Skip to content

Latest commit

 

History

History
204 lines (154 loc) · 13.4 KB

configuration.md

File metadata and controls

204 lines (154 loc) · 13.4 KB

Configuration Language Specification

This document specifies the configuration language for the automatic extraction of task dataframes and cohorts from structured EHR data organized either via the MEDS format (recommended) or the ESGPT format. This extraction system works by defining a configuration object that details the underlying concepts, inclusion/exclusion, and labeling criteria for the cohort/task to be extracted, then using a recursive algorithm to identify all realizations of valid patient time-ranges of data that satisfy those constraints from the raw data. For more details on the recursive algorithm, see Algorithm Design.

As indicated above, these cohorts are specified through a combination of concepts (realized as event predicate functions, aka "predicates") which are dataset specific and inclusion/exclusion/labeling criteria which, conditioned on a set of predicate definitions, are dataset agnostic.

Predicates are currently limited to "count" predicates, which are predicates that count the number of times a boolean condition is satisfied over a given time window, which can either be a single timestamp, thus tracking whether how many observations there were that satisfied the boolean condition in that event (aka at that timestamp) or over 1-dimensional windows. In the future, predicates may expand to include other notions of functional characterization, such as tracking the average/min/max value a concept takes on over a time-period, etc.

Constraints are specified in terms of time-points that can be bounded by events that satisfy predicates or temporal relationships on said events. The windows between these time-points can then either be constrained to contain events that satisfy certain aggregation functions over predicates for these time frames.

In the machine form used by ACES, the configuration file consists of three parts:

  • predicates, stored as a dictionary from string predicate names (which must be unique) to either {py:class}aces.config.PlainPredicateConfig objects, which store raw predicates with no dependencies on other predicates, or {py:class}aces.config.DerivedPredicateConfig objects, which store predicates that build on other predicates.
  • trigger, stored as a string to EventConfig
  • windows, stored as a dictionary from string window names (which must be unique) to {py:class}aces.config.WindowConfig objects.

Below, we will detail each of these configuration objects.

Predicates: PlainPredicateConfig and DerivedPredicateConfig

{py:class}aces.config.PlainPredicateConfig: Configuration of Predicates that can be Computed Directly from Raw Data

These configs consist of the following four fields:

  • code: The string expression for the code object that is relevant for this predicate. An observation will only satisfy this predicate if there is an occurrence of this code in the observation. The field can additionally be a dictionary with either a regex key and the value being a regular expression (satisfied if the regular expression evaluates to True), or a any key and the value being a list of strings (satisfied if there is an occurrence for any code in the list).

    Note: Each individual definition of PlainPredicateConfig and code will generate a separate predicate column. Thus, for memory optimization, it is strongly recommended to match multiple values using either the List of Values or Regular Expression formats whenever possible.

  • value_min: If specified, an observation will only satisfy this predicate if the occurrence of the underlying code with a reported numerical value that is either greater than or greater than or equal to value_min (with these options being decided on the basis of value_min_inclusive, where value_min_inclusive=True indicating that an observation satisfies this predicate if its value is greater than or equal to value_min, and value_min_inclusive=False indicating a greater than but not equal to will be used).

  • value_max: If specified, an observation will only satisfy this predicate if the occurrence of the underlying code with a reported numerical value that is either less than or less than or equal to value_max (with these options being decided on the basis of value_max_inclusive, where value_max_inclusive=True indicating that an observation satisfies this predicate if its value is less than or equal to value_max, and value_max_inclusive=False indicating a less than but not equal to will be used).

  • value_min_inclusive: See value_min

  • value_max_inclusive: See value_max

  • other_cols: This optional field accepts a 1-to-1 dictionary of column names to column values, and can be used to specify further constraints on other columns (ie., not code) for this predicate.

A given observation will be gauged to satisfy or fail to satisfy this predicate in one of two ways, depending on its source format.

  1. If the source data is in MEDS format (recommended), then the code will be checked directly against MEDS' code field and the value_min and value_max constraints will be compared against MEDS' numeric_value field.

    Note: This syntax does not currently support defining predicates that also rely on matching other, optional fields in the MEDS syntax; if this is a desired feature for you, please let us know by filing a GitHub issue or pull request or upvoting any existing issue/PR that requests/implements this feature, and we will add support for this capability.

  2. If the source data is in ESGPT format, then the code will be interpreted in the following manner: a. If the code contains a "//", it will be interpreted as being a two element list joined by the "//" character, with the first element specifying the name of the ESGPT measurement under consideration, which should either be of the multi-label classification or multivariate regression type, and the second element being the name of the categorical key corresponding to the code in question within the underlying measurement specified. If either of value_min and value_max are present, then this measurement must be of a multivariate regression type, and the corresponding values_column for extracting numerical observations from ESGPT's dynamic_measurements_df will be sourced from the ESGPT dataset configuration object. b. If the code does not contain a "//", it will be interpreted as a direct measurement name that must be of the univariate regression type and its value, if needed, will be pulled from the corresponding column.

