood samples of infected individuals at 1, 3, and 6 months after COVID-19 infection. Results were obtained from FTIR spectrometry experiments. The results indicate a very effective ability to identify the different states of infection, and between 1 and 6 months even perfect. Specific spectrometry wavelength ranges can also be distinguished as medical markers., (© 2022 The Author(s). Published by Elsevier B.V.)

Medical data are often tricky to get mined for patterns even by the generally demonstrated successful modern methodologies of deep learning. This paper puts forward such a medical classification task, where patient registers of two of the categories are sometimes hard to be distinguished because of samples showing characteristics of both labels in turn in several repetitions of the screening procedure. To this end, the current research appoints a pre-processing clustering step (through self-organizing maps) to group the data based on shape similarity and relabel it accordingly. Subsequently, a deep learning approach (a tandem of convolutional and long short-term memory networks) performs the training classification phase on the 'cleaned' samples. The dual methodology was applied for the computational diagnosis of electrooculography tests within spino-cerebral ataxia of type 2. The accuracy obtained for the discrimination into three classes was of 78.24%. The improvement that this duo brings over the deep learner alone does not stem from significantly higher accuracy results when the performance is considered for all classes. The major finding of this combination is that half of the presymptomatic cases were correctly found, in opposition to the single deep model, where this category was sacrificed by the learner in favor of a good accuracy overall. A high accuracy in general is desirable for any medical task, however the correct identification of cases before the symptoms become evident is more important., Competing Interests: Ruxandra Stoean is an Academic Editor of PLOS ONE. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

Application of deep learning (DL) to the field of healthcare is aiding clinicians to make an accurate diagnosis. DL provides reliable results for image processing and sensor interpretation problems most of the time. However, model uncertainty should also be thoroughly quantified. This paper therefore addresses the employment of Monte Carlo dropout within the DL structure to automatically discriminate presymptomatic signs of spinocerebellar ataxia type 2 in saccadic samples obtained from electrooculograms. The current work goes beyond the common incorporation of this special type of dropout into deep neural networks and uses the uncertainty derived from the validation samples to construct a decision tree at the register level of the patients. The decision tree built from the uncertainty estimates obtained a classification accuracy of 81.18% in automatically discriminating control, presymptomatic and sick classes. This paper proposes a novel method to address both uncertainty quantification and explainability to develop reliable healthcare support systems.

Stock price prediction is a popular yet challenging task and deep learning provides the means to conduct the mining for the different patterns that trigger its dynamic movement. In this paper, the task is to predict the close price for 25 companies enlisted at the Bucharest Stock Exchange, from a novel data set introduced herein. Towards this scope, two traditional deep learning architectures are designed in comparison: a long short-memory network and a temporal convolutional neural model. Based on their predictions, a trading strategy, whose decision to buy or sell depends on two different thresholds, is proposed. A hill climbing approach selects the optimal values for these parameters. The prediction of the two deep learning representatives used in the subsequent trading strategy leads to distinct facets of gain., Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: Ruxandra Stoean is an Academic Editor of PLOS ONE. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

Current histopathological diagnosis involves human expert interpretation of stained images for diagnosis. This process is prone to inter-observer variability, often leading to low concordance rates amongst pathologists across many types of tissues. Further, since structural features are mostly just defined for epithelial alterations during tumor progression, the use of associated stromal changes is limited. Here we sought to examine whether digital analysis of commonly used hematoxylin and eosin-stained images could provide precise and quantitative metrics of disease from both epithelial and stromal cells. We developed a convolutional neural network approach to identify epithelial breast cells from their microenvironment. Second, we analyzed the microenvironment to further observe different constituent cells using unsupervised clustering. Finally, we categorized breast cancer by the combined effects of stromal and epithelial inertia. Together, the work provides insight and evidence of cancer association for interpretable features from deep learning methods that provide new opportunities for comprehensive analysis of standard pathology images., (Copyright © 2019 Mittal, Stoean, Kajdacsy-Balla and Bhargava.)

The current research study is concerned with the automated differentiation between histopathological slides from colon tissues with respect to four classes (healthy tissue and cancerous of grades 1, 2 or 3) through an optimized ensemble of predictors. Six distinct classifiers with prediction accuracies ranging from 87% to 95% are considered for the task. The proposed method of combining them takes into account the probabilities of the individual classifiers for each sample to be assigned to any of the four classes, optimizes weights for each technique by differential evolution and attains an accuracy that is significantly better than the individual results. Moreover, a degree of confidence is defined that would allow the pathologists to separate the data into two distinct sets, one that is correctly classified with a high level of confidence and the rest that would need their further attention. The tandem is also validated on other benchmark data sets. The proposed methodology proves to be efficient in improving the classification accuracy of each algorithm taken separately and performs reasonably well on other data sets, even with default weights. In addition, by establishing a degree of confidence the method becomes more viable for use by actual practitioners., Competing Interests: DL is employed by Wolfram Research, Inc. and received research materials in that his employer permitted the use of his desktop computer in this work. There are no patents, products in development or marketed products associated with this research to declare. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

This paper presents an automatic tool capable to learn from a patients data set with 24 medical indicators characterizing each sample and to subsequently use the acquired knowledge to differentiate between five degrees of liver fibrosis. The indicators represent clinical observations and the liver stiffness provided by the new, non-invasive procedure of Fibroscan. The proposed technique combines a hill climbing algorithm that selects subsets of important attributes for an accurate classification and a core represented by a cooperative coevolutionary classifier that builds rules for establishing the diagnosis for every new patient. The results of the novel method proved to be superior as compared to the ones obtained by other important classification techniques from the literature. Additionally, the proposed methodology extracts a set of the most meaningful attributes from the available ones., (Copyright © 2011 Elsevier Ltd. All rights reserved.)

