> For clean Markdown of any page, append .md to the page URL. > For a complete documentation index, see https://docs.emtelligent.com/llms.txt. > For AI client integration (Claude Code, Cursor, etc.), connect to the MCP server at https://docs.emtelligent.com/_mcp/server. # Temporality Relations In this section, we explain how structured data for temporality relations is represented in the NLP API JSON output and in the Output Database schema. We also provide sample queries to illustrate the key aspects of the data schema. After reading this page, we hope you will: * Understand what temporality relations are. * Understand what attributes are extracted. * Know how to find temporality relations and their attributes. The content in this section builds on the concepts and data models explained in earlier sections (see [Entity-level Tables](/nlp/database/schema/core/entity-tables)). The current implementation extracts `historical` mentions such as phrases containing `history of`, or `pmh of` (past medical history) and `specific` mentions like `day one`, `6 months`, or `10/07/2017`. ## What are Temporality Relations Temporality relations relate a subject to a temporal entity, where the subject can be any disorder, finding or other target concept in sentences where there are terms which provide some temporal information about the subject. Currently the NLP API recognizes 2 categories of temporal information: `historical` and `specific`. ## Historical Temporality Temporal qualifiers like `history of` or `status post` indicate that an event or disease process occurred in the past, often without giving a specific date of when the event happened. Being able to detect to distinguish that the disease had occurred in the past is relevant for the patient’s current care. The historical context of a disease mention or event may assist in understanding the velocity of disease progression, and provides important context for understanding the patient’s current state of health. Temporality relations are extracted when the subject term is mentioned in conjunction with a `historical` temporal term within a clause. Temporality relations relate a subject entity (e.g. disorders, findings or other target concepts) to an entity which provides temporal context. A temporal entity represents terms like `h/o`, `longstanding history of`, and `s/p`, and many other lexical variants. Consider the following sentence: ![Temporality relation arguments](/_fern-img/eb8affe22bf1810b9af0be368d29d47bc23d07820c2efcc1ea1aa86a5a2ae7c2.webp) The temporality relation attributes extracted by the NLP API are: * **Subject**: the subject modified by the temporal context. In the example above, the subject is `FALL`. * **Temporal_entity**: the entity which describes the temporal status of the subject. In the example above, the temporal entity is extracted for the term `S/P`. * **Polarity**: the polarity attribute for this relation indicates whether the temporality status itself is negated or asserted e.g. polarity is negated for the phrase `NO FALL`. * **Category**: for the example above, the category is `historical`. ### Found vs Assumed Temporal Entities The temporal entity which represents the `historical` term can be represented by a found entity (an entity with **type\_** `found` in the **entity** table) or an **assumed** entity (an entity type of `assumed` in the **assumedentity** table). Consider this example: ```text PAST MEDICAL HISTORY Coronary artery disease with previous MI. ``` In this case, the temporal relations extracted is an assumed temporal entity with a value of `past medical history`. The temporal entity for `Coronary artery disease` and `MI` in this example is an **assumed** entity because it occurs within a section that is categorized as `historical`. ## Specific Temporality Medical reports are rich sources of date mentions because they record when a patient experienced medically significant events, when tests and procedures were performed, and when medications were administered. We see a very large variety of ways in which dates are recorded, for example in structured forms like these: * **2024/12/22 - 12/16/2010**: Admitted with UTI and pneumonia; had a gout flare. * Discharge Date: **2015 JUNE 10th** * Pulmonary hypertension (PA pressure 