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Results for “Predictive modeling”

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At least 145 records · Page 8Linked to original sources

PubMed1987

Development and application of a prediction model for dental caries.

The development and validation of a caries prediction model comprising 13 sociodemographic and dental examination variables on Grade 1 and Grade 5 children in the National Preventive Dentistry Demonstration Program are described. The objective was to derive a method of predicting children at high risk to caries early in order that preventive measures might be undertaken. True high risk children were defined in two ways: highest 25% of children based on their 4-yr DMFS increment, and their total DMFS score at the end of the study. In both cases, children predicted to be at high risk were defined as the 25% with the highest discriminant score. Discriminant function and logistic regression analyses were used to determine the extent to which the 13 variables collectively discriminated between true high risk and non-high risk children so defined. Sensitivity was approximately 0.50 and specificity around 0.82, using the 4-yr increment as the criterion for defining true high risk, and approximately 0.64 and 0.88, respectively, using the final DMFS score for defining true high risk.

Child↗

PubMed1998

From twitch to tetanus for human muscle: experimental data and model predictions for m. triceps surae.

In models describing the excitation of muscle by the central nervous system, it is often assumed that excitation during a tetanic contraction can be obtained by the linear summation of responses to individual stimuli, from which the active state of the muscle is calculated. We investigate here the extent to which such a model describes the excitation of human muscle in vivo. For this purpose, experiments were performed on the calf muscles of four healthy subjects. Values of parameters in the model describing the behaviour of the contractile element (CE) and the series elastic element (SEE) of this muscle group were derived on the basis of a set of isokinetic release contractions performed on a special-purpose dynamometer as well as on the basis of morphological data. Parameter values describing the excitation of the calf muscles were optimized such that the model correctly predicted plantar flexion moment histories in an isometric twitch, elicited by stimulation of the tibial nerve. For all subjects, the model using these muscle parameters was able to make reasonable predictions of isometric moment histories at higher stimulation frequencies. These results suggest that the linear summation of responses to individual stimuli can indeed give an adequate description of the process of human muscle excitation in vivo.

Adult↗

PubMed1989

Usefulness of a single-dose prediction model for the determination of long-term maintenance therapy of valproic acid.

The single-dose prediction model (SDPM) of Slattery et al. has been shown accurately to predict short-term (3 to 7 days) steady-state valproic acid (VPA) concentrations in normal volunteers and in hospitalized patients receiving monotherapy. To assess the long-term usefulness of the SDPM in a real-life clinic setting, six ambulatory patients ranging in age from 2 to 8 years were studied. Blood samples were drawn at least 6 h after the initial dose but before the second dose of VPA, A steady-state trough concentration (Cminss) was measured 3 to 7 days after the initiation of therapy. Dosage adjustments and alterations in therapeutic regimen were allowed and another Cminss was measured after 1 to 12 months. Predicted Cminss values were calculated for both the short-term and long-term VPA regimens. Predictive performance analysis demonstrated that the SDPM was unbiased in predicting both short-term and long-term VPA Cminss values and precise in predicting only short-term VPA Cminss values. The SDPM is not a reliable predictor of long-term total VPA concentrations in seizure patients in an outpatient clinic setting.

Child↗

PubMed1990

Validity of knee flexion and extension peak torque prediction models.

The primary purpose of this study was to test the validity of predictive models relating isokinetic knee torque production to anthropometric and demographic variables. Subjects were 23 healthy female and 15 healthy male volunteers between the ages of 10 and 77 years. We measured subjects' peak knee flexion and extension torque production at two angular velocities. For each torque dependent variable, we calculated a Pearson product-moment correlation coefficient between the measured torque values and the values obtained with prediction equations. The difference between the squared value of the correlation coefficients and the regression multiple R2 values obtained for an original group of 134 subjects ranged between .05 and .10 for the torque dependent variables. The results indicate the validity of the regression models at the level specified by the multiple regression R2 values. Clinicians can use the prediction equations presented in this article to establish rehabilitation goals for patients and can estimate the error involved in applying each prediction equation.

Adolescent↗

PubMed1993

Wave V latency shifts with age and sex in normals and patients with cochlear hearing loss: development of a predictive model.

