Methods for selecting, developing and improving diagnostic tests for pregnancy-related conditions

Claim 2

A computer-based method of assessment of the risk of preterm delivery or the risk of delivery within a selected period of time, comprising: (a) obtaining a set of candidate variables based upon data obtained by making querying and examining patients at risk for preterm delivery, designating the candidate variables as a first set of candidate variables and entering them into a computer memory or computer-readable storage medium; (b) selecting a set of important selected variables by: (i) providing a set of selected important variables and designating it the current set of selected important variables, wherein the set of selected important variables is initially empty; (ii) taking candidate variables from the set of candidate variables one at a time and evaluating each by training a computer-based decision-support system on that variable combined with the current set of selected important variables; (iii) selecting the best of the candidate variables, wherein the best variable is any one that gives the highest performance of the decision-support system, and if the best candidate variable improves performance compared to the performance of the selected important variables, adding it to the selected important variable set, removing it from the candidate set and continuing evaluating at step (ii), wherein, when the best candidate variable does not improve performance, the process is completed, thereby producing a set of selected variables (c) training a decision-support system using the selected final set of important selected variables to produce a test for assessment of the risk of preterm delivery or risk of delivery within a selected time period, wherein assessment of delivery within a selected period of time refers either to prediction of delivery at a particular gestational age, or the risk of delivery within a given time interval (d) storing the test for assessment of the risk in a computer memory or on a computer-readable medium.

Claim 3

The method of claim 1 , wherein in step (i) the candidate variables are obtained from patients and include historical data and/or biochemical data.

Claim 4

The method of claim 1 , wherein the method of diagnosis assesses the likelihood of preterm delivery.

Claim 5

The method of claim 1 , wherein the candidate variables include biochemical test data.

Claim 6

A computer readable storage medium produced by the method of claim 1 .

Claim 7

A computer system programmed with instructions for performing the method of claim 1 .

Claim 8

A computer-readable medium, comprising instructions for performing the method of claim 1 .

Claim 9

A computer-readable medium, comprising the set variables produced as the output of claim 1 .

Claim 10

A method of improving the effectiveness of a diagnostic biochemical test for preterm delivery or for the risk of delivery within a selected period of time, comprising: (a) selecting a set of important selected variables by the method of claim 1 ; (b) performing the biochemical test on test subjects to obtain test data, wherein the biochemical test is performed before, after or during the selecting step; and (c) training a decision-support system using the selected final set of important selected variables and the biochemical test data to produce a test that is more effective in assessing the risk or preterm delivery or delivery within a selected time period than the biochemical test alone. (d) storing the test for assessment of the risk in a computer memory or on a computer-readable medium.

Claim 11

The method of claim 9 , wherein the candidate variables are responses to queries selected from the group consisting of: Age; Ethnic origin Caucasian; Ethnic origin Black; ethnic origin Asian; ethnic origin Hispanic; ethnic origin Native American; ethnic origin other than the Native American, Hispanic, Asian, Black, or Caucasian; marital status single; marital status married; marital status divorced or separated; marital status widowed; marital status living with partner; marital status other than married, divorced/separated, widowed, or living with partner; education unknown; education less than high school; education high school graduate; education college or trade school; patient has Uterine Contractions with or without pain; patient has intermittent lower abdominal pain, dull, low backache pelvic pressure; patient has bleeding during the second or third trimester; patient has menstrual-like or intestinal cramping; patient has change in vaginal discharge or amount, color, or consistency; patient is not “feeling right”; pooling; ferning; nitrazine; estimated gestational age (EGA) based on last menstrual period (LMP); EGA by sonogram (SONO); EGA by best, wherein EGA by best refers to the best of EGA by SONO and EGA by LMP determined as follows: if EGA by SONO is <13 weeks, then EGA best is EGA SONO; if the difference by EGA by LMP and EGA by SONO is >2 weeks, then EGA best is EGA by SONO; otherwise EGA best is EGA by LMP; EGA at sampling; cervical dilatation (CD); gravity; parity-term; parity-preterm; parity-abortions, wherein the number of abortions include spontaneous and elective abortions; parity-living; sex within 24 hrs prior to sampling for fFN; vaginal bleeding at time of sampling; cervical consistency at time of sampling; uterine contractions per hour as interpreted by the physician; no previous pregnancies; at least one previous pregnancy without complications; at least one preterm delivery; at least one previous pregnancy with a premature rupture of membrane (PROM); at least one previous delivery with incompetent cervix; at least on previous pregnancy with pregnancy induced hypertension (PIH)/preeclampsia; at least one previous pregnancy with spontaneous abortion prior to 20 weeks; and at least one previous pregnacy with a complication not listed above.

