Clinical decision support system

Light chain amyloidosis is a plasma-cell disorder with a poor prognosis. It is a progressive condition, causing worsening pain, disability, and life-limiting complications involving multiple organ systems. The medical regimen can be complex, including chemotherapy or immunotherapy for the disease itself, as well as treatment for pain, gastrointestinal and cardiorespiratory symptoms, and various secondary symptoms. Patients and their families must have a realistic awareness of the illness and of the goals and limitations of treatments in making informed decisions about medical therapy, supportive management, and end-of-life planning...

Opioid administration is particularly challenging in the perioperative period. Computerized-based Clinical Decision Support Systems (CDSS) are a promising innovation that might improve perioperative pain control. We report the development and feasibility validation of a knowledge-based CDSS aiming at optimizing the management of perioperative pain, postoperative nausea and vomiting (PONV), and laxative medications. This novel CDSS uses patient adaptive testing through a smartphone display, literature-based rules, and individual medical prescriptions to produce direct medical advice for the patient user...

BACKGROUND: Respiratory syncytial virus (RSV) affects children, causing serious infections, particularly in high-risk groups. Given the seasonality of RSV and the importance of rapid isolation of infected individuals, there is an urgent need for more efficient diagnostic methods to expedite this process. OBJECTIVE: This study aimed to investigate the performance of a machine learning model that leverages the temporal diversity of symptom onset for detecting RSV infections and elucidate its discriminatory ability...

OBJECTIVES: To develop and validate a risk model for 1-year mortality based on variables available from early prehospital emergency attendance of patients with infection. MATERIAL AND METHODS: Prospective, observational, noninterventional multicenter study in adults with suspected infection transferred to 4 Spanish hospitals by advanced life-support ambulances from June 1, 2020, through June 30, 2022. We collected demographic, physiological, clinical, and analytical data...

BACKGROUND: CHIVID is a telemedicine solution developed under tight time constraints that assists Thai healthcare practitioners in monitoring non-severe COVID-19 patients in isolation programs during crises. It assesses patient health and notifies healthcare practitioners of high-risk scenarios through a chatbot. The system was designed to integrate with the famous Thai messaging app LINE, reducing development time and enhancing user-friendliness, and the system allowed patients to upload a pulse oximeter image automatically processed by the PACMAN function to extract oxygen saturation and heart rate values to reduce patient input errors...

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PURPOSE: Machine learning (ML) models presented an excellent performance in the prognosis prediction. However, the black box characteristic of ML models limited the clinical applications. Here, we aimed to establish explainable and visualizable ML models to predict biochemical recurrence (BCR) of prostate cancer (PCa). MATERIALS AND METHODS: A total of 647 PCa patients were retrospectively evaluated. Clinical parameters were identified using LASSO regression. Then, cohort was split into training and validation datasets with a ratio of 0...

OBJECTIVE: To develop and validate the Asthma Severity-Health Search (AS-HScore), predicting severe asthma risk in Italian primary care. According to the current asthma treatment guidelines, the AS-HScore intended to serve as a clinical decision support system (CDSS) for General Practitioners (GPs). METHODS: Using the Health Search Database (HSD), a cohort of 32,917 asthma-diagnosed patients between 2013 and 2021 was identified. The AS-HScore was developed using multivariable Cox regression in a two-part cohort: development and validation...

BACKGROUND: Diabetic ketoacidosis (DKA) is a frequent acute complication of diabetes mellitus (DM). It develops quickly, produces severe symptoms, and greatly affects the lives and health of individuals with DM.This article utilizes machine learning methods to examine the baseline characteristics that significantly contribute to the development of DKA. Its goal is to identify and prevent DKA in a targeted and early manner. METHODS: This study selected 2382 eligible diabetic patients from the MIMIC-IV dataset, including 1193 DM patients with ketoacidosis and 1186 DM patients without ketoacidosis...

BACKGROUND: Early research indicates that cancer patients are more vulnerable to adverse outcomes and mortality when infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Nonetheless, the specific attributes of SARS-CoV-2 in lung Adenocarcinoma (LUAD) have not been extensively and methodically examined. METHODS: We acquired 322 SARS-CoV-2 infection-related genes (CRGs) from the Human Protein Atlas database. Using an integrative machine learning approach with 10 algorithms, we developed a SARS-CoV-2 score (Cov-2S) signature across The Cancer Genome Atlas and datasets GSE72094, GSE68465, and GSE31210...

Using deep learning has demonstrated significant potential in making informed decisions based on clinical evidence. In this study, we deal with optimizing medication and quantitatively present the role of deep learning in predicting the medication dosage for patients with Parkinson's disease (PD). The proposed method is based on recurrent neural networks (RNNs) and tries to predict the dosage of five critical medication types for PD, including levodopa, dopamine agonists, monoamine oxidase-B inhibitors, catechol-O-methyltransferase inhibitors, and amantadine...

