WHAT IS MEDICAL DIFFERENTIAL DIAGNOSIS?

WHAT IS MEDICAL DIFFERENTIAL DIAGNOSIS?
http://www.medparse.com/whatdfdx.htm
© 2001 G. William Moore, MD, PhD. [1,2,3]

From: Pathology and Laboratory Medicine Service (113), Baltimore VA Maryland Health Care System [1], Baltimore, MD.
Department of Pathology, University of Maryland School of Medicine [2], Baltimore, MD.
Department of Pathology, The Johns Hopkins Medical Institutions [3], Baltimore, MD.

WHAT IS MEDICAL DIFFERENTIAL DIAGNOSIS?

MEDICAL DIFFERENTIAL DIAGNOSIS (MDD) is a general paradigm in medicine, in which a single finding or process known about a patient generates a list of possibilities, which includes either the diagnosis or another concept that might lead to a diagnosis (Greenberger, 1998). For example: Neonatal-meconium-ileus
HAS THE DIFFERENTIAL DIAGNOSIS:
Cystic-fibrosis
Intestinal-stenosis
Meckel's-diverticulum.
In this listing, neonatal-meconium-ileus is the HEADER and the remaining diseases are TRAILERS.

In this report, we develop the idea that MDD can serve as the cornerstone for a broader theory of medical diagnostic reasoning. In earlier papers in medical informatics, it was believed that a direct, set theory or symbolic logic representation of this information would suffice for a theory of medical diagnosis, as for example in SYMBOLIC LOGIC NOTATION: Neonatal-meconium-ileus IMPLIES Cystic-fibrosis OR Intestinal-stenosis OR Meckel's-diverticulum.
or in SET THEORY NOTATION: Neonatal-meconium-ileus IS-SUBSET-OF Cystic-fibrosis UNION Intestinal-stenosis UNION Meckel's-diverticulum.
The basic idea is reasonable, but it fails on many, significant details. In this report, we propose to address these details, using ONTOLOGY LAYERING LOGIC.

FAILURE OF EXPERT SYSTEMS.

      MEDICAL DIAGNOSTIC REASONING BY COMPUTER is already well-trod ground over the past three decades of medical informatics, and in my opinion, has been largely a flop. Very few physicians use computer systems, so-called EXPERT SYSTEMS, to assist in making medical diagnoses, although computers are well-integrated into the remainder of the physician's workplace. The stereotype of the computerphobic, older physician is untenable in modern medicine: the old guys have either retired or learned how to use computers. In fact, the actual failed concept is that of expert systems themselves, the idea that computers render better diagnoses than trained physicians. Much to the surprise of many (and to the gratification of some older physicians), modern computer systems are remarkably poor at making Gestalt perceptions, or HIGH ORDER PATTERN RECOGNITION about the patient as-a-whole; this skill remains the unchallenged domain of the experienced, trained physician. Rather, the principal, emerging value of computers in medicine over the past several decades has been one of organizing and communicating fussy, detailed information about patients, such as physician-orders and medical records (Murff, 2001). The physician is still the best overall diagnostician.

      One of the growing areas of interest, that requires many of the same computer paradigms as expert system design, is QUALITY ASSURANCE in medical diagnosis (Borkowski et al, 2001). That is, an existing medical record (or a significant subset thereof, such as the pathology reports) is reviewed by computer for signal events that might instigate further review of the record by trained human reviewers. The most obvious quality assurance monitors are date inconsistencies, such as patient biopsied before admission, or receives a diagnosis before biopsy, or is autopsied before death. These details are currently managed in LABORATORY INFORMATION SYSTEMS at the data-entry step, but even for something as simple as this, there is no public-domain, standardized list of required date-consistencies for surgical pathology reports (Moore and Berman, 2000).

      As we shall demonstrate in this report, quality assurance monitors can be applied almost automatically to event-tracking in an ELECTRONIC MEDICAL RECORD (EMR), and can perform a task that physicians do NOT like to do, and do NOT do very well, namely, tracking signal events, especially if they can be caught and corrected before any significant harm befalls a patient. It goes without saying that any medical administrator that puts such a mechanism in place in his/her institution should make the system equally available to physicians "in the trenches", who can spot and correct errors in a timely manner. This capability is one of the strengths of the VISTA computer system used by the DEPARTMENT OF VETERANS AFFAIRS (Murff, 2000).

FAILURE OF THE CLASSICAL LOGIC PARADIGM.

      THE CLASSICAL LOGIC PARADIGM in MDD is the assertion that lists of MDDs, in the style of Greenberger et al (1998), can be managed with the usual operations of classical set theory or symbolic logic. This assertion fails mathematically at three levels, and computationally at a fourth level. FIRST, there is often an implicit frequency-ordering in the trailer-elements of a MDD, where the first diagnosis is the most common. In the example given, cystic-fibrosis is far-and-away the most common cause of neonatal-meconium-ileus. Classical logic does not recognize, nor exploit in any way, this ordering of the trailer-elements in a MDD. This is so-called SUTTON'S LAW or the ZEBRA RULE. However, human physicians who solve diagnostic problems almost certainly exploit this knowledge and experience.

