Articles producció científica> Enginyeria Informàtica i Matemàtiques

Formalization and acquisition of temporal knowledge for decision support in medical processes

  • Dades identificatives

    Identificador: imarina:5132331
    Autors:
    Kamisalic, AidaRiano, DavidWelzer, Tatjana
    Resum:
    Background : In medical practice, long term interventions are common and they require timely planning of the involved processes. Unfortunately, evidence-based statements about time are hard to find in Clinical Practice Guidelines (CPGs) and in other sources of medical knowledge. At the same time, health care cen- ters use medical records and information systems to register data about clinical processes and patients, including time information about the encounters, prescriptions, and other clinical actions. Consequently, medical records and health care information systems are promising sources of data from which we can detect temporal medical knowledge. Objective : The objectives were to (1) Analyze and classify the sorts of time constraints in medical processes, (2) Propose a formalism to represent these sorts of clinical time constraints, (3) Use these formalisms to enable the automatic generation of temporal models from clinical data, and (4) Study the adherence of these intervention models to CPG recommendations. Methods : In order to achieve these objectives, we carried out four studies: The identification of the sort of times involved in the long-term diagnostic and therapeutic medical procedures of fifty patients, the supervision of the indications about time contained in six CPGs on chronic diseases, the study of the time structures of two standard data models, as well as ten languages to computerize CPGs. Based on the provided studies, we synthesized two representation formalisms: Micro- and macro-temporality. We developed three algorithms for automatic generation of generalized time constraints in the form of micro- and macro-temporalities from clinical databases, which were double tested. Results : A full classification of time constraints for medical procedur
  • Altres:

    Autor segons l'article: Kamisalic, Aida; Riano, David; Welzer, Tatjana;
    Departament: Enginyeria Informàtica i Matemàtiques
    Autor/s de la URV: RIAÑO RAMOS, DAVID
    Paraules clau: Time modelling Time constraints generation Temporal knowledge representation Medical procedural knowledge Decision support systems Decision support Clinical practice guidelines Cardiovascular diseases time constraints generation temporal knowledge representation medical procedural knowledge decision support clinical practice guidelines cardiovascular diseases
    Resum: Background : In medical practice, long term interventions are common and they require timely planning of the involved processes. Unfortunately, evidence-based statements about time are hard to find in Clinical Practice Guidelines (CPGs) and in other sources of medical knowledge. At the same time, health care cen- ters use medical records and information systems to register data about clinical processes and patients, including time information about the encounters, prescriptions, and other clinical actions. Consequently, medical records and health care information systems are promising sources of data from which we can detect temporal medical knowledge. Objective : The objectives were to (1) Analyze and classify the sorts of time constraints in medical processes, (2) Propose a formalism to represent these sorts of clinical time constraints, (3) Use these formalisms to enable the automatic generation of temporal models from clinical data, and (4) Study the adherence of these intervention models to CPG recommendations. Methods : In order to achieve these objectives, we carried out four studies: The identification of the sort of times involved in the long-term diagnostic and therapeutic medical procedures of fifty patients, the supervision of the indications about time contained in six CPGs on chronic diseases, the study of the time structures of two standard data models, as well as ten languages to computerize CPGs. Based on the provided studies, we synthesized two representation formalisms: Micro- and macro-temporality. We developed three algorithms for automatic generation of generalized time constraints in the form of micro- and macro-temporalities from clinical databases, which were double tested. Results : A full classification of time constraints for medical procedures is proposed. Two formalisms called micro- and macro-temporality are introduced and validated to represent these time constraints. Time constraints were generated automatically from the data about 8781 Arterial Hypertension (AH) patients. The generated macro-temporalities restricted visits to be between 1-7 weeks, whereas CPGs recommend 2-4 weeks. Micro-temporal constraints on drug-dosage therapies distinguished between the initial dosage and the target dosage, with visits every 1-6 weeks, and 2-5 months, respectively. Our algo- rithms obtained semi-complete maps of dosage increments and the maximum dosages for 7 drug types. Data-based time limits for lifestyle change counsels and blood pressure (BP) check-ups were fixed to 6 and 3 months, for patients with low- and high-BP, respectively, when CPGs specify a general 3-6 month range. Conclusions : Experience-based temporal knowledge detected using our algorithms complements the evidence-based knowledge about clinical procedures contained in the CPGs. Our temporal model is sim- ple and highly descriptive when dealing with general or specific time constraints' representations, of- fering temporal knowledge representation of varying detail. Therefore, it is capable of capturing all the temporal knowledge we can find in medical procedures, when dealing with chronic diseases. With our model and algorithms, an adherence analysis emerges naturally to detect CPG-compliant interventions, but also deviations whose causes and possible rationales can call into question CPG recommendations (e.g., our analysis of AH patients showed that the time between visits recommended by CPGs were too long for a proper drug therapy decision, dosage titration, or general follow-up).
    Àrees temàtiques: Software Saúde coletiva Psicología Odontología Medicina iii Medicina ii Medicina i Medical informatics Matemática / probabilidade e estatística Interdisciplinar Health informatics General medicine Engineering, biomedical Engenharias iv Engenharias iii Engenharias ii Educação física Computer science, theory & methods Computer science, interdisciplinary applications Computer science applications Ciências biológicas ii Ciências biológicas i Ciências ambientais Ciências agrárias i Ciência da computação Biotecnología
    Accès a la llicència d'ús: https://creativecommons.org/licenses/by/3.0/es/
    Adreça de correu electrònic de l'autor: david.riano@urv.cat
    Identificador de l'autor: 0000-0002-1608-0215
    Data d'alta del registre: 2022-07-09
    Versió de l'article dipositat: info:eu-repo/semantics/acceptedVersion
    Enllaç font original: https://www.sciencedirect.com/science/article/abs/pii/S0169260717309756?via%3Dihub
    Referència a l'article segons font original: Computer Methods And Programs In Biomedicine. 158 207-228
    Referència de l'ítem segons les normes APA: Kamisalic, Aida; Riano, David; Welzer, Tatjana; (2018). Formalization and acquisition of temporal knowledge for decision support in medical processes. Computer Methods And Programs In Biomedicine, 158(), 207-228. DOI: 10.1016/j.cmpb.2018.02.012
    URL Document de llicència: https://repositori.urv.cat/ca/proteccio-de-dades/
    DOI de l'article: 10.1016/j.cmpb.2018.02.012
    Entitat: Universitat Rovira i Virgili
    Any de publicació de la revista: 2018
    Tipus de publicació: Journal Publications
  • Paraules clau:

    Computer Science Applications,Computer Science, Interdisciplinary Applications,Computer Science, Theory & Methods,Engineering, Biomedical,Health Informatics,Medical Informatics,Software
    Time modelling
    Time constraints generation
    Temporal knowledge representation
    Medical procedural knowledge
    Decision support systems
    Decision support
    Clinical practice guidelines
    Cardiovascular diseases
    time constraints generation
    temporal knowledge representation
    medical procedural knowledge
    decision support
    clinical practice guidelines
    cardiovascular diseases
    Software
    Saúde coletiva
    Psicología
    Odontología
    Medicina iii
    Medicina ii
    Medicina i
    Medical informatics
    Matemática / probabilidade e estatística
    Interdisciplinar
    Health informatics
    General medicine
    Engineering, biomedical
    Engenharias iv
    Engenharias iii
    Engenharias ii
    Educação física
    Computer science, theory & methods
    Computer science, interdisciplinary applications
    Computer science applications
    Ciências biológicas ii
    Ciências biológicas i
    Ciências ambientais
    Ciências agrárias i
    Ciência da computação
    Biotecnología
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