Forecasting peculiarities of intracerebral pressure and local cerebral blood flow data and methods of outcome forecasting in the early postoperative period.
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Blood Pressure↗
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A model for statistical forecasting of menu item demand.
Foodservice planning necessarily begins with a forecast of demand. Menu item demand forecasts are needed to make food item production decisions, work force and facility acquisition plans, and resource allocation and scheduling decisions. As these forecasts become more accurate, the tasks of adjusting original plans are minimized. Forecasting menu item demand need no longer be the tedious and inaccurate chore which is so prevalent in hospital food management systems today. In most instances, data may be easily collected as a by-product of existing activities to support accurate statistical time series predictions. Forecasts of meal tray count, based on a rather sophisticated model, multiplied by average menu item preference percentages can provide accurate predictions of demand. Once the forecasting models for tray count have been developed, simple worksheets can be prepared to facilitate manual generation of the forecasts on a continuing basis. These forecasts can then be recorded on a worksheet that reflects average patient preference percentages (of tray count), so that the product of the percentages with the tray count prediction produces menu item predictions on the same worksheet. As the patient preference percentages stabilize, data collection can be reduced to the daily recording of tray count and one-step-ahead forecase errors for each meal with a periodic gathering of patient preference percentages to update and/or verify the existing date. The author is more thoroughly investigating the cost/benefit relationship of such a system through the analysis of new empirical data. It is clear that the system offers potential for reducing costs at the diet category or total tray count levels. It is felt that these benefits transfer down to the meal item level as well as offer ways of generating more accurate predictions, with perhaps only minor (if any) labor time increments. Research in progress will delineate expected savings more explicitly. The approach requires statistical and computer expertise primarily during the development of the tray count model and patient preference percentage table. The results of this effort can be transferred to a form that is easily utilized by food management personnel manually to generate menu item demand forecasts.
Computers↗
Forecasting individual pharmacokinetics.
Often drug dosage may be chosen rationally by use of plasma concentration (CP) as the "therapeutic" end point. The ability to accurately forecast the CP resulting from a dosage regimen is central to choosing that regimen. Tradionally forecasting has been attempted only by accounting for known influences on pharmacokinetics, such as sex, age, and renal disease. One must also adjust for previously observed CPs. Herein, we discuss and explain an approach to both of these tasks, mainly focusing on the latter. The approach balances observed outcomes against prior expectations taking account of observation CP error. For digoxin, use of 1 measured CP, as opposed to none, improves forecast precision for future CPs by 40% (decrement in variance of forecast error), and 2 CPs improve it by 67%. There is also an increase in forecast accuracy (decrement in mean of forecast error) as the number of CPs used increases. After only 2, forecast accuracy and precision are as good as theoretically possible. Moreover, information from CPs is far more valuable for forecasting than that from observable patient features-sex, age, and the like; use of all the latter information does not improve accuracy and precision as much as only 1 CP.
Adult↗
Forecasting hospital laboratory procedures.
Improved forecasts of hospital laboratory procedures can provide the basis for better resource planning and enhanced operating efficiency. The research reported here-in describes how multiple regression models can be both a source of insight into causal relationships and a tool for achieving accurate monthly forecasts. Past research in this area may have overstated the statistical significance of findings because of a failure to address the potential effect of serial correlation. The present study uses the Cochrane-Orcutt regression procedure, rather than OLS, to overcome this problem. A model using inpatient admissions, acuity days, length of stay, discharge days and seasonal dummy variables is shown to account for 87% of the variation in the number of billable laboratory procedures. A simpler multiple regression model and a Winters' exponential smoothing model were found to provide excellent forecasts for laboratory procedures. In a one year out of sample evaluation, the annual percent forecast error was 0.7% for the regression model. This compares favorably to a percentage forecast error of 11.6% using subjective forecasting methods.
Forecasting↗
Statistical forecasting in a hospital clinical laboratory.
Three forecasting methodologies were applied to monthly laboratory test count data in order to arrive at a best procedure for forecasting ahead to cover the next fiscal year. The purpose of the forecasting was, first, to aid in reimbursement and income decisions and, second, to assist in operations management decisions within the laboratory itself. The Box-Jenkins ARIMA models were found to be superior in all cases, and forecasts for individual test counts (as opposed to packages of tests billed as a unit) were improved if forecasts for inpatients and outpatients were done separately and then aggregated. With 2 years of experience to go on, the annual forecast error stands at around 4.5%.
Clinical Laboratory Techniques↗
A multi-echelon menu item forecasting system for hospitals.
