Timing Is Everything Temporal Reasoning in Medicine Temporal

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					    Temporal Reasoning and Planning in Medicine

      Temporal Reasoning
In Medical Information Systems (I)

      Yuval Shahar M.D., Ph.D.
      Time as Symbolic Tokens
• “Chest pain, substernal, lasting less than 20 minutes”
  (the INTERNIST-I system for internal-medicine diagnosis
  [Miller et al., 1982])
   – No ability to note [at least a potential] contradiction with “Chest
     pain, substernal, lasting more than 20 minutes”
• “Significant organisms isolated within the past 30 days”
  (The MYCIN system for infectious-diseases diagnosis and
• Answers are always Yes or No (Boolean predicates) and no
  further reasoning on the structure of the token is possible
• Typically, no transaction or valid times, only a duration
Fagan’s Ventilator Management (VM) System
                            (Fagan, 1980)

• Designed for management of patients on ventilators in ICUs

• Rule based; rules were specific to contexts (states)

• Explicit reasoning about time units, rates of change

• Context-specific classifications mapped parameters into
  a context-sensitive range of values (e.g., acceptable), enabling
  context-free rules and context-free aggregation of abstractions

• Expiration dates of parameters represented in a good-for slot
  => constants, continuous, volunteered, deduced parameter types

• Special state-change rules inferred a new context (mode)

• Data could not be accepted out of temporal order (no valid time)
                   Blum’s Rx System

• Analyzed time-oriented clinical databases to produce a set of
  possible causal relations
• Used two modules in succession:
   – A Discovery Module, for automated discovery of statistical relations
   – A Study Module, to rule out spurious correlations, by using a medical
     knowledge base and by creating and testing a statistical model of the
• Applied to data in the Stanford American Rheumatic
  Association Medical Information System (ARAMIS), which
  evolved from the Time Oriented Database (TOD)
• ARAMIS and TOD are three-dimensional historical databases
   – a patient ID indexes <patient visit, clinical parameter, parameter value>
       Data Representation in Rx
• Point Events (e.g., laboratory test)
• Interval Events (e.g., a disease spanning several visits)
   – required an extension to the TOD
• Used a hierarchical derivation tree: Event A can be defined in
  terms of events B1, B2; these can be defined by C11, C12, and
  C21, C22…
   – to assess a value, traverse its derivation tree and collect all values
• Sometimes a clinical parameter needs to be assessed when
  not measured (a latent variable), using proxy variables
• Time-dependent database access functions (delayed_action,
  delayed_effect, previous_value)
• Parameter-specific knowledge determined inter-episode gaps
  Downs’ Medical-record Summarization Program

• Designed specifically to automate the summarization of online medical records
• Like Rx, used the Stanford ARAMIS database
• A knowledge base represented two classes of parameters:
    – abnormal attributes (abnormal findings)
    – derived attributes (including diseases that can be inferred from data)
• Each abnormal attribute points to a list of derived attributes that should be
  considered if its value is True
• Inference based on a hypothetico-deductive approach: Data-driven hypothesis
  generation produces a list of potential diagnoses; discrimination among
  competing hypotheses follows, using positive and negative evidence
• Probabilistic, Bayesian reasoning: Prior likelihood ratios are updated by each
  relevant datum
• Temporal predicates used, such as “the last 5 creatinine values are above 2.0”
Down’s Summarization Program’s Knowledge Base

                                                       G L OM E R U L O N E P H R I T I S

                                                       N EP H R OT I C. SY N D ROM E
                    D ER I VED .AT TR I BU T ES
                                                       UT I
                                                       A CUT E . R EN A L . FA I L U RE

  ATT R I BU T ES                                      CH RON I C.REN AL .F AI L U RE
                                                       A ZOT E M I A
                                                       HE M A T URI A
                    A B NOR M AL . A T TR I B UT E S
                                                       H I GH . CH OL E S T ER OL

                                                       H I GH . CRE A T I NI N E

                                                       P ROT EIN U RI A

                                                       PY URIA
            De Zegher-Geets’ IDEFIX System

• Goal: Intelligent summarization of online patient records
   – Explain, for a given visit, all manifestations in that visit, given previous data
• Influenced by and extends Down’s summarization program
• Use the ARAMIS database, especially for systemic lupus
  erythematosus (SLE) patients
• Updated disease likelihoods by Bayesian odds-update functions
• Distinguished static medical knowledge from dynamic patient data
• Three levels of abstraction in the knowledge base’s ontology:
   – abnormal primary attributes (APAs) such as presence of protein in urine
   – abnormal states, such as nephrotic syndrome
   – diseases, such as SLE-related nephritis
 The IDEFIX Inference Methods
• Two phase inference:
   – Goal-driven strategy explains given APAs, using a list of
     complications of the current disease
   – Followed by a data-driven strategy that tries to explain remaining
     APAs using a Cover and Differentiate approach based on odds-
     likelihood ratios, similar to Downs’ program
• Unlike Downs’ program, used severity levels using severity
  fiunctions for manifestations as well as for states or diseases
• Time-Oriented Probabilistic Functions (TOPFs) returned
  conditional probability of disease D given manifestation M as
  a function of a time interval (e.g., duration of D)
   – Used only to compute positive evidence; did not use context, severity
A Time-Oriented Probabilistic Function (TOPF)


    of Lupus



                        1   x                                  365
                                Time since apparition of SLE
Russ's Temporal Control Structure [TCS] System

 • Manages data dependencies over time

 • Data driven

 • Supports decomposition of reasoning into static and dynamic parts

 • Performs the necessary bookkeeping needed to ensure propagation
   of information in the system as well as completeness in processing

 • A point-based approach with exact dating

 • Performs procedural abstractions

 • Supports hindsight as more data accumulate

 • Evaluated as part of a diabetic ketoacidosis monitoring system
    The TCS Environment

System     Control
database   system    environment

                       Rule set
Partitioning the Database by TCS




    i1   i2       i3   i4   i5
A Process-Chain in TCS

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