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PubMedDate not supplied

The clinical algorithm nosology: a method for comparing algorithmic guidelines.

Concern regarding the cost and quality of medical care has led to a proliferation of competing clinical practice guidelines. No technique has been described for determining objectively the degree of similarity between alternative guidelines for the same clinical problem. The authors describe the development of the Clinical Algorithm Nosology (CAN), a new method to compare one form of guideline: the clinical algorithm. The CAN measures overall design complexity independent of algorithm content, qualitatively describes the clinical differences between two alternative algorithms, and then scores the degree of similarity between them. CAN algorithm design-complexity scores correlated highly with clinicians' estimates of complexity on an ordinal scale (r = 0.86). Five pairs of clinical algorithms addressing three topics (gallstone lithotripsy, thyroid nodule, and sinusitis) were selected for interrater reliability testing of the CAN clinical-similarity scoring system. Raters categorized the similarity of algorithm pathways in alternative algorithms as "identical," "similar," or "different." Interrater agreement was achieved on 85/109 scores (80%), weighted kappa statistic, k = 0.73. It is concluded that the CAN is a valid method for determining the structural complexity of clinical algorithms, and a reliable method for describing differences and scoring the similarity between algorithms for the same clinical problem. In the future, the CAN may serve to evaluate the reliability of algorithm development programs, and to support providers and purchasers in choosing among alternative clinical guidelines.

Algorithms↗
PubMed1992

Hemodynamic and oxygen transport patterns for outcome prediction, therapeutic goals, and clinical algorithms to improve outcome. Feasibility of artificial intelligence to customize algorithms.

A generalized decision tree or clinical algorithm for treatment of high-risk elective surgical patients was developed from a physiologic model based on empirical data. First, a large data bank was used to do the following: (1) describe temporal hemodynamic and oxygen transport patterns that interrelate cardiac, pulmonary, and tissue perfusion functions in survivors and nonsurvivors; (2) define optimal therapeutic goals based on the supranormal oxygen transport values of high-risk postoperative survivors; (3) compare the relative effectiveness of alternative therapies in a wide variety of clinical and physiologic conditions; and (4) to develop criteria for titration of therapy to the endpoints of the supranormal optimal goals using cardiac index (CI), oxygen delivery (DO2), and oxygen consumption (VO2) as proxy outcome measures. Second, a general purpose algorithm was generated from these data and tested in preoperatively randomized clinical trials of high-risk surgical patients. Improved outcome was demonstrated with this generalized algorithm. The concept that the supranormal values represent compensations that have survival value has been corroborated by several other groups. We now propose a unique approach to refine the generalized algorithm to develop customized algorithms and individualized decision analysis for each patient's unique problems. The present article describes a preliminary evaluation of the feasibility of artificial intelligence techniques to accomplish individualized algorithms that may further improve patient care and outcome.

Algorithms↗
PubMed1991

An algorithm for matching anonymous hospital discharge records used in occupational disease surveillance: anonymous record matching algorithm.

The expense of collecting primary data, coupled with limited authority to mandate reporting, requires alternative methods of implementing an occupational disease registry in Illinois. One alternative data source for surveillance of some occupational diseases is hospital discharge records. Because these records lack personal identifiers, it has been impossible historically to match records belonging to the same individual and obtain reliable case estimates. To circumvent this difficulty, an algorithm has been developed to match anonymous hospital discharge records collected from all Illinois hospitals. The algorithm was based on the assumption that specific combinations of occupational disease code, sex, zip code, and date of birth would identify an individual to whom multiple hospitalizations belong. Matching with the algorithm reduced the 1986 case estimates from 597 to 499 for all cases of coal workers' pneumoconiosis, asbestosis, and silicosis.

Algorithms↗
PubMed2024

Algorithms for the identification of prevalent diabetes in the All of Us Research Program validated using polygenic scores.

