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R T U New York State Center of Excellence in Bioinformatics & Life Sciences Grand Rounds, Psychiatry Advancing Translational Research in Psychiatry through Realism-based Ontology UVM College of Medicine - Department of Psychiatry Fletcher Allen Health Care Burlington, VT, USA, August 15, 2008 Werner CEUSTERS, MD Center of Excellence in Bioinformatics and Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences 1977 1959 - 2008 2006 Short personal history 1989 2004 1992 2002 1995 1993 1998 R T U New York State Center of Excellence in Bioinformatics & Life Sciences Past work and research in Psychiatry • • 1990-1991 1990-1993 • 1990 • 1990 • 1991-1992 • • 1991-1999 1994-1998 • Ceusters W, Van de Wiele L, Van Moffaert M. Therapeutical value of an automated observation scale for psychiatric inpatients, in MIC-Proceedings 1990, Noordwijkerhout, The Netherlands, Nov 9-10, 1990;:212-23. Ceusters W, De Cuypere G, Jannes C, Hoes M, Pluymakers J. Rational and efficient use of computers in psychiatry: the AMDP as a European standard for psychiatric record systems ? in: Proc MIC 1991, Willems JL (ed.), 1991;:117-126. • • Head of Department of Neuropsychiatry, Military Hospital Soest, Germany. Consultant in Medical Informatics, Department of Psychiatry, University Hospital of Ghent. Egmond Prize for Medical Informatics (1990) (for automated observation scale for psychiatric in-patients). Dutch and Belgian Medical Informatics Conference Prize of the Belgian Society for Medical Informatics (for best paper and presentation). Member, Belgian Ministry of Health Working Group on the Registration of Psychiatric Patients. Neuropsychiatrist, Queen Astrid Military Hospital of Brussels. Member, Mental Health Standards Working Group of WHO, Division of Mental Health. Ceusters W, Smith B. Referent Tracking for Treatment Optimisation in Schizophrenic Patients. Journal of Web Semantics 4(3) 2006:229-36; R T U New York State Center of Excellence in Bioinformatics & Life Sciences Ongoing Research in Psychiatry • ‘UB Task Force for ontology-based IT support for large scale field studies in Psychiatry’ – Sponsor: John R. Oishei Foundation ($148,328) – Specific aims: • to assess the functional and technical requirements to be fulfilled by a data management system able to do justice to both the dimensional and categorical approach in psychiatric diagnosis; • to design an implementation and funding plan for the technical infrastructure to be built in order to support data collection and analyses in large-scale field studies in psychiatry, and; • to initiate the collaborations needed to deliver data collection and analyses services to provide the answers to the questions raised in the DSM-V research agenda. R T U New York State Center of Excellence in Bioinformatics & Life Sciences What has this research to do with translational medicine ? R T U New York State Center of Excellence in Bioinformatics & Life Sciences Presentation overview • • • • Some key aspects of Translational Research Philosophy & Psychiatry Ontology & Informatics Connecting the dots: a holistic approach to Evidence Based Medicine (in Vermont) R T U New York State Center of Excellence in Bioinformatics & Life Sciences I. Translational Research What is it ? R T U New York State Center of Excellence in Bioinformatics & Life Sciences Translational Research • Research in which ideas, insights, and discoveries generated through basic scientific inquiry are applied to the treatment or prevention of human disease. • Two categories: – T1: from ‘bench to bedside’ – T2: from bedside to community: enhance adoption of effective programs and practices R T U New York State Center of Excellence in Bioinformatics & Life Sciences Further distinctions translation to humans translation to patients translation to practice Westfall, J. M. et al. JAMA 2007;297:403-406. R T U New York State Center of Excellence in Bioinformatics & Life Sciences Translational Research & NIMH • Division of Developmental Translational Research – promotes an integrated program of research across basic behavioral/psychological processes, environmental processes, brain development, pediatric psychopathology and