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PubMed2006

Developmental delay and outcomes in paediatric cochlear implantation: implications for candidacy.

OBJECTIVE: Criteria for paediatric cochlear implant candidacy continue to evolve, as research indicates an increasingly broad range of children for whom the procedure can produce benefit. Children with difficulties in addition to their deafness, or global developmental delay, are not routinely excluded. The aim of this study is to explore the association between developmental delay in young paediatric cochlear implant candidates and progress with the device. MATERIALS AND METHODS: The study is a retrospective case series analysis of 32 children, aged between 1.2 and 2.8 years at pre-implant assessment. Children were assessed using the Schedule of Growing Skills II and IT-MAIS before implantation. Progress in speech perception and Speech Intelligibility was monitored using the E2L toy test and Speech Intelligibility Rating (SIR), respectively, 1 and 2 years after switch-on. RESULTS: Data were subjected to regression analyses, with either the E2L or SIR as the outcome variables, and variables derived from the Schedule of Growing Skills II (general development and cognitive functioning), IT-MAIS, age at switch-on and average aided hearing loss pre-implant as the predictor variables. In each analysis the strongest (and statistically significant) predictors of speech perception and Speech Intelligibility were the measures of general development and cognitive functioning, accounting for around 40% of the variance in outcomes. CONCLUSION: This study indicates the value of routinely assessing the general development of young paediatric cochlear implant candidates, as part of the pre-implant decision-making process. Significant developmental delay is predictive of poor outcomes, but children with a mild delay do make appreciable progress. Developmental assessment of young deaf children is therefore important in terms of candidacy decisions and counselling parents regarding potential benefit from a cochlear implant.

Cochlear Implantation↗
PubMed1997

Reasoning and learning by analogy.

Analogy is a powerful cognitive mechanism that people use to make inferences and learn new abstractions. The history of work on analogy in modern cognitive science is sketched, focusing on contributions from cognitive psychology, artificial intelligence, and philosophy of science. This review sets the stage for the 3 articles that follow in this Science Watch section.

Association Learning↗
PubMed2001

Development of a case-based system for the supervision of an activated sludge process.

The development of a case-based reasoning system for the supervision of an activated sludge process is presented here. The methodology proposed permits the use of past experiences to solve new problems that arise in the process. These experiences are classified as cases or situations. The adaptation of cases and the generation of new cases are used to tune the response of the system and to learn from the new information generated by the process. The case and the case library definition, the initial seed, the search and retrieval process, the adaptation, the action, the evaluation and the learning steps are presented and outlined. The process studied is the wastewater treatment plant of Girona, Spain. Two examples of the response of the system to two different operational situations are presented. The paper also outlines the integration of different fields in a multidisciplinary approach as the most optimal solution to ensure the successful control and supervision of complex processes like the activated sludge process. With this aim the integration of an array of specific supervisory intelligent systems (for the logical analysis and reasoning) and numerical computations for detailed engineering is suggested.

Bacteria↗
PubMed2005

Executive functioning in depressed patients with suicidal ideation.

OBJECTIVE: Suicidal thinking has been associated with cognitive rigidity, however, not all depressed patients contemplate suicide. Therefore, we hypothesized that compared with depressed subjects without suicidal ideation, depressed individuals with suicidal ideation would display poorer performance on measures of executive functioning that involve mental flexibility. METHOD: In-patients with a current major depressive episode who had no current suicidal ideation (n=28) were compared with those who had current suicidal ideation (n=5) on measures of executive functioning and two neurocognitive tests that predominantly assess non-frontal regions. RESULTS: Compared with non-suicidal depressed patients, depressed suicidal patients performed significantly worse on several measures of executive functioning after controlling for age, IQ, severity of depression and prior suicide attempts. The two groups performed similarly on tests that predominantly assess non-frontal regions. CONCLUSION: Depressed individuals contemplating suicide have cognitive rigidity, which does not appear to be a global brain dysfunction. Suicidal mental states may result from dysfunctional executive decision-making that is associated with the frontal lobe.