The All of Us Research Program (AoU) is an initiative designed to gather a comprehensive and diverse dataset from at least one million individuals across the USA. This longitudinal cohort study aims to advance research by providing a rich resource of genetic and phenotypic information, enabling powerful studies on the epidemiology and genetics of human diseases. One critical challenge to maximizing its use is the development of accurate algorithms that can efficiently and accurately identify well-defined disease and disease-free participants for case-control studies. This study aimed to develop and validate type 1 (T1D) and type 2 diabetes (T2D) algorithms in the AoU cohort, using electronic health record (EHR) and survey data. Building on existing algorithms and using diagnosis codes, medications, laboratory results, and survey data, we developed and implemented algorithms for identifying prevalent cases of type 1 and type 2 diabetes. The first set of algorithms used only EHR data (EHR-only), and the second set used a combination of EHR and survey data (EHR+). A universal algorithm was also developed to identify individuals without diabetes. The performance of each algorithm was evaluated by testing its association with polygenic scores (PSs) for type 1 and type 2 diabetes. We demonstrated the feasibility and utility of using AoU EHR and survey data to employ diabetes algorithms. For T1D, the EHR-only algorithm showed a stronger association with T1D-PS compared to the EHR + algorithm (DeLong p-value = 3 × 10-5). For T2D, the EHR + algorithm outperformed both the EHR-only and the existing T2D definition provided in the AoU Phenotyping Library (DeLong p-values = 0.03 and 1 × 10-4, respectively), identifying 25.79% and 22.57% more cases, respectively, and providing an improved association with T2D PS. We provide a new validated type 1 diabetes definition and an improved type 2 diabetes definition in AoU, which are freely available for diabetes research in the AoU. These algorithms ensure consistency of diabetes definitions in the cohort, facilitating high-quality diabetes research.

Humans↗
PubMed1989

High-spatial-frequency (bone) algorithm improves quality of standard CT of the thorax.

The high-spatial-frequency (bone) reconstruction algorithm has been shown to be superior to the standard algorithm in the assessment of thin-section images of the lung parenchyma. We compared the high-spatial-frequency and standard algorithms in the evaluation of normal and abnormal lung parenchyma and mediastinum on conventional 10-mm-collimation CT scans. Three observers, blinded to the reconstruction algorithm used, compared both algorithms on identical images in 31 patients with a variety of diseases. Lung parenchymal detail was improved with the bone algorithm in 94% of scans (p less than .001). The definition of mediastinal contents with the bone algorithm was judged equal or superior to the standard algorithm in 94% of images (p less than .001). Scanning of a line-pair-resolution phantom demonstrated a 28% improvement in image resolution with the bone algorithm. Although the bone algorithm resulted in an increase in visible noise, the overall visual quality of the images was considered equal to or greater than that of those reconstructed on the standard algorithm in 100% of parenchymal (p less than .001) and 85% of mediastinal (p less than .001) images. We conclude that routine use of the bone algorithm results in improved spatial resolution and definition of pulmonary parenchymal detail without appreciably degrading the overall visual quality of mediastinal images.

Algorithms↗
PubMed1999

An optimal algorithm for automatic genotype elimination.

In an effort to accelerate likelihood computations on pedigrees, Lange and Goradia defined a genotype-elimination algorithm that aims to identify those genotypes that need not be considered during the likelihood computation. For pedigrees without loops, they showed that their algorithm was optimal, in the sense that it identified all genotypes that lead to a Mendelian inconsistency. Their algorithm, however, is not optimal for pedigrees with loops, which continue to pose daunting computational challenges. We present here a simple extension of the Lange-Goradia algorithm that we prove is optimal on pedigrees with loops, and we give examples of how our new algorithm can be used to detect genotyping errors. We also introduce a more efficient and faster algorithm for carrying out the fundamental step in the Lange-Goradia algorithm-namely, genotype elimination within a nuclear family. Finally, we improve a common algorithm for computing the likelihood of a pedigree with multiple loops. This algorithm breaks each loop by duplicating a person in that loop and then carrying out a separate likelihood calculation for each vector of possible genotypes of the loop breakers. This algorithm, however, does unnecessary computations when the loop-breaker vector is inconsistent. In this paper we present a new recursive loop breaker-elimination algorithm that solves this problem and illustrate its effectiveness on a pedigree with six loops.

