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Medical diagnosis as a linguistic game

BMC Medical Informatics and Decision MakingBMC series – open, inclusive and trusted201717:103

https://doi.org/10.1186/s12911-017-0488-3

Received: 2 September 2016

Accepted: 13 June 2017

Published: 10 July 2017

Abstract

Background

We present a formalized medical knowledge system using a linguistic approach combined with a semantic net.

Method

Diseases are defined and coded by natural linguistic terms and linked via a complex network of attributes, categories, classes, lists and other semantic conditions.

Results

We have isolated more than 4600 disease entities (termed pathosoms using a made-up word) with more than 100.000 attributes sets (termed pathophemes using a made-up word) and a semantic net with more than 140.000 links. All major-medical thesauri like ICD, ICD-O and OPS are included.

Conclusions

Memem7 is a linguistic approach to medical knowledge approach. With the system, we performed a proof of concept and we conclude from our data that our or similar approaches provides reliable and feasible tools for physicians given a formalized history taking is available. Our approach can be considered as both a linguistic game and a third opinion to a set of patient’s data.

Keywords

Formalized medical knowledge Medical data base Linguistic game Disease entity

Background

Medicine involves doctors and patients discussing risk and reaching complex decisions together about treatment options, amongst other things. Yet it is known that diagnostic errors occur, with a frequency (depending on how terms are defined) ranging between 2.5% and 20% [1, 2]. Most errors have only moderate negative consequences, but some do not. Modern computing technology is claimed to decrease the number of erroneous diagnosis [35]. Nowadays, numerous systems of collecting patient data (electronic history taking systems) exist. Only generalist publications are cited ([516], for review [5, 16]). Our approach has as starting point the fact that the collection of patient data can be considered as a patient data vector. Assignment of the symptoms or signs of a patient to a certain diagnosis is possible by a bundle of different not exclusive methods: (1) by the knowledge of the physician, (2) by comparison of the patient data with clinical pathways and guidelines, (3) by boards of specialized physicians, (4) a second opinion by an experienced medical doctor, and (5) by computer-assisted comparison with a data base of medical knowledge. The approach proposed in (5) implies, that we use formalized systems allowing sampling of medical knowledge (prototypic diseases) in a formalized manner. We describe thereafter a database of prototypes of diseases, which are oriented on the concept of vectors (patient vectors and disease vectors). For this approach three constituents are necessary: (1) patient vector, (2) a pool of disease vectors (described in this paper) (3) an algorithm allowing an interaction between both vectors. For the approach mentioned in (2) and (3) clinical decision support systems (CDSS and inference engine technology) are available [1719]. Most approaches concerning computer-assisted data bases of medical knowledge are based on classical object-oriented structures and an entity-relationship-model. Substantial improvement of CDSS may be provided by IT-systems improving knowledge acquisition such as MetaMap [20] and cTake [21].

Our system shows that it is possible to use original atomistic language element and an intuitive, flexible semantic network to get medical context and structures which allow complex diagnostic understanding. The diagnosis process appears as a system driven dialogue which tries to match system semantics with patient semantics. This procedure can be understood as a “linguistic game”, which provides a “third opinion” for the patient’s symptoms.

Methods

For the demonstration of our approach we used 4 examples of patients, fitting the symptoms of these patients to possible disease entities (pathosoms in our terminology).

Examples

Case 1: The patient suffers from cytopenia, anemia and bone marrow: hypercellular (3 tuple, one with an attribute).

Case 2: A female, aged 55 years suffering from a firm lump in the mamma (2-tuple with 1 and 2 attributes).

Case 3: A newborn suffering from “polydactyly”, “renal cyst” and “encephalocele” (3-tuple).

Case 4: A female patient suffering from proteinuria, hematuria and fever (3-tuple).

