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Exploring a new best information algorithm for Iliad.

Iliad is a diagnostic expert system for internal medicine. One important feature that Iliad offers is the ability to analyze a particular patient case and to determine the most cost-effective method for pursuing the work-up. Iliad's current "best information" algorithm has not been previously validated and compared to other potential algorithms. Therefore, this paper presents a comparison of four new algorithms to the current algorithm. The basis for this comparison was eighteen "vignette" cases derived from real patient cases from the University of Utah Medical Center. The results indicated that the current algorithm can be significantly improved. More promising algorithms are suggested for future investigation.

Algorithms

Stochastic simulation algorithms for query networks.

One of the barriers to using belief networks for medical information retrieval is the computational cost of reasoning as the networks become large. Stochastic simulation algorithms allow one to compute approximations of probability values in a reasonable amount of time. We previously examined the performance of five stochastic simulation algorithms applied to four simple belief networks networks and found that the Self-Importance algorithm performed well. In this paper, we examine how the same five algorithms perform when applied to a belief network derived from the cardiovascular subtree of the Medical Subject Headings (MeSH). Both the Likelihood Weighting and Self-Importance algorithms perform well when applied to the MeSH-derived network, suggesting that stochastic simulation algorithms may provide reasonable performance in medical information retrieval settings.

Algorithms

Use of immunoglobulin heavy-chain and light-chain measurements in a multicenter trial to investigate monoclonal components: II. Classification by use of computer-based algorithms.

We describe a computer algorithm for classifying serum monoclonal proteins (MC) based on serum protein electrophoresis (SPE) and the automated measurement of kappa and lambda light chains and IgG, IgA, and IgM. We developed the algorithm by using a large database of unselected samples containing MC collected in a multicenter study. The performance of the algorithm was optimized by using iterative computational procedures and was tested on both the development database and on an independent set of MC-containing samples. With the development database, the algorithm correctly classified 50% and misassigned 2.5% of the MC. Where the MC were present in concentrations greater than 10 g/L, the rate of successful classification increased to 72% with 3% misclassification. When the algorithm was tested on a group of 101 MC-containing samples from an independent source, 67% were correctly classified and 8% misclassified, half of the latter being unusual IgD myelomas. We discuss the scope for the application of the algorithm in routine laboratory practice involving personal computer software.

Algorithms

Algorithm to predict triple-vessel/left main coronary artery disease in patients without myocardial infarction. An international cross validation.

Logistic regression was applied to the clinical, risk factor, and exercise data of consecutive angiographic referrals without prior myocardial infarction to determine an algorithm predicting the probability of triple-vessel/left main coronary artery disease. These data were obtained from a total of 1,074 such subjects from patient populations at four centers (Cleveland Clinic Foundation, Cleveland, Ohio; Hungarian Institute of Cardiology, Budapest, Hungary; the university hospitals, Zurich and Basel, Switzerland; and the Veterans Administration Medical Center, Long Beach, Calif.) and used to derive four separate probability algorithms. Each algorithm is based on patient data from study samples at three of the four centers and consists of 272 logistic functions, which are related to linear combinations of 13 variables (age, sex, type of chest pain, systolic blood pressure, resting electrocardiogram, serum cholesterol, fasting blood sugar, achieved exercise work load, achieved heart rate, exercise-induced angina and hypotension, heart rate-adjusted resting ST depression, and exercise ST slope). The four algorithms were cross validated by testing them on the populations not involved in their derivation. The resulting probabilities in the four test groups were then compared with the angiographic findings of triple-vessel/left main coronary artery disease. The discriminatory power of all the algorithms was fair to good (area under receiver operating characteristic curve, 0.68, 0.75, 0.82, 0.85) in the test groups. The algorithm did not significantly underestimate or overestimate disease probability except in one center (Long Beach).(ABSTRACT TRUNCATED AT 250 WORDS)

Algorithms

[The use of a computer for the diagnosis of comatose states in diabetics (a differential diagnostic algorithm)].

An algorithm for differential diagnosis of comatose conditions in patients with diabetes mellitus has been devised. The algorithm is intended for the general practitioner and non-specialized department. The algorithm uses the minimum of the crucial signs of comatose conditions, ensuring their diagnosis under the conditions of any hospital. The algorithm can be applied to the recognition of the typical variants of comatose conditions in a "pure" form. The amount of algorithm steps is minimized. The program of differential diagnosis is written in the Fokal language and realized on the computer "Elektronika BK 0010". The program is run in the dialogue mode. The algorithm is used in clinical practice and in the training process, with its efficacy being independent of the professional skills of the user.

Algorithms

Prediction of protein helices with a derivative of the strip-of-helix hydrophobicity algorithm.