Adult↗
PubMed1990

Building an effective competitive intelligence system for health care service providers.

With the increasing competitiveness of the health care marketplace, the need for information by service providers has increased concomitantly. In response to this need, strategic and competitive intelligence systems have emerged as a vital source of information. This article establishes a basis for the development and operation of a competitive intelligence system. Initially, strategic and competitive intelligence systems are conceptualized, then followed by a discussion of the areas which are candidates for inclusion in the intelligence system. The remainder of the article focuses on system development and operation. Attention also is directed toward information utilization and integration.

Data Collection↗
PubMed1992

The effect of two intraoperative heat-conserving methods on orthopedic patients receiving regional and general anesthesia.

Postoperative hypothermia is a serious complication of surgery. The best way to prevent postoperative hypothermia is to prevent perioperative heat loss. The purpose of this study was to compare the efficacy of Thermadrape (O.R. Concepts, Inc., Roanoke, TX), a heat-retaining surgical covering, with cotton blankets in maintaining core body temperature for patients receiving general or regional anesthesia. One hundred seventeen patients having hand, wrist, or elbow surgery were covered with either cotton blankets or Thermadrape covering throughout the perioperative period. Core body temperature was measured using a tympanic membrane device (FirstTemp, Intelligent Medical Systems, Carlsbad, CA) preoperatively, on PACU admission, and at discharge. Thermadrape covering and cotton blankets were equally effective in conserving body heat. Patients receiving general anesthesia were cooler on PACU admission than those receiving regional anesthesia, regardless of the type of covering. Hospital decisions regarding choice of perioperative heat conservation interventions will be guided by the cost of the intervention as well as efficacy. If overall hospital cost of cotton blankets is greater than that associated with Thermadrape covering, a decision to use Thermadrape covering may be warranted. Important factors for consideration in future research are presented and discussed.

Anesthesia, Conduction↗
PubMed1995

Dynamic patient data bases: the foundation of an integrated approach to outcome measures for the healthcare professionals.

In recent years there has been a tremendous need among healthcare professionals to assess the effectiveness, efficiency, and appropriateness of the patient care services being provided through criteria-based outcome and program evaluation. Although the need for a tool which could evaluate the effectiveness of patient care is widely recognized, such an undertaking has been severely limited due to the lack of any automated means to collect and analyze patient data on a routine, continuous basis within a clinical setting. We have developed and implemented at Mineral Springs Hospital, Banff, Alberta an integrated and automated hospital information system that not only continuously collects administrative, financial, and patient data, but also contains an intelligent component for automated outcome measure and program evaluation. The system collects various non-duplicated data elements from each routine work process within the facility on a continuous basis. Through the creation of a dynamic patient database, data is transformed into information--a powerful decision support tool. The system provides flexible user-defined reports in patient-specific resource utilization, direct and/or indirect specific financial costs, result reporting of each intervention, service provided and user-defined criteria-based outcome, and program evaluation. The system design incorporates expert rules, dynamic data entry forms, quantitative models, and user-defined access control. Using information derived from the dynamic common database, managers and front-line clinicians can easily evaluate and modify management decisions or careplans on a macro or micro level. An external review is planned to evaluate whether the system has helped the assessment of effectiveness, efficiency and appropriateness of healthcare services being provided at the hospital. The fundamental concept behind the system design is that the patient is the center of activity for data collection. The system provides the answers to the 5 W's (who, what, where, when, and why) together with intervention and service result reports. A dynamic common patient database is the center of the system and is accessible to all with proper authorization. Common data elements are collected from routine work flow without extra data entry and this information is subsequently shared. Data collection is a continuous process. We believe that every process is the outcome of another sub-process or event. The design of the dynamic patient database incorporates patient-specific costing and outcome evaluation, user-defined flexible data entry forms, user-defined access control, outcome evaluation rules and information semantic rules. Such a patient database would provide the flexibility needed to accommodate diverse methodologies to evaluate outcomes whether it they be medical, cost, access and/or other combination of measures. The system was developed on a PC-based Network technology, using FOXPRO (XBase) as the database development tool incorporating advanced technology such as distributed processing and fault tolerant computing. We chose PC-based technology because it is economical, having relatively low maintenance costs and requires no major dependency on vendors. The developed system produces patient-specific reports with many dimensions. The reports are user-defined. The system reports general data, CMG, RGN, LOS, Expected LOS, and other user-defined demographic data. Resource utilization, financial costs, and result reportings are produced together with rule-based outcome assessments of any type of measures, including, but not limited to, pre-set functional/health goals, user satisfaction, clinicianUs text or codified comments etc. It provides the framework for continually capturing data at a practical, work-flow level. The incorporation of a dynamic patient database as the driving forece of an integrated, rule-based administration, financial and patient data system will provdie the tools for healthcar