Objective: Hepatic fibrosis, the principal pointer to the development of a liver disease within chronic hepatitis C, can be measured through several stages. The correct evaluation of its degree, based on recent different non-invasive procedures, is of current major concern. The latest methodology for assessing it is the Fibroscan and the effect of its employment is impressive. However, the complex interaction between its stiffness indicator and the other biochemical and clinical examinations towards a respective degree of liver fibrosis is hard to be manually discovered. In this respect, the novel, well-performing evolutionary-powered support vector machines are proposed towards an automated learning of the relationship between medical attributes and fibrosis levels. The traditional support vector machines have been an often choice for addressing hepatic fibrosis, while the evolutionary option has been validated on many real-world tasks and proven flexibility and good performance., Methods and Materials: The evolutionary approach is simple and direct, resulting from the hybridization of the learning component within support vector machines and the optimization engine of evolutionary algorithms. It discovers the optimal coefficients of surfaces that separate instances of distinct classes. Apart from a detached manner of establishing the fibrosis degree for new cases, a resulting formula also offers insight upon the correspondence between the medical factors and the respective outcome. What is more, a feature selection genetic algorithm can be further embedded into the method structure, in order to dynamically concentrate search only on the most relevant attributes. The data set refers 722 patients with chronic hepatitis C infection and 24 indicators. The five possible degrees of fibrosis range from F0 (no fibrosis) to F4 (cirrhosis)., Results: Since the standard support vector machines are among the most frequently used methods in recent artificial intelligence studies for hepatic fibrosis staging, the evolutionary method is viewed in comparison to the traditional one. The multifaceted discrimination into all five degrees of fibrosis and the slightly less difficult common separation into solely three related stages are both investigated. The resulting performance proves the superiority over the standard support vector classification and the attained formula is helpful in providing an immediate calculation of the liver stage for new cases, while establishing the presence/absence and comprehending the weight of each medical factor with respect to a certain fibrosis level., Conclusion: The use of the evolutionary technique for fibrosis degree prediction triggers simplicity and offers a direct expression of the influence of dynamically selected indicators on the corresponding stage. Perhaps most importantly, it significantly surpasses the classical support vector machines, which are both widely used and technically sound. All these therefore confirm the promise of the new methodology towards a dependable support within the medical decision-making., (Copyright © 2010 Elsevier B.V. All rights reserved.)

In an attempt to accelerate the antitetanic vaccine-induced immune response necessary particularly in subjects with tetanigenic wounds, groups of guinea pigs or rabbits were i.d. or s.c. immunized with a nonadsorbed purified and concentrated toxoid. Concomitantly, lots of control animals were i.m. immunized with tetanic vaccine adsorbed on aluminium phosphate. The immune response was estimated by testing the serum antitetanus antitoxic titre and the resistance of guinea pigs to tetanic intoxication. The results obtained revealed a variety of responses to vaccination in which differed terms of the species of animals and the applied immunization schedule. When single vaccine doses were used the protective antitetanus limit was reached in seven days in rabbits and after ten days in guinea pigs; the adsorbed vaccine induced a stronger immune response than the non-adsorbed vaccine. When multiple immunization doses were used the non-adsorbed tetanus vaccine induced, especially in rabbits, a more rapid response that the adsorbed vaccine. The vaccination schedules used in laboratory animals and which have yielded the best results in this experiment will be analysed and checked on human subjects in a further study.

The evaluation of Schick test results as compared to the results obtained by determination of diphtheria antitoxin concentration in blood using the neutralizing test on rabbit--Jensen on the one hand and to those obtained by the passive hemagglutination reaction on the other has revealed some discrepancies against the two tests: 7.09% (4.08% pseudo-protected, 3.01 pseudo-susceptible) as against TN-Jensen and 8.6% (5.59% pseudo-protected and 3.01% pseudo-susceptible) as against the passive hemagglutination reaction results. The reported discrepancies were due to the fact that the results of Schick test did not correlate perfectly with the amount of circulating antitoxin, the diphtheria immunity level was wrongly indicated by the Schick test. The passive hemagglutination reaction has proved to be adequate to mass-screening investigations being capable to replace Schick test.

