In some instances, two cases or more in the database had equally small distances to the case that was to be diagnosed. In this situation, the new case was assigned the diagnosis of the database case belonging to the most prevalent of the syndromes with the same distance.

We used the Lisp programming language. The programs were run on a PC with the Linux operating system.

The ID3 starts by dividing the patients into two subgroups, where each subgroup is as homogeneous as possible. Homogeneous in our context means that the patients have the same clinical signs. After the first division into subgroups, each subgroup is subdivided into two new subgroups, and so on. This procedure builds a tree, where the original group is the root/ trunk, subgroups are branches, subsubgroups are twigs, and the basic unit of analysis is a leaf. The basic unit of analysis is e.g. an individual patient or a syndrome. The signs used to discriminate between groups, are the branching points in the tree.

To decide how homogeneous a group is, the ID3 algorithm uses an information theory formula:

∑bnbnt×∑c−nbcnblog2nbcnb

where nb is the number of instances in branch b, nt is the total number of instances in the whole tree, and nbc is the total of instances in branch b of type c. In our context, ‘type c’ stands for ‘syndrome patients who have a certain clinical sign’. At each branching point in the tree, the remaining syndrome patients are divided into two groups, those who have the clinical sign and those who do not have the clinical sign.

With the cluster analysis runs, we used the same data sets as we used for the runs using the basic artificial intelligence methods.

We ran cluster analyses using average linkage between groups, and nearest neighbour as the clustering method. Since our data were binary, we used a binary measure of similarity (‘Sokal and Sneath 5’, the squared geometric mean of conditional probabilities of positive and negative matches). Clinical signs were used as the basic unit of analysis.

Theoretically, the optimal way of finding which clinical signs have the largest predictive value, is using a calculation based on Bayes’ formula. This formula takes into account the sensitivity as well as the specificity of the clinical sign, and the prior probability of the syndrome.

There are two problems with using ‘Bayes’ formula. First, it assumes that clinical signs are independent. This does not always hold true. ‘Upward slanting palpebral fissures’ as a sign clearly is not independent mathematically from ‘downward slanting palpebral fissures’. ‘Low set ears’ and ‘upward slanting palpebral fissures’ probably occur together more often than expected by chance, etc.

Secondly, the figures that go into Bayes’ formula are often not readily available.

The vector method had a low diagnostic error rate. This holds true for the global error rate, as well as for the error rates of the individual syndromes. The set method attained a high predictive value for most of the sets of clinical signs.

These basic artificial intelligence methods were easy to implement, rapid, and showed consistent results in repeated runs.

There was a good correspondence between comparable methods.

The set method on the one hand, and the cluster analysis using clinical signs as the basic unit on the other hand, gave signs or groups of signs that match.

With the vector method algorithm, there was no learning phase. This algorithm directly diagnosed the patients.

As seen in Table 4, the predictive values were high, with the lowest being 94.9 for fragile X syndrome. Fragile X syndrome does not have many distinguishing features in the newborn period.

Table 4

Vector Method/ Nearest Neighbour Run

The global error rate is satisfactory. However, quite low sensitivities were observed for some syndromes, with Smith-Lemli-Opitz syndrome (SLOS) at a low of 69.6%.

The vector method did not produce any output other than diagnoses. Thus, the algorithm did not have to make any concessions for the sake of readability. The algorithm could therefore use all available information without doing any pruning. (‘Pruning’ here means removing twigs on an ID3 tree, or parts of other search results which do not cover many cases, but which contribute to making it more complex). The nearest neighbour algorithm attained very high specificities, at one hundred per cent, or close to a hundred per cent.

The set method table (Table 6) lists the sets of signs found by the set method, along with their clinical indices. It should be stressed that these are sets of signs, i.e. either the full set listed is present, or it is not. This theoretically should have the effect of lowering sensitivity and increasing specificity. The impression from the tables is definitely that the specificity is higher than is usual, in many instances 100%.

Table 6

Sets of Clinical Signs Versus Syndromes, ‘Set Method’ Results

Yet, the sensitivity does not seem to be dramatically lowered, though e.g. Smith-Lemli-Opitz syndrome (SLOS) with the set method is down to a 15% sensitivity. Although the predictive value is very good, this particular set will therefore not be a very useful set of signs in diagnosis.