35/15 by RHC in **3/2022**) * **6/6/2018 12:10PM** BLOOD Glucose-463\* UreaN-20 Creat-1.2\* Na-132\* K-3.6 Cl-101 HCO3-18\* AnGap-17 The NLP API is trained to recognize many date and date-time formats to maximize recall of date terms. While dates are prevalent in structured and semi-structured sections of medical reports, temporal information is also recorded in narrative text sections in various ways, often more informally. Examples of such sentences are given with the date terms highlighted: * hernia repair **2020** * Right heart failure with an echocardiogram in **Sep of this year** showing mild left ventricular hypertrophy * On **3-25**, the patient had another PEA arrest * The patient had an appendectomy **five years ago** Consider the following sentence: ![Temporality relation arguments](/_fern-files/emtelligent-prod.docs.buildwithfern.com/2627537b3cd46d2c8563ffbc66bb67ad485d6c5a8b9a11b9f2fda215da2495ff/assets/nlp/temporalityrelation_anno_sp.png) The temporality relation attributes extracted by the NLP API are: * **Subject**: the subject modified by the temporal status. In the example above, the subject is `appendectomy`. * **Temporal_entity**: the entity that represents the temporal status of the subject. In the example above, a temporal entity is extracted for the term `1992`. * **Polarity**: the polarity attribute for this relation indicates whether the temporality status itself is negated or asserted e.g. polarity is **asserted** for the entity `appendectomy`, because the patient `had an appendectomy`. * **Category**: the type of temporality relation recognized is `specific`. ## Temporality vs Experiencer Relations It should also be noted that a `historical` or `specific` mention may be experienced by persons other than the patient. For example: ```text The patient’s mother has a history of breast cancer ``` In contrast to: ```text The patient has a history of breast cancer ``` Likewise for `specific` temporality relations: ```text His mother had CABG in 2000 at age 55. ``` In contrast to: ```text The patient had CABG in 2019. ``` The experiencer is extracted as an experiencer relation while the temporal status of the cancer mention is extracted as a temporality relation. To obtain both pieces of information for all temporality relations a query can be written to first retrieve the `foundentities` for those mentions experienced by an experiencer (e.g. the patient) using the experiencer relation and then retrieve temporality information for that subset of `foundentities` using the temporality relation. An example of this query pattern is provided [here](#query-find-all-historical-mentions-for-patient). > **Warning** > > > **When using temporality relations, use the SNOMED CT ontology.** > > Compound concepts such `history of depression` exist in some ontologies; for example SNOMED has the concept `History of depression (situation)| 161469008`. When the NLP API encounters such terms, it extracts a relation between the base forms of the SNOMED concepts `depression` and `history of` but not the compound concept `History of depression (situation)`.This is deliberate. By extracting the term `depression` as `Depressive disorder (disorder) | 35489007` the NLP API allows users to retrieve all disorder mentions by querying for the `disorder` SNOMED semantic tag. By contrast the `situation` semantic tag covers a diverse range of concepts making a less useful query parameter. Likewise, some ontologies contain compound concepts like `no history of drug abuse`; however the NLP API avoids using the negated compound concepts, and instead stores the negated temporal context as the `polarity` attribute of the temporality relation. ## JSON Data Model This section explains the NLP API JSON output for `historical` and `specific` temporality relations. ### Historical Temporality The JSON below shows the NLP API’s JSON result output for a `historical` temporality relation extracted from this statement: ![Temporality relation arguments](/_fern-img/eb8affe22bf1810b9af0be368d29d47bc23d07820c2efcc1ea1aa86a5a2ae7c2.webp) ```json { "relations": { "temporalities": [ { "label": "RT0", "attributes": { "confidence": 1, "polarity": "asserted", "category": "historical" }, "args": { "subject": { "ref": "E3", "text": [ "FALL" ] }, "modifiers": [], "temporal_entity": { "ref": "E1", "text": [ "S/P" ] } }, "concept_links": [] } ] } } ``` From the JSON output above, we see that: 1. The `temporalities` array is an element of the `relations` object. Each element of this array is a single object representing a temporality relation instance. 