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PubMed1995

An object-oriented, knowledge-based system for cardiovascular rehabilitation--phase II.

The Heart Monitor is an object-oriented, knowledge-based system designed to support the clinical activities of cardiovascular (CV) rehabilitation. The original concept was developed as part of graduate research completed in 1992. This paper describes the second generation system which is being implemented in collaboration with a local heart rehabilitation program. The PC UNIX-based system supports an extensive patient database organized by clinical areas. In addition, a knowledge base is employed to monitor patient status. Rule-based automated reasoning is employed to assess risk factors contraindicative to exercise therapy and to monitor administrative and statutory requirements.

Artificial Intelligence↗

PubMed2005

An evolution based classifier for prediction of protein interfaces without using protein structures.

MOTIVATION: The number of available protein structures still lags far behind the number of known protein sequences. This makes it important to predict which residues participate in protein-protein interactions using only sequence information. Few studies have tackled this problem until now. RESULTS: We applied support vector machines to sequences in order to generate a classification of all protein residues into those that are part of a protein interface and those that are not. For the first time evolutionary information was used as one of the attributes and this inclusion of evolutionary importance rankings improves the classification. Leave-one-out cross-validation experiments show that prediction accuracy reaches 64%.

Algorithms↗

PubMedDate not supplied

Semi-automated entry of clinical temporal-abstraction knowledge.

OBJECTIVES: The authors discuss the usability of an automated tool that supports entry, by clinical experts, of the knowledge necessary for forming high-level concepts and patterns from raw time-oriented clinical data. DESIGN: Based on their previous work on the RESUME system for forming high-level concepts from raw time-oriented clinical data, the authors designed a graphical knowledge acquisition (KA) tool that acquires the knowledge required by RESUME. This tool was designed using Protégé, a general framework and set of tools for the construction of knowledge-based systems. The usability of the KA tool was evaluated by three expert physicians and three knowledge engineers in three domains-the monitoring of children's growth, the care of patients with diabetes, and protocol-based care in oncology and in experimental therapy for AIDS. The study evaluated the usability of the KA tool for the entry of previously elicited knowledge. MEASUREMENTS: The authors recorded the time required to understand the methodology and the KA tool and to enter the knowledge; they examined the subjects' qualitative comments; and they compared the output abstractions with benchmark abstractions computed from the same data and a version of the same knowledge entered manually by RESUME experts. RESULTS: Understanding RESUME required 6 to 20 hours (median, 15 to 20 hours); learning to use the KA tool required 2 to 6 hours (median, 3 to 4 hours). Entry times for physicians varied by domain-2 to 20 hours for growth monitoring (median, 3 hours), 6 and 12 hours for diabetes care, and 5 to 60 hours for protocol-based care (median, 10 hours). An increase in speed of up to 25 times (median, 3 times) was demonstrated for all participants when the KA process was repeated. On their first attempt at using the tool to enter the knowledge, the knowledge engineers recorded entry times similar to those of the expert physicians' second attempt at entering the same knowledge. In all cases RESUME, using knowledge entered by means of the KA tool, generated abstractions that were almost identical to those generated using the same knowledge entered manually. CONCLUSION: The authors demonstrate that the KA tool is usable and effective for expert physicians and knowledge engineers to enter clinical temporal-abstraction knowledge and that the resulting knowledge bases are as valid as those produced by manual entry.

Acquired Immunodeficiency Syndrome↗

PubMed1994

Heart signal recognition by Hidden Markov Models: the ECG case.

Wave recognition in ECG signals by Hidden Markov Models (HMMs) relies on the stationary assumption for the set of parameters used to describe ECG waves. This approach seems unnatural and consequently generates severe errors in practice. A new class of HMMs called Modified Continuous Variable Duration HMMs is proposed to account for the specific properties of the ECG signal. An application of the latter, coupled with a multiresolution front-end analysis of the ECG is presented. Results show these methods can increase the performance of ECG recognition compared to classical HMMs.

Artificial Intelligence↗

PubMed2025

Accurate identification of abnormal ploidy using an artificial intelligence model in preimplantation genetic testing.

