About Aaushi

From Aaushi
Jump to navigation Jump to search

One who is careful, knowledgeable and blessed with a long life.

Aaushi is a free, web-based, intelligent medical reference for inpatient and outpatient diagnosis and management of patients in internal medicine and geriatrics. The reference is organized to provide rapid access to specific information at the point of patient care. It is a tool that works in the clinic, at the bedside, in lectures, or in study sessions for board examinations.

Contact: aaushiLLC@gmail.com

About the author:

Sheldon S Ball, Ph.D, M.D.


M.D., University of Miami, 1983
Ph.D., University of California at Davis, 1978, Chemistry
B.S., University of California at Davis, 1974


Board Certification in Clinical Pathology, 1989
Board Certification in Internal Medicine, 1999-2029
Board Certification in Geriatrics, 2002-2022


Los Angeles Jewish Home for the Aging

About the evolution of Aaushi:

Aaushi began in 1988 from a project started as a medical informatics fellow at Yale University. The project initially focused on knowledge representation in the domain of molecular pathology, especially on diseases of old age. Over the next decade, it became apparent that the success of medical knowledge was ultimately driven by the delivery of patient care and that there was an opportunity within informatics to translate developments in molecular pathology to delivery of patient care at the point of patient contact. Below, we take you through the story of the evolution of Aaushi. In the final paragraph, we describe the rationale for this MediaWiki interface.

Aaushi is a functional prototype addressing:

 1) representation of medical & biological information,
 2) presentation of data  
 3) reasoning with this information.  

It is written in the robust, portable object-oriented programming environment of Common Lisp. This provides a single development environment, from prototyping to application delivery on multiple platforms.

Aaushi links presentation of an object directly to its semantics. This facilitates the separation of the internal representation of objects from the presentation of information to users. Aaushi facilitates both the translation of user input into methods manipulating Aaushi objects & the presentation of the data represented in Aaushi objects to the user. Thus the interface may be designed to optimize user friendliness and data presentation without compromising features of the underlying Common Lisp representation.

Aaushi development began as a project in knowledge representation. In particular, how can the following questions be addressed in the form of computer representations?

 - What are the essential features of elements that give rise to 
   the formation of molecules & ions?
 - What are the essential features of biomolecules that give rise 
   to their function within cellular compartments & individual 
 - What are the essential features of intra- & intermolecular 
   interactions that participate in molecular pathways & cascades?
 - What are the features of pathways & cascades that facilitate 
   cellular metabolism, cell division, & cell death?
 - What are the features of cells that give rise to their 
   interactions with other cells, within tissues, organs & 
   organ systems?
 - What are the features of diseases that are essential to 
   understand for the practice of medicine & how are those 
   features related to changes in organ system, organ, tissue, 
   cellular, compartmental & molecular function?

The questions addressed in knowledge representation must also accept that uncertainty will always be associated with scientific data. The uncertainty may be quantitative or qualitative in nature. Qualitative uncertainty shares representational issues with generalizations [2], the distinction being especially important when reasoning with information.

The object-oriented paradigm of the Common Lisp Object System (CLOS) [6] provides a means of generating structures of arbitrary complexity and also a means of organizing information within a classification structure. The CLOS classification* within Aaushi serves several functions, including:

 - structure for representation of biological information, 
 - inheritance of properties common to similarly classified 
 - customized operations through methods defined on specific 
 - representation of qualitative uncertainty & generalization, 
 - entry points for specification of queries. 

Along with the classification structure of Aaushi (type-of relationships) there is a component-of hierarchy for use with anatomy & histology.

Extensive use of object-oriented techniques has been employed in the development of Aaushi [1,2,3,5]. These include object- oriented user interface techniques and the metaobject protocol [4]. The metaobject protocol provides a framework for the system to examine its own behavior (introspection) & modify it to achieve a desired effect. This facilitates development of an application tailored for knowledge representation in multiple domains.

Objects have slots which provide a means of describing an object in detail. Slots are specialized descriptions of objects defined with the most generalized class to have that attribute or property. For example, molecule has slots 'compartment' & 'size'; molecular complex has the additional slot 'subunits'. Slots may assume default values specified with the class definitions & most slot values are themselves objects or lists of objects. Slots & their default slot values are inherited through the classification structure [4]. The inherited slot default values may be accepted as is or may be further specified. Classes of objects may have multiple supertypes so their properties may be determined by inheritance of multiple slot default values.

