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Results for “Decision Making, Computer-Assisted”

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PubMed1991

Smoking cessation program preferences associated with stage of quitting.

The purpose of this article is to report results from three exploratory studies which examined smoker preferences for cessation program attributes associated with stage of quitting. Study 1 was a telephone survey of 205 randomly selected smokers, in "precontemplative," "contemplative," and "ready-for-action" stages of quitting, who were queried about their preference for a wide variety of cessation program attributes. Study 2 entailed a computer-assisted telephone interview of 218 smokers in the same three stages of quitting. Interview data were used for multiattribute utility analysis of (a) preference for specific program attributes, selected partially on the basis of Study 1 results; and (b) preference for six different types of cessation programs, evaluated through computer simulations. Study 3 replicated Study 2 procedures using a smaller mall intercept sample which allowed in-person interviews and the use of display materials. Results suggested that there are limited but potentially significant differences among smokers in the three stages concerning program attribute preferences (e.g., use of self-help vs. facilitator-led programs, inclusion of relaxation techniques, quitting cold turkey, strategies for gaining social support). Nonetheless, there were no statistically significant differences among the three subgroups in level of preference for the six types of programs evaluated. Implications of these preliminary results are discussed.

Adult↗
PubMed2026

A multi-scale fusion model based on multi-phase contrast-enhanced CT for predicting pancreatic cancer resectability.

Purpose.Develop a multi-scale fusion model (MSFM) based on multi-phase contrast-enhanced computed tomography (CECT) to predict pancreatic cancer (PC) resectability, thereby assisting expert decision-making.Methods.This retrospective study enrolled 280 patients with PC from four institutions, which were randomly divided into a training cohort (202 patients) and an independent test cohort (78 patients). Three-phase CECT images (arterial, venous, and delayed phases) were used for modeling. The MSFM comprises two sub-networks: (1) a multi-phase fusion network for extracting cross-phase shared fusion features, (2) a phase-specific branch network for capturing phase-specific features; and a post-fusion strategy to generate the final predictive score by integrating the shared fusion features and three groups of phase-specific features. Additionally, a human-machine fusion deep learning model (HMfDL) was constructed by fusing the predictive score of the MSFM with expert assessments.Results.In the independent test, the MSFM achieved an AUC (area under the receiver operating characteristic curve) of 0.8385 (95% CI: 0.7521-0.9249), accuracy of 84.62%, sensitivity of 72.00%, and specificity of 90.57%. This performance outperformed single-phase models (AUC range: 0.7638-0.7781), two-phase models (AUC range: 0.7826-0.7864), and ten states-of-the-art classifiers (AUC range: 0.7404-0.7796). The HMfDL further improved the performance, reaching an AUC of 0.8626 (95% CI: 0.7853-0.9400), accuracy of 91.03%, sensitivity of 80.00%, and specificity of 96.23%. Notably, the HMfDL corrected 58.82% of misdiagnosis made by experts.Conclusions. The MSFM effectively fuses multi-phase CECT to enable highly accurate predictions of PC resectability, and provides valuable support for expert decision-making through HMfDL.

Humans↗
PubMedDate not supplied

Towards computer analysis of pulmonary infiltration.

A feasibility study is described to provide quantitative texture measures to distinguish between normal lung, alveolar infiltrates and interstitial infiltrates. Advanced computer imaging technology and decision making processes were applied to distinguish between these textural patterns. The results, based on computer extracted quantitative measures, show an excellent separation of the three classes considered with 95% accuracy in the training phase and 90% accuracy in the testing phase.

Diagnosis, Computer-Assisted↗
PubMed1990

The Bin Area Method: a computationally efficient technique for analysis of ventricular and atrial intracardiac electrograms.