A new type of concentrated unadsorbed Tetanus vaccine was administered in animals by intradermal route, associated or not with Typhoid vaccine. The results of the laboratory tests demonstrated that this vaccine is innocuous and produces minimal reactions. A single doses of Tetanus vaccine inoculated in guinea pig or rabbit resulted in a relevant titer which increased when the Typhoid vaccine was associated. Also, in such immunized guinea pigs, a remarkable resistance to tetanic toxin was achieved. The levels of the active or passive typhoid-protective power as well as that of the agglutinating antibodies were not influenced by the association of the Tetanus vaccine to the Typhoid vaccine. Therefore, when the Typhoid vaccine suspended in Tetanus vaccine was administered intradermally to the animals by Jet injector, it had not a negative effect for none of the two components, had a beneficial effect upon the levels of determined antibody production and allowed an easier and more rapid administration of these vaccines.

A lyophilized, heat-killed, phenol preserved typhoid vaccine (5 x 10(8) cells) suspended in 0.1 ml unadsorbed concentrated tetanus vaccine (10 Lf) was administered in man by intradermal route. This association of the two vaccines resulted in milder postvaccinal reactions: moreover, the immunological properties of typhoid vaccine, as certified by the passive protection test of the mouse with sera of the vaccines and the H and O agglutinin titres found in these immune sera, were perfectly conserved. Consequently, the mixed typhoid-tetanus vaccination in man by intradermal route is possible and advantageous for practical and economical reasons.

The immunogenicity and the reactogenicity of an unadsorbed Tetanus vaccine, intradermally administered in man as a booster immunization associated or not with lyophilized Typhoid vaccine, were comparatively studied. There were no differences in postvaccinal reactions between the Tetanus vaccine administered alone or associated with Typhoid vaccine as well as between the unadsorbed and adsorbed Tetanus vaccine. The booster inoculation carried out with Tetanus vaccine by i.d. route with doses of 10 Lf/0.1 ml proved to be effective, inducing to all the subjects a definite protective titre, maintaining for at least one year.

Investigations of anti-tetanus response, in 404 subjects, most of them aged 60, being non-immunized for at least 10 years, stressed out the fact that 28.7% were not protected and 6.18% presented a protecting titer of 0.01 IU/ml, evaluated by "in vivo" protection test in mice. Some subjects were immunized with unadsorbed Tetanus vaccine (10 Lf/0.1 ml/dose) by i.d. route, using Jet-injector, and the others with adsorbed Tetanus vaccine (0.5 ml/dose), by i.m. route, using the needle and syringe. The vaccines were well tolerated and adverse reactions were not recorded. After 30 days, a single vaccine dose produced a protecting effect in 97.45% of non-protected subjects, belonging to i.d. immunized group, and also in 93.33% belonging to i.m. immunized group. 30 days after the administration of a second dose, protection set up in all subjects, no matter of vaccine type and administration route used. For a continuous reduction of tetanus morbidity, the authors suggest a specific periodical immunization of non-protected persons, selected by serological screening, using unadsorbed Tetanus vaccine, administered by i.d. route by means of the Jet-injector.

The geometrical mean of antitoxic titers determined in a lot of immunized young men who received, 5-7 years before, a booster with diphtheria-tetanus bivaccine, was of 1.54 (x/divided by 10.54) I.U./ml. At one month after another booster, performed with Tetanus toxoid (adsorbed), an increase of 34 times was recorded, the geometrical mean reaching the value of 52.67 (x/divided by 10.42 I.U./ml). The covering coefficients of the minimal protective antitoxin level have increased from 154 to 5267, in the estimation performed at the level of geometrical mean (calculating the ratio between the value of this indicator and that of the protection limit) and from 1623 to 54935, or from 15 to 505, respectively, in the estimations performed at the levels of the limits of the statistical range of one geometric standard deviation (Mg x/divided by D Sg). The study of the relations between pre- and post-antitoxic titers, performed by the linear regression analysis, leads to an equation whose slope evidenced the lower amplitude of the booster titers, along the increase of previous titers. The study of the inter-methods (neutralization-passive haemagglutination) regression allowed to obtain a correlation coefficient, between methods, of r = 0.83 and to perform the transposition of the minimal protective N.T. level (0.01 I.U.) in the terms of passive haemagglutination reaction, at the titer of 160 (0.3 PHAU/ml), for the p less than or equal to 0.05 probability level.

In this study we have investigated the immune humoral response in the associated vaccination with smallpox, tetanus and typhoid fractionated vaccine (trivaccine) administered in two series at 1 month interval, by dermojet, in a group of young people of 18-20 years old. The results were comparatively estimated with those obtained in two groups of young people of the same age (control group), separately immunized with two components of the tri-vaccine: fractionated smallpox vaccine and tetanus toxoid, following-up the humoral response to the two vaccine components. It was find that, at the end of the surveillance period, similar results were obtained for the testing group and control group, the antibody titers (in geometric mean) presenting very close values: 1/1,140,463 for the testing group, and 1/1,053,583 for the control group against vaccinia component, and 1/1,86,880,586 for the testing group and 1/79,900,431 for the control group, against the tetanus component. The results obtained entitle us to propose this vaccination scheme for the vaccination practice.