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Early detection of pulmonary hypertension with heart sounds analysis pilot study.

There are strong evidences to support that the modification of the characteristics of the second heart sound has a high correlation with the pulmonary arterial pressure (PAP). It is hence postulated that a specific heart sound spectrum for this disease group could be generated as a decision support system to help healthcare workers for the early detection of pulmonary hypertension. This paper described the design of a case-control study on identifying the heart sound pattern of people with pulmonary hypertension. In the proposed study, the heart sound of patients having pulmonary hypertension will be captured by an electronic stethoscope and processed into digital sound spectra which will be analysed to identify a specific heart sound pattern. In the future, an intelligent device will be developed based on the identified pattern to identify and diagnose early stage of pulmonary hypertension.

Decision Support Systems, Clinical↗

Diagnostic utility of the Learning Disability Index.

The Learning Disability Index (LDI) is one of many diagnostic indicators proposed for the identification of students with learning disabilities that relies on patterns of performance on cognitive tests. The LDI is hypothesized to relate to students' specific neuropsychological deficits. The present study investigated the diagnostic utility of the LDI with the third edition of the Wechsler Intelligence Scale for Children by comparing students previously diagnosed with learning disabilities (n = 2,053) to students without learning disabilities (n = 2,200). Subsamples of youth with specific reading (n = 445) and math (n = 168) disabilities permitted further assessment of the efficacy of the LDI. Receiver operating characteristic (ROC) curves revealed that the LDI resulted in a correct diagnostic decision only 55% to 64% of the time. These results demonstrate that the LDI is not a valid diagnostic indicator of learning disabilities.

Adolescent↗

NewYork-Presbyterian Hospital: translating innovation into practice.

BACKGROUND: NewYork-Presbyterian (NYP) Hospital, a 2,242-bed not-for-profit academic medical center, was formed by a merger of The New York Hospital and The Presbyterian Hospital in the City of New York. It is also the flagship for the NewYork-Presbyterian Healthcare System, with 37 acute care facilities and 18 others. OVERALL APPROACH TO QUALITY AND SAFETY: The hospital embeds safety in the culture through strategic initiatives and enhances service and efficiency using Six Sigma and other techniques to drive adoption of improvements. Goals are selected in alignment with the annual strategic initiatives, which are chosen on the basis of satisfaction surveys, patient and family complaints, community advisory groups, and performance measures, among other sources. USE OF INFORMATION TO SET AND EVALUATE QUALITY GOALS AND PRIORITIZE INITIATIVES: A new business intelligence system enables online, dynamic analysis of performance results, replacing static paper reports. Advanced features in the clinical information systems include computerized physician order entry; interactive clinical alerts for decision support; a real-time infection control tracking system; and a clinical data warehouse supporting data mining and analysis for quality improvement, decision making, and education. APPROACH TO ADDRESSING THE SIX IOM QUALITY AIMS: To achieve clinical, service, and operational excellence, NYP focuses on all Institute of Medicine quality aims.

Hospital Bed Capacity, 500 and over↗

Dimensions of time in illness: an objective view.

It is almost impossible to try to represent and analyze clinical data in the absence of a temporal dimension. The temporal aspect is especially important when automated decision support is used for patient care over substantial periods. This paper emphasizes the crucial role that tasks of temporal reasoning and temporal maintenance play in modern medical information and decision support systems; it also discusses the implications of providing automated support to clinicians who must perform such tasks as part of broader clinical tasks (for example, monitoring and therapy). Temporal reasoning tasks mainly involve intelligent analysis of time-oriented clinical data, and temporal maintenance tasks focus on effective storage and retrieval of these data. Both types of tasks, however, are highly relevant for such applications as patient monitoring, proper use of therapeutic guidelines, assessment of the quality of guideline use, and visualization and exploration of time-oriented biomedical data. Scientists in medical informatics should view the integration of these two areas as a major research and development goal. This paper demonstrates one approach to integration by presenting the concept of a temporal mediator, which combines temporal reasoning and temporal maintenance. Use of the temporal mediator in several clinical tasks is also presented and discussed.