      SECOND, ipso facto, many published differential diagnosis lists have missing, infrequent trailer-elements, because the person who prepared the lists could not think of all the possibilities. The requirement for absolute completeness, if enforced, would impose an almost impossible burden upon the creators and maintainers of such lists, and would, eventually, inhibit their creation and publication. Would-be list-creators and committees would sit and stew on them forever, lest they miss something. In classical logic, two less-than-complete but different trailer-lists could result in contradictions. In ONTOLOGY LAYERING LOGIC (described herein), such list-differences actually enrich the solution software.

      THIRD, classical logic offers no preferred order in which to perform calculations leading to solution. In ONTOLOGY LAYERING LOGIC, an optimal stepwise solution procedureÿ20is readily suggested by the formalism.

      FINALLY, the classical solution to the general logic problem proposed above is NON-POLYNOMIAL COMPLETE, that is to say, computationally too burdensome for all but the most trivial, sample problems. Again in ONTOLOGY LAYERING LOGIC, an optimal, polynomial solution procedureÿ20is readily suggested by the formalism.

ONTOLOGY LAYERING LOGIC PARADIGM.

      In the ONTOLOGY LAYERING LOGIC PARADIGM, logic expressions are assigned to LAYERS of increasingly less certainty. LAYER ZERO represents direct observations on the patient, such as physical findings or laboratory findings (Moore, Brown and Miller, 2001). LAYER ONE represents objective tautologies, such as: if a patient is at least thirty years old, then that same patient is at least sixty years old. LAYER TWO represents assertions that are nearly always true, but have occasional exceptions, etc. The ONTOLOGY LAYERING THEOREM, guarantees that the logic-solution algorithm will never result in an inconsistency.

      As a general rule, it is OK to have redundant MDDs, as long as an MDD with MORE ELEMENTS appears in a LOWER NUMBERED LAYER than an MDD with FEWER ELEMENTS. The CERTAINTY SUPERSET THEOREM guarantees that such an ordering of MDDs is optimal. If you know the frequency distribution (so-called ZIPF DISTRIBUTION) of trailer-elements in a MDD, then it is OK to create redundant MDDs, consisting of stepwise more inclusive subsets, as for example: Neonatal-meconium-ileus IMPLIES Cystic-fibrosis.
and: Neonatal-meconium-ileus IMPLIES Cystic-fibrosis OR Intestinal-stenosis OR Meckel's-diverticulum.

      The POLYNOMIAL COMPLETE THEOREM guarantees that if your algorithm solves the system of logic starting from layer zero, then it will proceed in N³ steps. Thus, the PCT gives both a recipe for programming the algorithm, and a guarantee that the algorithm will finish.

      In the following discussion, we employ so-called POLISH NOTATION. In traditional notation, the operator (IMPLIES, OR) appears BETWEEN the two operands, as for example: Neonatal-meconium-ileus IMPLIES Cystic-fibrosis OR Intestinal-stenosis OR Meckel's-diverticulum.
or: Neonatal-meconium-ileus IMPLIES -> Cystic-fibrosis | Intestinal-stenosis | Meckel's-diverticulum.
In Polish notation, the operator appears BEFORE the two operands, as for example: -> Neonatal-meconium-ileus
|| Cystic-fibrosis
Intestinal-stenosis
Meckel's-diverticulum.

      The troubles with this speciously simple paradigm are:

1. Sometimes the list has a subtle ordering.
2. Different contexts -> different lists.
3. Nomenclature.

For this and many other reasons, there has been no standardized list in wide usage in medicine.

CONTEXT FOR MEDICAL DIFFERENTIAL DIAGNOSIS.

For the context issue, make sure that the context is somehow built into the list, e.g., for cystic fibrosis:

-> Gastrointestinal disease
|||| Cystic-fibrosis
Tracheoesophageal-fistula
Dysphagia-lusoria
Colonic-adenocarcinoma
Gastric-adenocarcinoma.

and:

-> respiratory disease
|||||| Cystic-fibrosis
Lung-adenocarcinoma
Adult-respiratory-distress-syndrome
Pulmonary-sequestration
Hypoplastic-left-lung-syndrome
Chronic-bronchitis
Emphysema.

and:

-> Congenital disease
||||||| Cystic-fibrosis
Sickle-cell-disease
Ventricular-septal-defect
Pulmonary-sequestration
Hypoplastic-left-lung-syndrome
Prune-belly-syndrome
Gastroschisis
Duodenal-atresia.