therapeutic interventions. • Division of Adult Translational Research and Treatment Development – understanding the pathophysiology of mental illness and hastening the translation of behavioral science and neuroscience advances into innovations in clinical care. • Geriatric Translational Neuroscience Program • Geriatric Translational Behavioral Science Program R T U New York State Center of Excellence in Bioinformatics & Life Sciences Translational psychiatry Cellular events behavior cognitive functionings • Forward translational psychiatry: – attempts to explain how neuronal activity, beginning at the molecular level, 'translates' to elicitation of behavior • Reverse translational psychiatry: – attempts to determine the molecular underpinnings that contribute to the expression of abnormal behavior. R T U New York State Center of Excellence in Bioinformatics & Life Sciences A key challenge: understanding how disorders at molecular level lead to disorders at mesoscopic level www.thefencingpost.com/mary/ www.williams.ngo.hu/ medgen.genetics.utah.ed u/.../pages/williams.htm http://www.williams-syndrome.org/ Williams Syndrome: a rare genetic disorder characterized by mild to moderate mental retardation or learning difficulties, a distinctive facial the ? overfriendliness appearance,Any and aideas unique about personality thatabove combines and high levels of empathy with anxiety. R T U New York State Center of Excellence in Bioinformatics & Life Sciences Another key challenge: multi-disciplinarity e.g.: Translational Research and the cause of Alzheimer Disease • • • • • • Disciplines mouse genetics cell biology animal neuropsychology protein biochemistry neuropathology … • • • • Hypotheses ADDL Amyloid cascade Alternative amyloid cascade … Difficult process Conflicting outcomes R T U New York State Center of Excellence in Bioinformatics & Life Sciences Barriers for Translational Research Sung NS et al. Central challenges facing the national clinical research enterprise. JAMA. 2003;289:1278–1287. R T U New York State Center of Excellence in Bioinformatics & Life Sciences Common approach: semantic annotation of scientific data <ms/ms_peak_list> <parameter instrument=“micromass_QTOF_2_quadropole_time_of_fli ght_mass_spectrometer” mode = “ms/ms”/> <parent_ion_mass>830.9570</parent_ion_mass> <total_abundance>194.9604</total_abundance> <z>2</z> <mass_spec_peak m/z = 580.2985 abundance = 0.3592/> <mass_spec_peak m/z = 688.3214 abundance = 0.2526/> <mass_spec_peak m/z = 779.4759 abundance = 38.4939/> <mass_spec_peak m/z = 784.3607 abundance = 21.7736/> <mass_spec_peak m/z = 1543.7476 abundance = 1.3822/> <mass_spec_peak m/z = 1544.7595 abundance = 2.9977/> <mass_spec_peak m/z = 1562.8113 abundance = 37.4790/> <mass_spec_peak m/zms/ms = 1660.7776 abundance = Annotated peaklist data 476.5043/> <ms/ms_peak_list> Amit Sheth. Semantic Web Technology in Support of Bioinformatics for Glycan Expression. W3C workshop on Semantic Web for Life Sciences, October 28, 2004, Cambridge MA R T U New York State Center of Excellence in Bioinformatics & Life Sciences Lost in translation Various reporting formalisms and data formats Various levels of granularity R T U New York State Center of Excellence in Bioinformatics & Life Sciences Not the least in … psychiatry BTP: BTR: BPR: Barriers for translational psychiatry = barriers for translational research op? barriers for psychiatric research BTP = BTR+BPR or BTR*BPR or BTR(BPR) ? R T U New York State Center of Excellence in Bioinformatics & Life Sciences II. Philosophy & Psychiatry R T U New York State Center of Excellence in Bioinformatics & Life Sciences A little test: who said … • “Too little attention has been paid in psychiatric education and training to the philosophical underpinnings of our field, and we believe that many problems with the way in which psychiatry is both perceived from the outside and practiced from the inside are attributable to a lack of clarity - or simply an absence of thought - on this topic.” Waterman, GS. & Schwartz, RJ. The Mind-Body Problem. Letter to the Editor. Am J Psychiatry 159:878-879, May 2002 R T U New York State Center of Excellence in Bioinformatics & Life Sciences Ontology and Psychiatry • Ontology: – (roughly) the branch of philosophy that deals with what exists and with how the entities that exist relate to each other. – representing reality in IT systems • Psychiatry: – (roughly) the branch of medicine that deals with the diagnosis, treatment, and prevention of ‘mental and emotional disorders’. • Ontology applied to psychiatry: – Studying the nature of ‘mental disorders’ and their place in pathological anatomy and pathophysiology; – Finding better ways to build IT systems to support research in and practice of psychiatry. R T U New York State Center of Excellence in Bioinformatics & Life Sciences ‘Ontology’ as the study of what exists • Key questions: – What exists ? – How do things that exist relate to each other ? • Some hypotheses: – – – – An external reality, time, space Ideas, concepts Particulars, universals, objects, processes God • Ontologists from distinct ‘schools’ differ in opinion about the existence of some of the above: – Realism, nominalism, conceptualism, monism, … R T U New York State Center of Excellence in Bioinformatics & Life Sciences A parallel: some wonder whether ‘mental disorders’ exist The Antipsychiatry Coalition • ‘there are no biological abnormalities responsible for so-called mental illness, mental disease, or mental disorder, therefore mental illness has no biological existence. • Perhaps more importantly, however, mental illness also has no non-biological existence, • except in the sense that the term is used to indicate disapproval of some aspect of a person's mentality.’ Lawrence Stevens, J.D, 1999 R T U New York State Center of Excellence in Bioinformatics & Life Sciences The “Myth of Mental Illness” • “I maintain – that the mind is not the brain, – that mental functions are not reducible to brain functions, and – that mental diseases are not brain diseases, – indeed, that mental diseases are not diseases at all. • When I assert the latter, I do not imply that distressing personal experiences and deviant behaviors do not exist. Anxiety, depression, and conflict do exist--in fact, are intrinsic to the human condition-but they are not diseases in the pathological sense.” Thomas S. Szasz (MD), Mental Disorders Are Not Diseases. USA Today (Magazine) January 2000 R T U New York State Center of Excellence in Bioinformatics & Life Sciences Another parallel: the ‘categorical – dimensional’ debate on the classification of mental disorders • Rough distinction: – “Categorical”: ‘mental disorders’ can be classified as single, discrete and mutually exclusive types, of which a particular patient does or does not exhibit an instance. DSM – “Dimensional”: any particular ‘mental disorder’ in a patient is an instance of just one single type and differences between cases are a matter of ‘scale’. • ‘Rough’, because – the literature is huge and vague – descriptions are (philosophically) very incoherent R T U New York State Center of Excellence in Bioinformatics & Life Sciences DSM under fire • severely ill inpatients often meet criteria for more than one DSM-IV personality disorder; • many outpatients do not meet the criteria for any of the specific categories identified in DSM-IV; • patients with the same categorical diagnosis often vary substantially with respect to signs and symptoms; • frequent revision of the diagnostic thresholds separating what is normal from what is disordered; • a number of the diagnostic categories mentioned in DSMIV lack any developing scientific base for an understanding of the corresponding disorder types. R T U New York State Center of Excellence in Bioinformatics & Life Sciences Medical Models and the Dimensions of Categorization • ‘Theories about […] psychiatric disorders […] often contain a range of assumptions about what counts as real, valid, relevant, and useful. They also often assume different notions about the nature of causal processes in psychiatric illness.’ Zachar P, Kendler KS. Psychiatric Disorders: A Conceptual Taxonomy. Am J Psychiatry 2007; 164:557–565 R T U New York State Center of Excellence in Bioinformatics & Life Sciences But: some dimensionalists also use flawed arguments • “Diagnostic categories defined by their syndromes should be regarded as valid only if they have been shown to be discrete entities with natural boundaries that separate them from other disorders.” Kendell R, Jablensky A. Distinguishing between the validity and utility of psychiatric diagnoses. Am J Psychiatry 2003; 160:4–12. • “there is no empirical evidence for natural boundaries between major syndromes” … “the categorical approach is fundamentally flawed” Cloninger CR: A new conceptual paradigm from genetics and psychobiology for the science of mental health. Aust N Z J Psychiatry 33:174–186, 1999. R T U New York State Center of Excellence in Bioinformatics & Life Sciences Is there empirical evidence for this boundary ? And if not, do these mountains exist ? R T U New York State Center of Excellence in Bioinformatics & Life Sciences Key issue: constructs & reality www.perseus.tufts.edu/.../Hpix/1992.06.1227.jpeg mcgonnigle.files.wordpress.com/.../lightning.jpg Just as what used to be seen as Zeus’s thunderbolts can still be lethal, what is currently referred to as mind can certainly be—and clearly is—causally efficacious. Waterman, GS. & Schwartz, RJ. The Mind-Body Problem. Letter to the Editor. Am J Psychiatry 159:878-879, May 2002 R T U New York State Center of Excellence in Bioinformatics & Life Sciences Attempts to resolve the problem (1) • Mental disorders as ‘practical kinds’ – ‘stable patterns that can be identified with varying levels of reliability and validity’ and which are justified by their usefulness for specific purposes – such as giving an appropriate treatment Zachar, P. 2000b. Psychiatric disorders are not natural kinds. Philosophy, Psychiatry and Psychology 7:167–94. R T U New York State Center of Excellence in Bioinformatics & Life Sciences Basis: ‘epistemic value commitments’ • ‘values involved in making and advancing epistemologically-relevant claims, such as scientific ones’: Coherence Instrumental efficacy Consistency Originality Comprehensiveness Relevance Fecundity Precision Simplicity JZ. Sadler. Epistemic Value Commitments in the Debate over Categorical vs. Dimensional Personality Diagnosis. Philosophy, Psychiatry, & Psychology 3.3 (1996) 203-222 R T U New York State Center of Excellence in Bioinformatics & Life Sciences Attempts to resolve the problem (2) Non-arbitrary basis for drawing a categorical distinction No Yes Non-kind ‘severity’ ‘neuroticism’ This basis is an objective discontinuity No Yes Practical kind ‘essential hypertension’ ‘depression’ Haslam N. Kinds of Kinds: A Conceptual Taxonomy of Psychiatric Categories. Philosophy, Psychiatry, & Psychology, 9 (2002), 203-218 The discontinuity is sharp and binary No Yes Fuzzy kind ‘borderline personality’ The discontinuity is constituted by an ‘essence’ No Yes Discrete kind ‘melancholia’ Natural kind ‘Williams syndrome’ R T U New York State Center of Excellence in Bioinformatics & Life Sciences DSM-IV-TR currently plays it both ways • “In DSM-IV, there is no assumption that each category of mental disorder is a completely discrete entity with absolute boundaries dividing it from other mental disorders or from no mental disorder” • “DSM-IV is a categorical classification that divides mental disorders into types based on criterion sets with defining features” Diagnostic and Statistical Manual of Mental Disorders, 4th Edition, Text Revision [DSM-IV-TR]; AmericanPsychiatric Association [APA], 2000, p. xxxi). R T U New York State Center of Excellence in Bioinformatics & Life Sciences … but asks for research in preparation of DSM-V • Some desiderata: 1. generate acceptable definitions for mental disorder; disease and illness; 2. provide a framework for validating the correctness of assignments of instances to disorder categories; 3. provide assessment of the arguments to the effect that a dimensional view is needed in addition to the categorical view; 4. reduce the discrepancies between DSM-V and ICD-11; 5. ensure that DSM-V can be used cross-culturally; 6. ensure that DSM-V can be used in non-psychiatric settings. Kupfer DJ, First MB, Regier DA (eds.) A Research Agenda for DSM-V. American Psychiatric Association 2002. R T U New York State Center of Excellence in Bioinformatics & Life Sciences … but asks for research in preparation of DSM-V • to establish, among