Recent studies have reported a significant false positive rate in delivery of therapy by implantable antitachycardia devices utilizing detection algorithms based on sustained high rate. More selective decision schemes for the recognition of life-threatening arrhythmias have been recently proposed that use analysis of the intrinsic electrogram rather than rate alone. Morphological discrimination of abnormal electrograms using correlation waveform analysis (CWA) has been proposed as an effective method of intracardiac electrogram analysis, but its computational demands limit its use in implantable devices. A new method for intracardiac electrogram analysis, the bin area method (BAM), was created to detect abnormal cardiac conduction with computational requirements of one-half to one-tenth those of CWA. Like CWA, BAM is a template matching method that is sensitive to conduction changes revealed in the electrogram morphology and is independent of amplitude and baseline fluctuations. Performance of BAM and CWA were compared using bipolar right ventricular and right atrial electrode recordings from 47 patients undergoing clinical cardiac electrophysiology studies. Nineteen patients had 31 distinct monomorphic ventricular tachycardias (VTs) induced (group I), thirteen patients had paroxysmal bundle branch block of supraventricular origin (BBB) induced (group II), and 19 patients had retrograde atrial activation during right ventricular overdrive pacing (group III). (One patient was common to all three groups, and two patients were common to groups II and III.) Using the ventricular electrogram, both BAM and CWA distinguished VT from sinus rhythm in 28/31 (90%) cases, and BBB from Normal Sinus Rhythm (NSR) in 13/13 (100%) patients. Using the atrial electrogram, both BAM and CWA distinguished anterograde from retrograde atrial activation in 19/19 (100%) patients. BAM achieves similar performance to CWA with significantly reduced computational demands, and may make real-time analysis of intracardiac electrograms feasible for implantable pacemakers and antitachycardia devices.

Algorithms↗
PubMed1979

An approach to quality and performance control in a computer-assisted clinical chemistry laboratory.

A locally developed, computer-based clinical chemistry laboratory system has been in operation since 1970. This utilises a Digital Equipment Co Ltd PDP 12 and an interconnected PDP 8/F computer. Details are presented of the performance and quality control techniques incorporated into the system. Laboratory performance is assessed through analysis of results from fixed-level control sera as well as from cumulative sum methods. At a simple level the presentation may be considered purely indicative, while at a more sophisticated level statistical concepts have been introduced to aid the laboratory controller in decision-making processes.

Chemistry, Clinical↗
PubMed1975

On the evolution of the physiological model.

Most of us who have concerned ourselves with models can perceive outlines like those above to catalog the future evolution of the expository function of models. In the context of a single class of computerized mathematical models of respiratory physiology, we can observe at once the burgeoning interest among scientists, and the similarities between model activity and the general organization of scientific information for use. Although physiological models have become quite advanced in their subject control, there is relatively little coordinated activity in the mechanization of the purposes and philosophical potential of automata. The outlines, however, are visible. An assiduous pursuit of the notion of "explanation" by machine is a major evolutionary step next to occur. It appears to us that various diagrams similar to Figures 5 or 6 can be created and investigated in terms of their relation to the human mind and in terms of formalizing rules for traversing from one plane to the next. The evolution of models will require program-making programs which can decide when and how to aggregate for deductive inference, and how far to penetrate top-down for explanation. The rules for identifying "second order" effects must be established. The decision to ignore or use these rules will be crucial. These are the means whereby the systems are traversed from plane to plane. In a word, models need to synthesize the means to ignore, "forget," and gloss over; only then will we have useful tools for taking informed action in physiology, diagnosis in medicine, or the writing of "scholarly" reviews.

Computers↗
PubMed1991

An objective, noninvasive method for the diagnosis of temporomandibular joint disorders.