Databases, Factual↗

Beyond data and technology: the need for new thinking to enable the era of precision prevention.

BACKGROUND: Global flagship initiatives increasingly advocate for proactive health maintenance to alleviate the growing burden on reactive, disease-focused healthcare systems. Precision prevention is conceived as the targeted modulation of causal pathways across the disease continuum, from latent risk and pre-disease states to clinical manifestation, surpassing conventional public health prevention strategies that prioritise managing population-level risk factors. Traditional discovery and implementation models, however, remain poorly aligned with the pace and breadth of scientific and technological advances. This review outlines key barriers to scaling precision prevention and argues for the integration of conceptual, methodological, and policy perspectives into a single implementation‑oriented framework. MAIN: Individualised risk stratification lies at the core of precision prevention. Genomics serves as a stable substrate for lifetime susceptibility assessment, while meaningful prediction in multifactorial chronic disease requires additional risk monitoring using dynamic intermediate molecular markers and high-resolution exposomic data. Machine learning and other artificial intelligence (AI) methods are increasingly helpful tools for integrating large, heterogeneous and temporally structured real-world data to generate personalised predictions of health trajectories. Trustworthy AI-enabled risk prediction or decision-support systems are expected to provide transparency about model logic, assumptions and performance. In discovery, existing diagnostic classifications and conventional case-control designs can obscure mechanistic heterogeneity. Shifting toward precision phenotyping and biologically grounded disease redefinition could reveal a new layer of molecular understanding. Evidence generation strategies that reflect the temporal change of disease, including high‑risk enrichment, surrogate endpoints, and adaptive, trajectory-based monitoring, are particularly important for common conditions with prolonged latency periods (e.g., cancer, cardiovascular disease). Features often dismissed as "noise", such as stochastic molecular variation and minimal exposures, may in fact encode meaningful individual-level signals and thus merit investigation. CONCLUSION: To shift healthcare from reactive treatment toward proactive health maintenance requires coordinated action from stakeholders to reshape the pillars of discovery, reform outcome assessments and modernise implementation strategies.

Humans↗

Expert systems for medical consultation.

After reviewing some of the elements that permit a clearer understanding of what characterizes the "expert systems" approach, representation of medical concepts, symbolic manipulation rather than numeric calculation, causal reasoning, etc., this paper will try to deal with some of the most noteworthy developments in medical decision aid, teaching, research, before discussing some problems raised by the evaluation of expert systems in medicine.

Artificial Intelligence↗

Patient result validation services.

Patient result validation is a vital final stage of laboratory quality assurance and is usually the responsibility of senior laboratory staff. Computerised validation systems have recently been introduced to autovalidate data meeting certain pre-defined criteria, thereby allowing senior staff to focus on problematic cases. This article describes the patient results validation service module developed for an advanced instrument workstation in the OpenLabs project. Using knowledge-based techniques, the module provides a range of locally configurable advanced validation procedures (e.g., internal consistency checks, delta checks, and checks for selected specific errors) in addition to the standard reference range and pathological limits checks offered by many laboratory information systems.

Artificial Intelligence↗

U.S. intelligence system: model for corporate chiefs?

A fully dedicated intelligence support function for senior management is no longer a luxury but a necessity. Companies can enhance their intelligence capabilities by using the government model as a rough blueprint to structure such a program.

Administrative Personnel↗

A support vector machines classifier to assess the severity of idiopathic scoliosis from surface topography.

A support vector machines (SVM) classifier was used to assess the severity of idiopathic scoliosis (IS) based on surface topographic images of human backs. Scoliosis is a condition that involves abnormal lateral curvature and rotation of the spine that usually causes noticeable trunk deformities. Based on the hypothesis that combining surface topography and clinical data using a SVM would produce better assessment results, we conducted a study using a dataset of 111 IS patients. Twelve surface and clinical indicators were obtained for each patient. The result of testing on the dataset showed that the system achieved 69-85% accuracy in testing. It outperformed a linear discriminant function classifier and a decision tree classifier on the dataset.