2. A temporality relation instance has several named arguments: the `subject` and the `temporal_entity`. 3. Note that both the `subject` argument and `temporal_entity` argument are required. Each temporality relation corresponds to exactly one subject and temporal entity pair. However, note that many-to-many relation between subject and temporal entities is possible. For example a procedure could be administered on multiple dates, and conversely it is possible for multiple procedures to be administered for the date. 4. Note that the `modifiers` property is not currently used. ### Specific Temporality The example below shows the NLP API’s JSON output for a `specific` temporality relation extracted from the following sentence: ![Temporality relation arguments](/_fern-files/emtelligent-prod.docs.buildwithfern.com/2627537b3cd46d2c8563ffbc66bb67ad485d6c5a8b9a11b9f2fda215da2495ff/assets/nlp/temporalityrelation_anno_sp.png) ```json { "relations": { "temporalities": [ { "label": "RT0", "attributes": { "confidence": 1, "polarity": "asserted", "category": "specific" }, "args": { "subject": { "ref": "E1", "text": [ "patient" ] }, "modifiers": [], "temporal_entity": { "ref": "E0", "text": [ "1992" ] } }, "concept_links": [] }, { "label": "RT1", "attributes": { "confidence": 1, "polarity": "asserted", "category": "specific" }, "args": { "subject": { "ref": "E2", "text": [ "appendectomy" ] }, "modifiers": [], "temporal_entity": { "ref": "E0", "text": [ "1992" ] } }, "concept_links": [] } ] } } ``` From the JSON output above, we see that the `temporal_entity` RT1 contains all the same the properties as the `historical` temporality relation example in the previous section except for the following: 1. The `category` is `specific`. 2. The `subject` is `appendectomy` and the temporal entity is `1992`. ## Temporality Relation Tables ![Entity-Relation diagram showing temporalityrelation and related tables.](/_fern-img/d6553802229b07b3f960c785578e25b7cbb817761b6ffe6085529e565bf1fa08.webp) A few key points about this schema: 1. Each temporality relation relates to **exactly one** subject term. Each subject mention is represented by an `entity` in the Output Database schema, and has a corresponding row in the **entity** and **foundentity** tables respectively. 2. Each temporality relation also extracts **exactly one** temporal qualifier term as a temporal entity. Temporal entities may be represented by an `entity` of type `found` or `assumed` in the Output Database schema, which have corresponding rows **foundentity** or **assumedentity** tables respectively 3. In addition to the entity attributes stored in the **foundentity** table, such as in the column **section_name**, many other attributes about an entity can be found from performing SQL `JOINs` with entity-related tables. For more information see [Entity-level tables](/nlp/database/schema/core/entity-tables) . 4. The **temporalityrelationmodifier** table is currently not populated with modifiers. ## Sample Queries In this section, we present SQL queries that illustrate how to retrieve information about medication relations for initial data exploration. Five queries are provided: We begin by querying all temporality relations: * [Query: Find All Temporality Relations](#query-find-all-temporality-relations) Next, we show examples of queries that target `historical` temporality relations: * [Query: Find All Historical Mentions for Patient](#query-find-all-historical-mentions-for-patient) * [Query: Find All Assumed Temporality Mentions](#query-find-all-assumed-temporality-mentions) Finally, we retrieve data about `specific` temporality relations: * [Query: Find Temporal Terms Associated with Procedure Mentions](#query-find-temporal-terms-associated-with-procedure-mentions) * [Query: Find Temporal Terms Associated with Disorder, Findings or Morphologic Abnormality Mentions](#query-find-temporal-terms-associated-with-disorder-findings-or-morphologic-abnormality-mentions) ### Query: Find All Temporality Relations This query returns all the terms and corresponding sentences for all temporality relations in an Output Database. This query is useful for reviewing the output of the temporality relation extraction feature. The query uses the `temporalityrelation` table and `JOINs` with related **foundentity** tables to retrieve the term (annotated text), section heading, and sentence. Note that as both the subject and the temporal status are represented by entities, we need to join with the entity table twice, and take care to use the appropriate aliases to reference their entity attributes e.g. `sub_fe` is the table alias for the subject entity and `temporal_e` is the table alias for the temporal status entity respectively. ```sql SELECT t.id, t.category, sub_fe.section_name AS section, sub_fe.text AS subject_term, temporal_fe.text AS temporal_entity_term, s.text AS sentence FROM temporalityrelation t JOIN entity sub_e ON t.subject_id = sub_e.id AND sub_e.type_ = 'found' JOIN foundentity sub_fe ON sub_e.entity_id = sub_fe.id JOIN foundentitylocation sub_fel ON sub_fe.id = sub_fel.found_entity_id JOIN location l ON sub_fel.location_id = l.id AND l.type_ = 'sentence' JOIN sentencelocation s ON l.location_id = s.id JOIN entity temporal_e ON t.temporal_entity_id = temporal_e.id AND temporal_e.type_ = 'found' JOIN foundentity temporal_fe ON temporal_e.entity_id = temporal_fe.id ON temporal_fe.id = temporal_fes.found_entity_id ORDER BY l.document_id, sub_fe.start; ``` | id | category | section | subject_term | temporal_entity_term | sentence | | -- | ---------- | -------------- | --------------------------------- | --------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------- | | 1 | specific | IMPRESSION | endoscopy | 3 years | Follow up surveillance endoscopy for Barrett's in 3 years (if pathology confirms this diagnosis). | | 3 | specific | IMPRESSION | Barrett's | 3 years | Follow up surveillance endoscopy for Barrett's in 3 years (if pathology confirms this diagnosis). | | 2 | specific | IMPRESSION | Follow up | 3 years | Follow up surveillance endoscopy for Barrett's in 3 years (if pathology confirms this diagnosis). | | 4 | specific | COMPARISON | CT of the abdomen and pelvis | yesterday | COMPARISON: Comparison is made to CT of the abdomen and pelvis from yesterday. | | 6 | specific | HISTORY | benign | five to six years ago | This is a 51-year-old African-American female who had undergone Whipple's procedure approximately five to six years ago for a benign pancreatic mass. | | 7 | specific | HISTORY | pancreatic mass | five to six years ago | This is a 51-year-old African-American female who had undergone Whipple's procedure approximately five to six years ago for a benign pancreatic mass. | | 5 | historical | IMPRESSION | Whipple's pancreaticoduodenectomy | Status post | Status post Whipple's pancreaticoduodenectomy. | | 8 | specific | HISTORY | Whipple's procedure | five to six years ago | This is a 51-year-old African-American female who had undergone Whipple's procedure approximately five to six years ago for a benign pancreatic mass. | | 9 | historical | PROCEDURE NOTE | hiatal hernia repair | previous | There was no evidence of stricturing or extrinsic narrowing from her previous hiatal hernia repair. | | 12 | historical | IMPRESSION | cholecystectomy | Status post | Status post cholecystectomy. | | 11 | historical | FINDINGS | cholecystectomy | status post | The patient is status post cholecystectomy. | | 10 | historical | INDICATION | colon cancer | history of | The patient's sister has a history of colon cancer. | ### Query: Find All Historical Mentions for Patient This query finds all temporality relations with the following criteria: * The temporality mention is experienced by the patient himself (whether explicitly referenced or inferred as the subject of the report) * The relation is positively asserted (i.e. patient has a history of ‘x’) * The `category` is `historical`. We also want to return the SNOMED concepts for the subject of the relation. ```sql WITH explicitpatients AS (SELECT er.id AS relation_id, c.description AS person, er.experienced_id AS experienced_entity_id FROM experiencerrelation er JOIN entity e ON er.experiencer_id = e.id -- NOTE: find