many other things, – under which circumstances one or the other of the two views should be adopted, – the categories which will then need to be recognized, and – the thresholds for associated criteria. • The proposed research is to be based on large scale crosscultural clinical, genetic, pathophysiologic, etiologic and outcome assessments, and thus requires the collection of vast amounts of data of diverse sorts. R T U New York State Center of Excellence in Bioinformatics & Life Sciences III. Ontology and Informatics R T U New York State Center of Excellence in Bioinformatics & Life Sciences Three major views on reality Realism • Basic questions: – What does a general term such as ‘psychosis’ refer to? – Do generic things exist? Conceptualism Nominalism Universal Concept Collection of particulars yes: in particulars perhaps: in minds no R T U New York State Center of Excellence in Bioinformatics & Life Sciences Types of realism • Naive realism: – things really are as they seem • Scientific realism: – things really are as science determines (or ultimately will determine) them to be – science discovers objective truths R T U New York State Center of Excellence in Bioinformatics & Life Sciences Scientific Research (realism-based) Reality universals particulars William M.K. Trochim. Idea of Construct Validity. Research Method Knowledge Base 2006. http://www.socialresearchmethods.net/kb/considea.php R T U New York State Center of Excellence in Bioinformatics & Life Sciences Realism-based ontology • Basic assumptions: 1. reality exists objectively in itself, i.e. independent of the perceptions or beliefs of cognitive beings; 2. reality, including its structure, is accessible to us, and can be discovered through (scientific) research; 3. the quality of an ontology is at least determined by the accuracy with which its structure mimics the pre-existing structure of reality. R T U New York State Center of Excellence in Bioinformatics & Life Sciences Three levels of reality in Realist Ontology Representation and Reference representational units (3) Representational units in various forms about (1), (2) or (3) cognitive units communicative units universals particulars (2) Cognitive entities which are our beliefs about (1) (1) Entities with objective existence which are not about anything First Order Reality R T U New York State Center of Excellence in Bioinformatics & Life Sciences Realist ontology: a modern version of Alberti’s grid ! R T U New York State Center of Excellence in Bioinformatics & Life Sciences Application in preparation of DSM-V (1) • For each ‘mental disorder’ – Express the criteria in terms of the core ontological entities and their possible co-occurrence in concrete cases • For each particular case (‘disorder in patient’) – Describe the case using the core ontological entities and their actual co-occurrence, i.e. • Assign IUIs • Express in RT-formalism R T U New York State Center of Excellence in Bioinformatics & Life Sciences Application in preparation of DSM-V (2) • Create an adequate IT infrastructure: – For case registration: RTU-based electronic patient record – For data collection: RTU back-end • Use the DSM-criteria as hypotheses that need to be validated on the basis of the data collected, and adjust when needed. R T U New York State Center of Excellence in Bioinformatics & Life Sciences IV. Connecting the dots in Vermont R T U New York State Center of Excellence in Bioinformatics & Life Sciences Vermont Health Information Technology Plan • Core objectives for 2007-2012: – increase the amount of health information that exists in electronic form, i.e. in electronic health record systems – achieve a secure electronic health information exchange to achieve the plan’s vision. – empower consumers to take an active role in electronic health information initiatives in Vermont. – enable public health agencies to use HIT to monitor and ensure the public’s health more transparently and quickly R T U New York State Center of Excellence in Bioinformatics & Life Sciences Some key choices identified in VHITP • Appropriate national standards for technical, semantic, and process interoperability; – But cave: too many “standards” for semantic interoperability exhibit one or more problems: • • • • very superficial semantics bad design no solid foundation (lack a serious benchmark) not compatible with other standards • A hybrid technology architecture with both centralized and distributed data service components; R T U New York State Center of Excellence in Bioinformatics & Life Sciences Adoption of the (Chronic) Care Model Wagner EH, Austin BT, Davis C, Hindmarsh M, Schaefer J, Bonomi A. Improving chronic illness care: translating evidence into action. Health Aff (Millwood). 