***TASK*** the task is to summarize an input biomedical literature in six sentences 
    ***INPUT*** the input is a biomedical literature 
    ***OUTPUT*** the output is the summary of an input biomedical literature in six sentences 
    ***DOCUMENTATION*** 
    ***EXAMPLES*** 
    Input: Many believe that medical expert systems have great potential to improve health care , but few of these systems have been rigorously evaluated , and even fewer are in routine use . We propose the evaluation of  medical expert systems  in two stages : laboratory and field testing . In the former , the perspectives of both prospect i ve users and experts responsible for implementation are valuable . In the latter , the study must be design ed to test , in an unbiased manner , whether the system is used in clinical practice , and if it is used , how it affects the structure , process and outcome of health care encounters . We conclude with proposals for encouraging the objective evaluation of these systems While heart rate variability has been measured in many clinical setting s and has offered insights into how HR is controlled , rarely has it offered unique information that has led to changes in patient management . We review our experience in developing continuous HR characteristics monitoring to aid in the early diagnosis of sepsis in premature infants in the neonatal intensive care unit . A predictive algorithm , developed at one center and vali date d at another , has led to diagnosis and treatment of this subacute and potentially catastrophic illness prior to appearance of symptoms of severe illness The multistate Kalman filter was applied to develop a heparin dose proposal system and to detect  coagulation disturbances during neonatal extracorporeal membrane oxygenation ( ECMO ) . A system containing this filter was based on the activated clotting time ( ACT ) values and the heparin doses administered every hour during ECMO . If the ACT value can be predicted accurately from the previous heparin dose , a heparin dose proposal can be given to achieve or maintain the required ACT level . The analysis was done on 6,356 ACT level measurements in 44 ECMO neonates . The multistate Kalman filter technique showed an unbiased prediction of ACT , with a st and ard deviation of 23 seconds . Two out of three cases of disseminated intravascular coagulation ( DIC ) were detected . ACT values were predicted sufficiently accurately by the multistate Kalman filter technique to justify a prospect i ve study on the performance of the heparin dose proposal system and its ability to detect DIC Open-loop computer control of inspired oxygen concentration was evaluated in 16 newborn infants requiring mechanical ventilation .  FIO2 and oxygen saturation were compared for 2-hour periods of computer versus routine manual FIO2 adjustment . During computer-assisted FIO2 adjustment , patients spent more time at the target SaO2 and less time with SaO2 < 90 % Objective . To compare multiple logistic regression and neural network models in predicting death for extremely low birth weight neonates at 5 time points with cumulative data sets , as follows : scenario A , limited prenatal data ; scenario B , scenario A plus additional prenatal data ; scenario C , scenario B plus data from the first 5 minutes after birth ; scenario D , scenario C plus data from the first 24 hours after birth ; scenario E , scenario D plus data from the first 1 week after birth . Methods . Data for all infants with birth weights of 401 to 1000 g who were born between January 1998 and April 2003 in 19 National Institute of Child Health and Human Development Neonatal Research Network centers were used ( n = 8608 ) . Twenty-eight variables were selected for analysis ( 3 for scenario A , 15 for scenario B , 20 for scenario C , 25 for scenario D , and 28 for scenario E ) from those collected routinely . Data sets censored for prior death or missing data were created for each scenario and divided r and omly into training ( 70 % ) and test ( 30 % ) data sets . Logistic regression and neural network models for predicting subsequent death were created with training data sets and evaluated with test data sets . The predictive abilities of the models were evaluated with the area under the curve of the receiver operating characteristic curves . Results . The data sets for scenarios A , B , and C were similar , and prediction was best with scenario C ( area under the curve : 0.85 for regression ; 0.84 for neural networks ) , compared with scenarios A and B. The logistic regression and neural network models performed similarly well for scenarios A , B , D , and E , but the regression model was superior for scenario C. Conclusions . Prediction of death is limited even with sophisticated statistical methods such as logistic regression and nonlinear modeling techniques such as neural networks . The difficulty of predicting death should be acknowledged in discussion s with families and caregivers about decisions regarding initiation or continuation of care OBJECTIVE To assess whether the provision of computerized physiologic trend data could improve