STUDY QUESTION: Can ultra-low-coverage whole-genome sequencing (ulc-WGS) accurately identify abnormal ploidy during preimplantation genetic testing (PGT)? SUMMARY ANSWER: The artificial intelligence (AI)-based PGT-Plus model demonstrates high accuracy in ploidy detection, offering a cost-effective solution that enhances clinical utility of PGT. WHAT IS KNOWN ALREADY: The predominant PGT for aneuploidy can identify chromosomal aneuploidies but cannot determine ploidy status. Transferring embryos with ploidy abnormalities can result in miscarriage and molar pregnancy. On the other hand, in ART, fertilization is assessed by morphological pronuclear assessment at the zygote stage. However, it has a low specificity in the prediction of abnormal ploidy status and embryos deemed abnormally fertilized can yield healthy pregnancies. Accurately identified abnormal ploidy in PGT-A can resolve current limitations and expand the utility range of PGT-A. Several studies have identified ploidy abnormalities; however, they were mainly based on single-nucleotide polymorphism (SNP) arrays or needed to combine additional targeted-next-generation sequencing (NGS) information. Studies based on ulc-WGS remain scarce. STUDY DESIGN SIZE DURATION: The study consisted of two stages: methodology establishment and validation. An AI model, named PGT-Plus, was developed using 653 samples with known ploidy status, which was further validated using 792 different ploidy status samples. In the clinical application stage, the approach was used to analyse the ploidy status of 19 103 normally fertilized PGT blastocysts and 140 single pronucleus (1PN)-derived blastocysts collected between May 2022 and December 2023. All blastocysts were tested using trophectoderm biopsy and NGS. PARTICIPANTS/MATERIALS SETTING METHODS: The methodology is based on the ulc-WGS data. First, based on samples with known ploidy status: the heterozygosity rate of high-frequency biallelic SNPs, the likelihood ratio (LLR) of alleles was calculated under different assumptions ('both parental homologs' [BPH] from a single parent, 'single parental homolog' [SPH] from each parent, disomy, and monosomy) by leveraging allele frequencies and linkage disequilibrium (LD) measured in the 1000 genomes project database. Twenty-three continuous candidate features derived from heterozygosity rates and LLRs of chromosomes or selected windows were included to establish the ploidy prediction AI model. Gini importance analysis and multicollinearity mitigation was performed for feature selection, then the performance of Random Forest (RF), Support Vector Machine (SVM), and Logistic Regression for modelling was compared. Subsequently, the parameter optimization was performed based on the RF model. Ploidy constitution concordance was evaluated in known ploidy status samples. The frequency of abnormal ploidy in normal fertilized PGT blastocysts and 1PN-derived blastocysts (including conventional IVF and ICSI) was evaluated. MAIN RESULTS AND THE ROLE OF CHANCE: Eleven features were collected for model architecture compared to SVM and Logistic Regression; RF achieved superior performance for ploidy detection. The AI model achieved an AUC of 1 for genome-wide-uniparental diploidy (GW-UPD), 1 for triploidy, and 0.99 for diploidy. For the 792 validation samples, 99.5% of samples were successfully detected using the AI model, and the model showed 100% accuracy for ploidy classification. In the clinical application stage, out of 19 103 PGT samples, 19 069 were successfully analysed using the model, with 110 (0.57%) identified as having abnormal ploidy embryos. Among these, 12.7% (14/110) were identified as GW-UPD, and 87.3% (96/110) were triploid. Among 5563 diploid blastocysts transferred, 3478 clinical pregnancies were achieved. Subsequent ploidy analysis was performed for 217 spontaneous abortion and 935 prenatal diagnostic samples, and no abnormal ploidy was identified. Furthermore, of the 140 1PN embryos tested, 40 (28.6%) exhibited GW-UPD, 3 (2.1%) exhibited triploidy, and 97 (69.3%) were determined to be biparental and normally fertilized. Among the 97 biparental embryos, 46 were diploid, 11 were mosaic, and 40 were aneuploid. In terms of the insemination pattern, the percentage of abnormal ploidy in ICSI was significantly higher than in conventional IVF (P < 0.01, 37.1% vs. 2.9%, respectively). With full informed consent, 20 patients without euploidy from normal fertilization chose 1PN-derived biparental and diploid blastocysts to transfer, resulting in 10 clinical pregnancies and 9 ongoing pregnancies. LARGE-SCALE DATA: N/A. LIMITATIONS REASONS FOR CAUTION: Some rare ploidy abnormalities, such as polyploidy with an equal number of identical sets of chromosomes and ploidy mosaicism cannot be accurately identified. Moreover, the origin of abnormal ploidy was not identified due to the unavailability of DNA from both parents. WIDER IMPLICATIONS OF THE FINDINGS: The PGT-Plus AI model provides a ploidy evaluation method based on the conventional PGT-A data and integrates directly into standard PGT-A workflows. Clinical utility results suggest that the model is a valuable tool for identifying embryos with abnormal ploidy in PGT-A and rescuing normal diploid embryos from abnormally fertilized embryos. These findings demonstrate that PGT-Plus significantly enhances the diagnostic accuracy of PGT. STUDY FUNDING/COMPETING INTERESTS: This study was supported by grants from Major Scientific Program of CITIC Group (No. 2023ZXKYB34100, to Ge.L.), Hunan Provincial Grant for Innovative Province Construction (2019SK4012), Hunan Xiangjiang New District (Changsha High-tech Zone) key core technology research project in 2023, and Science Foundation of Hunan Province (Grant 2023JJ30422). All authors declared no conflicts of interest..