2001;20:64 R T U New York State Center of Excellence in Bioinformatics & Life Sciences CCM: Clinical Information Systems • Provide reminders for providers and patients • Identify relevant patient subpopulations for proactive care • Facilitate individual patient care planning • Share information with providers and patients • Monitor performance of team and system R T U New York State Center of Excellence in Bioinformatics & Life Sciences ACIC 3.5: Clinical Information Systems Scores Components D Level C Level B Level A Registry …includes name, diagnosis, contact information and date of last contact either on paper or in a computer database. …allows queries to sort subpopulations by clinical priorities. …is tied to guidelines which provide prompts and reminders about needed services. Reminders to Providers … include general notification of the existence of a chronic illness, but does not describe needed services at time of encounter. …includes indications of needed service for populations of patients through periodic reporting. …includes specific information for the team about guideline adherence at the time of individual patient encounters. Feedback …is provided at infrequent intervals and is delivered impersonally. …occurs at frequent enough intervals to monitor performance and is specific to the team’s population. …is timely, specific to the team, routine and personally delivered by a respected opinion leader to improve team performance. Relevant Subgroups of Patients Needing Services …can only be obtained with special efforts or additional programming. …can be obtained upon request but is not routinely available. …is provided routinely to providers to help them deliver planned care. Patient Treatment Plans …are achieved through a standardized approach. …are established collaboratively and include self management as well as clinical goals. …are established collaborative an include self management as well as clinical management. Follow-up occurs and guides care at every point of service. R T U New York State Center of Excellence in Bioinformatics & Life Sciences VITL's EHR Pilot Project Vermont Information Technology Leaders Inc.’s Pre-Screened EHR Product List R T U New York State Center of Excellence in Bioinformatics & Life Sciences Getting it all: Referent Tracking Portion of Reality Entity Configuration represents Universal Particular contains is about Non-referring particular Information bearer corresponds-to Representation RT-tuple Defined class Representational unit Denotator denotes CUI IUI UUI denotes denotes R T U New York State Center of Excellence in Bioinformatics & Life Sciences Referent Tracking System Components • Referent Tracking Software Manipulation of statements about facts and beliefs • Referent Tracking Datastore: • IUI repository A collection of globally unique singular identifiers denoting particulars • Referent Tracking Database A collection of facts and beliefs about the particulars denoted in the IUI repository Manzoor S, Ceusters W, Rudnicki R. Implementation of a Referent Tracking System. International Journal of Healthcare Information Systems and Informatics 2007;2(4):41-58. R T U New York State Center of Excellence in Bioinformatics & Life Sciences Referent Tracking System Environment User User External Information System Referent Tracking System IUI Component RTS Proxy Peer Referent Tracking System User Interface(s) Referent Tracking Server (Peers) Reasoning Server Referent Tracking Data Access Server RTS Server Proxy Peer Internal Ontology Referent Tracking Data Store Terminology Server Vocabulary or Thesaurus or Nomenclature or Concept System or Realism-based Ontology R T U New York State