Claim 12

The method of claim 10 , wherein the biochemical test is a test that detects fetal fibronectin in cervico/vaginal samples; determines the level of a local inflammatory product protein in cervico/vaginal samples; or estriol or estretol in saliva.

Claim 13

The method of claim 9 , wherein the candidate variables are responses to queries selected from the group consisting of: Caucasian; living with partner; EGA by sonogram; EGA at sampling; estimated date of delivery by best; cervical dilatation (CM); Parity-preterm; vaginal bleeding at time of sampling; cervical consistency at time of sampling; and previous pregnancy without complication.

Claim 14

The method of claim 9 , wherein the candidate variables are responses to queries selected from the group consisting of: Caucasion; Uterine contractions with or without pain; Parity-abortions; Vaginal bleeding at time of sampling; Uterine contractions per hour; and No previous pregnancies.

Claim 15

A computer-based diagnostic test produced by the method of claim 4 .

Claim 18

The method of claim 15 , wherein the decision support system is a neural network.

Claim 20

The method of claim 15 , wherein the decision-support system has been trained using a set of variables that do not include the results of a test that detects fetal fibronectin in samples of mammalian body tissue and fluids.

Claim 21

The method of claim 18 , wherein the first trained neural network comprises a three-layer network containing an input layer, a hidden layer and an output layer, the input layer having seven input nodes, first, second, third, fourth and fifth hidden layer nodes, a hidden layer bias for each hidden layer node, first and second output layer nodes in the output layer, and an output layer bias for each output layer node.

Claim 22

The method of claim 19 , further including the steps of: (c1) applying the observation values and the relevant biochemical test results from the memory means to a plurality of the second neural networks, each one of the first neural networks being trained on the samples of the specified factors with starting weights for each training being randomly initialized; (d1 ) extracting from each one of the first trained neural networks, output value pairs for each one of the first neural networks; and (e) forming a linear combination of the first ones of the output value pairs and forming a linear combination of the second ones of the output value pairs, to obtain a confidence index pair, the confidence index pair being the indicator of the risk for delivery in 14 days or fewer days.

Claim 23

The method of claim 15 , wherein the set of variables further includes the result of a test that detects fetal fibronectin in cervico/vaginal samples.

Claim 26

The method of claim 22 , wherein the decision-support system comprises a neural network.

Claim 27

The method of claim 22 , wherein at least five factors are selected.

Claim 28

The method of claim 22 , wherein the clinical factors further comprise the result of a test that detects fetal fibronectin in cervico/vaginal samples; the result of a test that determines the level of a local inflammatory product protein in cervico/vaginal samples; or the result of a test that assesses estriol or estretol in saliva.

Claim 29

The method of claim 25 , wherein the selected factors include the result of the test.

Claim 30

The method of claim 26 , further comprising: b1) applying said observation values from said memory means to a plurality of the first decision-support system, wherein each one of the first decision-support systems is trained on the samples of the specified factors with different starting weights for each training; c1) extracting from the first decision-support system, output value pairs for each one of said first neural networks; and d) forming a linear combination of said first ones of said output value pairs and forming a linear combination of said second ones of said output value pairs, to obtain a confidence index pair, said confidence index pair being said quantitative objective aid.

Claim 31

The method of claim 27 , wherein the first decision support system is a neural network that comprises a three-layer network containing an input layer, a hidden layer and an output layer, the input layer having eleven input nodes, first, second and third second hidden layer nodes, a hidden layer bias for each hidden layer node, first and second output layer nodes in the output layer, and an output layer bias for each output layer node.

Claim 32

The method of claim 27 , wherein the first decision support system is a neural network and each of the plurality of first trained neural networks comprises a three-layer network comprising an input layer, a hidden layer and an output layer.

Claim 33

The method of claim 22 , further comprising: b1) applying said observation values from said memory means to a plurality of the first decision-support system, wherein each one of the first decision-support systems is trained on the samples of the specified factors with different starting weights for each training; c1) extracting from the first decision-support system, output value pairs for each one of said first neural networks; and d) forming a linear combination of said first ones of said output value pairs and forming a linear combination of said second ones of said output value pairs, to obtain a confidence index pair, said confidence index pair being said quantitative objective aid.