The lists of signs found by the set method have been pruned to make them more accessible to a human reader. We have tried to strike a balance between two concerns. The lists of clinical signs that we present are few per syndrome, and fairly short, in some instances the list of signs is just one single clinical sign. The sensitivity and specificity are still in general quite acceptable. The signs found make sense from a clinical point of view. The most cumbersome diagnoses are trisomy 13 and Zellweger syndrome. In trisomy 13 four lists of three clinical signs each are presented. In Zellweger syndrome, the longest list has four clinical signs that have to be present simultaneously.

On the other hand, ten of the seventeen syndromes have fairly predictive lists of only one sign.

The clinical sign ‘short palpebral fissures’ has a predictive value of one hundred per cent. It has a sensitivity of 89%, so this is a useful clinical sign.

As seen in Table 5, the global error rate is low for the ID3 run, at about the same level as the vector method.

Table 5

ID3run

Table 7 shows a dendrogram, after a cluster analysis has been run, where the clinical signs were used as the basic measure of analysis.

Table 7

Dendrogram from a Hierarchical Cluster Analysis Using Single Linkage, Showing the Relationship Between Clinical Signs

The triad of long eyelashes, synophrys and hirsutism is characteristic of Cornelia de Lange syndrome. Hepatosplenomegaly and omphalocoele are less distinctive, but point to Beckwith-Wiedeman syndrome. The hepatosplenomegaly alone also fits with Zellweger syndrome or congenital cytomegalovirus infection (CMV). Though not syndrome specific, this sign could be seen as pointing to this group of syndromes. ‘Cat like cry’ and round face are hallmarks of ‘Cri du chat’ or monosomy of the short arm of chromosome five. Williams syndrome is characterized by broad nasal tip/ broad nasal bridge. The single sign occipital encephalocoele is a strong pointer to Meckel syndrome.

Three syndromes have several similarities as far as clinical signs are concerned: Noonan syndrome, Turner syndrome and Klippel-Feil syndrome. The next group of clinical signs, hypertelorism, downward slanting palpebral fissures, short neck, and low hairline fit these syndromes. It can be seen from the arbitrary scale of the dendrogram that hyperelorism and downward slanting palpebral fissures are closely related, and in comparison stand apart from short neck and low hairline. This may distinguish Noonan syndrome from Turner syndrome and Klippel-Feil syndrome. Turner syndrome patients when newborn also have edema of hands and feet, found as a single clinical sign at line nine from the bottom of the dendrogram.

A large group of clinical signs, from polydactyly to simian crease, denote the trisomies (trisomy 21, 18 and 13). The first and smallest subgroup of this group fits trisomy 13 and 18, with the signs polydactyly, prominent calcaneus, cryptorchidism and micrognathia. The second, larger subgroup of clinical signs here is consistent with trisomy 21 (Down syndrome).

Because of the relatively high prevalence of the trisomies, some clinical signs seem to have been ‘stolen’ from the less prevalent syndromes. An example of this is the Prader-Willi syndrome (hypotonia, cryptorchidism).

No individual syndrome springs to mind for hypogenitalism as a single sign. In this context, however, hypogenitalism would strongly suggest Prader-Willi syndrome. Similarly, large ears strongly indicate Fragile X/ Martin-Bell syndrome.

Short palpebral fissures, long philtrum, and midface hypoplasia define fetal alcohol syndrome.

This leaves the signs microphtalmia, low birth weight and microcephaly as signs with no associated syndrome.

The syndromes that have not been taken into account are Smith-Lemli-Opitz syndrome (SLOS) and to a certain degree Zellweger syndrome and congenital cytomegalovirus infection. Smith-Lemli-Opitz syndrome (SLOS) seems to be difficult to diagnose for several of the methods with the data used here.

The results for the ‘naive Bayes’ ‘calculations are listed in Table 8. Although the difference is not large, the naive Bayes’ calculations attain the lowest global error rate of diagnosis. Like in the vector method runs, the naive Bayes’ calculation uses all available information, and does not have to compromise to satisfy a demand for human readability.

Table 8

‘Naїve Bayes’ Calculation

These three methods did roughly equally well as judged by the overall error rate. None of the methods did very badly in any of the syndrome groups. (It would have been possible to have a good overall performance, even with a poor performance in the smaller syndrome groups).

Small variations in specificity could lead to relatively large variations in predictive value.