Thesauri

A thesaurus was defined as a collection of atomistic terms with a coding system (numeric, alpha-numeric or string). Many thesauri could be obtained from Internet or official institutions as the DIMDI (Deutsches Institut für medizinische Dokumentation) [22]. Examples of such medical thesauri are: ICD (International Classification of Diseases, injuries and causes of death [23], ICD-O (International Classification of Diseases, Oncology) [24], ORPHANET (Portal für seltene Krankheiten und Orphan Drugs) [25], TA (Taxonomy of Anatomy) [26], OPS (Operationen-und Prozedurenschlüssel -Internationale Klassifikation der Prozeduren in der Medizin 2015) [27], OMIM (Online Mendelian Inheritance in Man) [28], CAS (common chemistry data base) [29], EC (comprehensive enzyme information system) [30], HGNC (Hugo Gene Nomenclature Committee 2015) [31], ATC (Anatomical Therapeutic Chemical Classification) [32] and LOINC (Logical Observation identifiers and names) [33]. All these thesauri terms and codes are harmonized and included in Memem7 using our own coding system with links to the original code.

Software

The system is focused on design flexibility using common website programming development environments like common SQL databases and Javascript.

Atomistic approach

Each descriptor of a symptom or sign (pathological sign, laboratory sign, EKG report (electrocardiogram) or genetic findings are transformed to an atomistic term. Atomistic terms are the basic form of our system. An atomistic term can be considered as a stem word with a singular code and some added attributes. One example of such an atomistic term is proteinuria with a numeric attribute (g/ml) or headache with the attribute: strong. Attributes may be cardinal, ordinal or numeric.

Prototypic diseases (pathosom)

A disease entity or a prototype of a disease is considered as the sum of all descriptors in a WHO classification or clinical pathway. All these descriptors must be atomic (see below for definition of a pathophem). Pictures are, so far, included by internet links. All these descriptors were class-divided as shown in Table 1. A pathosom is therefore defined as the sum of a vector: c (j1…jn). Each element j can be assigned to certain classes (see Table 1) or thesaurus (see Table 2).
Table 1

Classes of Memem7

No

Class

Subclass

Subclass2

Elements

1100

Description

Definition

 

7241

1400

Description

System/Lokalisation

 

9875

1500

Description

Struktur

ElementOf

1733

1540

Description

Struktur

HasElement

6251

1550

Description

Struktur

HasVariante

1651

1570

Description

Struktur

Sekundare Form

177

5100

Symptoms

Anamnese

 

7406

5110

Symptoms

Anamnese

Akut

182

5130

Symptoms

Anamnese

Vorgeschichte

479

5140

Symptoms

Anamnese

Familie

47

5150

Symptoms

Anamnese

Demografisch

10

5160

Symptoms

Anamnese

Sozial

88

5300

Symptoms

Vital

 

7597

5400

Symptoms

Physikal

 

768

5410

Symptoms

Physikal

Spirometry

22

5500

Symptoms

Labor

 

4423

5510

Symptoms

Labor

Clinical

696

5520

Symptoms

Labor

Agent

1370

5530

Symptoms

Labor

Toxicology

35

5600

Symptoms

Imaging

 

401

5610

Symptoms

Imaging

Ultrasound

119

5620

Symptoms

Imaging

Radiology

570

5630

Symptoms

Imaging

MRT

187

5640

Symptoms

Imaging

CT

132

5650

Symptoms

Imaging

Endoskopie

16

5700

Symptoms

Pathologie

 

1795

5710

Symptoms

Pathologie

Makroskopie

1252

5720

Symptoms

Pathologie

Mikroskopie

8576

5725

Symptoms

Pathologie

Elektronenmikroskopie

291

5730

Symptoms

Pathologie

Spezialfarbung

236

5735

Symptoms

Pathologie

Enzymhistochemie

96

5738

Symptoms

Pathologie

Zytologie

894

5740

Symptoms

Pathologie

Immunhistochemie

3596

5745

Symptoms

Pathologie

FACS

29

5750

Symptoms

Pathologie

In-Situ Hybridisierung

39

5760

Symptoms

Pathologie

Molekularbiologie

30

5770

Symptoms

Pathologie

Stoffe

95

5795

Symptoms

Pathologie

Differenzialdiagnose

1356

5800

Symptoms

Genetik

 