Claim 34

The method of claim 30 , wherein the first decision support system is a neural network that comprises a three-layer network containing an input layer, a hidden layer and an output layer, the input layer having eleven input nodes, first, second and third second hidden layer nodes, a hidden layer bias for each hidden layer node, first and second output layer nodes in the output layer, and an output layer bias for each output layer node.

Claim 35

The method of claim 30 , wherein the first decision support system is a neural network and each of the plurality of first trained neural networks comprises a three-layer network comprising an input layer, a hidden layer and an output layer.

Claim 36

The method of claim 22 , wherein the clinical factors further comprise the result of a test that detects fetal fibronectin in mammalian body tissue and fluid samples.

Claim 37

In a computer system, a method for assessing the risk of delivery in a subject prior to completion of 35 weeks of gestation, comprising the steps of: (a) querying and examining the subject to collect observation values reflecting presence and absence of specified factors and storing the observation factors in storage means of the computer system, the specified factors comprising: Ethnic Origin Caucasian, Marital Status living with partner, EGA by sonogram, EGA at sampling, estimated date of delivery by best, cervical dilatation (CM), parity-preterm, vaginal bleeding at time of sampling, cervical consistency at time of sampling, and previous pregnancy without complication; (b) testing a patient and obtaining results of the test from the patient, wherein the test detects fetal fibronectin (fFN) in mammalian body tissue and fluid samples, and/or the test determines the level of a local inflammatory product protein in cervico/vaginal samples; and/or of a test that assesses estriol or estretol in saliva, wherein the test is performed prior to, during or after step (a); (c) applying the observation values and the fFN test results from the memory means to a neural network trained on samples of the specified factors and the test results; and thereupon (d) extracting from the trained neural network an output value pair, the output value pair being a preliminary indicator for the risk of delivery prior to 35 weeks of gestation.

Claim 38

In a computer system, a method for assessing the risk of delivery in a subject prior to completion of 35 weeks of gestation, comprising the steps of: (a) querying and examining the subject to collect observation values reflecting presence and absence of specified factors and storing the observation factors in storage means of the computer system, the specified factors comprising: Ethnic Origin Caucasian, Marital Status living with partner, EGA by sonogram, EGA at sampling, estimated date of delivery by best, cervical dilatation (CM), parity-preterm, vaginal bleeding at time of sampling, cervical consistency at time of sampling, and previous pregnancy without complication; (b) testing a patient and obtaining results of the test from the patient, wherein the test detects fetal fibronectin (fFN) in mammalian body tissue and fluid samples, and/or the test determines the level of a local inflammatory product protein in cervico/vaginal samples; and/or of a test that assesses estriol or estretol in saliva, wherein the test is performed prior to, during or after step (a); (c) applying the observation values and the fFN test results from the memory means to a neural network trained on samples of the specified factors and the test results; and thereupon (d) extracting from the trained neural network an output value pair, the output value pair being a preliminary indicator for the risk of delivery prior to 35 weeks of gestation.

Claim 39

The method of claim 34 , further including the steps of: (c1) applying the observation values and the relevant biochemical test results from the memory means to a plurality of the second neural networks, each one of the first neural networks being trained on the samples of the specified factors with starting weights for each training being randomly initialized; (d1) extracting from each one of the first trained neural networks, output value pairs for each one of the first neural networks; and (e) forming a linear combination of the first ones of the output value pairs and forming a linear combination of the second ones of the output value pairs, to obtain a confidence index pair, the confidence index pair being a final indicator for the risk of delivery prior to 35 weeks of gestation.

Claim 40

The method of claim 34 , wherein the first trained neural network comprises a three-layer network containing an input layer, a hidden layer and an output layer, the input layer having eleven input nodes, first, second and third hidden layer nodes, a hidden layer bias for each hidden layer node, first and second output layer nodes in the output layer, and an output layer bias for each output layer node.

Claim 41

The method of claim 34 , wherein the sample is a cervico/vaginal sample.

Claim 42

A computer-based diagnostic test produced by the method of claim 24 .

Claim 43

A computer readable medium or computer memory, comprising a decision-support system produced by the method of claim 24 .

Claim 44

A computer-readable medium produced by the method of claim 24 .

Claim 47

The method of claim 41 , wherein: the variables further include the result of a test for to detect fetal fibronectin (fFN) in a cervico/vaginal sample and/or the result of a test that determines the level of a local inflammatory product protein in cervico/vaginal samples and/or the result of a test that assesses estriol or estretol in saliva; the selected variables include the results of the test; and the method measures the risk of delivery in 7 days or few days from obtaining the sample for the fFN.