outcome in newborn infants requiring intensive care . DESIGN R and omized , controlled trial , with subsidiary question naire studies . SETTING Tertiary neonatal intensive care unit with 12 intensive care cots . PATIENTS All infants admitted between January 1991 and September 1993 who were < or = 32 wks gestation or > 32 wks gestation , and ventilated for > 4 hrs or asphyxiated . INTERVENTIONS R and omization to one of four groups for first 7 days of life : A ) no display of trend data ; B ) continuous display of trend data ; C1 ) alternating 24-hr display of trend data , starting with display in first 24 hrs ; and C2 ) alternating 24-hr display of trend data , starting with no display in first 24 hrs . MEASUREMENTS AND MAIN RESULTS The short-term effects of monitoring on patient outcome was judged by volume of colloid given , number of blood gases taken , and by measurement taken from cranial Doppler ultrasound . Medium-term measures included time ventilated , time given supplemental oxygen , death , time to death or discharge , and cranial ultrasound at discharge . Long-term outcome was assessed by neurodevelopmental status at age 1 to 4 yrs of age . Staff and parent question naires assessed their respective attitudes to the introduction of this technology . None of the patient outcome measures , short- , medium- , or long-term , demonstrated any significant benefit from the provision of computerized physiologic trend monitoring . Staff question naires demonstrated an acceptance of the system and an improved underst and ing of neonatal physiology as a result of computerized physiologic trends . Parent question naires demonstrated increased anxiety caused by the system in 11 % of parents , although only 1 % of parents continued to have concerns if the system were able to help their child . CONCLUSIONS A r and omized , controlled trial was unable to demonstrate any benefit to patients result ing from the introduction of a computerized physiologic trend monitoring system . Benefits of the system have been recognized , however , in subsidiary studies , staff education , and research studies An assessment of neonatal outcome may be obtained from analysis of blood in the umbilical cord of an infant immediately after delivery . This can provide information on the health of the new-born infant , guide requirements for neonatal care , but there are problems with the technique . Sample s frequently contain errors in one or more of the important parameters , preventing accurate interpretation and many clinical staff lack the expert knowledge required to interpret error-free results . The development and implementation of an expert system to overcome these difficulties has previously been described . This expert system vali date s the raw data , provides an interpretation of the results for clinicians and archives all the results , including the quality control and calibration data , for permanent storage . Issues regarding the clinical evaluation of this system are now detailed further , along with some clinical results illustrating the potential of such a system Research using a computer-assisted instructional program developed for teaching well-newborn care is described . Third-year medical students were r and omly assigned during their week-long nursery rotation into three groups to study the acceptability and effectiveness of using the computer-assisted instructional lessons . Group A , the control group , was not required to complete any additional educational task . Group B was assigned specific reading material . Group C completed the microcomputer-based tutorial . ( The term tutorial is often used interchangeably to describe the computer-assisted instructional program . ) Both groups B and C improved dramatically on a posttest in comparison with group A. There was no statistically significant difference between groups B and C on the improvement from pretest to posttest scores . However , significantly less time was spent by group C completing the tutorial compared with the total time spent reading by group B. The results of this study provide further illustration of the feasibility and effectiveness of using a computer-assisted instructional program for medical student education Intraventricular haemorrhage ( IVH ) incidence is used to assess peri‐/neonatal therapy , and to make intra‐ and inter‐hospital quality assessment s. Unbiased assessment is complicated by the amount of confounding factors . Is an artificial neural network ( ANN ) able to early and accurately forecast the occurrence of severe IVH in an individual patient ? Is it superior to classic multiple logistic regression ? We conducted an observational study on pre‐existing routine data . Admission data were available from 890 preterm neonates ( gestational age < 32 weeks , birthweight < 1500 g ) . Patients were r and omly assigned to either a training , or a validation set ( 50%/50 % ) . Using the training set data an ANN was trained . A second predictive model was developed by stepwise multiple logistic regression analysis . Using the validation set input data both models delivered estimates of the