OBJECTIVES:  We conducted a focus group to assess the attitudes of primary care physicians (PCPs) toward prostate-specific antigen (PSA)-screening algorithms, perceptions of using decision support tools, and features that would make such tools feasible to implement. METHODS:  A multidisciplinary team (primary care, urology, behavioral sciences, bioinformatics) developed the decision support tool that was presented to a focus group of 10 PCPs who also filled out a survey...

OBJECTIVES:  Our objective was to pilot test an electronic health record-embedded decision support tool to facilitate prostate-specific antigen (PSA) screening discussions in the primary care setting. METHODS:  We pilot-tested a novel decision support tool that was used by 10 primary care physicians (PCPs) for 6 months, followed by a survey. The tool comprised (1) a risk-stratified algorithm, (2) a tool for facilitating shared decision-making (Simple Schema), (3) three best practice advisories (BPAs: <45, 45-75, and >75 years), and (4) a health maintenance module for scheduling automated reminders about PSA rescreening...

HKS21542, a highly selective activator of peripheral kappa opioid receptor agonists, plays a critical role in antinociception and itch inhibition during clinical development. Due to its indication population and elimination characteristics, it is imperative to evaluate the potential HSK21542 systemic exposure in individuals with renal impairment, hepatic impairment, the elderly, and the geriatric population. Here, a physiologically-based pharmacokinetic (PBPK) model for HSK21542 was developed based on in vitro metabolism and transport characteristics and in vivo elimination mechanism...

Deep Learning models such as Convolutional Neural Networks (CNNs) are very effective at extracting complex image features from medical X-rays. However, the limited interpretability of CNNs has hampered their deployment in medical settings as they failed to gain trust among clinicians. In this work, we propose an interactive framework to allow clinicians to ask what-if questions and intervene in the decisions of a CNN, with the aim of increasing trust in the system. The framework translates a layer of a trained CNN into a measurable and compact set of symbolic rules...

AIM: To study the clinical characteristics and prognosis of patients with functional class (FC) III-IV chronic heart failure (CHF) who meet the criteria for inclusion in the palliative care program. MATERIAL AND METHODS: A short registry of severe CHF forms was conducted at 60 outpatient and inpatient clinics in the Samara region for one month (16.05.2022-15.06.2022). The registry included patients with FC III-IV CHF who sought medical help during that period. Lethal outcomes were assessed at 90 days after the inclusion in the registry using the Mortality Information and Analytics system...

Artificial intelligence or machine learning (AI/ML) based systems can be used to help personalize prescribing decisions for individual patients. These AI/ML clinical decision support systems may provide either specific or more open-ended recommendations for the most appropriate medications to prescribe. These systems must fundamentally relate to the label of the medicines involved. The label of a medicine is an approved guide that indicates how to prescribe the drug in a safe and effective manner. The label for a medicine may evolve as new information on safety and effectiveness emerges, leading to the addition or removal of warnings, drug-drug interactions, or to permit new indications...

An echocardiogram is a sophisticated ultrasound imaging technique employed to diagnose heart conditions. The transthoracic echocardiogram, one of the most prevalent types, is instrumental in evaluating significant cardiac diseases. However, interpreting its results heavily relies on the clinician's expertise. In this context, artificial intelligence has emerged as a vital tool for helping clinicians. This study critically analyzes key state-of-the-art research that uses deep learning techniques to automate transthoracic echocardiogram analysis and support clinical judgments...

BACKGROUND: In the evolving field of health care, multimodal generative artificial intelligence (AI) systems, such as ChatGPT-4 with vision (ChatGPT-4V), represent a significant advancement, as they integrate visual data with text data. This integration has the potential to revolutionize clinical diagnostics by offering more comprehensive analysis capabilities. However, the impact on diagnostic accuracy of using image data to augment ChatGPT-4 remains unclear. OBJECTIVE: This study aims to assess the impact of adding image data on ChatGPT-4's diagnostic accuracy and provide insights into how image data integration can enhance the accuracy of multimodal AI in medical diagnostics...

PURPOSE: Clinical decision support systems (CDSS) are used to identify drugs with potential need for dose modification in patients with renal impairment. ChatGPT holds the potential to be integrated in the electronic health record (EHR) system to give such dosing advices. In this study, we aim to evaluate the performance of ChatGPT in clinical rule-guided dose interventions in hospitalized patients with renal impairment. METHODS: This cross-sectional study was performed at Tergooi Medical Center, the Netherlands...