Slot default values provide a means of programming biological knowledge into Aaushi. Slots are defined in Aaushi as logical 'AND' or logical 'OR' slots. For example, the slot 'compartment' is a logical 'OR' slot, in that a single molecule may be located in the cytoplasm or nucleus, but not both at the same time. In contrast, the slot 'motif' is a logical 'AND' slot. Proteins contain structural elements that give rise to function(s) of the molecule. Most proteins consisting of a single polypeptide can be represented as an ordered set of motifs connected by peptide regions. These peptide regions may be further organized into domains. This representation of proteins facilitates the representation of molecular interactions. Genes are represented through similar considerations.

One of the useful features of Aaushi is the display of molecular structure. Small molecules are simply drawn from methods defined on specific classes of molecules. Proteins are drawn similarly using methods defined on specific classes of proteins. These methods use symbolic representations & generate line drawings that illustrate protein motifs & domains containing the motifs. The drawings themselves contain selectable regions to show further detail. Proteins containing multiple transmembrane domains are also drawn using similar but more complex methods, taking into account loop size, cytoplasmic domain & exoplasmic domain size & intramolecular bonds across loops. Molecular complexes are drawn using methods that align subunits & draw intra- & intermolecular bonds. Genes are drawn using methods that show structural features of the 5'enhancer region, 5' promoter region, introns & exons & 3' region. Methods for drawing gene clusters have also been implemented.

Small molecules, proteins, molecular complexes & macromolecular complexes participate in molecular events, non-covalent bindings & chemical reactions (enzymatic & template-directed). These events may be linked in series to form molecular pathways or cascades. Molecular pathways cross-talk with other pathways in signaling networks. Aaushi contains structures to hold detailed information about molecular events & molecular pathways & to display this information in a graphical format. Display of molecular events is facilitated by methods that draw the events from symbolic representations of the substrate(s), product(s) & (if indicated) enzyme & template. Molecular pathways are drawn on an individual basis.

Sometimes it is known that a molecular event interacts with another, but the details of that interaction are not known. For example, phosphorylation of one protein may result in translocation of another to the nucleus (mediation) or inhibition of enzymatic activity of another protein phosphorylation (modulation). This type of information is often useful & Aaushi provides CLOS structures for capturing this information & utilizing it in analyses of signaling pathways.

Much of the structural framework for simulating signal transduction networks [6] is in place, representation of molecules, events (including kinetic parameters) & molecular interactions. Issues of generalization & uncertainty need special attention in the analysis of signaling pathways. Qualitative uncertainty is represented by using a sufficiently general class to describe a molecule. Generalization is represented by using the most general class of molecule to participate in a particular event (exceptions may be specified). A general class is interpreted by Aaushi to represent uncertainty, unless otherwise specified. Generalization is distinguished from uncertainty during the comparison of objects.

In parallel with the development of representation in molecular pathology, Aaushi provides a clinical component to integrate with molecular features of disease. This has been implemented as a point of patient contact medical reference for internal medicine and geriatrics, delivered as a MediaWiki web application www.aaushi.info. The goal has been to provide a familiar easy to use interface that allows rapidly focusing in on specific medical information needed in the clinic and at the bedside. A second website aaushi.org contains a functional lisp engine, facilitating real time computations such as differential diagnosis and various calculators. Both web applications have links to the underlying molecular pathology. A dedicated single user application serves as the development environment supporting many of the algorithmic graphical features described above.


1) Steele GL.

  Common LISP the Language. 2nd ed. Digital Press 1990

2) Ball SS, Mah VH.

  Senex: CLOS in molecular pathology. 
  Uncertainty, generalization, and the comparison of objects.
  In: Proceedings of the 4th Annual Lisp Users and Vendors 
  Conference; 1994 August 15-19; Berkeley, CA.

3) Keene SE.

  Object Oriented Programming in Common LISP, Addison-Wesley, Reading MA, 1989. 

4) Kiczales G.

  The Art of the Metaobject Protocol, MIT Press, Cambridge MA,1991.

5) Gu H, et al.

  Representing the UMLS as an object-oriented database: modeling issues and advantages. 
  Journal of the American Medical Informatics Association 2000;(7):66-80.

6) Lindvall JM, Blomberg KE, Smith CI.

  In silico tools for signal transduction research. 
  Brief Bioinform 4(4):315-24, 2003