4400

5900

Symptoms

Psychologie

 

175

6100

Characteristics

Historie

 

83

6300

Characteristics

Epidemologie

 

2029

6310

Characteristics

Epidemologie

Sex

557

6320

Characteristics

Epidemologie

Age

989

6330

Characteristics

Epidemologie

Race

66

6340

Characteristics

Epidemologie

Region

132

6350

Characteristics

Epidemologie

Inzidenz

198

6360

Characteristics

Epidemologie

Pravalenz

528

6400

Characteristics

Atiologie

 

607

6500

Characteristics

Pathophysiologie

 

2783

6600

Characteristics

Verlauf

 

588

6610

Characteristics

Verlauf

Beginn

236

6620

Characteristics

Verlauf

Verlauf

266

6630

Characteristics

Verlauf

Stadium

462

6640

Characteristics

Verlauf

Prognose

1122

6650

Characteristics

Verlauf

Komplikation

1506

6660

Characteristics

Verlauf

Risikofaktor

965

6700

Characteristics

Komorbiditat

 

1316

6800

Characteristics

Differentialdiagnose

 

7785

6900

Characteristics

Untersuchung

 

3016

8100

Therapy

Therapieprinzipien

 

1422

8200

Therapy

Medikamente

 

2302

8300

Therapy

Chirurgie

 

493

8400

Therapy

Strahlentherapie

 

32

8500

Therapy

Ambulance

 

8

8600

Therapy

ReHa

 

44

8700

Therapy

Psychotherapie

 

7

8800

Therapy

Alternative

 

369

9800

Therapy

Vorsorge

 

326

Table 2

Thesauri in medical use [2129, 35, 36, 38]

Thesaurus (acronym)

Content of diseases

Free available and used in memem7

ICD-10-GM

Diseases

yes

ICD-O

Morphology of tumors

yes

ORPHANET

Rare Diseases

yes

OPS

Medical interventions

yes

LOINC

Laboratory Codings

yes

SNOMED

Medical terms and ontology

no

OMIM

Human genes/Genetic Disorders

yes

TA

Taxanomica anatomica

yes

CAS

Molecules

yes

EC

Enzymes

yes

HGNC

Molecular biological terms

yes

ATC

Agents/Medicaments

yes

Pathophem

A pathophem is an object sentence in a classification, clinical pathway or a publication after its atomistic representation. After this breakdown, we have an object (a single word meaning a stem word in the linguistic sense) which can be further characterized by up to three attributes or any object. Attributes are time, intensity, color, taste etc. Each object can be either true or false or not available. Objects should be coded by the thesaurus systems as given in Table 2. All content is coded along our coding system and then termed pathophem. In addition, occurrence probabilities between 0 and 1 can be considered. “Lead” means that this pathophem is always present in a pathosom and signifies a probability of 1, “+++” means a frequency between 50% to 99.9% or a probability of 0.5–0.99, “++” means a frequency of 10% to 49.9% or a probability of 0,1–0.49, “+” 1% to 9.9% or a probability of 0.01–0.09, and “(+)” <1% or a probability <0.01. “NOT” means that a pathophem excludes a pathosom.

Diagnostic algorithm (linguistic game)

The algorithm uses various syntactical and semantic analysis procedures to provide a set of diagnoses. Proposal of diagnoses occurs by matching a patient vector (medical report replaced in our test system by a 5-tuple vector of patient data) with the disease vectors (termed pathosoms). First the patient vector must be analyzed syntactically. This verification ensures that all terms are within the basic thesauri and have a “primary meaning” within the semantic network. Our semantic network is a classical semantic network for knowledge representation. It is a directed graph consisting of vertices, which represent terms, objects and concepts, and edges, which represent semantic relations between terms like “is-a”, “is-part-of”, “has-attribut-of”, “is-class-of”.