Claim 48

The method of claim 42 , wherein the decision support system is a neural network.

Claim 49

The method of claim 42 , wherein the decision-support system has been trained using a set of variables that do not include biochemical test data.

Claim 50

The method of claim 41 , wherein: the variables further include the result of a test for to detect fetal fibronectin (fFN) in mammalian body tissue and fluid samples and/or the result of a test that determines the level of a local inflammatory product protein in cervico/vaginal samples and/or the result of a test that assesses estriol or estretol in saliva; the selected variables include the results of the test; and the method measures the risk of delivery in 7 days or few days from obtaining the sample for the fFN.

Claim 53

The method of claim 46 , wherein the decision-support system comprises a neural network.

Claim 54

The method of claim 46 , wherein at least five factors are selected.

Claim 55

The method of claim 46 , wherein the clinical factors further comprise the result of a test that detects fetal fibronectin in cervico/vaginal sample and/or the result of a test that determines the level of a local inflammatory product protein in cervico/vaginal samples and/or the result of a test that assesses estriol or estretol in saliva.

Claim 56

The method of claim 49 , wherein the selected factors include the result of the test.

Claim 57

The method of claim 50 , further comprising: b1) applying said observation values from said memory means to a plurality of the first decision-support system, wherein each one of the first decision-support systems is trained on the samples of the specified factors with different starting weights for each training; c1) extracting from the first decision-support system, output value pairs for each one of said first neural networks; and d) forming a linear combination of said first ones of said output value pairs and forming a linear combination of said second ones of said output value pairs, to obtain a confidence index pair, said confidence index pair being said quantitative objective aid.

Claim 58

The method of claim 51 , wherein the first decision support system is a neural network that comprises a three-layer network containing an input layer, a hidden layer and an output layer, the input layer having seven input nodes, first, second, third, forth and fifth second hidden layer nodes, a hidden layer bias for each hidden layer node, first and second output layer nodes in the output layer, and an output layer bias for each output layer node.

Claim 59

The method of claim 51 , wherein the first decision support system is a neural network and each of the plurality of first trained neural networks comprises a three-layer network comprising an input layer, a hidden layer and an output layer.

Claim 60

The method of claim 46 , further comprising: b1) applying said observation values from said memory means to a plurality of the first decision-support system, wherein each one of the first decision-support systems is trained on the samples of the specified factors with different starting weights for each training; c1) extracting from the first decision-support system, output value pairs for each one of said first neural networks; and d) forming a linear combination of said first ones of said output value pairs and forming a linear combination of said second ones of said output value pairs, to obtain a confidence index pair, said confidence index pair being said quantitative objective aid.

Claim 61

The method of claim 46 , wherein the first decision support system is a neural network that comprises a three-layer network containing an input layer, a hidden layer and an output layer, the input layer having six input nodes, first, second, third, forth and fifth second hidden layer nodes, a hidden layer bias for each hidden layer node, first and second output layer nodes in the output layer, and an output layer bias for each output layer node.

Claim 62

The method of claim 46 , wherein the first decision support system is a neural network and each of the plurality of first trained neural networks comprises a three-layer network comprising an input layer, a hidden layer and an output layer.

Claim 63

The method of claim 46 , wherein the clinical factors further comprise the result of a test that detects fetal fibronectin in mammalian body tissue and fluid samples.

Claim 64

In a computer system, a method for assessing the risk for delivery in 7 days or fewer days, comprising the steps of: (a) querying and examining the subject to collect observation values reflecting presence and absence of specified factors and storing the observation factors in storage means of the computer system, the specified factors comprising: Ethnic Origin Caucasian, Uterine contractions with or without pain, Parity-abortions, vaginal bleeding at time of sampling, uterine contractions per hour, prior to and No previous pregnancies; (b) testing a patient and obtaining results of the test from the patient, wherein the test detects fetal fibronectin (fFN) in mammalian body tissue and fluid samples, and/or the test determines the level of a local inflammatory product protein in cervico/vaginal samples; and/or of a test that assesses estriol or estretol in saliva, wherein the test is performed prior to, during or after step (a); (c) applying the observation values and the fFN test results from the memory means to a second neural network trained on samples of the specified factors and the test results; and thereupon (d) extracting from the second trained neural network an output value pair, the output value pair being a preliminary indicator for the risk of delivery in 7 days or few days from obtaining the cervico/vaginal sample.