The strip-of-helix hydrophobicity algorithm was devised to identify protein sequences which, when coiled as alpha or 3(10) helices, had one axial, hydrophobic strip and otherwise variably hydrophilic residues. The strip-of-helix hydrophobicity algorithm also ranked such sequences according to an index, the mean hydrophobicity of amino acids in the axial strip. This algorithm well predicted T cell-presented fragments of antigenic proteins. A derivative of this algorithm (the structural helices algorithm (SHA] was tested for the prediction of helices in crystallographically defined proteins. For the SHA, eight amino acid sequences, 2 cycles plus one amino acid in an alpha helix, with strip-of-helix hydrophobicity indices greater than 2.5, were selected with overlapping segments joined. These selections were terminated according to simple "capping rules," which took into account the roles of N-terminal Asn or Pro and C-terminal Gly in the stability of helices. In analyses of 35 crystallographically defined proteins with known alpha and 3(10) helices, the predictions with the SHA overlapped (had overlap indices x greater than or equal to 0.5) with 34% of known helices, touched (had overlap indices 0.5 greater than x greater than 0) or overlapped with 66% of known helices, or were neighboring (came within 6 residues) or touched or overlapped with 82% of known helices. At each level of judging the quality of prediction, the SHA was usually less sensitive (correct predictions/total number of known helices) and more efficient (correct predictions/total number of predictions) than the Chou-Fasman and Garnier-Robson methods. It was simpler in design and calculation. The chemical mechanisms underlying these algorithms appear to apply both to protein folding and to selection of T cell-presented antigenic sequences.

Algorithms

Identification and estimation algorithm for stochastic neural system.

An algorithm for the estimation of stochastic processes in a neural system is presented. This process is defined here as the continuous stochastic process reflecting the dynamics of the neural system which has some inputs and generates output spike trains. The algorithm proposed here is to identify the system parameters and then estimate the stochastic process called neural system process here. These procedures carried out on the basis of the output spike trains which are supposed to be the data observed in the randomly missing way by the threshold time function in the neural system. The algorithm is constructed with the well-known Kalman filters and realizes the estimation of the neural system process by cooperating with the algorithm for the parameter estimation of the threshold time function presented previously (Nakao et al., 1983). The performance of the algorithm is examined by applying it to the various spike trains simulated by some artificial models and also to the neural spike trains recorded in cat's optic tract fibers. The results in these applications are thought to prove the effectiveness of the algorithm proposed here to some extent. Such attempts, we think, will serve to improve the characterizing and modelling techniques of the stochastic neural systems.

Animals

Analysis of a cooperative stereo algorithm.

Marr and Poggio (1976) recently described a cooperative algorithm that solves the correspondence problem for stereopsis. This article uses a probabilistic technique to analyze the convergence of that algorithm, and derives the conditions governing the stability of the solution state. The actual results of applying the algorithm to random-dot stereograms are compared with the probabilistic analysis. A satisfactory mathematical analysis of the asymptotic behaviour of the algorithm is possible for a suitable choice of the parameter values and loading rules, and again the actual performance of the algorithm under these conditions is compared with the theoretical predictions. Finally, some problems raised by the analysis of this type of "cooperative" algorithm are briefly discussed.

Depth Perception

Potential effect of self-care algorithms on the number of physician visits.

To assess the potential effect of self-care algorithms on the number of physician visits, actual visits from the Seattle Virus Watch were compared retrospectively with those recommended by clinical algorithms for common illnesses from the book, Take Care of Yourself, by Vickery and Fries. From a total of 3929 illnesses, records indicating the presence of the index symptom for eight algorithms were identified, determining whether the criteria for seeing a physician were met and whether a physician visit was recorded. The number of visits observed was compared to the number of visits recommended by the algorithms. Strict adherence would have increased the number of visits over that observed for five, remained the same for two, and decreased for one of the algorithms. These results indicate that adherence to some commonly promulgated self-care algorithms may increase rather than decrease the number of physician visits.

Activities of Daily Living

Clinical algorithms for prehospital cardiac care.

Algorithms for the prehospital management of cardiac arrhythmias were developed and their use by and value to paramedics evaluated. The algorithms, in booklet form, were distributed to half of the Philadelphia paramedic platoons; paramedics in the other platoons followed a narrative protocol that reflected identical contents. An arrhythmia recognition test given 18 months after the algorithm booklets were introduced showed that paramedics who received the booklets scored significantly higher in identifying life-threatening arrhythmias (p = 0.029) than did their counterparts without the booklets. Survival data for 459 patients in ventricular fibrillation treated by paramedics were collected 1 year before and 7 months after the introduction of the algorithm booklets. The paramedics using the algorithms improved their survival rate from 11.25 to 15.1 per cent, while the survival rate for patients treated by paramedics using the narrative protocols decreased from 12.4 to 7.7 per cent. The likelihood of obtaining a ratio of survival odds of this magnitude when there is no true difference is 0.092. Time-to-death was significantly different (p = 0.04) for the two groups of patients. Thus, the use of algorithm booklets as an inexpensive educational aid for paramedics is recommended.