artificial intelligence↗

PubMedDate not supplied

Validation and subsequent development of the DEREK skin sensitization rulebase by analysis of the BgVV list of contact allergens.

The DEREK knowledge-based computer system contains a subset of approximately 50 rules describing chemical substructures (toxophores) responsible for skin sensitization. This rulebase, based originally on Unilever historical in-house guinea pig maximization test data, has been subject to extensive validation and is undergoing refinement as the next stage of its development. As part of an ongoing program of validation and testing, the predictive ability of the sensitization rule set has been assessed by processing the structures of the 84 chemical substances in the list of contact allergens issued by the BgVV (German Federal Institute for Health Protection of Consumers). This list of chemicals is important because the biological data for each of the chemicals have been carefully scrutinized and peer reviewed, a key consideration in an area of toxicology in which much unreliable and potentially misleading data have been published. The existing DEREK rulebase for skin sensitization identified toxophores for skin sensitization in the structures of 71 out of the 84 chemicals (85%). The exercise highlighted areas of chemistry where further development of the rulebase was required, either by extension of the scope of existing rules or by generation of new rules where a sound mechanistic rationale for the biological activity could be established. Chemicals likely to be acting as photoallergens were identified, and new rules for photoallergenicity have subsequently been written. At the end of the exercise, the refined rulebase was able to identify toxophores for skin sensitization for 82 of the 84 chemicals in the BgVV list.

Allergens↗

PubMed2002

Identifying spots in microarray images.

Microarray technology has provided a way to quantitate the simultaneous expression of a large number of genes. This approach is dependent on reproducible, accurate identification and quantitation of spot intensities. In this paper, clustering-based image segmentation is described to extract the target intensity of the microarray spots. While the technique is generic, its effectiveness on extracting spot intensities on arrays obtained from a two-color (Cy3/Cy5) experiment is discussed. The approximate boundaries of the spots are determined initially by manual alignment of rectangular grids. The pixel intensities of the image (I) inside a grid, is mapped onto a one-dimensional vector (v). The k-means clustering technique is applied to generate a binary partition of v. The median value of the pixel intensities inside each of the clusters for a given spot determines its foreground and the local background intensity. The difference in the median value of the foreground and the background intensity is the desired target intensity of the spot. The results are compared against those obtained using a region growing approach.

Algorithms↗

PubMed2005

A model (in)validation approach to gait classification.

This paper addresses the problem of human gait classification from a robust model (in)validation perspective. The main idea is to associate to each class of gaits a nominal model, subject to bounded uncertainty and measurement noise. In this context, the problem of recognizing an activity from a sequence of frames can be formulated as the problem of determining whether this sequence could have been generated by a given (model, uncertainty, and noise) triple. By exploiting interpolation theory, this problem can be recast into a nonconvex optimization. In order to efficiently solve it, we propose two convex relaxations, one deterministic and one stochastic. As we illustrate experimentally, these relaxations achieve over 83 percent and 86 percent success rates, respectively, even in the face of noisy data.

Algorithms↗

PubMed1994

A clinical database as a component of a diagnostic hematology workstation.

A clinical database was designed as part of a comprehensive workstation for diagnostic laboratory hematology. The database stores coded findings pertinent to hematologic disorders including inherited abnormalities, previous surgery, malignancies, current therapy, and laboratory test results. The workstation includes knowledge-based systems for peripheral blood analysis, flow cytometry studies, and bone marrow morphology. The peripheral blood system renders an interpretive report based on data from a complete blood count with manual review of a blood smear by a technologist. The flow cytometry module interprets the immunophenotyping and DNA content results, and correlates them with the clinical findings and the peripheral blood data. The bone marrow system bases its report on all of the available information including the morphologic review of the bone marrow specimen by a physician, the peripheral blood data, immunophenotype, and clinical/laboratory findings. Before generating an interpretive report, each of the knowledge-based systems automatically searches the clinical database for specific information pertinent to the findings in the case. Since the workstation must function in situations where access to distributed databases is not feasible or not yet practical, a data entry module with a graphical user interface has been created.