Claim 65

In a computer system, a method for assessing the risk for delivery in 7 days or fewer days, comprising the steps of: (a) querying and examining the subject to collect observation values reflecting presence and absence of specified factors and storing the observation factors in storage means of the computer system, the specified factors comprising: Ethnic Origin Caucasian, Uterine contractions with or without pain, Parity-abortions, vaginal bleeding at time of sampling, uterine contractions per hour, prior to and No previous pregnancies; (b) testing a patient and obtaining results of the test from the patient, wherein the test detects fetal fibronectin (fFN) in mammalian body tissue and fluid samples, and/or the test determines the level of a local inflammatory product protein in cervico/vaginal samples; and/or of a test that assesses estriol or estretol in saliva, wherein the test is performed prior to, during or after step (a); (c) applying the observation values and the fFN test results from the memory means to a second neural network trained on samples of the specified factors and the test results; and thereupon (d) extracting from the second trained neural network an output value pair, the output value pair being a preliminary indicator for the risk of delivery in 7 days or few days from obtaining the cervico/vaginal sample.

Claim 66

The method of claim 58 , further including the steps of: (c1) applying the observation values and the relevant biochemical test results from the memory means to a plurality of the second neural networks, each one of the first neural networks being trained on the samples of the specified factors with starting weights for each training being randomly initialized; (d1 ) extracting from each one of the first trained neural networks, output value pairs for each one of the first neural networks; and (e) forming a linear combination of the first ones of the output value pairs and forming a linear combination of the second ones of the output value pairs, to obtain a confidence index pair, the confidence index pair being the indicator of the risk for delivery in 7 days or fewer days.

Claim 67

The method of claim 58 , wherein the first trained neural network comprises a three-layer network containing an input layer, a hidden layer and an output layer, the input layer having seven input nodes, first, second, third, fourth and fifth hidden layer nodes, a hidden layer bias for each hidden layer node, first and second output layer nodes in the output layer, and an output layer bias for each output layer node.

Claim 68

The method of claim 58 , wherein the clinical factors further comprise the result of a test that detects fetal fibronectin in mammalian body tissue and fluid samples and/or the result of a test that determines the level of a local inflammatory product protein in cervico/vaginal samples and/or the result of a test that assesses estriol or estretol in saliva.

Claim 71

The method of claim 62 , wherein: the variables further include the result of a test for to detect fetal fibronectin (fFN) in a cervico/vaginal sample and/or the result of a test that determines the level of a local inflammatory product protein in cervico/vaginal samples and/or the result of a test that assesses estriol or estretol in saliva; the selected variables include the results of the test; and the method measures the risk of delivery in 14 days or few days from obtaining the sample for the fFN.

Claim 72

The method of claim 63 , wherein the decision support system is a neural network.

Claim 73

The method of claim 63 , wherein the decision-support system has been trained using a set of variables that do not include biochemical test data.

Claim 74

The method of claim 63 , wherein the decision-support system has been trained using a set of variables that do not include the results of a test that detects fetal fibronectin in cervico/vaginal samples.

Claim 75

The method of claim 62 , wherein: comprise the result of a test that detects fetal fibronectin in mammalian body tissue and fluid samples and/or the result of a test that determines the level of a local inflammatory product protein in cervico/vaginal samples and/or the result of a test that assesses estriol or estretol in saliva the selected variables include the results of the test; and the method measures the risk of delivery in 14 days or few days from obtaining the sample for the fFN.

Claim 78

The method of claim 68 , wherein the decision-support system comprises a neural network.

Claim 79

The method of claim 68 , wherein at least five factors are selected.

Claim 80

The method of claim 68 , wherein the clinical factors further comprise the result of a test that detects fetal fibronectin in cervico/vaginal samples.

Claim 81

The method of claim 68 , wherein the selected factors include the result of the test.

Claim 82

The method of claim 72 , further comprising: b1) applying said observation values from said memory means to a plurality of the first decision-support system, wherein each one of the first decision-support systems is trained on the samples of the specified factors with different starting weights for each training; c1) extracting from the first decision-support system, output value pairs for each one of said first neural networks; and d) forming a linear combination of said first ones of said output value pairs and forming a linear combination of said second ones of said output value pairs, to obtain a confidence index pair, said confidence index pair being said quantitative objective aid.