These two methods are comparable in that they both rendered lists or clusters of clinical signs. We chose cluster analysis as a reference method since it is a mainstream mathematical method. The cluster analysis with clinical signs as the basic unit is most appropriate for comparison with the set method. This analysis did not name syndromes, it just grouped clinical signs. Given this restriction, the clinical signs grouped by the cluster analysis, and the sets of signs found by the set method match reasonably well. For example, Table 6 shows, from the top, that FAS (fetal alcohol syndrome) according to the set method has the signs short palpebral fissures, and midface hypoplasia. Table 7, the cluster anaysis, shows in line 5, 4 and 3 from the bottom, that short palpebral fissures, long philtrum, and midface hypoplasia are grouped closely together. Next, for trisomy 21 (Down syndrome) in Table 6 the set method found the signs flat occiput, upward slanting palpebral fissures, and flat face. In Table 7 (the cluster analysis) in the middle of the figure finds a narrow grouping of flat face, upward slanting palpebral fissures, and flat occiput.

Most studies on syndrome diagnosis accept established diagnoses. Diagnoses in single patients may be questioned, but the diagnostic groups themselves are often considered untouchable.

Using methods such as the vector method or cluster analysis, it is an option to challenge the existing diagnostic groups. Set up in this way, it is conceivable that the nearest neighbour algorithm could suggest lumping or splitting of diagnostic groups, that new groups with different boundaries should be formed, or that totally new groups should be established.

‘New’ syndromes As far as establishing new diagnoses is concerned, an objective method has advantages compared to the pattern recognition method.

The pattern recognition method would be dependent on a single clinician seeing enough cases of a new syndrome to realize it was actually a new syndrome.

The syndromologist would then have to report it, other syndromologists would have to read the report and recognize the syndrome themselves. This obviously works in many cases, since new syndromes are regularly reported.

It is a disturbing fact, though, that we cannot know how many syndromes are not reported. It is reasonable to think that an international central database of syndromes would be useful for awareness to detect new syndromes. One important group would be teratogenic syndromes, e.g. possibly caused by the mother living close to a nuclear plant, caused by estrogen-like pollutants in the environment, caused by maternal drug abuse etc.

The solution provided by the set method is a set of clinical signs that have to be present simultaneously.

This is different from the single-sign method, where one sign, when found, increases the probability of the syndrome, the next sign may increase or decrease the probability etc.

In general, the requirement for several signs to be present at the same time, increases specificity and decreases sensitivity.

This is reflected in the tables of the Results section, where several lists of signs have a very high specificity, often one hundred per cent. Once found, these clinical signs (the set of clinical signs) will be better predictor variables.

A very long list of signs that have to be present simultaneously, may not be of value to a human diagnostician. Such a list would make perfect sense in machine diagnosis, though.

The most common approach with syndrome diagnosis based on clinical signs, is to use single signs to get closer to a diagnosis. With other methods, like the ID3 method, one partitions the ‘universe’ of possible diagnoses and thus continually circles in the few diagnoses that remain. In artificial intelligence, this way of searching is common, whereas in clinical thinking it may not seem that natural (although many diagnosticians use this way of thinking, perhaps unconsciously).

In artificial intelligence, it is common to make explicit the concept of the ‘closed world’. Many studies make this assumption, but do not state it explicitly. In the closed world of our study, there were only seventeen syndromes. Thus, if sixteen of the syndromes could be ruled out, the diagnosis would have to be the seventeenth syndrome. This may be unrealistic in a real-world situation.

Syndromologists often speak of ‘handles’, i.e. clinical signs with a high positive predictive value. We kept to this standard approach of using positive signs, i.e. signs present.

Of course, signs not present may help single out diagnosis just as effectively. Conversely, a sign may have a high negative predictive value, i.e. if this sign is present, the diagnosis becomes much less likely.

DNA diagnosis and diagnosis based on the clinical phenotype could either give the same result, or different results. In some cases the problem is small, since there is no alternative to clinical classification and diagnosis.

In other cases, one might ask which would be the ‘correct’ classification.

The clinical classification may be more practical. The DNA diagnosis is easier, more clear cut, and may have a higher status [21Biesecker LG. Lumping and splitting: molecular biology in the genetics clinic Clin Genet 1998; 53: 3-7.].

However, clinical classification is not outdated, and never will be. What is of interest, is ultimately the phenotype, the human being. If the overlap between a phenotypic classification and a DNA classification is little, so is the interest in the DNA ‘defect’.