In order to study the combined effect of age, sex and cochlear hearing loss on the auditory brainstem response in an adult population, absolute wave V latency measurements were made in 117 ears of 64 patients with cochlear hearing loss and 105 ears of 58 otologically normal control subjects. Among normals, wave V latency was found to increase with age. Latencies in female subjects were significantly shorter than in male subjects. The best predictive model for wave V latency in normals was: wave V latency (ms) = 4.982 + 0.007 x age + 0.091 x sex, where age is measured in years and sex is given a value of 1 for females and 2 for males. In patients with hearing loss, wave V latency increase was determined largely by the degree of hearing loss and the effect of age was moderate. The influence of sex was minimal. Wave V latency in patients with hearing loss was predicted by the formula: wave V latency (ms) = 4.911 + 0.007 x hearing loss + 0.004 x age + 0.081 x sex. These predictive models can be utilized to accurately estimate expected wave V latency in patients with cochlear hearing loss.

Adult↗

PubMed2026

Development and preliminary validation of plasma cell-free DNA methylation-based diagnostic prediction model for colorectal cancer detection.

BACKGROUND: Colorectal cancer (CRC) is a common malignancy associated with genetic and epigenetic alterations. Several methylation biomarkers have been investigated for non-invasive CRC detection; however, their reported performance varies across clinical settings, and the detection of early-stage or precancerous disease and discrimination from non-malignant colorectal conditions remain challenging. This exploratory study aimed to identify reproducible CRC-associated plasma cell-free DNA (cfDNA) methylation regions and to develop and preliminarily evaluate diagnostic prediction model for distinguishing CRC from healthy controls and benign samples. METHODS: Public CRC tissue methylation datasets from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) were analyzed to identify reproducible CRC-associated methylation alterations. Plasma cfDNA methylation was profiled using methyl-CpG-binding-domain enrichment followed by paired-end sequencing in patients with CRC, patients with colorectal polyps, and healthy controls. After quality-control filtering, 30 CRC and healthy-control samples were randomly allocated at the participant level in a 7:3 ratio to a development set comprising 10 patients with CRC and 11 healthy controls and a held-out test set comprising 4 patients with CRC and 5 healthy controls. Hypermethylated regions were selected using least absolute shrinkage and selection operator (LASSO) logistic regression. The 12-region model was evaluated in the held-out test set and subsequently applied to 10 colorectal polyp samples without refitting or recalibration. RESULTS: Tissue methylation analysis identified reproducible CRC-associated alterations across independent datasets. In the plasma development set, 707 differentially methylated regions (DMRs) were identified between CRC and healthy-control samples, including 324 hypermethylated and 383 hypomethylated regions. LASSO regression selected a 12-region hypermethylation signature. In the held-out test set, the model achieved an area under the curve (AUC) of 0.85 [95% confidence interval (CI): 0.579-1.000]. At the development-set-derived threshold, sensitivity was 75.0% (3/4), specificity was 60.0% (3/5), and accuracy was 66.7% (6/9). When the original model was applied to colorectal polyp samples, model scores were significantly higher in both CRC and polyp samples than in healthy controls, while CRC samples showed a tendency toward higher scores than polyp samples. CONCLUSIONS: This exploratory study identified a 12-region plasma cfDNA hypermethylation signature associated with CRC and developed a LASSO-based diagnostic prediction model that showed preliminary discrimination between CRC and healthy controls in a small held-out test set. By integrating tissue methylation evidence with plasma cfDNA profiling, this study expands the repertoire of candidate region-level methylation markers for blood-based CRC detection.

Colorectal cancer (CRC)↗

PubMed1971

Prediction models for 90Sr in shed deciduous teeth and infant bone.

Shed deciduous teeth were collected in 1966-69 in Denmark, the Faroes and Greenland from children born in the period 1953-63. 235 samples of crowns were analysed for 90Sr. The 90Sr levels in deciduous tooth crowns were related to the fall-out rate and the accumulated fall-out. The tooth levels in children born in 1950-62 could be described with the same equation as the 90Sr bone levels in 1-yr-old infants born in 1962-68. The prediction models for 90Sr in teeth and bones showed that for given amount of fall-out the Faroese levels became nearly twice as high as the Danish. The maximum teeth and bone levels were found in children born in 1963, where the Faroese level was estimated from the prediction model to be 24 pCi 90Sr/g Ca.