An objective, noninvasive method for the diagnosis and monitoring of pathology referable to the temporomandibular joint is presented. A computerized temporomandibular joint analyzer was developed to obtain data from the patient's temporomandibular joint area to provide an objective record of clinical symptoms and signs. This data, used in conjunction with a complete history and physical examination, aids in the diagnosis and decision-making process for therapy. Fifty patients with temporomandibular complaints were evaluated. Diagnoses made using the temporomandibular joint analyzer in conjunction with complete histories and physical examinations were confirmed in 81% of the patients by further examinations with x-ray studies, computed tomography scans, magnetic resonance imaging, and arthroscopy. Patients' progress during treatment was monitored using the temporomandibular joint analyzer. Results of the temporomandibular joint analyzer were compatible with patient's progress as reported subjectively and with clinical evaluation in 97% of the cases. The temporomandibular joint analyzer can be used to reduce time and expense as well as to minimize the discomfort involved in the evaluation of temporomandibular joint disorders.

Adolescent↗
PubMed1991

A computer-controlled ventilator weaning system.

Weaning of patients from mechanical ventilation is a time-consuming, labor-intensive process. Because most weaning decisions are based on objective data, we tested a computer-directed weaning system on postoperative patients. We developed an automatic, computer-controlled ventilator weaning system which interfaces a laptop computer to a ventilator and a pulse oximeter. The laptop computer program accesses patient data through the ventilator and pulse oximeter to make weaning decisions. The computer directly controls the ventilator through an interface developed for this system. We tested the system in nine postoperative patients who met the following criteria: negative inspiratory force less than or equal to -20 cm H2O, vital capacity greater than 10 ml/kg, inspired oxygen concentration less than or equal to 40 percent, and satisfactory arterial blood gas parameters (pH between 7.32 and 7.48, PCO2 between 32 and 48, and oxygen saturation greater than or equal to 90 percent). The computer decreased the SIMV rate by 2 breaths/min every 5 min until a rate of 2 breaths/min was reached, then decreased pressure support by 4 cm H2O every 5 min as long as the patient met the following criteria: respiratory rate between 8 and 25 breaths/min, minute ventilation between 6 and 14 L, and pulse oximeter oxygen saturation greater than or equal to 90 percent. If unsatisfactory weaning criteria were noted, the system automatically returned the patient to the previous weaning level. We successfully weaned nine patients using the system. Additional studies are underway to determine if this system can be used in medical patients. We believe this computer-controlled ventilator weaning system can be used successfully in patients requiring mechanical ventilation and may decrease the time and cost associated with the care of these patients.

Adult↗
PubMed2026

Automated Classification of Lymphoma Subtypes From Histopathological Images Using a U-Net Deep Learning Model: Comparative Evaluation Study.

BACKGROUND: Accurate classification and grading of lymphoma subtypes are essential for treatment planning. Traditional diagnostic methods face challenges of subjectivity and inefficiency, highlighting the need for automated solutions based on deep learning techniques. OBJECTIVE: This study aimed to investigate the application of deep learning technology, specifically the U-Net model, in classifying and grading lymphoma subtypes to enhance diagnostic precision and efficiency. METHODS: In this study, the U-Net model was used as the primary tool for image segmentation integrated with attention mechanisms and residual networks for feature extraction and classification. A total of 620 high-quality histopathological images representing 3 major lymphoma subtypes were collected from The Cancer Genome Atlas and the Cancer Imaging Archive. All images underwent standardized preprocessing, including Gaussian filtering for noise reduction, histogram equalization, and normalization. Data augmentation techniques such as rotation, flipping, and scaling were applied to improve the model's generalization capability. The dataset was divided into training (70%), validation (15%), and test (15%) subsets. Five-fold cross-validation was used to assess model robustness. Performance was benchmarked against mainstream convolutional neural network architectures, including fully convolutional network, SegNet, and DeepLabv3+. RESULTS: The U-Net model achieved high segmentation accuracy, effectively delineating lesion regions and improving the quality of input for classification and grading. The incorporation of attention mechanisms further improved the model's ability to extract key features, whereas the residual structure of the residual network enhanced classification accuracy for complex images. In the test set (N=1250), the proposed fusion model achieved an accuracy of 92% (1150/1250), a sensitivity of 91.04% (1138/1250), a specificity of 89.04% (1113/1250), and an F1-score of 90% (1125/1250) for the classification of the 3 lymphoma subtypes, with an area under the receiver operating characteristic curve of 0.95 (95% CI 0.93-0.97). The high sensitivity and specificity of the model indicate strong clinical applicability, particularly as an assistive diagnostic tool. CONCLUSIONS: Deep learning techniques based on the U-Net architecture offer considerable advantages in the automated classification and grading of lymphoma subtypes. The proposed model significantly improved diagnostic accuracy and accelerated pathological evaluation, providing efficient and precise support for clinical decision-making. Future work may focus on enhancing model robustness through integration with advanced algorithms and validating performance across multicenter clinical datasets. The model also holds promise for deployment in digital pathology platforms and artificial intelligence-assisted diagnostic workflows, improving screening efficiency and promoting consistency in pathological classification.

