Exploring Explainable AI Techniques for Text Classification in Healthcare: A Scoping Review.

Text classification plays an essential role in the medical domain by organizing and categorizing vast amounts of textual data through machine learning (ML) and deep learning (DL). The adoption of Artificial Intelligence (AI) technologies in healthcare has raised concerns about the interpretability of AI models, often perceived as "black boxes." Explainable AI (XAI) techniques aim to mitigate this issue by elucidating AI model decision-making process.

Measured Performance and Healthcare Professional Perception of Large Language Models Used as Clinical Decision Support Systems: A Scoping Review.

The healthcare sector confronts challenges from overloaded tumor board meetings, reduced discussion durations, and care quality concerns, necessitating innovative solutions. Integrating Clinical Decision Support Systems (CDSSs) has a potential in supporting clinicians to reduce the cancer burden, but CDSSs remain poorly used in clinical practice. The emergence of OpenAI's ChatGPT in 2022 has prompted the evaluation of Large Language Models (LLMs) as potential CDSSs for diagnosis and therapeutic management.

Leveraging Rule-Based NLP to Translate Textual Reports as Structured Inputs Automatically Processed by a Clinical Decision Support System.

Using clinical decision support systems (CDSSs) for breast cancer management necessitates to extract relevant patient data from textual reports which is a complex task although efficiently achieved by machine learning but black box methods. We proposed a rule-based natural language processing (NLP) method to automate the translation of breast cancer patient summaries into structured patient profiles suitable for input into the guideline-based CDSS of the DESIREE project. Our method encompasses named entity recognition (NER), relation extraction and structured data extraction to systematically organize patient data.

Clinical Decision Support Systems Applied to the Management of Breast Cancer Patients: A Scoping Review.

Breast cancer is the most commonly diagnosed cancer worldwide, and its burden has been rising over the past decades. A significant advance in healthcare is the integration of Clinical Decision Support Systems (CDSSs) into medical practice, which support healthcare professionals improving clinical decisions, leading to recommended patient-specific treatments and enhanced patient care. Breast cancer CDSSs are thus currently expanding, whether applied to screening, diagnostic, therapeutic or follow-up tasks.

Methods Used to Compare Narrative Clinical Practice Guidelines: A Scoping Review.

Guideline-based clinical decision support systems (CDSSs) need the most recent evidence for reliable performance, making the provision of regularly updated clinical practice guidelines (CPGs) a major issue. Some international guidelines are renewed in short intervals and can be used for checking the status of given national guidelines with regard to the most recent evidence. Considering the volume of medical data and the number of CPGs published, computerized comparison of clinical guidelines can be an effective method.

Using Machine Learning on Imbalanced Guideline Compliance Data to Optimize Multidisciplinary Tumour Board Decision Making for the Management of Breast Cancer Patients.

Complex breast cancer cases that need further multidisciplinary tumor board (MTB) discussions should have priority in the organization of MTBs. In order to optimize MTB workflow, we attempted to predict complex cases defined as non-compliant cases despite the use of the decision support system OncoDoc, through the implementation of machine learning procedures and algorithms (Decision Trees, Random Forests, and XGBoost). F1-score after cross-validation, sampling implementation, with or without feature selection, did not exceed 40%.

Comparison of MetaMap, cTAKES, SIFR, and ECMT to Annotate Breast Cancer Patient Summaries.

Most clinical texts including breast cancer patient summaries (BCPSs) are elaborated as narrative documents difficult to process by decision support systems. Annotators have been developed to extract the relevant content of such documents, e.g.

Using Machine Learning and Deep Learning Methods to Predict the Complexity of Breast Cancer Cases.

In many countries, the management of cancer patients must be discussed in multidisciplinary tumor boards (MTBs). These meetings have been introduced to provide a collaborative and multidisciplinary approach to cancer care. However, the benefits of MTBs are now being challenged because there are a lot of cases and not enough time to discuss all the of them.

ABiMed: Towards an Innovative Clinical Decision Support System for Medication Reviews and Polypharmacy Management.