experience(r) entities JOIN foundentity fe ON fe.id = e.entity_id -- IMPORTANT: e.entity_id not e.id JOIN foundentityconcept fec ON fe.id = fec.found_entity_id JOIN concept c ON c.concept_id = fec.concept_id AND c.ontology = fec.concept_ontology WHERE c.ontology = 'snomed' AND e.type_ = 'found' -- IMPORTANT: this condition is required. AND c.concept_id = '116154003'), implicitpatients AS (SELECT er.id AS relation_id, ae.value AS person, er.experienced_id AS experienced_entity_id FROM entity e JOIN experiencerrelation er ON er.experiencer_id = e.id -- NOTE: experience(r) entities JOIN assumedentity ae ON ae.id = e.entity_id WHERE e.type_ = 'assumed' AND ae.value = 'patient'), -- NOTE: must specify value as 'patient' (NOT 'family') all_patients AS (SELECT * FROM implicitpatients UNION SELECT * FROM explicitpatients) SELECT t.id, t.category, pat.person, sub_fe.section_name AS section, sub_fe.text AS subject_term, sub_c.description AS subject_concept, sub_c.concept_id AS subject_concept_id, temporal_fe.text AS temporal_entity_term, s.text AS sentence FROM temporalityrelation t JOIN all_patients pat ON t.subject_id = pat.experienced_entity_id AND t.category = 'historical' JOIN entity sub_e ON t.subject_id = sub_e.id AND sub_e.type_ = 'found' JOIN foundentity sub_fe ON sub_e.entity_id = sub_fe.id JOIN foundentityconcept sub_fec ON sub_fe.id = sub_fec.found_entity_id JOIN concept sub_c ON sub_fec.concept_id = sub_c.concept_id AND sub_fec.concept_ontology = sub_c.ontology AND sub_c.ontology = 'snomed' -- IMPORTANT: Use SNOMED JOIN foundentitylocation sub_fel ON sub_fe.id = sub_fel.found_entity_id JOIN location l ON sub_fel.location_id = l.id AND l.type_ = 'sentence' JOIN sentencelocation s ON l.location_id = s.id JOIN entity temporal_e ON t.temporal_entity_id = temporal_e.id AND temporal_e.type_ = 'found' JOIN foundentity temporal_fe ON temporal_e.entity_id = temporal_fe.id WHERE t.polarity = 'asserted'; -- IMPORTANT: exclude 'no history of ...' ``` | id | category | person | section | subject_term | subject_concept | subject_concept_id | temporal_entity_term | sentence | | -- | ---------- | ---------------- | -------------------------- | --------------------------------- | -------------------------------------------- | ------------------ | --------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | 5 | historical | patient | IMPRESSION | Whipple's pancreaticoduodenectomy | Pancreaticoduodenectomy (procedure) | 116241004 | Status post | Status post Whipple's pancreaticoduodenectomy. | | 9 | historical | patient | PROCEDURE NOTE | hiatal hernia repair | Esophageal hiatus hernia repair (procedure) | 9292007 | previous | There was no evidence of stricturing or extrinsic narrowing from her previous hiatal hernia repair. | | 11 | historical | Patient (person) | FINDINGS | cholecystectomy | Cholecystectomy (procedure) | 38102005 | status post | The patient is status post cholecystectomy. | | 12 | historical | patient | IMPRESSION | cholecystectomy | Cholecystectomy (procedure) | 38102005 | Status post | Status post cholecystectomy. | | 13 | historical | patient | PROCEDURE | rupture | Rupture (morphologic abnormality) | 125671007 | previous | There was some serosanguineous fluid from the previous rupture. | | 28 | historical | patient | HISTORY OF PRESENT ILLNESS | anorexia | Loss of appetite (finding) | 79890006 | history of | This 55 year-old female presented to the emergency department with a five day history of right upper quadrant pain associated with nausea and vomiting as well as anorexia. | | 29 | historical | patient | HISTORY OF PRESENT ILLNESS | nausea and vomiting | Nausea and vomiting (disorder) | 16932000 | history of | This 55 year-old female presented to the emergency department with a five day history of right upper quadrant pain associated with nausea and vomiting as well as anorexia. | | 30 | historical | patient | HISTORY OF PRESENT ILLNESS | right upper quadrant pain | Right upper quadrant pain (finding) | 301717006 | history of | This 55 year-old female presented to the emergency department with a five day history of right upper quadrant pain associated with nausea and vomiting as well as anorexia. | | 31 | historical | patient | HISTORY OF PRESENT ILLNESS | fever | Fever (finding) | 386661006 | denies any history of | She denies any history of fever. | | 34 | historical | patient | PAST MEDICAL HISTORY | inguinal hernia repair | Repair of inguinal hernia (procedure) | 44558001 | Prior | Prior inguinal hernia repair | | 35 | historical | patient | PAST MEDICAL HISTORY | alcohol abuse | Harmful pattern of use of alcohol (disorder) | 15167005 | Remote history of | Remote history of alcohol abuse | | 36 | historical | Patient (person) | SOCIAL HISTORY | alcohol abuse | Harmful pattern of use of alcohol (disorder) | 15167005 | history of | The patient has a history of alcohol abuse in her 30’s and has been sober since then. | ### Query: Find All Assumed Temporality Mentions This query returns each temporality relation where the temporal term is assumed based on context, such as from sentences like `Diabetic on insulin` in the PAST MEDICAL HISTORY section of a clinical note. Note that this query returns both positively asserted and negated relation instances. Also note that assumed temporal entities are necessarily `historical`. ```sql SELECT t.id, t.category, t.polarity, sub_fe.text AS subject_term, temporal_ae.value AS temporal_entity_term, s.text AS sentence FROM temporalityrelation t -- Extract subject text, location, and sentence JOIN entity sub_e ON t.subject_id = sub_e.id AND sub_e.type_ = 'found' JOIN foundentity sub_fe ON sub_e.entity_id = sub_fe.id JOIN foundentitylocation sub_fel ON sub_fe.id = sub_fel.found_entity_id JOIN location sub_l ON sub_fel.location_id = sub_l.id AND sub_l.type_ = 'sentence' JOIN sentencelocation s ON sub_l.location_id = s.id -- Extract assumed temporal entity mentions JOIN entity temporal_e ON t.temporal_entity_id = temporal_e.id AND temporal_e.type_ = 'assumed' JOIN assumedentity temporal_ae ON temporal_ae.id = temporal_e.entity_id ORDER BY sub_l.document_id, sub_fe.start; ``` | id | category | polarity | subject_term | temporal_entity_term | sentence | | -- | ---------- | -------- | ---------------------------------- | -------------------- | ----------------------------------------------------------- | | 15 | historical | asserted | Crohn's Disease | past medical history | Crohn's Disease - uses entocort during acute flares | | 16 | historical | asserted | Recurrent small bowel obstructions | past medical history | Recurrent small bowel obstructions - treated conservatively | | 17 | historical | asserted | GERD | past medical history | GERD | | 18 | historical | asserted | Benign colonic polyps | past medical history | Benign colonic polyps | | 19 | historical | asserted | Hypertension | past medical history | Hypertension | | 20 | historical | asserted | Atrial fibrillation | past medical history | Atrial fibrillation | | 21 | historical | asserted | Rheumatoid arthritis | past medical history | Rheumatoid arthritis | | 22 | historical | asserted | Gout | past medical history | Gout | | 23 | historical | asserted | Allergic Rhinitis | past medical history | Allergic Rhinitis | | 24 | historical | asserted | Renal stones | past medical history | Renal stones - treated with lithotripsy in the past | | 25 | historical | asserted | lithotripsy | past medical history | Renal stones - treated with lithotripsy in the past | | 26 | historical | asserted | Obesity | past medical history | Obesity | ### Query: Find Temporal Terms Associated with Procedure Mentions This query finds the temporal information for procedures by searching for the ‘procedure’ entity type information found in the **foundentitytype** table, and `specific` temporality relations. Temporal terms include dates in many formats as well as other words and phrases. If you are searching for date-time terms only, some additional post-retrieval data cleaning would be required to exclude non-date temporal terms. ```sql SELECT l.document_id AS doc_id, t.category, sub_fe.section_name, sub_fe.text AS procedure_term, temporal_fe.text AS temporal_term, sl.text AS sentence FROM temporalityrelation t -- Extract subject text, location, and type JOIN entity sub_e ON t.subject_id = sub_e.id AND sub_e.type_ = 'found' JOIN foundentity sub_fe ON sub_e.entity_id = sub_fe.id JOIN foundentitytype sub_fet ON sub_fe.id = sub_fet.found_entity_id JOIN foundentitylocation sub_fel ON sub_fe.id = sub_fel.found_entity_id JOIN location