Artificial Intelligence↗

PubMed1996

Building intelligent alarm systems by combining mathematical models and inductive machine learning techniques Part 2--sensitivity analysis.

In an earlier study an approach was described to generate intelligent alarm systems for monitoring ventilation of patients via mathematical simulation and machine learning. However, ventilator settings were not varied. In this study we investigated whether an alarm system could be created with which a satisfactory classification performance could be obtained under a wide variety of ventilator settings, by varying inspiratory to expiratory time (I:E) ratio, tidal volume and respiratory rate. In a first experiment three patient data sets were modeled, each with a different I:E ratio. A part of each data set was used to construct an alarm system for each I:E ratio. The remaining part was used to test the performance of the alarm systems. The three training sets were also combined to construct one alarm system, which was tested with the three test sets. Finally, all alarm systems were tested with data generated by a patient simulator. Similar experiments were performed for the tidal volume and the respiratory rate. It was concluded that an optimally functioning alarm system should contain a library of rule sets, one for each set of ventilator settings. A second best alternative is to take all possible settings into consideration when constructing the training set. Classification performance of the trees that were trained with multiple ventilator settings ranged from 98 to 100% for all test sets. When tested with the independent patient simulator data the classification performance of these trees ranged from 80 to 100%.

Airway Resistance↗

PubMed1989

Diagnostic reasoning.

Research in cognitive science, decision sciences, and artificial intelligence has yielded substantial insights into the nature of diagnostic reasoning. Many elements of the diagnostic process have been identified, and many principles of effective clinical reasoning have been formulated. Three reasoning strategies are considered here: probabilistic, causal, and deterministic. Probabilistic reasoning relies on the statistical relations between clinical variables and is frequently used in formal calculations of disease likelihoods. Probabilistic reasoning is especially useful in evoking diagnostic hypotheses and in assessing the significance of clinical findings and test results. Causal reasoning builds a physiologic model and assesses a patient's findings for coherency and completeness against the model; it functions especially effectively in verification of diagnostic hypotheses. Deterministic reasoning consists of sets of compiled rules generated from routine, well-defined practices. Much human problem solving may derive from activation and implementation of such rules. A deeper understanding of clinical cognition should enhance clinical teaching and patient care.

Cognition↗

PubMed2005

Designing a neural network for the constraint optimization of the fitness functions devised based on the load minimization of the genetic code.

Nonrandom patterns in codon assignments are supported by many statistical and biochemical studies in the last two decades. The canonical genetic code is known to be highly efficient in minimizing the effects of mistranslational errors and point mutations, an ability, which in term is designated "load minimization". Prior studies have included many attempts at quantitative estimation of the fraction of randomly generated codes, which in terms of load minimization, score higher than the canonical genetic code. In this study, a neural network, which estimates a highly optimized genetic code in a relatively short period of time has been devised. Several fitness functions were used throughout this text. Meanwhile, we have made use of two cost measure matrices, PAM74-100 and mutation matrix.

Algorithms↗

PubMed2005

Human-centered design of a distributed knowledge management system.

Many healthcare technology projects fail due to the lack of consideration of human issues, such as workflow, organizational change, and usability, during the design and implementation stages of a project's development process. Even when human issues are considered, the consideration is typically on designing better user interfaces. We argue that human-centered computing goes beyond a better user interface: it should include considerations of users, functions and tasks that are fundamental to human-centered computing. From this perspective, we integrated a previously developed human-centered methodology with a Project Design Lifecycle, and we applied this integration in the design of a complex distributed knowledge management system for the Biomedical Engineer (BME) domain in the Mission Control Center at NASA Johnson Space Center. We analyzed this complex system, identified its problems, generated systems requirements, and provided specifications of a replacement prototype for effective organizational memory and knowledge management. We demonstrated the value provided by our human-centered approach and described the unique properties, structures, and processes discovered using this methodology and how they contributed in the design of the prototype.

Artificial Intelligence↗

PubMed2005

Interpreting procedures from descriptive guidelines.

Errors in clinical practice guidelines may translate into errors in real-world clinical practice. The best way to eliminate these errors is to understand how they are generated, thus enabling the future development of methods to catch errors made in creating the guideline before publication. We examined the process by which a medical expert from the American College of Physicians (ACP) created clinical algorithms from narrative guidelines, as a case study. We studied this process by looking at intermediate versions produced during the algorithm creation. We identified and analyzed errors that were generated at each stage, categorized them using Knuth's classification scheme, and studied patterns of errors that were made over the set of algorithm versions that were created. We then assessed possible explanations for the sources of these errors and provided recommendations for reducing the number of errors, based on cognitive theory and on experience drawn from software engineering methodologies.