Databases, Factual↗
PubMed2026

Beyond predictive performance: A systematic review and critical methodological appraisal of AI/ML and conventional modelling strategies in breast, colorectal, and pancreatic Cancer.

BACKGROUND: Predictive modelling for cancer risk, treatment-related complications, and survival is central to precision oncology. Conventional logistic regression (LR) and Cox proportional hazards (CoxPH) regression remain widely used but are limited when modelling nonlinear interactions, high-dimensional imaging features, and multimodal clinical-metabolic predictors. Artificial intelligence (AI) and machine learning (ML) methods offer expanded capability through automated feature extraction, ensemble learning, and flexible survival modelling, but the evidence on when AI/ML adds value over conventional models across cancer sites and predictive tasks remains fragmented. OBJECTIVE: To systematically evaluate the methodological performance, validation strategies, and translational limitations of AI/ML models compared with conventional statistical models in published predictive-modelling studies for breast, colorectal, or pancreatic cancer. METHODS: PubMed, Scopus, and Web of Science were searched for studies published between January 2019 and March 2025. Two reviewers independently conducted title-and-abstract screening, full-text eligibility assessment, and PROBAST risk-of-bias assessment. Sixty-five studies (n = 907,567 participants) were narratively synthesised by cancer site, predictive task, model family, comparator, validation strategy, predictor modality, and calibration or explainability reporting. RESULTS: The 65 studies comprised breast cancer (n = 35), colorectal cancer (n = 21), and pancreatic cancer (n = 9). AI/ML superiority over LR and CoxPH was task- and data-dependent. CNN- and U-Net-based models predominated in imaging and body-composition tasks, tree-based ensembles consistently outperformed LR for tabular perioperative complication prediction, and CoxPH remained competitive, and in the largest pancreatic risk study, superior to XGBoost (C-index 0.802 vs 0.723) in well-structured datasets. PROBAST analysis-domain risk was moderate in 54 of 65 studies (83%), driven by limited external validation, sparse calibration reporting (11/65), and few decision-curve analyses (7/65). CONCLUSION: AI/ML adds the most methodological value in imaging-derived feature extraction and nonlinear perioperative prediction, while conventional regression remains preferable in large, structured datasets with linear predictors. Clinical translation requires standardised body-composition definitions, external validation, calibration assessment, decision-curve analysis, and explainability, in line with TRIPOD+AI and CLAIM standards.

Humans↗
PubMed2026

The Rise of Plant Pan-Genomes: From Genome Variation to Predictive Breeding.

Plant pan-genomics is entering a new phase beyond genome variation discovery, requiring a shift from cataloguing genomic diversity toward understanding how variation generates biological function and breeding value. Here, we propose that the future of plant pan-genomics will be shaped by three conceptual transitions. First, structural variation (SV), presence-absence variation (PAV), and haplotype diversity should be interpreted not merely as genomic differences, but as regulatory components that influence gene networks, chromatin organization, and complex traits. Second, the expansion from species-level pan-genomes to genus-level super pan-genomes provides an evolutionary framework for uncovering adaptive genetic modules preserved in wild relatives and overlooked during domestication. Third, integrating pan-genomes with pan-omics, three-dimensional genome analyses, and artificial intelligence will enable the transformation of genomic variation into predictive models for crop improvement. We further propose that the ultimate value of pan-genomes lies not in generating increasingly complete genome collections, but in establishing a mechanistic bridge between genome diversity, biological function, and breeding decisions. This transition will move crop improvement from empirical selection toward rational genome design, where evolutionary diversity can be systematically interpreted, predicted, and engineered.

