The Cooperation Databank: Machine-Readable Science Accelerates Research Synthesis.

Publishing studies using standardized, machine-readable formats will enable machines to perform meta-analyses on demand. To build a semantically enhanced technology that embodies these functions, we developed the Cooperation Databank (CoDa)-a databank that contains 2,636 studies on human cooperation (1958-2017) conducted in 78 societies involving 356,283 participants. Experts annotated these studies along 312 variables, including the quantitative results (13,959 effects).

Machine Learning Explainability in Breast Cancer Survival.

Machine Learning (ML) can improve the diagnosis, treatment decisions, and understanding of cancer. However, the low explainability of how "black box" ML methods produce their output hinders their clinical adoption. In this paper, we used data from the Netherlands Cancer Registry to generate a ML-based model to predict 10-year overall survival of breast cancer patients.

Ten years of knowledge representation for health care (2009-2018): Topics, trends, and challenges.

Background: In the last ten years, the international workshop on knowledge representation for health care (KR4HC) has hosted outstanding contributions of the artificial intelligence in medicine community pertaining to the formalization and representation of medical knowledge for supporting clinical care. Contributions regarding modeling languages, technologies and methodologies to produce these models, their incorporation into medical decision support systems, and practical applications in concrete medical settings have been the main contributions and the basis to define the evolution of this field across Europe and worldwide.

Objectives: Carry out a review of the papers accepted in KR4HC in the 2009-2018 decade, analyze and characterize the topics and trends within this field, and identify challenges for the evolution of the area in the near future.

Analyzing interactions on combining multiple clinical guidelines.

Accounting for patients with multiple health conditions is a complex task that requires analysing potential interactions among recommendations meant to address each condition. Although some approaches have been proposed to address this issue, important features still require more investigation, such as (re)usability and scalability. To this end, this paper presents an approach that relies on reusable rules for detecting interactions among recommendations coming from various guidelines.

Predictive modeling of colorectal cancer using a dedicated pre-processing pipeline on routine electronic medical records.

Over the past years, research utilizing routine care data extracted from Electronic Medical Records (EMRs) has increased tremendously. Yet there are no straightforward, standardized strategies for pre-processing these data. We propose a dedicated medical pre-processing pipeline aimed at taking on many problems and opportunities contained within EMR data, such as their temporal, inaccurate and incomplete nature.

Identification of patients at risk for colorectal cancer in primary care: an explorative study with routine healthcare data.

Background: Early diagnosis of colorectal cancer (CRC) is likely to reduce burden of disease and improve treatment success. Estimation of the individual patient risk for CRC diagnostic determinants in a primary care setting has not been very successful as yet. The aim of our study is to improve prediction of CRC in patients selected for colonoscopy in the primary healthcare setting using readily available routine healthcare data.

Enhancing reuse of structured eligibility criteria and supporting their relaxation.

Patient recruitment is one of the most important barriers to successful completion of clinical trials and thus to obtaining evidence about new methods for prevention, diagnostics and treatment. The reason is that recruitment is effort consuming. It requires the identification of candidate patients for the trial (the population under study), and verifying for each patient whether the eligibility criteria are met.

Influence of data quality on computed Dutch hospital quality indicators: a case study in colorectal cancer surgery.

Background: Our study aims to assess the influence of data quality on computed Dutch hospital quality indicators, and whether colorectal cancer surgery indicators can be computed reliably based on routinely recorded data from an electronic medical record (EMR).

Methods: Cross-sectional study in a department of gastrointestinal oncology in a university hospital, in which a set of 10 indicators is computed (1) based on data abstracted manually for the national quality register Dutch Surgical Colorectal Audit (DSCA) as reference standard and (2) based on routinely collected data from an EMR. All 75 patients for whom data has been submitted to the DSCA for the reporting year 2011 and all 79 patients who underwent a resection of a primary colorectal carcinoma in 2011 according to structured data in the EMR were included.

Formalization and computation of quality measures based on electronic medical records.

Objective: Ambiguous definitions of quality measures in natural language impede their automated computability and also the reproducibility, validity, timeliness, traceability, comparability, and interpretability of computed results. Therefore, quality measures should be formalized before their release. We have previously developed and successfully applied a method for clinical indicator formalization (CLIF).

Barriers to the reuse of routinely recorded clinical data: a field report.

Today, clinical data is routinely recorded in vast amounts, but its reuse can be challenging. A secondary use that should ideally be based on previously collected clinical data is the computation of clinical quality indicators. In the present study, we attempted to retrieve all data from our hospital that is required to compute a set of quality indicators in the domain of colorectal cancer surgery.

Automated generation of patient-tailored electronic care pathways by translating computer-interpretable guidelines into hierarchical task networks.

Objective: This paper describes a methodology which enables computer-aided support for the planning, visualization and execution of personalized patient treatments in a specific healthcare process, taking into account complex temporal constraints and the allocation of institutional resources. To this end, a translation from a time-annotated computer-interpretable guideline (CIG) model of a clinical protocol into a temporal hierarchical task network (HTN) planning domain is presented.

Materials And Methods: The proposed method uses a knowledge-driven reasoning process to translate knowledge previously described in a CIG into a corresponding HTN Planning and Scheduling domain, taking advantage of HTNs known ability to (i) dynamically cope with temporal and resource constraints, and (ii) automatically generate customized plans.

The reproducibility of CLIF, a method for clinical quality indicator formalisation.

In order to be able to automatically calculate clinical quality indicators, we have proposed CLIF, a stepwise method for clinical quality indicator formalisation. Quality indicators are used for external accountability and hospital comparison. As clinical quality indicators are computed in a decentralised manner by the hospitals themselves, reproducibility of the formalisation method is essential to ensure the comparability of calculated values.

Using model checking for critiquing based on clinical guidelines.

Objective: Medical critiquing systems compare clinical actions performed by a physician with a predefined set of actions. In order to provide useful feedback, an important task is to find differences between the actual actions and a set of 'ideal' actions as described by a clinical guideline. In case differences exist, the critiquing system provides insight into the extent to which they are compatible.

Extraction and use of linguistic patterns for modelling medical guidelines.

Objective: The quality of knowledge updates in evidence-based medical guidelines can be improved and the effort spent for updating can be reduced if the knowledge underlying the guideline text is explicitly modelled using the so-called linguistic guideline patterns, mappings between a text fragment and a formal representation of its corresponding medical knowledge.

Methods And Material: Ontology-driven extraction of linguistic patterns is a method to automatically reconstruct the control knowledge captured in guidelines, which facilitates a more effective modelling and authoring of medical guidelines. We illustrate by examples the use of this method for generating and instantiating linguistic patterns in the text of a guideline for treatment of breast cancer, and evaluate the usefulness of these patterns in the modelling of this guideline.

Protocure: supporting the development of medical protocols through formal methods.

Medical guidelines and protocols describe the optimal care for a specific group of patients and therefore, when properly applied, improve the quality of patient care. During the last decade, a large number of medical guidelines and protocols have been published. However, the work done on developing and disseminating them far outweighs the efforts on guaranteeing their quality.