There will be a continuous dialog by offering alternative meanings if all terms are completely matched using synonyms, clarifying double meanings etc. The first result will be a set of matching pathosoms with a ranking. If the set is too large the system provides a second analysis to reduce the set by asking more details about other essential symptoms of the pathosoms. If the set is very small or empty the system tries to broaden the patient vector by using semantic network context methods like “is-class” and others. For example: If you are searching for a concrete finger symptom/pathophem and you cannot find anyone there might be a corresponding symptom/pathophem about the hand which may fit.

Linguistic problems (German/English)

More than 85% of the used terms are already translated in English.

Statistical methods

For calculations, we used R statistic package (version 3.1.1). For analysis of the ICD-10 (German version) we used the tm package of R [34]. The question to be answered was of how many atomized terms the ICD-10 consisted of and to integrate those lacking in memem7.

Description of software

  • Project name: Memem7 Medical Semantic Network

  • Project home page: not yet determined

  • Archived version: not yet determined

  • Operating system(s): Platform independent

  • Programming language: Coldfusion, Javascript, HTML5

  • Other requirements: Coldfusion Server

  • License: Not yet determined

  • Any restrictions to use by non-academics: Not yet determined

Evaluation of Memem7

Testing the quality of Memem7 was done with 190 artificial reports (simulated cases not representing a patient), originally developed for testing CLEOS [35]. Each artificial case consists of a short medical report and/or symptoms with a mono-causal medical explanation.

Results

Examples for using Memem7 for searching

This means comparing the patient vector (a tuple with up to 5 elements) with the set of pathosoms, looking for those pathosoms which fits best to the patient vector using a linguistic approach.

Case 1

The search terms in English are cytopenia (41), anemia (657), bone marrow (91): hypercellular. This is a patient vector with 3 elements. The numbers in parenthesis signify how often a pathophem is mentioned in Memem7. For the intersection of the three pathophems only one pathosom was found, namely RAEB (M3501, see Additional file 1).

Case 2

The search terms are female (597): aged 55 years, nodule (365): firm (90), nodule: mamma (434). The pathosom invasive breast cancer was found and (excluding the attribute firm) 6 additional pathosoms are proposed: angiosarcoma of breast, secretory breast cancer, benign breast tumor, pseudoangiomatoid stroma hyperplasia, fibroadenoma and breast papilloma were recognized.

Case 3

The search terms are polydactyly (40), renal cyst (14), encephalocele (6) in a newborn infant. 3 pathosoms fitted to the search terms: Meckel syndrome type 1,3,6.

Case 4

The search terms are being proteinuria (61) and hematuria (72). As a result, 25 pathosoms are given by Memem7 as possible disease vectors fitting both symptoms. Adding fever (622) to the atomistic terms gives 8 additional pathosoms: cryoglobulinemia with vasculitis, periarteriitis nodosa, microscopic polyangiitis, hanta virus infection, hemorrhagic fever with with renal syndrome, emphysematous pyelonephritis, infective glomerulonephritis and acute interstitial nephritis.

Examples of pathosoms fitting the cases 1–4

Case 1: RAEB (refractory anemia with excess of blasts)

As an example, we have chosen the disease entity of RAEB being a subset of myelodysplasia. This entity is described by two pages (pp100–101, 22] in the WHO classification of tumors of hematopoietic and lymphoid tissue [35]. The ICD-10-GM coding is D46.2. The ICD-O-coding 9983/3. The first sentence is: refractory anemia with excess blasts is a myelodysplastic syndrome (MDS) with 5–19% myeloblasts in the bone marrow (BM) or 2–19% blasts in the peripheral blood. In our system, this sentence is transformed in a vector with atomistic terms: c (refractory anemia, myeloblasts: bone marrow > 5%: AND: myeloblasts: bone marrow < 20%) OR (blasts: peripheral blood > 2%). The whole Boole term must be TRUE. This is an example of generating pathophemes in the pathosom RAEB. The coded version of this sentence is: M3094|; (O812| & 10209|: 5..20%) | (10221 T| &10209 T|: 2..20%). For details see also Additional file 1: Tables S1 to S3.