A multi-echelon system was designed to generate statistical forecasts of menu-item demand in hospitals from one- through twenty-eight-day intervals prior to patient meal service. The three interdependent echelons were: (1) Forecasting patient census, (2) estimating diet category census, and (3) calculating menu-item demand. Eighteen weeks of supper data were utilized to analyze diet category distribution patterns and menu-item preferences, to test forecasting models, and to evaluate the performance of the forecasting system. A cost function was used to evaluate the efficiency of the mathematical forecasting system and manual technique over a nine-week period. The cost of menu-item forecast errors resulting from the use of adaptive exponential smoothing and Box-Jenkins formulations was approximately 40 per cent less than costs associated with the manual system.
Computers↗
The use of time-series analysis to forecast bont tick (Amblyomma hebraeum) infestations in Zimbabwe.
Studying the dynamics of tick infestations on cattle is an essential step in developing optimal strategies for tick control. Successful strategic tick control requires accurate predictions of when tick infestations will reach predetermined threshold levels. In the case of Amblyomma hebraeum, earlier work has shown that there is no consistent pattern of seasonal activity. This means that a statistical model for predicting A. hebraeum infestations cannot reliably use climatic factors as the only independent variables. An alternative method is to apply time-series, or auto-regressive moving-average (ARMA), analysis which uses only the past population patterns to predict future trends. This technique was applied to a data set consisting of 108 weekly tick counts of A. hebraeum (adult males, standard females, flat females and standard nymphs), conducted at an experimental station in southeastern Zimbabwe. The ability of the ARMA models to fit and predict actual tick infestations was judged using two sets of criteria. The first set focused on the goodness-of-fit, and used the adjusted R2 values, Q statistic and the Akaike Information Criteria. The second set of criteria measured the forecasting accuracy of an estimated equation, and consisted of regressing a 9-period forecast against an actual out-of-sample data set not used in the estimation process. The root mean square error of the forecast was also considered when comparing several models for the same data set. Using these criteria, the models estimated using the ARMA technique were judged to both fit and forecast with sufficient accuracy to warrant their use in strategic tick control. Although the success of using ARMA to forecast A. hebraeum is partly due to the non-seasonal behavior of the species, the results presented here suggest that it is worthwhile exploring the use of ARMA techniques to model the dynamics of other tick species. Where independent variables exert considerable influence on the dynamics of a tick species, these variables can be incorporated into an ARMA-style model.
Animals↗
Forecasting bed needs and recommending facilities plans for community hospitals: a review of past performance.
A university-based hospital consulting group reviewed six studies of Michigan hospitals retrospectively in 1975. The studies represented all those done between 1967 and 1971 requiring forecasts of acute bed supply and service needs. The original studies developed forecasts using empirical studies of patient origin and rigorously prepared authoritative forecasts of county populations. The 1975 review compared forecasts of population, service population, and bed need against current values and also interviewed clients to assess retrospective satisfaction with the recommendations. Although the consultants strove steadily to minimize the bed supply and base population forecasts were accurate, the studies overestimated bed needs. Further, the clients were often dissatisfied with the original recommendations, and frequently acted to exceed them. Comparing the 1975 actual with what would now be recommended by the consultant indicates that the "error" cost the communities about $50 per person per year.
Bed Occupancy↗
Menu item forecasting systems in hospital foodservice. A cost comparison of two- and three-echelon systems.
The forecasting efficiency of two- and three-echelon menu item forecasting systems was compared. Two forecasting models were used with each system, adaptive exponential smoothing and a Box-Jenkins model. The two systems were compared in terms of forecast error costs. The two-echelon system, using adaptive exponential smoothing, was recommended. This technique resulted in the lowest forecast error costs at a majority of the lead times which probably would be used most frequently. Also, this technique was the least complicated of the four techniques which were compared.
Computers↗
Developing a demand forecasting system for a foodservice operation.
In foodservice operations, accurate and dependable forecasts of food production demands can help control food and labor costs. A decreased incidence of menu item over- and under-production should lower scheduled labor and production time and optimize use of equipment. Each foodservice system has specified characteristics and patterns of activity. A procedure to develop, establish, control, and evaluate a forecasting system is described. The objectives of the foodservice and the proposed forecasting system must be defined. A cycle menu and historical data bases are two key inputs. It is more accurate to forecast menu item demand than diet category demand because of the complexity in categorizing multi-restricted diets. Control of the system is maintained by establishing policies and procedures and conducting routine subjective and objective evaluations.
Computers↗
A time series approach to forecasting Australian total live-births.
The relationship between classical demographic deterministic forecasting models, stochastic structural econometric models and time series models is discussed. Final equation autoregressive moving average (ARMA) models for Australian total live-births are constructed. Particular attention is given to the problem of transforming the time series to stationarity (and Gaussianity) and the properties of the forecasts are analyzed. Final form transfer function models linking births to females in the reproductive age groups are also constructed and a comparison of actual forecast performance using the various models is made. Long-run future forecasts are generated and compared with available projections based on the deterministic cohort model after which some policy implications of the analysis are considered.
Australia↗
HiCForecast: dynamic network optical flow estimation algorithm for spatiotemporal Hi-C data forecasting.