l ON sub_fel.location_id = l.id AND l.type_ = 'sentence' JOIN sentencelocation sl ON l.location_id = sl.id -- Extract temporal entity text JOIN entity temporal_e ON t.temporal_entity_id = temporal_e.id AND temporal_e.type_ = 'found' JOIN foundentity temporal_fe ON temporal_e.entity_id = temporal_fe.id WHERE sub_fet.type_name = 'procedure' AND sub_fet.ontology = 'snomed' AND t.category = 'specific' ORDER BY l.document_id, sub_fe.start; ``` | doc_id | category | section_name | procedure_term | temporal_term | sentence | | ------ | -------- | --------------------------------- | ---------------------------- | ----------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------- | | 2 | specific | IMPRESSION | Follow up | 3 years | Follow up surveillance endoscopy for Barrett's in 3 years (if pathology confirms this diagnosis). | | 2 | specific | IMPRESSION | endoscopy | 3 years | Follow up surveillance endoscopy for Barrett's in 3 years (if pathology confirms this diagnosis). | | 3 | specific | COMPARISON | CT of the abdomen and pelvis | yesterday | COMPARISON: Comparison is made to CT of the abdomen and pelvis from yesterday. | | 4 | specific | HISTORY | Whipple's procedure | five to six years ago | This is a 51-year-old African-American female who had undergone Whipple's procedure approximately five to six years ago for a benign pancreatic mass. | | 13 | specific | PROCEDURES AND TREATMENT PROVIDED | follow-up | within 2 months | Numerous subcentimeter lung nodules were also identified for which a follow-up chest CT should be performed within 2 months was recommended. | | 13 | specific | PROCEDURES AND TREATMENT PROVIDED | chest CT | within 2 months | Numerous subcentimeter lung nodules were also identified for which a follow-up chest CT should be performed within 2 months was recommended. | | 19 | specific | PLAN | phosphate | Friday | Check CMP and phosphate every Monday, Wednesday, and Friday for refeeding syndrome noted by following potassium and phosphate. | | 22 | specific | PROCEDURES AND TREATMENT PROVIDED | antimicrobial therapy | 6 weeks | He was also seen by the ID service who recommended 6 weeks of antimicrobial therapy. | | 28 | specific | COMPARISION | Right wrist x-ray | 2008/10/11 | COMPARISION: Right wrist x-ray from 2008/10/11. | | 30 | specific | COMPARISON | study | March 11th, 2013 | COMPARISON: This study was compared to the prior study of March 11th, 2013. | | 32 | specific | IMPRESSION | follow-up | within 2 months further | Numerous subcentimeter lung nodules, for which a follow-up chest CT should be performed within 2 months further characterization. | | 32 | specific | IMPRESSION | chest CT | within 2 months further | Numerous subcentimeter lung nodules, for which a follow-up chest CT should be performed within 2 months further characterization. | ### Query: Find Temporal Terms Associated with Disorder, Findings or Morphologic Abnormality Mentions This query finds all temporal terms associated with mentions of disorders, findings, and morphologic abnormalities by using the `specific` temporality relation and `JOINs` to retrieve concept and entity type information from the **concept** and **foundentitytype** tables respectively. ```sql SELECT d.id AS doc_id, t.category, sub_fe.section_name, sub_fe.text AS subject_term, temporal_fe.text AS temporal_term, sub_c.description AS subject_concept, temporal_c.description AS temporal_concept, s.text AS sentence FROM temporalityrelation t -- Extract subject text, location JOIN entity sub_e ON t.subject_id = sub_e.id AND sub_e.type_ = 'found' AND t.category = 'specific' JOIN foundentity sub_fe ON sub_e.entity_id = sub_fe.id JOIN foundentitytype sub_fet ON sub_fe.id = sub_fet.found_entity_id JOIN foundentitylocation sub_fel ON sub_fe.id = sub_fel.found_entity_id JOIN location l ON sub_fel.location_id = l.id AND l.type_ = 'sentence' JOIN sentencelocation s ON l.location_id = s.id -- Extract subject SNOMED concept JOIN foundentityconcept sub_fec ON sub_fe.id = sub_fec.found_entity_id JOIN concept sub_c ON sub_fec.concept_id = sub_c.concept_id AND sub_fec.concept_ontology = sub_c.ontology AND sub_c.ontology = 'snomed' -- Extract temporal entity text, location JOIN entity temporal_e ON t.temporal_entity_id = temporal_e.id AND temporal_e.type_ = 'found' JOIN foundentity temporal_fe ON temporal_e.entity_id = temporal_fe.id -- Extract temporal entity SNOMED concept JOIN foundentityconcept temporal_fec ON temporal_fe.id = temporal_fec.found_entity_id JOIN concept temporal_c ON temporal_fec.concept_id = temporal_c.concept_id AND temporal_fec.concept_ontology = temporal_c.ontology AND temporal_c.ontology = 'snomed' -- Find document filename JOIN document d ON l.document_id = d.id WHERE sub_fet.type_name IN ('disorder', 'finding', 'morphologic abnormality') AND sub_fet.ontology = 'snomed' ORDER BY regexp_replace(d.filename, '.