Bone and Bones↗

PubMed1998

Predictive model for the diagnosis of intraabdominal abscess.

RATIONALE AND OBJECTIVES: The authors investigated the use of an artificial neural network (ANN) to aid in the diagnosis of intraabdominal abscess. MATERIALS AND METHODS: An ANN was constructed based on data from 140 patients who underwent abdominal and pelvic computed tomography (CT) between January and December 1995. Input nodes included data from clinical history, physical examination, laboratory investigation, and radiographic study. The ANN was trained and tested on data from all 140 cases by using a round-robin method and was compared with linear discriminate analysis. A receiver operating characteristic curve was generated to evaluate both predictive models. RESULTS: CT examinations in 50 cases were positive for abscess. This finding was confirmed by means of laboratory culture of aspirations from CT-guided percutaneous drainage in 38 patients, ultrasound-guided percutaneous drainage in five patients, surgery in five patients, and characteristic appearance on CT scans without aspiration in two patients. CT scans in 90 cases were negative for abscess. The sensitivity and specificity of the ANN in predicting the presence of intraabdominal abscess were 90% and 51%, respectively. Receiver operating characteristic analysis showed no statistically significant difference in performance between the two predictive models. CONCLUSION: The ANN is a useful tool for determining whether an intraabdominal abscess is present. It can be used to set priorities for CT examinations in order to expedite treatment in patients believed to be more likely to have an abscess.

Abdominal Abscess↗

PubMed1987

Keeping juvenile delinquents in school: a prediction model.

The purpose of this study was to test an empirically based prediction model of school dropout on a sample of 137 juvenile delinquents, some who have dropped out and some who have remained in school. The specific factors among the many found in previous research that are salient for predicting whether delinquent youths will drop out or remain in school were determined. An important finding of this study is that it required only four factors to yield a high level of prediction: misbehavior in school, disliking school, the negative influence of peers with respect to dropping out and getting into trouble, and a marginal or weak relationship with parents. The four factors identified create a model that is directly applicable to prevention strategies and is extremely parsimonious.

Adolescent↗

PubMed2026

ToxiVerse: chemical bioprofiling, toxicity data sharing and customizable predictive modeling.

MOTIVATION: Chemical toxicity assessment is critical for drug development and environmental safety. Computational models have emerged as a promising alternative to animal testing and now play a significant role in efficiently evaluating new chemicals. To address the urgent need for user-friendly machine learning tools in computational toxicology, we developed ToxiVerse, a public web-based platform. RESULTS: ToxiVerse provides automatic chemical bioprofiling, curated toxicity datasets, and a predictive modeling interface designed for researchers who lack programming expertise. The platform comprises three integrated modules: (i) Bioprofiler, which provides chemical descriptors by combining chemical-bioactivity data from PubChem assays with a machine learning-based data gap-filling procedure; (ii) Database, which hosts ∼50 000 curated chemicals covering diverse toxicity endpoints; and (iii) Cheminformatics, which enables dataset upload, chemical curation, and automatic generation of quantitative structure-activity relationship models for toxicity prediction. AVAILABILITY: The tool is accessible at www.toxiverse.com, and source code is available at https://github.com/zhu-research-group/toxiverse.

Quantitative Structure-Activity Relationship↗

PubMed1996

Final height prediction models for pubertal boys.

Accurate adult height prediction is of clinical importance in assessing the need for pharmacological intervention and in the evaluation of the outcome of therapy. The methods currently in use are subject to a wide range of error, one source of which is the use of bone age (BA) measurements. We have developed a computer model for predicting adult height in pubertal boys without using BA determinations. The model is based on the existing Infancy-Childhood-Puberty model and calculates the onset of the pubertal growth spurt. Predicted adult height was assessed using this new model and four others in a group of normal boys and in a group of short normal boys receiving growth hormone. Calculated final heights by all the methods were not significantly different. Incorporation of paternal height into the prediction equations increased the accuracy of the prediction. It was concluded that our new model is as accurate as existing methods of predicting final height that involve assessing BA.