{py:class}aces.config.DerivedPredicateConfig: Configuration of Predicates that Depend on Other Predicates

These configuration objects consist of only a single string field--expr--which contains a limited grammar of accepted operations that can be applied to other predicates, containing precisely the following:

  • and(pred_1_name, pred_2_name, ...): Asserts that all of the specified predicates must be true.
  • or(pred_1_name, pred_2_name, ...): Asserts that any of the specified predicates must be true.

Note: Currently, and's and or's cannot be nested. Upon user request, we may support further advanced analytic operations over predicates.

Events: {py:class}aces.config.EventConfig

The event config consists of only a single field, predicate, which specifies the predicate that must be observed with value greater than one to satisfy the event. There can only be one defined "event" with an "EventConfig" in a valid configuration, and it will define the "trigger" event of the cohort.

The value of its field can be any defined predicate.

Windows: {py:class}aces.config.WindowConfig

Windows contain a tracking name field, and otherwise are specified with two parts: (1) A set of four parameters (start, end, start_inclusive, and end_inclusive) that specify the time range of the window, and (2) a set of constraints specified through two fields, dictionary of constraints (the has field) that specify the constraints that must be satisfied over the defined predicates for a possible realization of this window to be valid.

Time Range Fields

start and end

Valid windows always progress in time from the start field to the end field. These two fields define, in symbolic form, the relationship between the start and end time of the window. These two fields must obey the following rules:

  1. Linkage to other windows: Firstly, exactly one of these two fields must reference an external event, as specified either through the name of the trigger event or the start or end event of another window. The other field must either be null/None/omitted (which has a very specific meaning, to be explained shortly) or must reference the field that references the external event.

  2. Linkage reference language: Secondly, for both events, regardless of whether they reference an external event or an internal event, that reference must be expressed in one of the following ways.

    1. $REFERENCING = $REFERENCED + $TIME_DELTA, $REFERENCING = $REFERENCED - $TIME_DELTA, etc. In this case, the referencing event (either the start or end of the window) will be defined as occurring exactly $TIME_DELTA either after or before the event being referenced (either the external event or the end or start of the window).

      Note: If $REFERENCED is the start field, then $TIME_DELTA must be positive, and if $REFERENCED is the end field, then $TIME_DELTA must be negative to preserve the time ordering of the window fields.

    2. $REFERENCING = $REFERENCED -> $PREDICATE, $REFERENCING = $REFERENCED <- $PREDICATE In this case, the referencing event will be defined as the next or previous event satisfying the predicate, $PREDICATE.

      Note: If the $REFERENCED is the start field, then the "next predicate ordering" ($REFERENCED -> $PREDICATE) must be used, and if the $REFERENCED is the end field, then the "previous predicate ordering" ($REFERENCED <- $PREDICATE) must be used to preserve the time ordering of the window fields. These forms can lead to windows being defined as single point events, if the $REFERENCED event itself satisfies $PREDICATE and the appropriate constraints are satisfied and inclusive values are set.

    3. $REFERENCING = $REFERENCED In this case, the referencing event will be defined as the same event as the referenced event.

  3. null/None/omitted: If start is null/None/omitted, then the window will start at the beginning of the patient's record. If end is null/None/omitted, then the window will end at the end of the patient's record. In either of these cases, the other field must reference an external event, per rule 1.

start_inclusive and end_inclusive

These two fields specify whether the start and end of the window are inclusive or exclusive, respectively. This applies both to whether they are included in the calculation of the predicate values over the windows, but also, in the $REFERENCING = $REFERENCED -> $PREDICATE and $REFERENCING = $PREDICATE -> $REFERENCED cases, to which events are possible to use for valid next or prior $PREDICATE events. E.g., if we have that start_inclusive=False and the end field is equal to start -> $PREDICATE, and it so happens that the start event itself satisfies $PREDICATE, the fact that start_inclusive=False will mean that we do not consider the start event itself to be a valid start to any window that ends at the same start event, as its timestamp when considered as the prospective "window start timestamp" occurs "after" the effective timestamp of itself when considered as the $PREDICATE event that marks the window end given that start_inclusive=False and thus we will think of the window as truly starting an iota after the timestamp of the start event itself.

Constraints Field

The constraints field is a dictionary that maps predicate names to tuples of the form (min_valid, max_valid) that define the valid range the count of observations of the named predicate that must be found in a window for it to be considered valid. Either min_valid or max_valid constraints can be None, in which case those endpoints are left unconstrained. Likewise, unreferenced predicates are also left unconstrained.

Note: As predicate counts are always integral, this specification does not need an additional inclusive/exclusive endpoint field, as one can simply increment the bound by one in the appropriate direction to achieve the result. Instead, this bound is always interpreted to be inclusive, so a window would satisfy the constraint for predicate name with constraint name: (1, 2) if the count of observations of predicate name in a window was either 1 or 2. All constraints in the dictionary must be satisfied on a window for it to be included.