Claim 83

The method of claim 72 , wherein the first decision support system is a neural network that comprises a three-layer network containing an input layer, a hidden layer and an output layer, the input layer having seven input nodes, first, second, third, forth and fifth second hidden layer nodes, a hidden layer bias for each hidden layer node, first and second output layer nodes in the output layer, and an output layer bias for each output layer node.

Claim 84

The method of claim 72 , wherein the first decision support system is a neural network and each of the plurality of first trained neural networks comprises a three-layer network comprising an input layer, a hidden layer and an output layer.

Claim 85

The method of claim 68 , further comprising: b1) applying said observation values from said memory means to a plurality of the first decision-support system, wherein each one of the first decision-support systems is trained on the samples of the specified factors with different starting weights for each training; c1) extracting from the first decision-support system, output value pairs for each one of said first neural networks; and d) forming a linear combination of said first ones of said output value pairs and forming a linear combination of said second ones of said output value pairs, to obtain a confidence index pair, said confidence index pair being said quantitative objective aid.

Claim 86

The method of claim 68 , wherein the first decision support system is a neural network that comprises a three-layer network containing an input layer, a hidden layer and an output layer, the input layer having six input nodes, first, second, third, forth and fifth second hidden layer nodes, a hidden layer bias for each hidden layer node, first and second output layer nodes in the output layer, and an output layer bias for each output layer node.

Claim 87

The method of claim 68 , wherein the first decision support system is a neural network and each of the plurality of first trained neural networks comprises a three-layer network comprising an input layer, a hidden layer and an output layer.

Claim 88

The method of claim 68 , wherein the clinical factors further comprise the result of a test that detects fetal fibronectin in mammalian body tissue and fluid samples and/or the result of a test that determines the level of a local inflammatory product protein in cervico/vaginal samples and/or the result of a test that assesses estriol or estretol in saliva.

Claim 89

In a computer system, a method for assessing the risk for delivery in 14 days or fewer days, comprising the steps of: (a) querying and examining the subject to collect observation values reflecting presence and absence of specified factors and storing the observation factors in storage means of the computer system, the specified factors comprising: Ethnic Origin Hispanic, Marital Status living with partner, Uterine contractions with or without pain, cervical dilatation, Uterine contractions per hour, and No previous pregnancies; (b) testing a patient and obtaining results of the test from the patient, wherein the test detects fetal fibronectin (fFN) in mammalian body tissue and fluid samples, and/or the test determines the level of a local inflammatory product protein in cervico/vaginal samples; and/or of a test that assesses estriol or estretol in saliva, wherein the test is performed prior to, during or after step (a); (c) applying the observation values and the fFN test results from the memory means to a second neural network trained on samples of the specified factors and the test results; and thereupon (d) extracting from the second trained neural network an output value pair, the output value pair being a preliminary indicator for the risk of delivery in 14 days or few days from obtaining the cervico/vaginal sample.

Claim 90

In a computer system, a method for assessing the risk for delivery in 14 days or fewer days, comprising the steps of: (a) querying and examining the subject to collect observation values reflecting presence and absence of specified factors and storing the observation factors in storage means of the computer system, the specified factors comprising: Ethnic Origin Hispanic, Marital Status living with partner, Uterine contractions with or without pain, cervical dilatation, Uterine contractions per hour, and No previous pregnancies; (b) testing a patient and obtaining results of the test from the patient, wherein the test detects fetal fibronectin (fFN) in mammalian body tissue and fluid samples, and/or the test determines the level of a local inflammatory product protein in cervico/vaginal samples; and/or of a test that assesses estriol or estretol in saliva, wherein the test is performed prior to, during or after step (a); (c) applying the observation values and the fFN test results from the memory means to a second neural network trained on samples of the specified factors and the test results; and thereupon (d) extracting from the second trained neural network an output value pair, the output value pair being a preliminary indicator for the risk of delivery in 14 days or few days from obtaining the cervico/vaginal sample.

Claim 91

The method of claim 80 , wherein the sample is a cervico/vaginal sample and the test detects fetal fibronectin.

Claim 92

A computer system, comprising a neural network or plurality thereof trained for assessing the risk of preterm delivery or imminent delivery within in a predetermined time frame.

Claim 93

A computer system, comprising a neural network or plurality thereof trained for assessing the risk of preterm delivery or imminent delivery within in a predetermined time frame.

Claim 94

The system of claim 82 , wherein the time frame is 7 days.

Claim 95

The system of claim 82 , wherein the time frame is 14 days.

Claim 96

The system of claim 82 , wherein the risk of delivery before 35 weeks is assessed.