International Journal of Medical Informatics

Infant mortality is one of the most important socioeconomic and health quality indicators in the world. In Brazil, neonatal mortality accounts to 70% of the infant mortality. Despite its importance, neonatal mortality shows increasing signals, which causes concerns about the necessity of efficient and effective methods able to help reducing it. In this paper a new approach is proposed to classify newborns that may be susceptible to neonatal mortality by applying supervised machine learning metho...

BackgroundPost-discharge opioid consumption is an important source of data in guiding appropriate opioid prescribing guidelines, but its collection is tedious and requires significant resources. Furthermore, the reliability of post-discharge opioid consumption surveys is unclear. Our group developed an automated short messaging service (SMS)-to-web survey for collecting this data from patients. In this study, we assessed its effectiveness in estimating opioid consumption by performing causal adj...

BackgroundOpioids are a widely prescribed class of medication for pain management. However, they have variable efficacy and adverse effects among patients, due to complex interplay between biological and clinical factors. Pharmacogenetic (PGx) testing can be utilized to match patients genetic profiles to individualize opioid therapy, improving pain relief and reducing the risk of adverse effects. Despite its potential, PGx uptake--utilization of PGx testing--remains low due to a range of barrier...

IntroductionSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and influenza virus are contagious respiratory pathogens with similar symptoms but require different treatment and management strategies. This study investigated whether laboratory blood tests can discriminate between SARS-CoV-2 and influenza infections at emergency department (ED) presentation. Methods723 influenza A/B positive (2018/1/1 to 2020/3/15) and 1,281 SARS-CoV-2 positive (2020/3/11 to 2020/6/30) ED patients were...

Due to the high economic and public health burden of chronic pain, and the risk of public health consequences of opioid-based treatments, there is a need to identify effective alternative therapies. The evidence basis for many alternative therapies is weak or nonexistent. Social media presents a unique opportunity to gather large-scale knowledge about such therapies self-reported by sufferers themselves. We attempted to (i) verify the presence of largescale chronic pain-related chatter on Twitte...

BackgroundCommunity-acquired pneumonia (CAP) is an acute respiratory condition associated with high mortality in adult populations and is potentially more serious in older patients. Accurate and consistently applied prediction of outcome may contribute to reduce in-hospital mortality. Currently, CAP outcomes are assessed with clinical scores like CURB65, based on signs and symptoms that are non-specific to the disease. Recent literature has shown that machine learning (ML) has the potential to i...

BackgroundIn clinical settings, patients often express dissatisfaction through narrative speech or written text. However, most complaints management systems still rely on manual review or rulebased methods that fail to capture the severity or urgency of complaints. This leads to inconsistent triage, delayed resolution and missed opportunities for systemic improvement. A novel model leveraging large language model-assisted content analysis (LACA) and machine learning (ML) can transform subjective...

ObjectiveEmergency department (ED) encounters represent valuable opportunities to initiate evidence-based treatments for patients with opioid misuse, but few receive such care. Universal manual screening has been proposed to improve patient identification but is uncommon due to its time and resource-intensive nature. We sought to determine the feasibility of identifying patients with opioid misuse at the time of ED triage using machine learning (ML). MethodsWe conducted a retrospective cohort s...

This report addresses, from a machine learning perspective, a multi-class classification problem to predict the first deterioration level of a COVID-19 positive patient at the time of hospital admission. Socio-demographic features, laboratory tests and other measures are taken into account to learn the models. Our output is divided into 4 categories ranging from healthy patients, followed by patients requiring some form of ventilation (divided in 2 cate-gories) and finally patients expected to d...

The sudden increase of COVID-19 cases is putting a high pressure on healthcare services worldwide. At the current stage, fast, accurate and early clinical assessment of the disease severity is vital. To support decision making and logistical planning in healthcare systems, this study leverages a database of blood samples from 404 infected patients in the region of Wuhan, China to identify crucial predictive biomarkers of disease severity. For this purpose, machine learning tools selected three b...

