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At least 1,513 records · Page 84Linked to original sources
PubMed1996

Comparison of an expert system with other clinical scores for the evaluation of severity of asthma.

"Asthmaexpert" was produced at the special request of several clinicians in order to obtain a better understanding of the medical decisions taken by clinical experts in the management of asthmatic patients. In order to assess the severity of asthma, a new score called Artificial Intelligence score (AI score), produced by Asthmaexpert, was compared with three other scores (Aas, Hargreave and Brooks). One hundred patients were enrolled prospectively in the study during their first consultation in the out-patient clinic. Distribution of severity level according to the different scores was studied, and the reliability between AI and other scores was evaluated by Kappa and MacNemar tests. Correlations with functional parameters were performed. The AI score assessed higher levels of severity than the other scores (Kappa = 18, 28 and 10% for Aas, Hargreave and Brooks, respectively) with significant MacNemar test in all cases. There was a significant correlation between AI score and forced expiratory volume in one second (FEV1) (r = 0.73). These data indicate that the AI score is a severity score which defines higher levels of severity than the chosen scores. Correlations for functional parameters are good. This score appears easy to use for the first consultation of an asthmatic patient.

Adult↗
PubMed1978

The validation of procedures to assess prevocational task preferences in retarded adults.

Three severely retarded young adults between the ages of 19 and 21 years participated in a prevocational training program, and worked regularly on six different tasks during the scheduled six-hour day. The study attempted to assess each subject's preferences for the six tasks: collating, stuffing, sorting, pulley assembly, flour-sifter assembly, and circuit-board stuffing. In Phase I, the procedure consisted of randomly pairing each task with all other tasks in a two-choice situation that required the subjects to select one task from each pair combination to work for a seven-minute period. The selection procedure consisted of presenting two representative task objects on a tray and requesting the subject to pick up one object and place it on the work table. The object selected represented the task worked for that period. The 15 possible pair combinations were presented randomly every two days for a period of 34 days to determine the preferences. During the validation phase (Phase II), each subject's least- and most-preferred tasks were paired separately with moderately-preferred tasks. As expected, these manipulations confirmed the baseline data, as choices for the moderately-preferred tasks decreased when consistently paired with the preferred tasks and increased when consistently paired with the least-preferred tasks.

Adult↗
PubMed1995

Classification and regression trees (CART) for prediction of function at 1 year following head trauma.

A cohort of 514 hospitalized head-injury survivors was identified based on their injury and 448 (87%) of them were followed for 1 year. Comprehensive neurobehavioral testing was performed 1 month and 1 year after injury. The authors developed predictions of six neuropsychological and two psychosocial outcomes 1 year after head injury. Prediction trees are presented for verbal IQ, Halstead's Impairment Index, and work status at 1 year. Early predictors of neurobehavioral outcome in survivors are similar to previously reported predictors of mortality. Extent (both depth and length) of coma and age are the medical and demographic variables most predictive of late outcome. Adding 1-month scores substantially improves prediction of neuropsychological variables. The classification and regression tree is a useful technique for predicting long-term outcome in patients with head injury. The trees are simple enough to be used in a clinical setting and, especially with 1-month scores, predictions are accurate enough for clinical utility.

Adolescent↗
PubMed1997

Of red planets and indigo computers: Mars database visualization as an example of platform downsizing.

The last decade has witnessed tremendous advancements in the computer hardware and software used to perform scientific visualization. In this paper, we consider how the visualization of a particular data set, the digital terrain model derived from the Viking orbiter imagery, has been realized in four distinct projects over this period. These examples serve to demonstrate how the vast improvements in computational performance both decrease the cost of such visualization efforts and permit an increasing level of interactivity. We then consider how even today's graphical systems require the visualization designer to make intelligent choices and tradeoffs in database rendering. Finally, we discuss how insights gleaned from an understanding of human visual perception can guide these design decisions, and suggest new options for visualization hardware and software.

Computer Graphics↗
PubMed2003

Internet based expert system for the management of gallstones, renal, ureteric and bladder calculi.

An Internet based expert system for the management of gallstones, Renal, Ureteric and bladder calculi based on ultrasound images is presented in this paper. Calculi are due to abnormal collection of certain chemicals like oxalate, phosphate and Uric acid. These calculi can be present in kidney, Ureter or in Urinary bladder and also in gall bladder. The expert system is designed to assist the physician to detect, extract, classify and diagnose calculi with greater accuracy. It also helps physicians in the management of calculi based on the etiological analysis of calculi. The Expert system takes an ultrasound image as input along with the symptoms of the patients. The expert system extracts the renal calculi and analyzes it using different image processing techniques to extract the image features like size, location and texture. These image features along with the clinical data of the patient enable the expert system to provide the decisions to decide the future course of treatment with more accuracy.