Adolescent↗

[Artificial intelligence in sleep analysis (ARTISANA)--modelling visual processes in sleep classification].

We describe a novel approach to the problem of automated sleep stage recognition. The ARTISANA algorithm mimics the behaviour of a human expert visually scoring sleep stages (Rechtschaffen and Kales classification). It comprises a number of interacting components that imitate the stepwise approach of the human expert, and artificial intelligence components. On the basis of parameters extracted at 1-s intervals from the signal curves, artificial neural networks recognize the incidence of typical patterns, e.g. delta activity or K complexes. This is followed by a rule interpretation stage that identifies the sleep stage with the aid of a neuro-fuzzy system while taking account of the context. Validation studies based on the records of 8 patients with obstructive sleep apnoea have confirmed the potential of this approach. Further features of the system include the transparency of the decision-taking process, and the flexibility of the option for expanding the system to cover new patterns and criteria.

Adult↗

Aqueous humour extracellular vesicle membrane protein profiling reveals pathological features of refractory macular edema.

BACKGROUND: Both diabetic macular edema (DME) and retinal vein occlusion-related macular edema (RVO-ME) can become refractory to anti-vascular endothelial growth factor (anti-VEGF) therapy, but the underlying mechanisms are unclear. Molecular discrimination of refractory disease could guide personalized treatment. This study examined whether aqueous humor-derived extracellular vesicle (EV) membrane proteins can characterize refractoriness and reveal etiology-specific pathways. METHODS: This prospective cohort study included 28 patients with DME or RVO-ME (14 each), further divided into treatment-naïve and refractory subgroups. Aqueous humour samples were collected before intravitreal anti-VEGF injection. EV membrane proteins were profiled using an EV Array chip targeting 435 antibodies. Differentially expressed proteins were analyzed by bioinformatics, including Gene Ontology, Kyoto encyclopaedia of genes and genomes (KEGG) pathway enrichment, Gene set enrichment analysis (GSEA), and cell-of-origin mapping using public single-cell RNA-seq data. RESULTS: VEGF/VEGFR2 were elevated in treatment-naïve DME and RVO-ME. Refractory DME showed upregulation of C5 and CD34 (complement/immune activation). Refractory RVO-ME exhibited increased CD68 and Annexin A1 with decreased PDGFR (chronic inflammation, vascular dysregulation). RANTES was commonly upregulated in refractory disease. Several EV proteins discriminated refractory cases with high accuracy (AUC 0.898-0.980). Cellular origin suggested immune cell-derived EVs in DME, retinal cell-derived EVs in RVO-ME. External validation confirmed key differences. CONCLUSION: Refractory ME involves distinct pathways: immune-inflammatory activation in DME versus chronic inflammation with vascular dysregulation in RVO-ME. EV membrane proteins from aqueous humor provide insights into therapeutic resistance and hold promise as biomarkers for personalized treatment decisions.

artificial intelligence↗

Prediction of clinical behaviour and treatment for cancers.

Prediction of clinical behaviour and treatment for cancers is based on the integration of clinical and pathological parameters. Recent reports have demonstrated that gene expression profiling provides a powerful new approach for determining disease outcome. If clinical and microarray data each contain independent information then it should be possible to combine these datasets to gain more accurate prognostic information. Here, we have used existing clinical information and microarray data to generate a combined prognostic model for outcome prediction for diffuse large B-cell lymphoma (DLBCL). A prediction accuracy of 87.5% was achieved. This constitutes a significant improvement compared to the previously most accurate prognostic model with an accuracy of 77.6%. The model introduced here may be generally applicable to the combination of various types of molecular and clinical data for improving medical decision support systems and individualising patient care.