Journal Article↗
PubMed2004

A multidisciplinary approach to teach responses to weapons of mass destruction and terrorism using combined simulation modalities.

STUDY OBJECTIVE: To reinforce concepts presented in the lectures; understand the complexity and speed of casualty and information generation during a Weapons of Mass Destruction and Terrorism (WMD/T) event; experience the novelty of combined weapons' effects; recognize the time course of the various chemical, biological, and radiation agents; and make challenging decisions with incomplete and conflicting information. SETTINGS: Two environments simulated simultaneously: one a major trauma center emergency room (ER) with two patient simulators and several human actors; the other an Emergency Operations Command Center (EOC). TARGET AUDIENCE: Students for this course included: clinicians, scientists, military and intelligence officers, lawyers, administrators, and logistic personnel whose jobs involve planning and executing emergency response plans to WMD/T. SIMULATION SCRIPT: A WMD/T attack in Washington, D.C., has occurred. Clinical students performed in their real life roles in the simulated ER, while nonclinical students did the same in the simulated EOC. Six ER casualties with combined WMD/T injuries were presented and treated over 40 minutes. In the EOC, each person was given his or her role title with identification tag. The EOC scenario took cues from the action in the ER via two television (TV) news feeds and telephone calls from other Emergency Operations Assets. PERFORMANCE EXPECTATIONS: Students were expected to actively engage in their roles. Student performances were self-evaluated during the debriefing. DEBRIEFING: The two groups were reunited and debriefed utilizing disaster crisis resource management tools. ASSESSMENT OF EFFECTIVENESS: Students answered an 18-point questionnaire to help evaluate the usefulness and acceptance of multimodality patient simulation. LESSONS LEARNED: Large-scale multimodality patient simulation can be used to train both clinicians and nonclinicians for future events of WMD/T. Students accepted the simulation experience and thought that scenario was appropriately realistic, complex, and overwhelming. Difficulties include the extensive man-hours involved in designing and presenting the live simulations. EOC-only sessions could be staged with only a few video cassette recorders, TVs, telephones, and callers.

Disasters↗
PubMed2005

Multimodal imaging and computer assisted diagnosis for functional tumour characterisation.

Radiological modalities, especially CT, mainly provide morphological and structural information with high spatial resolution covering large volumes. Novel developments, which are predominantly MR-based, also deliver 'functional' information, which can be used for individual characterisation of tumour biology. Both aspects and modalities, additionally complemented by ultrasound, have to be combined in the radiological workflow of cancer patients including volumetric visualisation, information extraction from multimodal imaging, quantitative surrogates, intelligent interpretation assistance and image-guided procedures. Based on volumetric visualisation and the generation of 3D+t maps, CAD tools have to address registration of different image series from different modalities, and extraction of quantitative surrogates. The latter will then serve tumour characterisation, therapeutic decision-making, image-guided procedures and efficacy evaluation.

Diagnosis, Computer-Assisted↗
PubMed2001

Magnetic resonance imaging in the evaluation of occult submucous cleft palate.