For the ordering requirement, place the diagnoses in approximate order of frequency, as known. This is often not possible, but at least put all the horses before all the zebras.

Have the computer reorder the lists, based upon:

a. Ontology Layering Theorem.
b. Certainty Superset Theorem.
c. Non-monotonic reasoning.
d. Goedel Quotient Logic Polynomial Theorem.

NOMENCLATURE FOR
MEDICAL DIFFERENTIAL DIAGNOSIS.

The NOMENCLATURE PROBLEM is the easiest to address, from a purely technical point of view. For research applications, one should use the UNIFIED MEDICAL LANGUAGE SYSTEM (UMLS) of the U. S. National Library of Medicine (USNLM). UMLS is cost-free to researchers worldwide, and is nearly comprehensive for medical terminology, although UMLS is relatively synonym-poor. As long as you sign and abide by the annual contract sent by the USNLM, and use UMLS strictly in your own laboratory, you are out of trouble. The problem comes when you try to export large chunks of UMLS nomenclature to a public venue, such as the Internet.

The fair-use restrictions on UMLS are currently under dispute. A large subset of the concepts in UMLS that would be of interest to pathologists was contributed by the College of American Pathologists (CAP), in the form of SNOMED III, which is copyrighted by the CAP. Another large, interesting subset of UMLS is the Clinical Codes of the British National Health Service (formerly, Read codes), which are owned by the British crown. Both institutions have spent millions of dollars to develop their concept systems, and are extremely jealous of their copyrights. The U. S. Federal Government Health Care Financing Administration is currently in negotiation with the CAP over the "liberation" of the SNOMED concept-space into the public domain, and so far, the CAP is winning. Public-use advocates are so discouraged that there is serious talk about constructing yet another nomenclature system from the ground up. See Dr. Christopher Chute's Keynote Address at the APIII-2001 Tissue Microarray Data Exchange Standards Workshop, at URL:

http://www.pathology.pitt.edu/apiii01/

Click on the TISSUE MICROARRAY DATA EXCHANGE STANDARDS WORKSHOP, Saturday, October 6, 2001.

My view of the situation is as follows. First, the USNLM has assigned CONCEPT UNIQUE IDENTIFIERS (CUIs) to each of its concepts, for example, lung=C0024109, and these concept identifiers must be public-domain. The fact that C0024109 also corresponds to the SNOMED-code, T-28000=Lung, NOS, is irrelevant, as long as one has as separate source of medical nomenclature. In fact, the Johns Hopkins Autopsy Resource, at URL:

http://www.netautopsy.org

offers such a public source of medical nomenclature, which corresponds to the language of Prof. William H. Welch, Sir William Osler, Prof. William Halstead, and other nineteenth century medical luminaries, and which easily predates any copyrights or fair-use restrictions applied by the CAP. It has been shown (Moore et al, 1988) that most of this vocabulary was already in use in the JHAR at the end of the nineteenth century, and thus long predates the existence of the CAP.

The biggest problems with not paying CAP's licensure fee for research projects are: (1) you can only use concepts that you already have in your research database (for the JHAR, less than 10% of SNOMED's total); and (2) you can't charge a fee for medical services that include a coding requirement. You can't use the entirety of any contributing concept system within the UMLS, without breaking copyright fair-use, but you can start small with a few concepts that fit into your own experience, and build up by cooperation with other webmasters.

MEDICAL ONTOLOGIES.

      1. An ONTOLOGY is a (Platonic) description of essential reality, i.e., what actually is, as opposed to what one can see (observation, accident), or what one can know (epistemiology). The term ontology was coined by two German philosophers, Göckel and Lorhard, in 1613, and first appeared in English in 1721. Quine views ontology as the metaphysical commitments or presuppositions embodied in the different natural sciences. For example, the belief that a cancer can metastasize would be an ONTOLOGICAL COMMITMENT. In the philosophy and practice of science, ontology goes under various names: essence, reality, Mind of God, nature, gold standard, or mathiverse. In medical informatics, ontology has come to mean a structured list of concepts, typically prepared by an expert or panel of experts.

      2. With the ease of posting structured lists on the Internet, and with EXTENDED MARKUP LANGUAGE (XML) as an emerging standard for such lists, it is likely that the next decade will witness an explosion of public medical ontologies, both amateur and professional.

      3. The importance of ontologies has been recognized by the U. S. Defense Advanced Research Projects Agency (DARPA), the original sponsor of the Internet, which has proposed guidelines for a formal ontology AGENT MARKUP LANGUAGE, that employs the ONTOLOGY INFERENCE LAYER.

      4. In this report, we propose that the MEDICAL DIFFERENTIAL DIAGNOSIS is the fundamental element of a medical ontology.

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Last modified, November 18, 2001, by G. William Moore, MD, PhD.