Humans↗
PubMedDate not supplied

Characteristics of the software for computer applications in medicine.

The requirements of clinical medicine which have tended to make the design and implementation of software for hospital computer systems more difficult than that elsewhere, are discussed in this paper. Specific constraints on the software for selected computer-assisted activities in a hospital environment are examined in considerable depth. It is shown that since some of these activities have counterparts elsewhere, hospital computing can benefit from the accumulated experience in dealing with similiar problems in business and scientific environments. The argument is put forward that developing countries, with their characteristic problem of acute shortage of skilled manpower in both medicine and computing, should initially concentrate on applying computers to these activities alone. Furthermore, medical education in such countries should incorporate programmes relating to computer technology in general and the software aspects in particular.

Computers↗
PubMedDate not supplied

Computer assisted instruction for preoperative and postoperative patient education in joint replacement surgery.

This article describes a comprehensive system for preoperative and postoperative patient education. The system offers a cost-effective method of instruction which encourages patient interaction and practice with decision making. The system was designed for patients undergoing total joint replacement surgery and includes two preoperative lessons, and a third lesson presented postoperatively at the bedside. The computer lessons were developed using data collected by a patient assessment instrument, and collaboratively with input from a nurse clinical specialist, orthopedic surgeon, physical therapist, and computer programmer. In this project, several advantages for using computer assisted instruction for preoperative and postoperative patient education were identified.

Computer-Assisted Instruction↗
PubMed1991

Ranking radiotherapy treatment plans using decision-analytic and heuristic techniques.

Radiotherapy treatment optimization is done by generating a set of tentative treatment plans, evaluating them and selecting the plan closest to achieving a set of conflicting treatment objectives. The evaluation of potential plans involves making tradeoffs among competing possible outcomes. Multiattribute decision theory provides a framework for specifying such tradeoffs and using them to select optimal actions. Using these concepts, we have developed a plan-ranking model which ranks a set of tentative treatment plans from best to worst. Heuristics are used to refine this model so that it reflects the clinical condition of the patient being treated and the practice preferences of the physician prescribing the treatment. A figure of merit is computed for each tentative plan, and is used to rank the plans. The approach described is very general and can be used for other medical domains having similar characteristics. The figure of merit can also be used as an objective function by computer programs that attempt to automatically generate an optimal treatment plan.

Artificial Intelligence↗
PubMed1991

Evaluation of a Computerized Clinical Information System (Micromedex).