Polypharmacy in elderly is a public health problem with both clinical (increase of adverse drug events) and economic issues. One solution is medication review, a structured assessment of patients' drug orders by the pharmacist for optimizing the therapy. However, this task is tedious, cognitively complex and error-prone, and only a few clinical decision support systems have been proposed for supporting it.

Are Semantic Annotators Able to Extract Relevant Complexity-Related Concepts from Clinical Notes?

Clinical decision support systems (CDSSs) implementing cancer clinical practice guidelines (CPGs) have the potential to improve the compliance of decisions made by multidisciplinary tumor boards (MTB) with CPGs. However, guideline-based CDSSs do not cover complex cases and need time for discussion. We propose to learn how to predict complex cancer cases prior to MTBs from breast cancer patient summaries (BCPSs) resuming clinical notes.

Deep Learning, a Not so Magical Problem Solver: A Case Study with Predicting the Complexity of Breast Cancer Cases.

Using guideline-based clinical decision support systems (CDSSs) has improved clinical practice, especially during multidisciplinary tumour boards (MTBs) in cancer patient management. However, MTBs have been reported to be overcrowded, with limited time to discuss all cases. Complex breast cancer cases that need further MTB discussions should have priority in the organization of MTBs.

Users' Assessement of the Visualization with Gauges of a Guideline-Based Decision Support System Output: A Qualitative Study of Focus Groups.

Computerized decision support systems (CDSSs) are still poorly routinely implemented in clinical practices mainly because of usability problems related to the technology interface. We previously proposed to use gauges to visualize the output of a guideline-based CDSS applied to malnutrition and pressure ulcer management in nursing homes. This interface was assessed by four focus groups including 16 healthcare professionals with expertise in geriatrics.

Reusing Decisions Made with One Decision Support System to Assess a Second Decision Support System: Introducing the Notion of Complex Cases.

The guideline-based decision support system (GL-DSS) of the DESIREE project and OncoDoc are two clinical decision support systems applied to the management of breast cancer. In order to evaluate the DESIREE GL-DSS, we decided to reuse a sample of clinical cases previously resolved by the multidisciplinary tumor board (MTB) of the Tenon Hospital (Paris, France) when using OncoDoc. Since we had two different knowledge representation models to represent clinical parameters and decisions, and two formalisms to represent guidelines, we developed a transformation sequence, involving the creation of synthetic patients, the enrichment of DESIREE ontology, and the translation of clinical cases and their decisions, to transform OncoDoc data into the DESIREE representation.

Easy Medication Reconciliation at Hospital Admission: The EzMedRec Decision Support System.

Medication reconciliation (MR) aims at preventing medication errors at care transitions. It is a complex, time-consuming, cognitively demanding pharmacological task. We have developed a decision support system, EzMedRec, to assist retroactive MR at hospital admission.

Preliminary Qualitative and Quantitative Evaluation of DESIREE, a Decision Support Platform for the Management of Primary Breast Cancer Patients.

The DESIREE project has developed a platform offering several complementary therapeutic decision support systems (DSSs) to improve care quality for breast cancer patients. A first assessment of the system was carried out in close-to-real tumor boards (TBs). Fourteen TB sessions were organized corresponding to a total of 125 exploitable decisions previously made without the system and re-played with the system after a washout period in three pilot sites.

Computerized Decision Support Systems for Nursing Homes: A Scoping Review.

Objectives: To summarize the research literature describing the outcomes of computerized decision support systems (CDSSs) implemented in nursing homes (NHs).

Design: Scoping review.

Methods: Search of relevant articles published in the English language between January 1, 2000, and February 29, 2020, in the Medline database.

Creating Synthetic Patients to Address Interoperability Issues: A Case Study with the Management of Breast Cancer Patients.

Interoperability issues are common in biomedical informatics. Reusing data generated from a system in another system, or integrating an existing clinical decision support system (CDSS) in a new organization is a complex task due to recurrent problems of concept mapping and alignment. The GL-DSS of the DESIREE project is a guideline-based CDSS to support the management of breast cancer patients.