Case 2: invasive breast cancer

Invasive breast cancer is a set of clinical and morphological diseases with a hierachical structure. There are 68 pathosoms of breast cancer including terms like non-invasive breast cancer, inflammatory breast cancer (see Additional file 1: Tables S5 and S6) or tubular carcinoma of breast, which are structured in a tree (“is-element”, “has-element”, “has-variant”). One example of these 68 pathosoms (inflammatory breast cancer) is given in Table 3 and Additional file 1: Tables S5 and S6.
Table 3

Part of the pathosom breast cancer with concern to anamnesis and examination by the physican (Vital)

Note that “tastbarer Knoten is coded by 10787Z and this code includes lump and palpable node

Case 3: genetic disorder of skeletal disease

For this example, we used the Meckel syndrome type IV (see Table 4) [36, 37]. The atomistic terms are coded. If different codes are available from different thesauri the selection of the assignment is random. As shown in Table 1, 6 different synonyms or codes (such as the OMIM code 612284) are provided in our system allowing to collect further information.
Table 4

Meckel syndrom VI as an example of a pathosom

Case 4: membrano-proliferative glomerulonephritis (MPGN) (see Table 5)

Classification of MPGN is a set of three subsets termed MPGN Type I, MPGN type II and MPGN type III [38]. The ICD coding is N05.5, N04.5, N03.5, N02.5, and N01.5 (see Additional file 1: Tables S7 and S8).
Table 5

Note that each pathosom is part of a hierarchical tree with superset and subsets

Note that syntactic differences or language problems are not of interest as “Nierenzyste”, “Nierencyste”, “renal cyst” and “renal cysts” are all coded by M5065

Description of Memem7

Thesauri

The thesauri used in our system are listed in Table 2. For the example RAEB (refractory anemia with excess of blasts) we have one alpha-numeric code ICD-10 D46.2. In the other thesauri RAEB is not mentioned. In the ICD-O classification [23], however, this disease entity is mentioned by the term refractory cytopenia with multilineage dysplasia. For the second example with genetic caused skeletal disorder different ICD-10 codes are available (ICD-10 Q65.–Q79.9). With the tm text analysis system of R, we obtained a list of 53947 atomized terms. 16798 (31.1%) of these ICD-10 terms were originally not part of memem7 and were integrated in the system. Mallet finger (Hammerfinger in German) is part of ICD-10 (M20.0), but originally not of Memem7. Tm identifies the term Hammerfinger as element of ICD-10 and this term was added to Memem7 (including its counterpart in English). The number of diagnoses in the ICD-10 code (not signs, symptoms) was estimated to ~ 9700.

Number of pathosoms and pathophemes

By now approx. 4600 pathosoms (prototypic diseases) with 104.200 pathophemes (prototypic symptoms) are included in our system. For writing the atomized pathophems we provide terms taken from different thesauri (see Table 2). In total, there are approx. 230.000 terms coded out of 1.550.000 terms including German, English and Latin in different grammatical forms. Therefore, Memem7 covers ~ 4600/9700 (47.2%) of the diagnoses of the ICD-10 classification system. We assume that each pathosom consists of at least 10…100 pathophemes depending of the complexity of the disease.

Comparison of pathosom and diagnosis

A pathosom is a data model which describes knowledge about a disease in a structured but flexible, non-deterministic way. It also may be considered as a vector of pathophems, each of them being TRUE (1), FALSE (0), or by an evidence between 0 and 1. A diagnosis is either an alpha-numeric code such as a ICD-10 code [23, 24, 35] or a written description of a disease such as refractory anemia with an excess of blasts (RAEB) [35]. RAEB exists in three variants, termed RAEB-1, RAEB-2 and RAEB-F, being subsets of the RAEB pathosom. A pathosom is a vector of pathophems. RAEB is a vector with 75 elements (so far) (see Additional file 1: Tables S1 to S4) with three variants. Each pathosom is part of a tree structure. RAEB has three subsets: RAEB-1, RAEB-2 and RAEB-F and is an element of the pathosom myelodysplasia. The tree structure is one of the basic structure for the semantic network and provides “has-attribute” relationships.