Algorithms↗
PubMed1991

Evaluation of a diagnostic algorithm for heart disease in neonates.

OBJECTIVE: To develop, test, and validate an algorithm for diagnosing disease in neonates during an over the telephone referral to a specialist cardiac centre. DESIGN: A draft algorithm requiring only data available to a referring paediatrician was generated. This was modified in the light of a retrospective review of case records. A questionnaire to elicit all the data required by the algorithm was then generated. There followed a prospective three phase evaluation during consecutive over the telephone referrals. This consisted of (a) a conventional phase with unstructured referral consultations, (b) a phase with referrals structured around the questionnaire but independent of the algorithm, and (c) a validation phase with the algorithm (and its previous errors) available during the referral consultation. SETTING: 59 paediatric centres in south east England and a central specialist paediatric cardiology unit. PATIENTS: Consecutive neonates (aged less than 31 days) referred with suspected heart disease. The retrospective review was of records of 174 neonates from 1979. In the prospective evaluation (1987-90) the conventional phase comprised 71 neonates (over 5.5 months), the structured phase 203 neonates (over 14 months), and the validation phase 195 neonates (over 12 months). MAIN OUTCOME MEASURES: Diagnostic accuracy (assigning patients to the correct diagnostic category (out of 27)), of the referring paediatrician, the specialist after the referral consultation, and the algorithm as compared with the definitive diagnosis by echocardiography at the specialist centre, and score for the appropriateness of management in transit. RESULTS: Simply structuring the consultation by questionnaire (that is, proceeding from the conventional phase to the structured phase) improved the diagnostic accuracy of both paediatricians (from 34% (24/71 cases) to 48% (97/203) correct) and specialists (from 54% (38/71 cases) to 64% (130/203) correct). The algorithm (structured phase) would have been even more accurate (78% (158/203 cases); p less than 0.01). Management scores in the structured phase were also better than in the conventional phase (80%(162/203 cases)v 58% (41/71) appropriate; p less than 0.01). Management scores would have improved to 91% appropriate (185/203; p less than 0.001) had the algorithmic diagnoses dictated management. The superiority of the algorithm was maintained but not bettered in the validation phase. CONCLUSIONS: Applying the algorithm should reduce the morbidity and mortality of neonates with critical heart disease by aiding clinicians in therapeutic decisions for in transit care.

Algorithms↗
PubMed1992

Dynamic programming algorithms for restriction map comparison.

For most sequence comparison problems there is a corresponding map comparison algorithm. While map data may appear to be incompatible with dynamic programming, we show in this paper that the rigor and efficiency of dynamic programming algorithms carry over to the map comparison algorithms. We present algorithms for restriction map comparison that deal with two types of map errors: (i) closely spaced sites for different enzymes can be ordered incorrectly, and (ii) closely spaced sites for the same enzyme can be mapped as a single site. The new algorithms are a natural extension of a previous map comparison model. Dynamic programming algorithms for computing optimal global and local alignments under the new model are described. The new algorithms take about the same order of time as previous map comparison algorithms. Programs implementing some of the new algorithms are used to find similar regions within the Escherichia coli restriction map of Kohara et al.

Algorithms↗
PubMed1990

A modified algorithm of the single pool urea kinetic model.