OBJECTIVE: To explore the application of magnetic resonance imaging (MRI) in the evaluation of patients with occult submucous cleft palate and to use the MRI information obtained to aid in the treatment decision to perform surgery versus behavioral speech therapy. DESIGN: Prospective study with magnetic resonance (MR) images of subjects suspected of having occult submucous cleft palate. SETTING: Hospital and university-based. PATIENTS: Two girls who were 4 years old at the time of palatal surgery. INTERVENTION: Furlow double-opposing Z-plasty. MAIN OUTCOME MEASURES: MR images and clinical speech evaluations. RESULTS: MR images provided evidence of an interruption of levator veli palatini muscle tissue in the midline and a substantial attachment of levator muscle tissue to the posterior border of the hard palate. In addition, MR images for both subjects demonstrated remarkably similar bilateral encapsulating sheaths that contained nonmuscular tissue, as confirmed subsequently during surgery. The encapsulating sheaths interrupted the normal progression of the levator muscle sling across the midline. The MR images led to the decision to perform surgery instead of speech therapy. Hypernasality was markedly reduced in both subjects after surgery. CONCLUSIONS: MRI is an effective technique for diagnosing occult submucous cleft palate and may be an important aid in the treatment decision regarding surgery versus behavioral speech therapy for patients diagnosed with occult submucous cleft palate.

Articulation Disorders↗
PubMed2026

Toward a Better Paradigm for Head and Neck Cancer Treatment Applying AI (HNC-TACTIC): Protocol for an International Cohort Study of Electronic Health Records.

BACKGROUND: Head and neck squamous cell carcinomas (HNSCCs) cause considerable morbidity and mortality. Multimodal treatment strategies can cause significant toxicity, and therapy options are limited for recurrent disease. Immunotherapy has emerged as a promising approach. However, patient response variability underscores the need for better predictive markers. OBJECTIVE: This study aims to use artificial intelligence to develop two predictive models in patients with HNSCC to assess (1) progression or recurrence following primary curative treatment and (2) long-term survival after immunotherapy schemes in recurrent and metastatic disease. This study will also describe the characteristics of patients with early, locally advanced, and recurrent or metastatic cancers. METHODS: This is a retrospective, observational study of data captured in electronic health records (EHRs) from participating hospitals between January 1, 2014, and December 31, 2021. This study's population comprises adults diagnosed with HNSCC at any stage. Study variables, including demographics, comorbidities, clinical variables, treatments, and outcomes, will be extracted using EHRead, a technology that applies natural language processing and machine learning to extract and analyze structured and unstructured clinical information in deidentified EHRs. Predictive models based on dynamic risk stratification for treatment response and progression or recurrence will be developed using multivariable logistic regressions, decision tree classifiers, and random forest approaches. Descriptive and outcome analyses will be shown for different anatomic subsites and stratified by stage and treatment. RESULTS: This study began enrolling sites in July 2021 and is currently ongoing. By December 2025, data from 10 centers has been collected, comprising a total of 151,934,990 EHRs from 2,159,719 patients. CONCLUSIONS: Development of predictive models using artificial intelligence will advance clinical understanding of HNSCC to improve patient outcomes.

Humans↗
PubMed1999

FOX-GA: a genetic algorithm for generating and analyzing battlefield courses of action.

This paper describes FOX-GA, a genetic algorithm (GA) that generates and evaluates plans in the complex domain of military maneuver planning. FOX-GA's contributions are to demonstrate an effective application of GA technology to a complex real world planning problem, and to provide an understanding of the properties needed in a GA solution to meet the challenges of decision support in complex domains. Previous obstacles to applying GA technology to maneuver planning include the lack of efficient algorithms for determining the fitness of plans. Detailed simulations would ideally be used to evaluate these plans, but most such simulations typically require several hours to assess a single plan. Since a GA needs to quickly generate and evaluate thousands of plans, these methods are too slow. To solve this problem we developed an efficient evaluator (wargamer) that uses course-grained representations of this problem domain to allow appropriate yet intelligent trade-offs between computational efficiency and accuracy. An additional challenge was that users needed a diverse set of significantly different plan options from which to choose. Typical GA's tend to develop a group of "best" solutions that may be very similar (or identical) to each other. This may not provide users with sufficient choice. We addressed this problem by adding a niching strategy to the selection mechanism to insure diversity in the solution set, providing users with a more satisfactory range of choices. FOX-GA's impact will be in providing decision support to time constrained and cognitively overloaded battlestaff to help them rapidly explore options, create plans, and better cope with the information demands of modern warfare.