Artificial Intelligence↗
PubMed1996

Ethical decision-making near the end of life.

For ethical decision-making near the end of life, autonomy is the moral North Star. At the same time, for some treatments, the burdens so clearly outweigh benefits that physicians may make a judgment not to offer the treatment. This is often clearer in surgery. A person with colon cancer and metastases may not insist on resection of the metastases. For some reason, some treatments have escaped these logical constraints. Attempted resuscitation of a dying patient is a good example. The circumstances in which a physician may make choices on behalf of a competent, terminally-ill patient without consent, and even without notification, are hotly debated, but data suggest that physicians do so frequently. Patients who lack capacity present even more difficult challenges. Advance directives, when available, can be extremely helpful, but even with them difficult problems can remain. If advance directives have not been established, family and close friends are an obvious source of guidance. Their legal role varies in different jurisdictions; in practice, they are crucial in bedside decision-making. Guardianship and alternatives to it remain a poor last resort. Euthanasia is a very difficult problem. We believe it is semantically misleading to lump under the term "passive euthanasia" those circumstances where potentially life-sustaining treatment is withheld or withdrawn. The tension between patient autonomy and medical common sense remains unresolved within the "futility" controversy. The authors believe it serves no purpose to discuss carefully with dying patients propositions that are nonsense. At the same time, physicians must not confuse decisions about quality of life with judgements about treatment effectiveness. We believe that what many, although not all, dying patients want are physicians with intelligent compassion who can take care of them through the dying process.

Advance Directives↗
PubMed1998

Scalable methodologies for distributed development of logic-based convergent medical terminology.

As the size and complexity of medical terminologies increase, terminology modelers are increasingly hampered by lack of tools and methods to manage the development process. This paper presents our use and ongoing evaluation of a description-logic classifier to support cognitive scalability of the underlying terminology and our enhancements to that classifier to support concurrent development utilizing semantics-based concurrency control methods. Our enhancements, collectively referred to as the Gálapagos, consist of several applications that take locally-developed terminology enhancements from multiple sites, identify conflicting design decisions, support the modelers' reconciliation of the conflicting designs, and efficiently disseminate updates tailored for locally enhanced terminologies. We have tested our ideas through concurrent evolutionary enhancement of SNOMED International at three Kaiser Permanente regions and the Mayo Clinic. We have found that the underlying environment has met our design objectives, and supports semantic-based concurrency control, and identification and resolution of conflicting design decisions.

Artificial Intelligence↗
PubMed2005

[Study on intelligent system of hyper-spectral data gathering based on GPS in farm machinery].

In order to meet the precision agriculture needs that highly dense and overall farmland information is gathered, this system uses Visual Basic and Map-Objects to carry on integration of GPS, GIS and RS. On the one hand, the system can provide the space seat attribute of high precision for the spectral data gathered by way of integration GPS, and realizes integration in spectrum data gathering, orientation and storage. On the other hand, by way of integrated GIS, the system has realized the function of visualizing analysis and management of the spectrum data, displaying real-time trace of moving point, providing some related attribute of the moving point in map layers, and calculating crop derivative spectra and crop vegetation index NDVI etc. The sufficient information is put forward for expert system and decision support system of precision agriculture.

Agriculture↗
PubMed2026

Integrating ex vivo platforms with AI to guide glioblastoma treatment.

PURPOSE: Ex vivo platforms can rapidly and cost-effectively screen patient-derived tumor cells or tissue. Artificial intelligence (AI) algorithms can search and identify patterns in large datasets and provide predictions. This review focuses on integrating microphysiological platforms with AI to inform physician and patient decision-making. METHODS AND RESULTS: Combining efficacy, safety, and pharmacology results from drug screens with the output of extensive AI searches can yield insights to guide physician and patient decision-making and potentially improve a patient's prognosis. We detail ex vivo platforms at different stages of development that represent the diversity of approaches: a microphysiological system and a high-throughput screen that assesses drug cytotoxicity in both bulk and drug-tolerant tumor cells. We review AI approaches that can enhance the utility of microphysiological platforms. CONCLUSION: Integrating emerging microphysiological platforms with AI is expected to significantly impact physician and patient choice of treatment.

Humans↗
PubMed1985

RED: a red-cell antibody identification expert module.