Evaluation of the system

In 90/190 (47.4%) artificial cases the proposed diagnosis was identified by memem7 (47.6%). The number of proposed pathosoms (diagnoses) ranged between 0 and 173 with a median of 3.

Discussion

Nowadays, it is common sense that both computational linguistics and computer technology will change the practice of medicine fundamentally [39]. However, the evidence, that this change the quality of medicine will improve is sparse [18].

Here, we describe a software system consisting of a pool of pathosoms (disease entities, syndromes, symptoms or sets of disease entities). The goal of this publication is to demonstrate that with some prerequisites a computer-assisted assignment of symptoms (in our preliminary system a 5n tuple) to certain disease entities is possible. The prerequisites are (1) linguistic knowledge is delivered atomistic (2) each pathosom (prototypic disease entity) is considered as a vector (3), and the results of the analysis should be a linguistic game assisting classification of a patient vector to different pathosoms without the use of defined mathematical methods or a distance measure. After the search function of Memem7 a list of possible pathosoms explaining the patient’s complaint is given, so far, with focus on linguistic and semantic network heuristics.

For a coming assistance system of decision making in medicine three different tools are mandatory: (1) an electronic history taking which can be transformed in a patient data vector [35] or extracts of electronic health care systems (2) a set of disease vectors (in our system called pathosoms) each reflecting a prototypic disease vector or an algorithms for a disease entity. (3) an algorithm comparing the patient vector with all pathosom vectors giving a ranking of possible pathosoms fitting to a given patient vector (inference engine).

Our system termed Memem7 allows today: (1) to construct a prototypic disease vector (2) to have a restricted dialogue with a 5n-tuple of a patient vector. Restricted dialogue means that, so far, only 5 tuples can be analyzed by Memem7. After this dialogue, the user is provided with n pathosoms, which fit to the 5-tuple patient vector. The goal of our approach was to demonstrate the feasibility of a knowledge collection in a formalized way.

The mathematical methods used in big data analysis will be of further interest for improving the tools allowing machine assisted medical treatment decision as described by Sun and coworkers, Slack and coworkers and Huang and coworkers [4042]. Our system covers approximately 4600 medical diagnoses (increasing constantly). Many trials of electronic history taking, even in very sophisticated technical settings as described by Morrison and coworkers [43] are published (70 up to 2010) ([3, 4], for review [5, 16]). However, so far, as the authors survey the literature only few open source application of an electronic history taking system are available such as OpenEMR and OpenEHR [44, 45]. In our linguistic game these histories taking systems were replaced by a 5n-tupel. The term linguistic game has some fundamental implications (1) error is part of any game (2) decision can be assisted, but not replaced by a linguistic game (3) a player can improve his results by improving his knowledge (in our system the disease vector of the pathosoms). Therefore, our system may be considered as a third opinion to a given patient vector with so far 5 tuples. Combining memem7 with tools recognizing medical texts like MetaMap [20] or cTake [21] are currently in work.

Can we give any data for accuracy of memem7? In a first testing of the system with up to 5 tuple of search terms we identified the proposed diagnosis of 190 artificial cases for 46.7%. The data available for assessment of the sensitivity for clinical decision support system (CDSS) are sparse and difficult to compare. In one recent publication of Müller and coworkers [46] a sensitivity of 96% was claimed, when case reports of the New England Journal of Medicine were used for the evaluation of the systems (DXplain, Isabel Healthcare, Diagnosis Pro, PEPID) [4750]. Our much less impressive data will improve with each new test as the mistakes found in the test are corrected after completing the test. Therefore, the tests are part of a continuous improvement quality circle which provides algorithms to enhance both sematic network and pathophemes (disease symptoms).

What are the differences of Memem7 to other existing medical decisions systems like e.g. HELP [18]. There are different approaches for use of computer-technology in CDSS such as probabilistic, Bayesian approaches or machine learning systems. We add a system based on atomized linguistic terms. It may be possible to combine probabilistic and linguistic approaches. The advantage of a linguistic system is that atomized terms (pathophems) can be easily added to a disease entity. The comparison between a linguistic and a probabilistic approach or its combined use is planned on basis of the next software version.