Urea Kt/V, calculated according to the variable volume single pool urea kinetic model (UKM), has been accepted as the yardstick reflecting the adequacy of haemodialysis therapy. However, the classical algorithm of UKM requires great care in dialyser urea clearance (K) measurement in order to avoid major inaccuracies in estimating the urea distribution volume (V). Thus, we suggest a modified algorithm of UKM which avoids the measurement of K. It assumes an arbitrary V value and then calculates kinetically K as a function of the assumed V value. The rationale of the modified algorithm can be derived from the knowledge that the classical algorithm imposes a proportionality ration between K and V: given a particular set of data, a change in the attributed value of K leads to a proportional change in the calculated V value, so that the ratio K/V remains nearly constant. Aims of the study were (1) to validate the modified algorithm by comparing the resulting Kt/V and normalised protein catabolic rate (NPCR) values with the homologous ones obtained using the classical algorithm in a group of 33 patients on thrice-weekly haemodialysis; plasma water urea concentrations were used with the classical algorithm (CApw) and the modified algorithm (MApw); and (2) to verify the possibility of using plasma urea concentrations with the modified algorithm (MAp) instead of the more rigorous plasma water concentrations. NPCR (g/kg per day) was 1.33 +/- 0.05 in CApw, 1.29 +/- 0.05 in MApw and 1.28 +/- 0.04 in MAp. Kt/V was 1.27 +/- 0.03 in CApw, 1.25 +/- 0.03 in MApw and 1.26 +/- 0.03 in MAp.(ABSTRACT TRUNCATED AT 250 WORDS)

Algorithms↗
PubMed2025

Quantifying and improving rheumatoid arthritis algorithm performance in biobank settings.

OBJECTIVE: To quantify and improve the performance of standard rheumatoid arthritis (RA) algorithms in a biobank setting. METHODS: This retrospective cohort study within the Mayo Clinic (MC) Biobank and MC Tapestry Study identified RA cases by presence of at least two RA codes OR positive anti-cyclic citrullinated peptide antibodies (CCP) plus disease-modifying anti-rheumatic drug (DMARD) prescription as of 7/18/2022. Rheumatology physicians manually verified all RA cases using RA criteria and/or rheumatology physician diagnosis plus DMARD use. All other biobank participants served as non-RA controls. We defined seropositivity as rheumatoid factor and/or anti-CCP positivity. We assessed rules-based and Electronic Medical Records and Genomics (eMERGE) RA algorithms using positive predictive value (PPV). Finally, we developed a novel RA algorithm using a LASSO-based machine learning approach with five-fold cross validation. RESULTS: We identified 1,316 confirmed RA cases (968 MC Biobank, 348 Tapestry, 70 % seropositive) and 82,123 non-RA controls (mean age 65, 61 % female). The PPV of 3 RA codes was 43 %, codes plus DMARD was 54 %, and codes plus DMARD plus seropositivity was 85 %. The PPV of eMERGE was 77 %. Available in the MC Biobank, self-reported RA (PPV 10 %) only minimally improved algorithm performance (PPV from 83 % to 85 %), whereas family history of RA (PPV 3 %) worsened performance. At 90 % PPV, the novel RA algorithm incorporating key variables such as anti-CCP and DMARD use increased sensitivity by 4-11 % compared to eMERGE. CONCLUSION: Rules-based and eMERGE RA algorithms had worse performance in biobank than administrative settings. Our novel RA algorithm outperformed these standard algorithms.

Humans↗
PubMed1989

Construction of clinical algorithms for educational programs.

Clinical algorithms have been used successfully in a variety of health care settings to assist health care professionals in the diagnosis and management of medical problems. In addition to their clinical applications, algorithms also serve as an instructional resource by themselves and when used in conjunction with other educational methodologies. A recommended algorithm development process is described for cancer educators who wish to take advantage of the unique contribution clinical algorithms can offer for their educational programs. Algorithm design conventions are reviewed and specific writing suggestions are offered for the guidance of educators who want to design their own clinical algorithms. Objections to clinical algorithms can often be attributed to a misunderstanding of their proper role, which is to facilitate, not dictate, the decision process and guide the application of management logic. Clinical algorithms are a valuable instructional resource that can be used in a wide range of educational settings from self-instruction units to the design of lecture presentations.