This paper summarizes data collected as part of a project designed to identify and assess the technical and organizational problems associated with the implementation and evaluation of a Computerized Clinical Information System (CCIS), Micromedex, in three U.S. Department of Veterans Affairs Medical Centers (VAMCs). The study began in 1987 as a national effort to implement decision support technologies in the Veterans Administration Decentralized Hospital Computer Program (DHCP). The specific objectives of this project were to (1) examine one particular decision support technology, (2) identify the technical and organizational barriers to the implementation of a CCIS in the VA host environment, (3) assess the possible benefits of this system to VA clinicians in terms of therapeutic decision making, and (4) develop new methods for identifying the clinical utility of a computer program designed to provide clinicians with a new information tool. The project was conducted intermittently over a three-year period at three VA medical centers chosen as implementation and evaluation test sites for Micromedex. Findings from the Kansas City Medical Center in Missouri are presented to illustrate some of the technical problems associated with the implementation of a commercial database program in the DHCP host environment, the organizational factors influencing clinical use of the system, and the methods used to evaluate its use. Data from 4581 provider encounters with the CCIS are summarized. Usage statistics are presented to illustrate the methodological possibilities for assessing the "benefits and burdens" of a computerized information system by using an automated collection of user demographics and program audit trails that allow evaluators to monitor user interactions with different segments of the database.

Computer Systems↗
PubMed1991

AI/Consult: a prototype directed history system based upon the AI/Rheum knowledge base.

An expert system is designed which uses the AI/Rheum knowledge base as the basis for a directed workup system for rheumatological disorders. A Turbo Pascal prototype is demonstrated which, unlike AI/Rheum, permits entry by the clinician of a patient's chief complaint(s) and subsequently develops a dynamic differential diagnosis for this limited set of findings. The system restricts its line of questioning to those pertinent in order to rule in or rule out items on this dynamic differential diagnosis, and, unlike AI/Rheum, it provides immediate notification to the user when a critical mass of information has been entered in order to meet a diagnosis at one of the AI/Rheum criteria table's three levels of diagnosis (possible, probable, definite). Thus the system allows for a more rapid, focused decision making approach than does AI/Consult, while it follows the trend established by QMR in that it abandons the Greek Oracle problem solving approach and instead adopts a physician-assisted hypothesis investigation approach. The system is currently a prototype, without AI/Rheum's page oriented and mouse driven interface, and thus the total user interaction time may be longer than with AI/Rheum even though fewer user interactions (responses) are required. Plans for future development include optimization of the knowledge base to allow for more efficient, problem oriented questioning and modification of the knowledge base compiler in order to dynamically rule out diseases through the addition of three new levels of diagnosis in the knowledge base criteria tables.

Diagnosis, Computer-Assisted↗
PubMed1991

Patient management in the ICU: the PDB System.

The Intensive Care Unit is the area in patient care where the amount of patient data from a variety of sources is particularly large. The problem for clinicians lies in the ability to gather, and use these data in the decision making process. A well designed computer based patient data management system, incorporating a variety of data analysis tools, would have a dramatic impact in patient care in an environment such as this. The PDB System has been in continuous use at the Montreal General Hospital's Surgical and Trauma Intensive Care Unit since Jan. 88. Its initial implementation in two beds in our SICU has allowed the complete replacement of the conventional patient paper record. It is used by all ICU staff, including nurses, physicians, and ward clerks for the recording/viewing of all patient vital data, laboratory data, medications, and optionally chart notes. In addition, medical staff has the option to use the entered data to perform a variety of data analysis procedures.

Critical Care↗
PubMed1991

Bayesian diagnostic probabilities without assuming independence of symptoms.

The paper describes an application of Bayes' Theorem to the problem of estimating from past data the probabilities that patients have certain diseases, given their symptoms. The data consist of hospital records of patients who suffered acute abdominal pain. For each patient the records showed a large number of symptoms and the final diagnosis to one of nine diseases or diagnostic groups. Most current methods of computer diagnosis use the "Simple Bayes" model in which the symptoms are assumed to be independent, but the present paper does not make this assumption. Those symptoms (or lack of symptoms) which are most relevant to the diagnosis of each disease are identified by a sequence of chi-squared tests. The computer diagnoses obtained as a result of the implementation of this approach are compared with those given by the "Simple Bayes" method, by the method of classification trees (CART), and also with the preliminary and final diagnoses made by physicians.

Abdominal Pain↗