There are already many systems and tools presented for biomedical text mining and biomedical concept recognition [51, 52]. The systems mainly focus on natural language processing and concept identification through dictionary matching and various machine learning mechanisms. In contrast to these approaches Memem7 is not focused on identifying concepts in natural languages but rather on using such concepts for identification of other complex objects like health disorders. In future, the system might also be able to handle the pre-process of concept identification of written data e.g. medical patient reports. Technically Memem7 is dealing with the problem of matching ambiguous fact concepts (like symptoms, medical events, lab data) with complex concept trees (like anatomy, medical body functions, diseases). Because of the plurivalent nature of the concepts and the heuristic of the algorithms the matching processing is more like a game than a deterministic process.

Conclusions

In conclusion, we present an electronic system of disease entities, which can enter in a formalized dialog with a patient data vector as proof of concept of a medical navigation system based on a linguistic game.

Abbreviations

ATC: 

Anatomical therapeutic chemical classification

CAS: 

Common chemistry data base

CDSS: 

Clinical decision support system

DIMDI: 

Deutsches Institut für medizinische Dokumentation

EC: 

Brenda, comprehensive enzyme information system

HGNC: 

Hugo gene nomenclature committee 2015)

HTML5: 

Hypertext markup language

ICD-10: 

International classification of diseases, injuries and causes of death

ICD-O: 

International classification of diseases, oncology

LOINC: 

Logical observation identifiers and names

MPNG: 

Membrano-proliferative glomerulonephritis

OMIM: 

Online Mendelian Inheritance in Man

OPS: 

Operationen-und Prozedurenschlüssel-Internationale Klassifikation der Prozeduren in der Medizin 2015

ORPHANET: 

Portal für seltene Krankheiten und Orphan Drugs

RAEB: 

Refractory anaemia with excess blasts

TA: 

Taxonomy of anatomy

WHO: 

World Health Organization

Declarations

Acknowledgements

We appreciate the long-standing and ongoing support of the Robert Bosch Foundation for the improvement of medical practice.

Funding

Supported by the Robert Bosch Foundation Stuttgart, Germany.

Availability of data and materials

Availability of data and materials: the system is in a proof-of-concept state and provides a limited test environment access. The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Authors’ contributions

PF was responsible for the input of medical knowledge, conceptual work, testing the system, preparing of the manuscript. AK was responsible for writing of the software, conceptual work and preparing of the manuscript. PA was engaged in writing of software and conceptual work. FK achieved the input of orphan diseases. CF was testing the system and worked on the input of medical knowledge. MDA achieved the conceptual work and was engaged in preparing the manuscript. All authors were involved in the reading of the manuscript and gave intellectual input to all stages of the preparation of the manuscript. All authors read and approved the final manuscript.

Competing interests

Dr. Peter Fritz: No competing interest.

Dr. Andreas Kleinhans: No competing interest.

Dipl-Inf. Patrick Albu: No competing interest.

Florian Kuisle: No competing interest.

Dr. Christine Fritz-Kuisle: No competing interest.

Prof. Mark Dominik Alscher: No competing interest.

Consent for publication

Not applicable.

Ethics approval and consent to participate

As no patient data are included in the publication, no ethic approval is necessary following the laws. All artificial patients are imagined by the help of textbooks of medicine. The ethical board of Baden-Wuerttemberg is responsible with bylaws stated in “Gesetz über das Berufsrecht und die Kammern der Ärzte, Zahnärzte, Tierärzte, Apotheker, Psychologischen Psychotherapeuten sowie der Kinder- und Jugendlichenpsychotherapeuten (Heilberufe-Kammergesetz – HBKG) in der Fassung vom 16. März 1995 (GBl. BW v. 17. Mai 1995 S. 314)”.

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Authors’ Affiliations

(1)
Department of Clinical Pathology, Robert-Bosch-Hospital
(2)
Department of General Internal Medicine and Nephrology, Robert-Bosch-Hospital
(3)
Klinikum Günzburg, Abteilung für Anästhesie

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© The Author(s). 2017