Algorithms↗
PubMed1990

A single TLD dose algorithm to satisfy federal standards and typical field conditions.

Modern whole-body dosimeters are often required to accurately measure the absorbed dose in a wide range of radiation fields. While programs are commonly developed around the fields tested as part of the National Voluntary Accreditation Program (NVLAP), the actual fields of application may be significantly different. Dose algorithms designed to meet the NVLAP standard, which emphasizes photons and high-energy beta radiation, may not be capable of the beta-energy discrimination necessary for accurate assessment of absorbed dose in the work environment. To address this problem, some processors use one algorithm for NVLAP testing and one or more different algorithms for the work environments. After several years of experience with a multiple algorithm approach, the Dosimetry Services Group of Yankee Atomic Electric Company (YAEC) developed a one-algorithm system for use with a four-element TLD badge using Li2B4O7 and CaSO4 phosphors. The design of the dosimeter allows the measurement of the effective energies of both photon and beta components of the radiation field, resulting in excellent mixed-field capability. The algorithm was successfully tested in all of the NVLAP photon and beta fields, as well as several non-NVLAP fields representative of the work environment. The work environment fields, including low- and medium-energy beta radiation and mixed fields of low-energy photons and beta particles, are often more demanding than the NVLAP fields. This paper discusses the development of the algorithm as well as some results of the system testing including: mixed-field irradiations, angular response, and a unique test to demonstrate the stability of the algorithm. An analysis of the uncertainty of the reported doses under various irradiation conditions is also presented.

Algorithms↗
PubMed1990

A randomized double-blind, cross-over study of the linear and nonlinear algorithms for the QT sensing rate adaptive pacemaker.

We have compared the pacing rate responses during cardiopulmonary exercise testing in 11 patients (mean 59 years, six female) with implanted QT sensing rate adaptive pacemakers who were randomly programmed to 1-month periods in the linear and nonlinear algorithms using a double-blind, cross-over design. Exercise testing was performed at the end of each month block and symptoms were scored with the MacMaster questionnaire. With exercise, the time to a 10 beats/min increment in rate was significantly less with the nonlinear compared to the linear algorithm (126 sec vs 255 sec, P = 0.02) but there were no significant differences in exercise duration, the peak pacing rate, the peak VO2, the VO2 at the anaerobic threshold or the mean correlation coefficients of the pacing rate VO2 relationship. Rate oscillation occurred in seven patients in the linear algorithm and in two patients in the nonlinear setting. Initial deceleration of the pacing rate at the onset of exercise occurred in seven patients in the linear algorithm and in four patients in the nonlinear setting. The nonlinear algorithm is associated with a faster response time during exercise and fewer instances of rate instability. However, it has not overcome the problem of a dip in the pacing rate at the beginning of exercise. The major difference in the function of the two algorithms is faster initial acceleration with the nonlinear algorithm. This is explained by the significantly higher values of the slope setting at the lower rate limit for the nonlinear versus the linear algorithm (6.3 ms/ms vs 5.1 ms/ms).

Adult↗
PubMedDate not supplied

Pencil-beam redefinition algorithm for electron dose distributions.