Adolescent↗

PubMed2025

Genetic mapping and predictive modeling of paralog synthetic lethality.

Paralogs are abundant in the human genome and thought to be a primary source of synthetic lethality, yet the vast paralogome remains largely uncharacterized. A digenic screen of 36,648 paralogous pairs in the human genome revealed that synthetic lethalities were infrequent and varied in penetrance in different tumor backgrounds. We hypothesized that the variable penetrance of synthetic lethalities resulted from complex polygenic interactions with different cellular contexts. A machine learning classifier of a subset of paralog pairs tested across 49 cancer models revealed that endogenous perturbations in related pathways predicted paralog synthetic lethality. Further, predictive modeling of paralog synthetic lethality showed that the strength of synthetic lethal interactions was largely due to the overlap and essentiality of the protein-protein interaction networks shared by the paralog pairs. Collectively, this study tested 36,648 digenic paralog interactions and delineated the key feature classes that underlie the heterogeneity of paralog synthetic lethalities.

Humans↗

PubMed1992

A comparative investigation of hepatic clearance models: predictions of metabolite formation and elimination.

Liver clearance models serve to improve our understanding of the relationships between the physiological determinants and hepatic clearance and predict changes in the disposition of substrates when homeostasis of the organ is perturbed. Their ability to describe metabolism was presently extended to the sequential formation and elimination of primary (M1), secondary (M2), and tertiary (M3) metabolites during a single passage of drug (P) across the liver, under steady state and first-order conditions. The well-stirred model is distinct from other models in that metabolite formation and elimination is independent of enzymic distributions, the number of steps involved in metabolite formation, and the intrinsic clearances of the precursors. This model predicts that the extraction ratio of a formed primary metabolite derived from drug (E[M1, P]) is identical to that for the preformed primary metabolite (E[M1]), and that the extraction ratios of a secondary metabolite derived from drug (E[M2, P]) and primary metabolite (E[M2, M1]) or preformed secondary metabolite (E[M2]) are identical. For the more physiologically acceptable, parallel-tube and dispersion models, metabolite sequential elimination is highly influenced by the intrinsic clearances of the precursors and the enzymic distributions that mediate removal of precursor species and the metabolites. Furthermore, the extent of sequential metabolism recedes as the number of steps involved for metabolite formation increases. These models predict that E[M1, P] less than E[M1], and E[M2, P] less than E[M2, M1] less than E[M2], with the magnitude of the changes being less for the dispersion model than for the parallel-tube model. Competing pathways that divert substrate from entering the sequential pathway were found to exert only minimal influence on the sequential pathway.

Liver↗

PubMed1994

A prediction model of performance in level II fieldwork in physical disabilities.

OBJECTIVES: A prediction model of performance in physical disabilities fieldwork was generated with grades received in the occupational therapy curriculum and in prerequisite courses. METHOD: Grades included those from functional anatomy, neuroanatomy, physical disabilities lecture, physical disabilities clinic, and prerequisite anatomy and physiology courses. Sampling was done collectively over graduated occupational therapy classes from 1987 to 1992 at the University of Puget Sound. A multiple regression analysis was performed and prediction equations were generated for each subscale of the Fieldwork Evaluation for the Occupational Therapist. Equations for combinations of the subscale categories were also produced. RESULTS: Adjusted R2 values were found to be less than 10% in all equations. CONCLUSION: This poor ability of grades to predict fieldwork performance suggests that future investigation be focused on variables other than grades. Such variables might include student motivation, rapport between the student and fieldwork supervisor, and hospital experience in physical disabilities.

Educational Measurement↗

PubMed1999

[A predictive model for affect of atopic dermatitis in infancy by neural network and multiple logistic regression].

OBJECTS: To analyze the predictive accuracy of the predictive model for affect of atopic dermatitis in infancy, from the data of the epidemiological survey, which were conducted for 10,000 of mothers of infants and children in 1993. SUBJECTS AND METHODS: A total of 4610 replies were received: 2714 from mothers of infants (12 month old) and 1,896 from mothers of children (2 years old). The sensitivity, specificity and predictive accuracy were calculated from probabilistic model by neural network analysis (NNA) and multiple logistic regression analysis (MLA). RESULTS: Risk factors for probabilistic model by NNA were family history (father, mother, siblings, grand father, grand mother), food restriction, food allergy, age, food restriction of mother, egg introduced time, cow's milk introduced time. The sensitivity, specificity and predictive accuracy of NNA model was 88.6%, 99.5% and 96.4%, respectively and MLA model was 75.1%, 82.6% and 82.3%, respectively. CONCLUSION: These results suggest that the NNA is a good and useful method for prediction of onset of AD than MLA. Furthermore, It is necessary to investigate the artificial neural networks for diagnosis and/or treatment by physician.