Algorithms↗
PubMed2000

Enhancing the expressiveness of structured reporting systems.

The overall goal of this research is to build a structured reporting system that reduces the cost, delays, and inconvenience associated with conventional dictation and speech recognition systems. We have implemented such a structured reporting system for radiology that replaces current dictation and transcription processes by allowing radiologists and other imaging professionals to select imaging findings from a medical lexicon. The system uses an imaging-specific information model, called a "description set,' to organize selected terms in a relational database. Unique features of the knowledge representation that enhance its expressiveness include its ability to codify uncertainty about an imaging observation and to represent explicitly the logical relationships among imaging findings. In addition, the system does not require the user to fill in "blanks' in a static text template. Instead, it allows entry of terms in arbitrary order and uses automated text-generation techniques to create a text report that referring physicians are accustomed to receiving. In parallel, the system also produces a multimedia report that the referring physician can use as a quick reference. Unlike the results of conventional dictation or speech recognition, each finding is coded in a relational database for later information processing. Thus, the structured report database can be used to index images by content, to provide real-time decision support, to enhance radiologists' performance, to conduct exploratory clinical research, and to transmit imaging report data to computer-based patient record systems.

Abstracting and Indexing↗
PubMed1994

Psychiatry and the death penalty: the landmark Supreme Court cases and their ethical implications for the profession.

The U.S. Supreme Court has made a number of recent rulings in regard to the death penalty that will likely have the effect of increasing the use of psychiatry during the trial and sentencing process in capital cases. Any such changes are bound to increase the number of ethical dilemmas faced by psychiatrists involved in such work. The rulings affecting psychiatry include: (1) The Eighth Amendment forbids the execution of persons who are mentally incompetent in regard to their ability to appreciate the reasons for punishment. (2) A mentally-ill prisoner may be forcibly given neuroleptics if he presents a danger to himself or others. (3) Forced medication may not be used during the trial and sentencing phase if it has the potential to change the defendant's demeanor significantly enough to affect his defense. (4) Aggravating psychological factors affecting a convictee may be balanced against mitigating factors in considering whether death sentence should be imposed. (5) The psychosocial impact of the crime upon the victim's family may be presented during the sentencing phase as factors relevant to sentencing. (6) Adolescents and retarded individuals are not immune from the death penalty simply by virtue of their age or level of intelligence.

Age Factors↗
PubMed2026

AI In Leukemia Diagnostics: Complementing the Pathologist's Role.

Artificial intelligence (AI) is reshaping every stage of leukemia diagnostics, from digital morphology and multiparameter flow cytometry to next-generation sequencing, multi-omics analysis, and emerging computational frontiers such as quantum-inspired feature selection. This review outlines how contemporary AI tools can automate labor-intensive quantitation, flag diagnostically salient patterns, and standardize interpretation, while the pathologist or hematologist retains authority over validation, context-specific integration, and clinical decision-making. We present an illustrative "human-in-the-loop" workflow that embeds AI modules within current laboratory information systems, emphasizing points where expert oversight mitigates algorithmic bias and resolves discordant findings. We further map the validator-integrator role across morphology, flow cytometry, and genomic/multi-omic interpretation and provide practical training competencies and use cases for AI-assisted hematopathology. Beyond technical deployment, the article addresses the educational transformation required for sustainable adoption. Drawing on international competency frameworks, including the Digital Health Competencies in Medical Education Framework and recently proposed AI-specific Entrustable Professional Activities, we map core skills that future hematopathologists must master: data-science literacy, critical appraisal of AI outputs, and ethical governance. We highlight evaluated training models such as the Pathology Informatics Essentials for Residents curriculum, Stanford Artificial Intelligence in Machine and Imaging workshops, and College of American Pathologists bootcamps and propose integration strategies adaptable across resource settings. By pairing rigorous validation with targeted education, AI can elevate rather than eclipse the diagnostic role of the leukemia specialist, enabling more timely, reproducible, and personalized patient care.

Humans↗