From Atomic Guideline-Based Recommendations to Complete Therapeutic Care Plans: A Knowledge-Based Approach Applied to Breast Cancer Management.

How textual clinical practice guidelines are written may have an impact on how they are formalized and on the kind of recommendations issued by the clinical decision support systems (CDSSs) that implement them. Breast cancer guidelines are mostly centered on the description of the different recommended therapeutic modalities, represented as atomic recommendations, but seldom provide comprehensive plans that drive care delivery. The objective of this work is to implement a knowledge-based approach to develop a care plan builder (CPB) that works on atomic recommendations to build patient-centered care plans as sequences of chronologically ordered therapeutic steps.

Visualization of Guideline-Based Decision Support for the Management of Pressure Ulcers in Nursing Homes.

Though a preventable risk, the management of pressure ulcers (PUs) in nursing homes is not satisfactory due to inadequate prevention and complex care plans. PUs early detection and wound assessment require to know the patient condition and risk factors and to have a good knowledge of best practices. We built a guideline-based clinical decision support system (CDSS) for the prevention, the assessment, and the management of PUs.

Implementation of an ontological reasoning to support the guideline-based management of primary breast cancer patients in the DESIREE project.

The DESIREE project has developed a platform offering several complementary therapeutic decision support modules to improve the quality of care for breast cancer patients. All modules are operating consistently with a common breast cancer knowledge model (BCKM) following the generic entity-attribute-value model. The BCKM is formalized as an ontology including both the data model to represent clinical patient information and the termino-ontological model to represent the application domain concepts.

Guideline-Based Decision Support System for Nursing Homes: A Case Study with the Management of Malnutrition.

Nursing home (NH) residents are known to be at risk of preventable adverse events due to inadequate monitoring or failure to provide necessary treatments. Missed care has been partially explained by nurses' lack of knowledge. We describe a guideline-based decision support system for the management of malnutrition in NHs.

Update on the DMP, the French Nationally Shared Medical Record: Did We Make It?

Different countries have solved the problem of care coordination by implementing a nationwide centralized framework of clinical information sharing with "new" secure online care records stored in specifically created platforms. The French DMP initially launched in 2006, relaunched in 2010, and re-relaunched in 2016 follows this model. After a difficult start and some governmental actions to promote its adoption, the DMP has been nationally deployed in November 2018.

Making a Subjective Notion Computer-Interpretable: The Case of the Tumour-Volume to Breast-Volume Ratio for the Surgical Decision of Breast Cancer.

Clinical practice guidelines (CPGs) often include ambiguous criteria making their translation as computer-interpretable guidelines a difficult task. In breast cancer management, whether to perform a breast conservative surgery (BCS) or not is one example. Most international CPGs recommend to perform a BCS when the tumour volume / breast volume ratio allows for good cosmetic results, which cannot be directly translated into a computable format.

Localisation, Personalisation and Delivery of Best Practice Guidelines on an Integrated Care and Cure Cloud Architecture: The C3-Cloud Approach to Managing Multimorbidity.

Background: C3-Cloud is an integrated care ICT infrastructure offering seamless patient-centered approach to managing multimorbidity, deployed in three European pilot sites. Challenge: The digital delivery of best practice guidelines unified for multimorbidity, customized to local practice, offering the capability to improve patient personalization and benefit.

Method: C3-Cloud has adopted a co-production approach to developing unified multimorbidity guidelines, by collating and reconciling best practice guidelines for each condition.

Clinical Decision Support Systems in Nursing Homes: A Scoping Review.

The world population is dramatically ageing, resulting in an increase of the prevalence of older dependent adults living in nursing homes (NHs). Because of insufficient resources in NHs, and nurses' lack of time and knowledge, adverse events, most of them being preventable, are often reported. Clinical decision support systems (CDSSs) have proven to improve the quality of care in various healthcare settings such as hospitals and primary care centers.