Child, Preschool↗

PubMed1991

Mathematical analysis of perifusion data: models predicting elution concentration.

System models are constructed and analyzed for combined convective flow and for dispersion in distorting concentrations of a chemical signal as it passes through a packed column. We derive general analytical solutions for these models. The results have applications to analyses such as in biological experiments involving hormonal stimulation of perifused cells, elution chromatography, adsorption columns, and studies of groundwater flow. The models reveal that the column distorts an incoming signal (such as a change in solute concentration in the flowing liquid) at the inlet. This distortion is greatest at low values of the Peclet number of the flow and is small at larger values. We explore the effects of the approximations inherent in the mathematical models of the system. Specification of the boundary conditions of the problem are shown to be particularly important. With the use of incorrect models, it is possible to obtain accurate interpolations to data obtained from perfusion experiments. However, the parameters derived (in particular the dispersion constant and the peak concentration of a solute concentration pulse) may be considerably in error. This may lead to errors when these parameter estimates are used to predict results in other experimental situations.

Animals↗

PubMed1999

[Treatment dropout as failed utilization--development of a predictive model for inpatient psychosomatic rehabilitation].

Premature discontinuance of rehabilitative measures can be considered a revised decision by the patient concerning participation or a revised decision by the clinic concerning admittance. Such termination of therapy suggests that the individual need for rehabilitation (defined as the fit between the patient's need for rehabilitation and the treatment offered by the rehabilitation facility) is not (or no longer) present, at least at this point in time. Assuming that the individual need for rehabilitation actually existed when treatment was requested or approval for rehabilitative measures was granted, the question arises as to when and how the need changed in such a way that would prevent the treatment from continuing as planned and being concluded in a regular fashion. This question will be taken into focus in the present article by developing a model for the prediction and explanation of prematurely discontinued treatment. This model will give central significance to the intention to co-operate, which is considered a dependent variable by reference to individual symptoms and treatment related expectations. Furthermore, various factors of influence during the stay in the clinic are formulated, which, firstly, have a presumed effect on the intention to cooperate and, secondly, have an influence on whether the intention to prematurely terminate treatment develops from a specific intention to cooperate and whether this is then realized. The model is discussed with regard to its practicability and possibilities for operationalization.

Germany↗

PubMed1999

[Risk factors, diseases and health care acceptance in perinatology: a predictive model of hospital use].

BACKGROUND: The goal of our study was to develop a predictive model of resource use for pregnancy and perinatal care based on the knowledge of the distribution of risk factors in a given population of pregnant women. METHODS: Data recorded in Outcome of Pregnancy Certificates (CIG) from 11 voluntary maternities of the district of Seine-Saint-Denis allowed us to identify those pathologies that were predictive of premature births and prenatal hospitalization of mothers. We built a classification of disease states and of risk level. A logistic regression using disease states as dependent variables and risk levels as independent variables allowed us to compute expected rates with their confidence intervals. RESULTS: Among singletons, malformations, diabetes, toxemia, intra-uterin growth retardation, premature rupture of membranes covered 25% of all pregnancies but explained 64% of maternal hospitalizations; 90% of all mothers hospitalized and with delivery before 37 weeks gestation had at least one of these disease states. But 85% of the women who did not belong to disease classes had a normal pregnancy and delivery. CONCLUSIONS: In a given population, the distribution of risk levels is predictive of the incidence of disease per class. Then, given the length of stay of mothers per class, the rate of transfer of babies and the length of stay in postnatal care, we can simulate bed occupancy and compute bed capacities. The precision of the model is globally good, despite the relatively modest size of our initial data base: it will improve with the use of the model and the expected more widespread availability of data in France.

Adult↗