Algorithms↗

Electronic Patient Information -- Pioneers and MuchMore. A vision, lessons learned, and challenges.

OBJECTIVES: This paper must fulfill three different tasks: First, to introduce the topic "Electronic Patient Information -- Pioneers and MuchMore", second, to introduce the invited authors of the symposium, and third, to serve as the author's academic farewell lecture as professor emeritus. RESULTS: The electronic patient record, with all its different kinds of patient information, can be structured in many ways. Here, an historical approach is presented with a primary focus on the development of an information system for in- and outpatients in Germany, especially in Frankfurt, but also in comparison with US systems. The "Stone Age" and "Bronze Age" of patient-related computer applications started with expensive and insufficient hardware, but some years later, the first systems for patient documentation, text generation, and data acquisition could be implemented. The "iron age" and "golden age" yielded until the mid-1970s, e.g. in Oakland, Boston, Salt Lake City, and Frankfurt, quite successful Hospital Information Systems with some special emphasis on natural language processing. The following dark years were filled primarily with administrative systems, but beginning in the early 1980s, an era of enlightenment started, e.g. with rather inexpensive and easy to use PC application, broadly distributed MUMPS systems, and improved thesaurus-based text analysis. Especially in modern times, the medical text processing and classifying has been extended and successfully applied. CONCLUSIONS: Somewhat in contrast to other approaches, in the future the use of medical linguistics for the development of a successful electronic patient record should be better supported. Electronic patient information should be available wherever and whenever needed. For this, intelligent and automated reporting and controlled data exchange is necessary. The computer should do all classification, coding, and administrative work, and the physician should get all relevant information necessary for decision making.

Diffusion of Innovation↗

Computer methods of assessment of patients state for practical use in clinical laboratory.

Results in the development of computer methods of assessment of patient state are presented as tasks of; diagnostics, gravity of disease assessment, selection of treatment based upon analysis of alternatives, forecast of the patient's state. Practice-oriented PC-based inquiry-consulting system is given as a result of this study. The system combines clinical data base and analysis functions, including the elements of artificial intelligence techniques in a user-friendly interface.

Algorithms↗

Intelligent system for topic survey in MEDLINE by keyword recommendation and learning text characteristics.

We have implemented a system for assisting experts in selecting MEDLINE records for database construction purposes. This system has two specific features: The first is a learning mechanism which extracts characteristics in the abstracts of MEDLINE records of interest as patterns. These patterns reflect selection decisions by experts and are used for screening the records. The second is a keyword recommendation system which assists and supplements experts' knowledge in unexpected cases. Combined with a conventional keyword-based information retrieval system, this system may provide an efficient and comfortable environment for MEDLINE record selection by experts. Some computational experiments are provided to prove that this idea is useful.

Artificial Intelligence↗

Global information infrastructure.

The High Performance Computing and Communications Program (HPCC) is a multiagency federal initiative under the leadership of the White House Office of Science and Technology Policy, established by the High Performance Computing Act of 1991. It has been assigned a critical role in supporting the international collaboration essential to science and to health care. Goals of the HPCC are to extend USA leadership in high performance computing and networking technologies; to improve technology transfer for economic competitiveness, education, and national security; and to provide a key part of the foundation for the National Information Infrastructure. The first component of the National Institutes of Health to participate in the HPCC, the National Library of Medicine (NLM), recently issued a solicitation for proposals to address a range of issues, from privacy to 'testbed' networks, 'virtual reality,' and more. These efforts will build upon the NLM's extensive outreach program and other initiatives, including the Unified Medical Language System (UMLS), MEDLARS, and Grateful Med. New Internet search tools are emerging, such as Gopher and 'Knowbots'. Medicine will succeed in developing future intelligent agents to assist in utilizing computer networks. Our ability to serve patients is so often restricted by lack of information and knowledge at the time and place of medical decision-making. The new technologies, properly employed, will also greatly enhance our ability to serve the patient.

Computer Communication Networks↗