Allied Health Personnel

In vivo comparison of different algorithms for the artificial beta-cell.

Using an extracorporeal artificial beta-cell in chronically diabetic dogs, the effects of four different mathematical models of glucose-controlled insulin dosage were compared: the Biostator algorithm (quadratic equation), Toronto algorithm (hyperbolic tangent function), Karlsburg algorithm (modified first-order derivative controller), and Ilmenau algorithm (second-order linear difference equation). The constants of all formulas implemented for the artificial beta-cell were obtained by regression analysis of paired blood glucose and plasma insulin data from normal control animals. Thus, they were biologically equivalent for all formulas. The patterns of blood glucose, insulin doses, and plasma insulin before, during, and after an intravenous glucose infusion test performed during the glucose-controlled insulin infusion showed no significant differences between the experimental groups subjected to the different algorithms. However, in no case were really normal blood glucose response curves restored by the artificial beta-cell. This might be due, first, to the fact that the algorithm parameters were not adapted to the actual individual insulin responsiveness, second, to the unphysiological peripheral venous route of insulin administration, and, third, to the lack of appropriate adaptation of the animals to normoglycemia.

Animals

Two-film brachytherapy reconstruction algorithm.

We have developed a new isocentric two-film reconstruction algorithm for brachytherapy seed and needle implants. The algorithm has no requirements that the two films be orthogonal, symmetric, or even be taken in a transverse plane. In addition, there is no requirement that the two films even have the same number of images. We have found removal of these usual constraints useful for head and neck implants where images are often obscured by patient anatomy. The inherent image matching ambiguities associated with traditional two-film techniques are minimized by considering the image end points, rather than just the image centroids. For two films, the new algorithm, which considers all image combinations at one time, matches all the end-point images on one film with those on the other, and then reconstructs the end-point positions of the seeds. The algorithm minimizes the difference between the actual images and the projected images from the reconstructed seeds. The new two-film image matching problem is shown to be equivalent to the well-known assignment problem. For an implant of N seeds, this equivalence allows the two-film problem to be solved by an algorithm (ACM algorithm 548) that scales with a polynomial power of N, rather than N! as is usually assumed. An implant of N seeds can be matched and reconstructed in approximately (N/20)2s on a VAX 11/780.

Brachytherapy

An excitation-pattern algorithm for the estimation of (2f1-f2) and (f2-f1) cancellation level and phase.

An excitation-pattern algorithm is described which provides an estimate of cancellation level and phase for the (2f1-f2) and (f2-f1) distortion products. An experiment is first conducted to demonstrate the need for such an algorithm for (f2-f1) level predictions. The results of this experiment, which employed three pairs of primaries having complementary input levels (L1 = 65, L2 = 85 dB; L1 = 85, L2 = 65 dB), do not agree with the predictions of another similar algorithm [E. Zwicker, J. Acoust. Soc. Am. 69, 1410-1413 (1981)]. A new excitation-pattern algorithm is then described. The predicted level behavior for (f2-f1) and (2f1-f2) is more accurate for the proposed algorithm. In addition, an accurate phase estimate is also provided by the new algorithm.

Acoustic Stimulation

Future promise, current clinical ambiguity: a systematic review of machine learning algorithm outputs predicting risk of cardiovascular disease.

OBJECTIVE: To examine whether the outputs of machine learning algorithms designed to predict risk of cardiovascular disease (CVD) address known deficiencies of the Framingham Risk Score (FRS) and improve risk estimates. METHODS: For this critical review, Medline, Embase and IEEE were searched from inception to 1 January 2025. Included were studies describing machine learning algorithms designed to specifically compare output of cardiovascular risk assessment with the FRS. Commentaries, letters, unpublished work or non-peer-reviewed papers were excluded.Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, two reviewers screened titles and abstracts independently, then populated a purpose-built data extraction form. A subsequent qualitative thematic analysis focused on algorithms' strengths, added value, potential harms, unintended consequences and equity implications.The main outcome assessed was whether, among healthy adults, the algorithm improved CVD risk prediction relative to the FRS. RESULTS: Of 707 studies retrieved, 29 met inclusion criteria. 23 reported improved predictive ability relative to the FRS. Most datasets and/or medical records used included sociodemographic predictors of CVD not included among FRS inputs. Some added costly diagnostic tests like CT angiography to FRS screening indicators. When they were defined, inputs and outcomes such as hypertension or myocardial infarction did not always adhere to FRS values. Statistical significance was generally taken as a proxy for clinical significance. Some algorithms overestimated the number at risk compared with the FRS without discussing whether that larger proportion might be at risk of overdiagnosis rather than CVD, while a few decreased the proportion found to be at risk. CONCLUSIONS: Use of artificial intelligence to improve accuracy of risk assessment for CVD demonstrates the technological capacity to merge known sociodemographic predictors with biologic variables and examine non-linear interactions among these. Still needed to achieve patient benefit is clinical insight, adherence to screening principles and cost-benefit assessment of inputs selected.