probability for severe IVH to occur in each individual patient . Receiver operating characteristic ( ROC ) curves were used to compare prognostic performance . The optimal ANN processed 13 input variables , whereas stepwise logistic regression analysis only identified five independent predictor variables . The area under the ROC curve was 0.935 for the ANN and 0.884 for the logistic regression model ( p= 0:001 ) . Adjusted for 95 % , 90 % , 85 % , 80 % and 75 % specificity , the sensitivity of the ANN was significantly superior to that of the logistic regression model . Due to its ability to give an accurate prognosis based solely on admission data , a trained ANN qualifies as a tool for local quality control A computer expert system is an alternative method of training and providing real-time clinical decision support for nurses to advance their practice s from a novice to a proficient level . The purpose of this study was twofold : ( 1 ) to develop a prototype of a computer expert system for mechanically ventilated neonates ( ES-MVN ) and ( 2 ) to assess the impact of the ES-MVN on the clinical judgment and information access capability of nurses . Five steps used in developing the prototype are described in this article . The ES-MVN is a multimedia interactive consultation-based program that contains 2 major parts : the nursing diagnosis and the knowledge base on nursing care of mechanically ventilated neonates . A rule-based ( Boolean frame ) was chosen for the nursing diagnosis decision model . The prototype was developed on the Web server and a combination of computer applications operated in a Microsoft Windows environment . A quasi-experimental , 1-group pretest-posttest design was used to measure the efficacy of the  ES-MVN  in 16 neonatal intensive care unit registered nurses . Case simulations were used to test the nurses ' clinical judgment performance . The results showed a significant increase in the nurses ' performance scores of diagnoses and managed care after using the ES-MVN ( t[15 ] = 17.21 , P = .0001 ) . The nurses ' scores for perceptions of their information access capability and clinical judgment ability after receiving the ES-MVN were significantly higher than before installing the ES-MVN in the neonatal intensive care unit ( ts[15 ] = 6.91 and = 17.53 , Ps = .0001 , respectively ) . The findings suggest the usefulness of the computer expert system as an effective tool to support nurses ' clinical judgment in critical care situations and to provide access to information at the practice site Objective To develop an easy-to-use bedside scoring system , composed of clinical variables , hematologic variables , and risk factors of infection , to predict nosocomial sepsis in neonatal intensive care unit patients . Setting A neonatal intensive care unit in a university hospital , Antwerp , Belgium . Patients Over 2 yrs , we analyzed two groups of patients . First , we prospect ively studied 104 episodes of presumed nosocomial sepsis in 80 neonates ( derivation cohort ) , and then we retrospectively studied 50 episodes in 39 neonates ( validation cohort ) . Interventions None . Measurements and Main Results We developed two versions of a scoring system to predict nosocomial sepsis in sick neonates . The first scoring system ( NOSEP-1 score ) was based on 15 clinical , 12 laboratory , and 17 historical variables potentially connected with infection ; the second one ( NOSEP-2 score ) also included the culture results of central vascular catheters . Based on the odds ratios of all independent variables , an additive and weighted score was developed and vali date d in a cohort of 39 patients screened for nosocomial sepsis in the same center . The NOSEP-1 score consisted of three laboratory variables ( C-reactive protein ≥14 mg/L , thrombocytopenia < 150 × 109/L , and neutrophil fraction > 50 % ) , one clinical factor ( fever > 38.2 ° C [ 100.8 ° F ] ) , and one risk factor ( parenteral nutrition for ≥14 days ) . The NOSEP-2 score consisted of the same variables plus catheter-hub and catheter insertion site colonization data . Receiver operating characteristic curve analysis demonstrated good predictor performance of the NOSEP-1 score ( area under the curve [ Az ] = 0.82 ± 0.04 [ sem ] ) and NOSEP-2 score ( Az = 0.84 ± 0.04 , p < .05 ) . We checked whether a complex computer-generated scoring system ( CD-1 and CD-2 scores ) based on the original numerical values of the items used in NOSEP-1 and NOSEP-2 would improve the prediction of nosocomial sepsis . The analysis showed the accuracy of bedside NOSEP-1 and NOSEP-2 scores to be comparable with the more cumbersome computer-generated CD-1 and CD-2 scores ( receiver operating characteristic curve , Az : CD-1 score = 0.81 ± 0.04 , p = .69 , and CD-2 score = 
    Output:
    
It was found that the time taken for calculation was significantly reduced and there was a significant reduction in the number of calculation errors .
There were no significant effects on mortality , volume of colloid infused , frequency of blood gases sampling ( sample s per day ) or severe ( Papile Grade 4 ) intraventricular haemorrhage .