*/', ''), sub_fe.start; ``` | doc_id | category | section_name | subject_term | temporal_term | subject_concept | temporal_concept | sentence | | ------ | -------- | ---------------------------------- | ---------------------------------- | ------------------- | --------------------------------------------------- | --------------------------------- | ------------------------------------------------------------------------------------------------------------------------- | | 138 | specific | ALLERGIES AND INTOLERANCES SECTION | Allergy to Eggs | 03-31-2010 10:00:00 | Allergy to egg protein (finding) | Temporal relationship (attribute) | • Allergy to Eggs \| UNII \| 291P45F896 \| Eggs \| Severe \| Very susceptible \| 03-31-2010 10:00:00 \| Active. | | 138 | specific | ENCOUNTERS SECTION | Inpatient Discharge | 05-28-2010 10:00:00 | Discharged from inpatient care (finding) | Temporal relationship (attribute) | • Encounter Inpatient Discharge \| CPT \| 99238 \| Hospital discharge day \| 05-28-2010 10:00:00 \| Performed. | | 138 | specific | PROBLEM SECTION | Coronary Artery Disease | 03-31-2010 10:00:00 | Coronary arteriosclerosis (disorder) | Temporal relationship (attribute) | • Coronary Artery Disease No MI \| SNOMED-CT \| 53741008 \| Coronary arteriosclerosis \| 03-31-2010 10:00:00 \| Active. | | 138 | specific | PROBLEM SECTION | MI | 03-31-2010 10:00:00 | Myocardial infarction (disorder) | Temporal relationship (attribute) | • Coronary Artery Disease No MI \| SNOMED-CT \| 53741008 \| Coronary arteriosclerosis \| 03-31-2010 10:00:00 \| Active. | | 138 | specific | PROBLEM SECTION | Coronary arteriosclerosis | 03-31-2010 10:00:00 | Coronary arteriosclerosis (disorder) | Temporal relationship (attribute) | • Coronary Artery Disease No MI \| SNOMED-CT \| 53741008 \| Coronary arteriosclerosis \| 03-31-2010 10:00:00 \| Active. | | 138 | specific | PROBLEM SECTION | Diabetes mellitus type 2 | 03-31-2010 10:00:00 | Diabetes mellitus type 2 (disorder) | Temporal relationship (attribute) | • Diabetes \| SNOMED-CT \| 44054006 \| Diabetes mellitus type 2 \| 03-31-2010 10:00:00 \| Active. | | 138 | specific | PROBLEM SECTION | Hypertension | 03-31-2010 10:00:00 | Hypertensive disorder, systemic arterial (disorder) | Temporal relationship (attribute) | • Hypertension \| SNOMED-CT \| 59621000 \| Essential hypertension \| 03-31-2010 10:00:00 \| Active. | | 138 | specific | PROBLEM SECTION | Essential hypertension | 03-31-2010 10:00:00 | Essential hypertension (disorder) | Temporal relationship (attribute) | • Hypertension \| SNOMED-CT \| 59621000 \| Essential hypertension \| 03-31-2010 10:00:00 \| Active. | | 138 | specific | PROBLEM SECTION | Acute Q wave myocardial infarction | 05-28-2010 10:00:00 | Acute Q wave myocardial infarction (disorder) | Temporal relationship (attribute) | • Myocardial Infarction \| SNOMED-CT \| 304914007 \| Acute Q wave myocardial infarction \| 05-28-2010 10:00:00 \| Active. | | 138 | specific | PROCEDURES SECTION | LVF | 05-24-2010 10:00:00 | Left heart failure (disorder) | Temporal relationship (attribute) | • LVF Assessment \| CPT \| 93543 \| Injection for heart x-rays \| 05-24-2010 10:00:00 \| Performed. | | 138 | specific | SOCIAL HISTORY SECTION | Never smoker | 03-31-2010 10:00:00 | Never smoked tobacco (finding) | Temporal relationship (attribute) | • Tobacco Use \| SNOMED-CT \| 266919005 \| Never smoker (Never Smoked) \| 03-31-2010 10:00:00 \| Active. | | 138 | specific | SOCIAL HISTORY SECTION | (Never Smoked) | 03-31-2010 10:00:00 | Never smoked tobacco (finding) | Temporal relationship (attribute) | • Tobacco Use \| SNOMED-CT \| 266919005 \| Never smoker (Never Smoked) \| 03-31-2010 10:00:00 \| Active. |