Humans

Efficient algorithms for generating interpolated (zoomed) MR images.

This paper discusses the two-dimensional implementation of a number of modified fast Fourier transform (FFT) algorithms that efficiently interpolate (zoom) magnetic resonance (MR) images. If the original image was sampled at a rate satisfying the Nyquist criterion, these algorithms would effectively increase the sampling rate, permitting image details to be more easily discerned. The Skinner interpolating fast Fourier transform (SIFFT) avoids many of the computationally unnecessary complex multiplications that occur when interpolating using the normal fast Fourier transform algorithm. The novel interpolating fast Fourier transform (NIFFT) offers further savings when a subimage is required. Theoretical and experimental timings that compare the use of the normal FFT, SIFFT, and NIFFT algorithms for interpolation are given using magnetic resonance image reconstruction examples. Time savings of a factor of 2 to 4 are possible in typical experimental situations. Time savings of factors of 5 to 20 are possible when zooming images using two-dimensional band selectable digital filtering (2D-BSDF) in combination with decimation and the SIFFT algorithm. In 2D-BSDF, the original MRI data set is reduced in size to retain only those frequency components corresponding to a desired subimage, thereby decreasing the computational load associated with further processing. A significant reduction in computation time is achieved when modeling is combined with 2D-BSDF and SIFFT as fewer points require modeling.

Algorithms

Prototype ventilator and alarm algorithm for the NASA space station.

An alarm algorithm was developed to monitor the ventilator on the National Aeronautics and Space Administration space station. The algorithm automatically identifies and interprets critical events so that an untrained user can manage the mechanical ventilation of a critically injured crew member. The algorithm was tested in two healthy volunteers by simulating 260 critical events in each volunteer while the volunteer breathed via the ventilator. Thirteen critical events were induced eight times in random order, for the five different modes of ventilation. These events included various ventilator tubing disconnects, leaks, and occlusions, as well as power and gas supply failures. The algorithm identified the critical events and generated alarms in response to 99.2% (516 of 520, total) of the events. The alarm textual messages were correct 98% (505 of 516 messages) of the time. The alarm algorithm is an improvement over current alarms found on most ventilators because its alarm messages specifically identify failures in the patient breathing circuit or ventilator. The system may improve patient care by helping critical care personnel respond more rapidly and correctly to critical events.

Algorithms

Parallel algorithms for the analysis of two-dimensional electrophoresis gels.

This paper describes some parallel processing algorithms for the analysis of two-dimensional electrophoresis images. The machine used for the processing was the CLIP4 Cellular Array Computer at University College, London, one of the largest processor arrays in the world. Included in this paper are an algorithm for centroid detection, Gaussian fitting algorithms, and an algorithm for the extraction of data out of the cellular array machine. It is shown that these parallel algorithms can run at a speed almost completely independent of the number of spots in the gel images.

Algorithms

Mathematical characterization of Chaos Game Representation. New algorithms for nucleotide sequence analysis.

Chaos Game Representation (CGR) can recognize patterns in the nucleotide sequences, obtained from databases, of a class of genes using the techniques of fractal structures and by considering DNA sequences as strings composed of four units, G, A, T and C. Such recognition of patterns relies only on visual identification and no mathematical characterization of CGR is known. The present report describes two algorithms that can predict the presence or absence of a stretch of nucleotides in any gene family. The first algorithm can be used to generate DNA sequences represented by any point in the CGR. The second algorithm can simulate known CGR patterns for different gene families by setting the probabilities of occurrence of different di- or trinucleotides by a trial and error process using some guidelines and approximate rules-of-thumb. The validity of the second algorithm has been tested by simulating sequences that can mimic the CGRs of vertebrate non-oncogenes, proto-oncogenes and oncogenes. These algorithms can provide a mathematical basis of the CGR patterns obtained using nucleotide sequences from databases.

Algorithms