AO_SCPLOWBSTRACTC_SCPLOWOpen source software that enable research and development of machine learning (ML) models for clinical use cases are fragmented, poorly maintained and fall short in functionality. CyclOps is a software framework designed to address this gap and help accelerate the development of ML models for health. In this paper, we describe the architecture, APIs and implementation details of CyclOps, while providing benchmarks on example clinical use cases. We emphasize that CyclOps i...

BackgroundTemporal variability in healthcare processes or protocols is intrinsic to medicine. Such variability can potentially introduce dataset shifts, a data quality issue when reusing electronic health records (EHRs) for secondary purposes. Temporal dataset shifts can present as trends, abrupt or seasonal changes in the statistical distributions of data over time, being particularly complex to address in multi-modal and highly coded data. These changes, if not delineated, can harm population ...

BackgroundFor accurate medication usage statistics and medication adherence calculations, we need to have an accurate days supply (DS) for each prescription. Unfortunately, often the DS or information needed for calculating the DS is not provided. Therefore, other methods need to be applied to acquire missing values or substituting incorrect values. ObjectiveThe aim of this study is to apply a variety of methods for managing incomplete and missing data to enhance the accuracy of calculating DS ...

BackgroundThe digitisation of healthcare records has generated vast amounts of unstructured data, presenting opportunities for improvements in disease diagnosis when clinical coding falls short, such as in the recording of patient symptoms. This study presents an approach using natural language processing to extract clinical concepts from free-text which are used to automatically form diagnostic criteria for lung cancer from unstructured secondary-care data. MethodsPatients aged 40 and above wh...

COVID-19 is an infectious disease discovered after the outbreak began in Wuhan, China, in December 2019. COVID-19 is still becoming an increasing global threat to public health. The virus has been escalated to many countries across the globe. This paper analyzed and compared the performance of three different supervised machine learning techniques; Linear Discriminant Analysis (LDA), Random Forest (RF), and Support Vector Machine (SVM) on COVID-19 dataset. The best level of accuracy between thes...

BackgroundDiabetic nephropathy (DN) is a serious microvascular complication that affects 40% of diabetes patients. In the last decade, artificial intelligence (AI) has been widely used in both structured and unstructured clinical data to improve the treatment of patients/potential patients with DN. MethodsThis systematic review aims to cover all applications of AI in the clinical use of DN or related topics. Studies were searched in four open-access databases (Pubmed, IEEE Xplore, DBLP Computer...

PurposeRare diseases are difficult to fully capture, and regularly call for large, geographically dispersed initiatives. Such initiatives are often met with data harmonisation challenges. These challenges render data incompatible and impede successful realisation. The STRONG AYA project is such an initiative, specifically focusing on adolescents and young adults (AYAs) with cancer. STRONG AYA is setting up a federated data infrastructure containing data of varying format. Here, we elaborate on h...

PurposeRecently introduced Large Language Models (LLMs) such as ChatGPT have already shown promising results in natural language processing in healthcare. The aim of this study is to systematically review the literature on the applications of LLMs in breast cancer diagnosis and care. MethodsA literature search was conducted using MEDLINE, focusing on studies published up to October 22nd, 2023, using the following terms: "large language models", "LLM", "GPT", "ChatGPT", "OpenAI", and "breast". ...

Machine learning (ML) models for analyzing medical data are critical for both accelerating development of novel diagnostic and treatment strategies and improving the accuracy of medical care delivery. Our objective was to comprehensively review supervised ML models for diagnosis or treatment prediction. Publications indexed in PubMed were reviewed to identify articles utilizing supervised predictive ML models in medicine. Articles published between 01/01/2020-01/01/2022 were included in this rev...

ObjectiveThe recent pandemic of novel coronavirus disease 2019 (COVID-19) is increasingly causing severe acute respiratory syndrome (SARS) and significant mortality. We aim here to identify the risk factors associated with mortality of coronavirus infected persons using a supervised machine learning approach. Research Design and MethodsClinical data of 1085 cases of COVID-19 from 13th January to 28th February, 2020 was obtained from Kaggle, an online community of Data scientists. 430 cases were...