Center of Excellence in Bioinformatics & Life Sciences Summary • Translational Research is a must, but has many barriers, especially in psychiatry. – Key issue: the problem of “emergence” • ‘mind’ emerging from brain activities • Philosophy, especially realism-based ontology, provides methods and techniques to deal with the issues. • Referent Tracking, as an implementation of realism-based ontology, is able to keep track of how scientific insights and individual care evolve over time. • Key players in Vermont understand what is at stake and are moving in the right direction. • Vermont is thus an ideal environment for crossfertilization. R T U New York State Center of Excellence in Bioinformatics & Life Sciences Additional slides R T U New York State Center of Excellence in Bioinformatics & Life Sciences Reality-based mapping of care assessment instruments BFO U16 Class-relations U1 U17 U2 U9 U8 U3 MDS Ontology U14 U7 U10 U4 U12 U6 U5 U13 U11 MDS terms MDS1 MDS2 MDS3 MDS4 MDS5 MDS6 … State R T U New York Adding the other assessments instruments Center of Excellence in Bioinformatics & Life Sciences BFO U16 U1 U17 U2 U9 U8 U3 U… U7 U10 U4 U12 U6 U5 MDS1 MDS2 OPO Ontology (MDS + CARE +…) U14 U13 U11 … MDS3 MDS4 MDS5 MDS terms MDS6 … New York State R T U Adding the other assessments instruments CARE terms Center of Excellence in Bioinformatics & Life Sciences U15 U1 U17 U2 U… U9 U8 U3 BFO U16 U7 U10 U4 U12 U6 U5 … MDS1 MDS2 OPO Ontology (MDS + CARE +…) U14 U13 U11 … MDS3 MDS4 MDS5 MDS terms MDS6 … R T U New York StateU ‘Poor man’s’ data linkage 16 Center of Excellence in Bioinformatics & Life Sciences U1 U17 U2 U9 U8 U3 U… U7 U10 U4 U12 U6 U5 MDS1 MDS Ontology U14 U13 U11 … MDS2 MDS3 pt4 MDS4 MDS5 pt3 MDS terms MDS6 … Patient data R T U New York State Center of Excellence in Bioinformatics & Life Sciences Data linkage using multiple instruments CARE terms U15 1 A R E s C X C A R E X U1 U17 C U3 X U7 U10 U12 4 C X U6 X X MDS1 X … MDS3 MDS2 MDS4 X X X X X X U13 U11 U5 … OPO Ontology (MDS + CARE +…) U14 U4 A R E U9 U8 3 E U2 A R X U… 2 nt tie a P BFO U16 X X MDS5 X MDS terms MDS6 … Patient 1 Patient 2 Patient 3 R T U New York State Center of Excellence in Bioinformatics & Life Sciences Problems with this level • Exclusive focus on universals, ignoring that in data collection (almost) everything is about particulars. • Therefore Referent Tracking must be brought in the picture. R T U New York State Center of Excellence in Bioinformatics & Life Sciences Referent Tracking solves this problem: • It is true that: – (1) ‘All Americans have one mother’ – (2) ‘All Americans have one president’ • But: – (1) ‘all Americans have a distinct mother’ – (2) ‘all Americans have a (numerically) identical president’ R T U New York StateU From ‘poor man’s’ to 16 Center of Excellence in ‘rich man’s’ data linkage Bioinformatics & Life Sciences U1 U17 U2 U9 U8 U3 U7 U10 U4 U12 U6 formula MDS1 MDS Ontology U14 U5 U13 U11 MDS terms MDS2 MDS3 pt4 MDS4 MDS5 pt3 MDS6 … Patient data R T U New York State Center of Excellence in Bioinformatics & Life Sciences Rich man’s data linkage: focus on particulars U6 U6 MDS3 pt4 U11 U11 MDS4 Instance-of IUI-1 IUI-2 IUI-3 IUI-4 Particular relations pt3 pt4 pt3 IUI-5 R T U New York State Center of Excellence in Bioinformatics & Life Sciences Many more combinations possible U6 IUI-1 IUI-2 pt4 IUI-3 U6 U11 IUI-4 IUI-5 pt3 • The terms used in MDS4 denote distinct particulars related to both patients IUI-1 IUI-3 pt4 IUI-2 U11 IUI-5 pt3 • One of the terms used in MDS4 denotes the same particular for both patients R T U New York State Center of Excellence in Bioinformatics & Life Sciences Many more combinations possible • If the same MDS (containing several referring terms) applies to different patients at t1, either – All terms denote always distinct particulars • ‘patient is able to recall what he did yesterday’ – Some terms denote the same particular • ‘patient is able to remember who he met yesterday’ • If the same MDS applies to the same patient at distinct times: – Some terms may/may not denote the same particular • ‘patient recognizes his room mate’ • If the same term occurs in distinct MDS – May/may not denote the same particular (at any time)