We describe a software module in an expert system RED, which interprets data related to red cell antibody identification. There are three portions to this module: the problem-solving component, which incorporates the knowledge required for antibody identification as a hierarchy of programs. The programs in the hierarchy organize within themselves small pieces of knowledge represented in the form of production rules, which are capable of making judgments concerning a specific hypothesis; an intelligent data base for storage of patient data, red cell attributes, and test results; the "overview critic" portion, which combines the atomic hypotheses judged favorably by the antibody programs into a unified judgment concerning the case. Overview makes the decision to terminate processing with a conclusion about which antibodies are actually present and what specific further tests need to be performed to resolve any remaining ambiguities.

Artificial Intelligence↗
PubMed2026

Emerging multidimensional biomarker system for cardiovascular-kidney-metabolic syndrome: from multi-omics integration to clinical artificial intelligence.

Cardiovascular-kidney-metabolic (CKM) syndrome is an emerging clinical entity that highlights the complex, bidirectional interplay among cardiovascular disease, chronic kidney disease, and metabolic disorders, representing a substantial and growing global health burden. This conceptualization marks a paradigm shift from viewing these conditions in isolation to understanding them as an interconnected disease continuum. Traditional biomarkers face significant limitations in the early detection, risk stratification, and precise management of CKM, necessitating a transition towards an integrated framework that captures its multisystem nature. This review systematically outlines an emerging multidimensional biomarker system encompassing key pathological axes such as metabolism, immuno-inflammation, oxidative stress, and biological aging, offering refined risk assessment beyond conventional metrics. The development of this system is propelled by revolutionary platforms, including accessible sampling techniques (e.g., dried blood spots), advanced in vitro models (e.g., multi-organ-on-a-chip), and multi-omics technologies. These platforms not only facilitate a deeper dissection of the heterogeneous origins and inter-organ crosstalk in CKM but also accelerate the discovery and validation of novel biomarkers. Concurrently, artificial intelligence serves as a pivotal tool for clinical translation, effectively integrating high-dimensional data to transform complex molecular profiles into actionable clinical insights. By enabling the construction of dynamic risk prediction and decision-support systems, this review charts a pathway toward proactive, individualized, and precise prevention and management of CKM syndrome.

Humans↗
PubMed2001

[Diagnosis of reading and spelling disorder].

The ICD-10 calls for the use of tables that account for the correlation between intelligence and spelling or reading, respectively (regression model) in the diagnosis of dyslexia. In this paper we discuss the consequences that arise from this recommendation with respect to the interpretation of psychometric tests. In addition, a table is presented that contains the data required to make diagnostic decisions based on the regression model. Furthermore, an expected prevalence rate was calculated using randomized computer data.

Adolescent↗
PubMed2003

Multivariate analyses of the profile stability of intelligence tests: high for IQs, low to very low for subtest analyses.

Profile stability involves the consistency of a set of scores over time. That is, does a profile of scores change on retesting and does this change affect clinical decisions? While psychologists routinely examine the reliability of individual scores, little research has examined the stability of a profile or set of scores. The first study described in this paper examined potential measures of profile stability using a simulation computer program. The results suggest that several measures show promise in this context, particularly Cattell's coefficient of pattern similarity (r(p)), salient variable similarity index (S), and the D(2) coefficient. In the second study, selected measures of profile stability were applied to Wechsler test-retest data. The results suggest that profiles composed of IQ and index scores demonstrate acceptable stability and can be usefully interpreted in clinical and research situations. However, subtest score profiles are inherently less stable and provide little useful clinical information.

Cattell Personality Factor Questionnaire↗
PubMed2025

Diagnosing the undiagnosed: AI-enhanced multimodal modeling for placental mesenchymal dysplasia in high-risk pregnancies.

Placental mesenchymal dysplasia (PMD) is a rare vascular placental disorder that mimics molar pregnancy but often coexists with a viable fetus, making its misdiagnosis potentially devastating. In high-risk pregnancies, artificial intelligence (AI)-enhanced multimodal modeling - incorporating imaging, genomics, proteomics, and clinical features - offers a transformative diagnostic strategy. Leveraging Bayesian hyperparameter optimization for model refinement, this approach improves diagnostic accuracy while reducing uncertainty and clinician hesitation. Recent clinical studies support its efficacy and interpretability through SHAP and LIME models, while real-time surgical enhancements using Bayesian methods highlight its broader clinical utility. Despite current challenges such as data heterogeneity and integration barriers, multimodal AI provides unprecedented resolution in placental analysis, enabling precise differentiation between PMD and similar fetopathies. Ultimately, this advancement supports timely, non-invasive diagnosis, personalized management, and emotionally informed decision-making aligned with ethical AI implementation standards.

Bayesian optimization↗