A pencil-beam redefinition algorithm has been developed for the calculation of electron-beam dose distributions on a three-dimensional grid utilizing 3-D inhomogeneity correction. The concept of redefinition was first used for both fixed and arced electron beams by Hogstrom et al. but was limited to a single redefinition. The success of those works stimulated the development of the pencil-beam redefinition algorithm, the aim of which is to solve the dosimetry problems presented by deep inhomogeneities through development of a model that redefines the pencil beams continuously with depth. This type of algorithm was developed independently by Storchi and Huizenga who termed it the "moments method." Such a pencil beam within the patient is characterized by a complex angular distribution, which is approximated by a Gaussian distribution having the same first three moments as the actual distribution. Three physical quantities required for dose calculation and subsequent radiation transport--namely planar fluence, mean direction, and root-mean-square spread about the mean direction--are obtained from these moments. The primary difference between the moments method and the redefinition algorithm is that the latter subdivides the pencil beams into multiple energy bins. The algorithm then becomes a macroscopic method for transporting the complete phase space of the beam and allows the calculation of physical quantities such as fluence, dose, and energy distribution. Comparison of calculated dose distributions with measured dose distributions for a homogeneous water phantom, and for phantoms with inhomogeneities deep relative to the surface, show agreement superior to that achieved with the pencil-beam algorithm of Hogstrom et al. in the penumbral region and beneath the edges of air and bone inhomogeneities. The accuracy of the redefinition algorithm is within 4% and appears sufficient for clinical use, and the algorithm is structured for further expansion of the physical model if required for site-specific treatment planning problems.

Algorithms↗
PubMed1989

Comparison of information-preserving and information-losing data-compression algorithms for CT images.

Data compression increases the number of images that can be stored on magnetic disks or tape and reduces the time required for transmission of images between stations. Two algorithms for data compression are compared in application to computed tomographic (CT) images. The first, an information-preserving algorithm combining differential and Huffman encoding, allows reconstruction of the original image. A second algorithm alters the image in a clinically acceptable manner. This second algorithm combines two processes: the suppression of data outside of the head or body and the combination of differential and Huffman encoding. Because the final image is not an exact copy, the second algorithm is information losing. Application of the information-preserving algorithm can double or triple the number of CT images that can be stored on hard disk or magnetic tape. This algorithm may also double or triple the speed with which images may be transmitted. The information-losing algorithm can increase storage or transmission speed by a factor of five. The computation time on this system is excessive, but dedicated hardware is available to allow efficient implementation.

Algorithms↗
PubMed1991

Classification of postoperative cardiac patients: comparative evaluation of four algorithms.

Four classification algorithms based on Bayes' rule for minimum error are compared by evaluating their ability to recognize high- and normal-risk cardio-surgical patients. These algorithms differ in the modelling of the probability density function (pdf) for each class and include: (a) two parametric algorithms based on the assumption of normal pdf; (b) two non-parametric algorithms using Parzen multidimensional approximation of pdf with normal kernels. In each case, classes with both equal and different covariance matrices were considered. A set of 200 patients in the 6 h immediately following cardiac surgery has been used to test the performance of the algorithms. For each patient the three measured variables most effective in representing the difference between the two classes were considered. We found that the two algorithms which explicitly incorporate the information on the different sample covariance between the physiological variables existing in the two classes generally provide better recognition of high- and normal-risk patients. Of these two algorithms the parametric one appears extremely attractive for practical applications, since it exhibits slightly better performance in spite of its great simplicity.

Algorithms↗
PubMed1992

Fast ECG data compression algorithms suitable for microprocessor systems.

ECG data compression techniques have received extensive attention in ECG analysis. Numerous data compression algorithms for ECG signals have been proposed during the last three decades. We describe two algorithms based on the scan-along polygonal approximation algorithm (SAPA) that are suitable for multichannel ECG data reduction on a microprocessor-based system. One represents a modification of SAPA (MSAPA) which adopts the method of integer division table searching to speed up data reduction; the other (CSAPA) combines MSAPA and TP, a turning-point algorithm, to preserve ST segment signals. Results show that our algorithms achieve a compression ratio of more than 5:1 and a percent rms difference (PRD) to the original signal of less than 3.5%. In addition, the maximum execution time of MSAPA for processing one data point is about 50 microseconds. Moreover, the CSAPA algorithm retains all of the details of the ST segment, which are important in ischaemia diagnosis, by employing the TP algorithm.

Algorithms↗