MOTIVATION: The exploration of the 3D organization of DNA within the nucleus in relation to various stages of cellular development has led to experiments generating spatiotemporal Hi-C data. However, there is limited spatiotemporal Hi-C data for many organisms, impeding the study of 3D genome dynamics. To overcome this limitation and advance our understanding of genome organization, it is crucial to develop methods for forecasting Hi-C data at future time points from existing timeseries Hi-C data. RESULT: In this work, we designed a novel framework named HiCForecast, adopting a dynamic voxel flow algorithm to forecast future spatiotemporal Hi-C data. We evaluated how well our method generalizes forecasting data across different species and systems, ensuring performance in homogeneous, heterogeneous, and general contexts. Using both computational and biological evaluation metrics, our results show that HiCForecast outperforms the current state-of-the-art algorithm, emerging as an efficient and powerful tool for forecasting future spatiotemporal Hi-C datasets. AVAILABILITY AND IMPLEMENTATION: HiCForecast is publicly available at https://github.com/OluwadareLab/HiCForecast.
Algorithms↗
Forecasting: which type is for you?
Planning--one of the most important functions of laboratory management--cannot be done efficiently without forecasts of external and internal conditions that affect the laboratory. The successful Delphi Method for external forecasting of technological events is discussed. The internal forecasting methods covered in this report include judge and jury methods, time series analysis, and regression models. A brief description of each of these methods is presented along with information on accuracy, typical applications, costs, and references that provide the details of how to perform the forecasts.
Humans↗
Forecasting patient census: commonalities in time series models.
Highly accurate patient census forecasting models are specified for five hospitals by use of a general equation for integrated autoregressive moving average (IARMA) forecasts. A general census forecasting model, based on features common to all five institution-specific models, is described and its forecasts are compared to those from the specific models.
Hospitals↗
A review of methods to forecast restorative treatment needs.
Decision makers in the areas of health policy, resource allocation, and manpower requirements rely implicitly on estimations of treatment needs on which to base their forecasts. The less specific the treatment-need estimate, the less precise the forecast. In previous decades, high caries rates were so prevalent that the dental profession could risk having inexact projections because overwhelming need and demand existed. However, rampant decay is no longer a common occurrence. Decay levels are declining in our nation's children and adults have fewer missing teeth. Therefore, restorative treatment needs and patterns in adult populations are transforming at a time when health care costs are spiraling and budget analysts at all levels of government are questioning the priority of continued support of dental care, dental education, and dental research at current levels. The purpose of this review is to present the existing methods of forecasting restorative treatment needs and to postulate the development of a new method based on the collective experiences of practicing dentists nationwide, an empirical method, to convert surface-specific oral health status data to restorative treatment need information. Need estimations based on empirical data would more accurately reflect the actual distribution of services that practicing dentists provide.
Adult↗
Forecasting patient tray census for hospital food service.
Five computerized forecasting models were tested with data on daily patient tray demand in a large medical center food service, and results were compared with intuitive forecasts made by the food service supervisor. All five models gave more accurate results than the intuitive procedure; an adaptive exponential smoothing model was most accurate. The effects of model complexity and data storage requirements are discussed, and simple exponential smoothing is suggested for forecasting patient tray demand in this setting.
Computers↗
[Grass pollen in Munich air. Preparations for a forecast (author's transl)].
To forecast pollen concentrations of the air of a particular place the following data of the area in question should be known: The basic grass pollen content of the air from volumetric measurements made during several years and its mean seasonal changes; the mean daily grass pollen content of 1 m3 of air and its changes, especially at the beginning of the main grass pollen season; the influence of temperature, humidity, precipitation and wind speed on the grass pollen content; weather forecast of the day for which the pollen forecast is to be made and the grass pollen content of the previous day; the composition of the grass flora in the surroundings and the state of flowering of the main components of the grass flora. These data for the city of Munich are presented and discussed. In addition the grass pollen content in the air during the summers of 1975 and 1976 is compared with allergic complaints of 96 and 45 persons respectively.
Air Movements↗
Food production relationships between entrée combinations and forecasted demand.
Macro elements and values for associated Time Measurement Units were applied to three classifications of entrées to synthesize production time for 100, 300, and 500 portions. Average handling and process time for each classification and time per portion were calculated. Data revealed that roast and single-item entrées required greater average handling time than combination entrées, because more individual handling of portions was required as forecasted demand increased. Process time for combination and single-item entrées increased as forecasted demand increased. The time for the single-item classification doubled for 300 and 500 portions, because oven capacity was exceeded. Production time data were applied to six combinations (menu mixes) and quantity levels of entrée classifications to determine production feasibility in a simulated foodservice system. Production problems were encountered in five of the six menu mixes because of system constraints. This study indicated that total production time estimates would be useful to foodservice managers when planning a menu mix, scheduling production personnel, and forecasting labor costs.
Food Handling↗