Time as Symbolic Tokens

1. Temporal Reasoning and Planning in MedicineTemporal ReasoningIn Medical Information Systems (I)Yuval Shahar M.D., Ph.D.

2. 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 therapy) • 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

3. 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)

4. Blum’s Rx System(1982) • 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 hypothesis • 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>

5. 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

6. Downs’ Medical-record Summarization Program(1986) • 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”

7. Down’s Summarization Program’s Knowledge Base

8. De Zegher-Geets’ IDEFIX System(1987) • 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

9. 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

10. A Time-Oriented Probabilistic Function (TOPF)

11. Russ's Temporal Control Structure [TCS] System (1983-1991) • 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

12. The TCS Environment

13. Partitioning the Database by TCS

14. A Process-Chain in TCS