Artificial Intelligence↗

PubMed2004

Dynamic proximity of spatio-temporal sequences.

Recurrent networks can generate spatio-temporal neural sequences of very large cycles, having an apparent random behavior. Nonetheless a proximity measure between these sequences may be defined through comparison of the synaptic weight matrices that generate them. Following the dynamic neural filter (DNF) formalism we demonstrate this concept by comparing teacher and student recurrent networks of binary neurons. We show that large sequences, providing a training set well exceeding the Cover limit, allow for good determination of the synaptic matrices. Alternatively, assuming the matrices to be known, very fast determination of the biases can be achieved. Thus, a spatio-temporal sequence may be regarded as spatio-temporal encoding of the bias vector. We introduce a linear support vector machine (SVM) variant of the DNF in order to specify an optimal weight matrix. This approach allows us to deal with noise. Spatio-temporal sequences generated by different DNFs with the same number of neurons may be compared by calculating correlations of the synaptic matrices of the reconstructed DNFs. Other types of spatio-temporal sequences need the introduction of hidden neurons, and/or the use of a kernel variant of the SVM approach. The latter is being defined as a recurrent support vector network (RSVN).

Action Potentials↗

PubMed2005

Separating reflection components of textured surfaces using a single image.

In inhomogeneous objects, highlights are linear combinations of diffuse and specular reflection components. A number of methods have been proposed to separate or decompose these two components. To our knowledge, all methods that use a single input image require explicit color segmentation to deal with multicolored surfaces. Unfortunately, for complex textured images, current color segmentation algorithms are still problematic to segment correctly. Consequently, a method without explicit color segmentation becomes indispensable and this paper presents such a method. The method is based solely on colors, particularly chromaticity, without requiring any geometrical information. One of the basic ideas is to iteratively compare the intensity logarithmic differentiation of an input image and its specular-free image. A specular-free image is an image that has exactly the same geometrical profile as the diffuse component of the input image and that can be generated by shifting each pixel's intensity and maximum chromaticity nonlinearly. Unlike existing methods using a single image, all processes in the proposed method are done locally, involving a maximum of only two neighboring pixels. This local operation is useful for handling textured objects with complex multicolored scenes. Evaluations by comparison with the results of polarizing filters demonstrate the effectiveness of the proposed method.

Algorithms↗

PubMed2005

A biologically inspired active compliant joint using local positive velocity feedback (LPVF).

Starting from studies which revealed that positive feedback is found in the control system for walking in arthropods, we have constructed a new positive feedback driven joint that can be used for solving compliant motion tasks. We propose two different joint constructions each of which shows passive compliance. Based on these joints we introduce three different local positive velocity feedback (LPVF) controllers and discuss their properties in the context of motion generation in closed kinematic chains. The third circuit named undelayed dLPVF is used for the control of a compliant planar manipulator which turns a crank. Our concept is of highly decentralized nature and follows the idea of embodiment. In our case this means that a process which is controlled by LPVF controllers reveals its nature when the controllers interact with this process.

Animals↗

PubMed2004

Measurement of mesothelioma on thoracic CT scans: a comparison of manual and computer-assisted techniques.

Our purpose in this study was to evaluate the variability of manual mesothelioma tumor thickness measurements in computed tomography (CT) scans and to assess the relative performance of six computerized measurement algorithms. The CT scans of 22 patients with malignant pleural mesothelioma were collected. In each scan, an initial observer identified up to three sites in each of three CT sections at which tumor thickness measurements were to be made. At each site, five observers manually measured tumor thickness through a computer interface. Three observers repeated these measurements during three separate sessions. Inter- and intra-observer variability in the manual measurement of tumor thickness was assessed. Six automated measurement algorithms were developed based on the geometric relationship between a specified measurement site and the automatically extracted lung regions. Computer-generated measurements were compared with manual measurements. The tumor thickness measurements of different observers were highly correlated (r > or = 0.99); however, the 95% limits of agreement for relative inter-observer difference spanned a range of 30%. Tumor thickness measurements generated by the computer algorithms also correlated highly with the average of observer measurements (r > or = 0.93). We have developed computerized techniques for the measurement of mesothelioma tumor thickness in CT scans. These techniques achieved varying levels of agreement with measurements made by human observers.

Adult↗