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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↗
PubMedDate not supplied

Sequential decision making with continuous disease states and measurements: II. Application to diastolic blood pressure.

The model and strategy for sequential decision making using normally distributed measurements proposed in a companion paper are applied to the problem of diagnosing diastolic hypertension. The assumptions of the model are discussed and justified clinically. Methods for assigning values to the model's parameters are explained and illustrated in the context of a hypothetical "generic" patient. Although current national recommendations and the sequential strategy both lead to an average of 1.89 measurements per patient prior to diagnosis, the sequential strategy applies a sequence of four or more measurements to 12% of patients. Fewer than 1% of patients would require ten or more measurements under this strategy. The sequential strategy leads to fewer patients' receiving unnecessary treatment and substantially higher expected utility for the patient. The role of multiple blood pressure determinations per visit is explored in the absence of appropriate estimates. Even under "best-case" assumptions, however, it is shown that obtaining more than one observation per visit is called for only in about 15% of visits. While the exact role of multiple determinations cannot be specified from existing data, it is likely to be much more limited than current recommendations suggest.

Computer Simulation↗
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↗
PubMedDate not supplied

[Computers in emergency medicine].

Computer's applications in emergency medicine are reviewed. In the Emergency Health Care computer science provides support to the solution of medical problems (with diagnostic and medical decision making softwares) and to the management of emergency departments. Computer interfaced to instrumental equipments allows the monitoring of biomedical signals and their transmission at distance. Finally, the possibilities of computer-aided instruction are presented. Teaching methods such as the tutor-system and the simulation are discussed.

Computer-Assisted Instruction↗
PubMed1990

[Computer and acid-base disorders].

The diagnosis of acid-base disorders has been subject to computerised analysis for many years. Expert systems, an application of artificial intelligence, have been validated. They are of help to clinical decision making, enabling the different diagnostic possibilities of given acid-base and hydro-electrolytic situations to be evaluated and appropriate therapy to be instituted. Computer networks improve the transmission of laboratory results to the intensive care units and the integration of data facilitates the monitoring of biochemical tendencies. Computers can also be used for teaching acid-base physiology and pathology.

Acid-Base Imbalance↗
PubMed1990

Computer applications in orthopaedics.

With the rapid developments in microprocessors, the widespread availability of computers has brought about broad applications in the field of orthopaedics. The present technology enables large quantities of data to be logically processed in a very short span of time. This has led to the development of information management database systems where relevant medical information may be retrieved very quickly and effectively. The analytical power of the computer has also been utilised in expert systems to assist in clinical-decision making process. Computer graphics have revolutionised the visualisation of physical features of internal and external body parts, providing new and improved modalities of diagnosis. In some centres, surgical planning and rehearsals are already being carried out at the computer terminal with the use of animation and computer graphics. Computer technology has also played an active role in the field of prosthetics and rehabilitation. Intelligent robotic systems and microprocessor with functional neuromuscular stimulation have been applied to benefit, and in some cases restore some motor functions to the physically disabled. With more collaboration between engineers, scientists and the medical community, several prototypes of computer-controlled prostheses and prosthesis designed and manufactured by Computer-Aided Design/Computer-Aided Manufacturing (CAD/CAM) technology are available today to assist the amputees in their daily living and ambulatory activities.

Computer Graphics↗