Differentiating viral and bacterial infections: A machine learning model based on routine blood test values.

The growing threat of antibiotic resistance necessitates accurate differentiation between bacterial and viral infections for proper antibiotic administration. In this study, a Virus vs. Bacteria machine learning model was developed to distinguish between these infection types using 16 routine blood test results, C-reactive protein concentration (CRP), biological sex, and age. With a dataset of 44,120 cases from a single medical center, the model achieved an accuracy of 82.2 %, a sensitivity of 79.7 %, a specificity of 84.5 %, a Brier score of 0.129, and an area under the ROC curve (AUC) of 0.905, outperforming a CRP-based decision rule. Notably, the machine learning model enhanced accuracy within the CRP range of 10-40 mg/L, a range where CRP alone is less informative. These results highlight the advantage of integrating multiple blood parameters in diagnostics. The "Virus vs. Bacteria" model paves the way for advanced diagnostic tools, leveraging machine learning to optimize infection management.

Disease Progression of Hypertrophic Cardiomyopathy: Modeling Using Machine Learning.

BACKGROUND: Cardiovascular disorders in general are responsible for 30% of deaths worldwide. Among them, hypertrophic cardiomyopathy (HCM) is a genetic cardiac disease that is present in about 1 of 500 young adults and can cause sudden cardiac death (SCD). OBJECTIVE: Although the current state-of-the-art methods model the risk of SCD for patients, to the best of our knowledge, no methods are available for modeling the patient's clinical status up to 10 years ahead. In this paper, we propose a novel machine learning (ML)-based tool for predicting disease progression for patients diagnosed with HCM in terms of adverse remodeling of the heart during a 10-year period. METHODS: The method consisted of 6 predictive regression models that independently predict future values of 6 clinical characteristics: left atrial size, left atrial volume, left ventricular ejection fraction, New York Heart Association functional classification, left ventricular internal diastolic diameter, and left ventricular internal systolic diameter. We supplemented each prediction with the explanation that is generated using the Shapley additive explanation method. RESULTS: The final experiments showed that predictive error is lower on 5 of the 6 constructed models in comparison to experts (on average, by 0.34) or a consortium of experts (on average, by 0.22). The experiments revealed that semisupervised learning and the artificial data from virtual patients help improve predictive accuracies. The best-performing random forest model improved R2 from 0.3 to 0.6. CONCLUSIONS: By engaging medical experts to provide interpretation and validation of the results, we determined the models' favorable performance compared to the performance of experts for 5 of 6 targets.

A machine learning-based risk stratification model for ventricular tachycardia and heart failure in hypertrophic cardiomyopathy.

BACKGROUND: Machine learning (ML) and artificial intelligence are emerging as important components of precision medicine that enhance diagnosis and risk stratification. Risk stratification tools for hypertrophic cardiomyopathy (HCM) exist, but they are based on traditional statistical methods. The aim was to develop a novel machine learning risk stratification tool for the prediction of 5-year risk in HCM. The goal was to determine if its predictive accuracy is higher than the accuracy of the state-of-the-art tools. METHOD: Data from a total of 2302 patients were used. The data were comprised of demographic characteristics, genetic data, clinical investigations, medications, and disease-related events. Four classification models were applied to model the risk level, and their decisions were explained using the SHAP (SHapley Additive exPlanations) method. Unwanted cardiac events were defined as sustained ventricular tachycardia occurrence (VT), heart failure (HF), ICD activation, sudden cardiac death (SCD), cardiac death, and all-cause death. RESULTS: The proposed machine learning approach outperformed the similar existing risk-stratification models for SCD, cardiac death, and all-cause death risk-stratification: it achieved higher AUC by 17%, 9%, and 1%, respectively. The boosted trees achieved the best performing AUC of 0.82. The resulting model most accurately predicts VT, HF, and ICD with AUCs of 0.90, 0.88, and 0.87, respectively. CONCLUSIONS: The proposed risk-stratification model demonstrates high accuracy in predicting events in patients with hypertrophic cardiomyopathy. The use of a machine-learning risk stratification model may improve patient management, clinical practice, and outcomes in general.

COVID-19 diagnosis by routine blood tests using machine learning.

Physicians taking care of patients with COVID-19 have described different changes in routine blood parameters. However, these changes hinder them from performing COVID-19 diagnoses. We constructed a machine learning model for COVID-19 diagnosis that was based and cross-validated on the routine blood tests of 5333 patients with various bacterial and viral infections, and 160 COVID-19-positive patients. We selected the operational ROC point at a sensitivity of 81.9% and a specificity of 97.9%. The cross-validated AUC was 0.97. The five most useful routine blood parameters for COVID-19 diagnosis according to the feature importance scoring of the XGBoost algorithm were: MCHC, eosinophil count, albumin, INR, and prothrombin activity percentage. t-SNE visualization showed that the blood parameters of the patients with a severe COVID-19 course are more like the parameters of a bacterial than a viral infection. The reported diagnostic accuracy is at least comparable and probably complementary to RT-PCR and chest CT studies. Patients with fever, cough, myalgia, and other symptoms can now have initial routine blood tests assessed by our diagnostic tool. All patients with a positive COVID-19 prediction would then undergo standard RT-PCR studies to confirm the diagnosis. We believe that our results represent a significant contribution to improvements in COVID-19 diagnosis.

Mobile App for Symptom Management and Associated Quality of Life During Systemic Treatment in Early Stage Breast Cancer: Nonrandomized Controlled Prospective Cohort Study.

BACKGROUND: Providing patients with cancer who are undergoing systemic therapy with useful information about symptom management is essential to prevent unnecessary deterioration of quality of life. OBJECTIVE: The aim was to evaluate whether use of an app for symptom management was associated with any change in patient quality of life or use of health resources. METHODS: Outpatients with early stage breast cancer receiving systemic therapy were recruited at the Institute of Oncology in Ljubljana, Slovenia. Patients who received systemic therapy between December 2017 and March 2018 (control group) and between April 2018 and September 2018 (intervention group) were eligible. All patients received standard care, but only those in the intervention group were asked to use mPRO Mamma, an Android-based smartphone app, in addition. The app supported daily tracking of 50 symptoms, allowed users to grade their symptom severity (as mild, moderate, or severe), and also provided in-depth descriptions and recommendations based on reported symptom level. Patient-reported outcomes in both groups were assessed through the European Organisation for Research and Treatment of Cancer (EORTC) core (C-30) and breast cancer (BR-23) questionnaires, as well as a questionnaire about health resources use. The primary outcomes were the difference in the global quality of life between groups and the difference in summary score of the EORTC C-30 questionnaire between groups after 3 time periods (the first week of treatment, the first treatment cycle, and the entire treatment). The secondary outcome was the use of health resources (doctor visits and hospitalizations) in each time period. Other scales were used for exploratory analysis. RESULTS: The mean difference between the intervention group (n=46) and the control group (n=45) in global quality of life (adjusted for baseline and type of surgery) after the first week was 10.1 (95% CI 1.8 to 18.5, P=.02). The intervention group summary scores were significantly higher than those of the control group after the first week (adjusted mean difference: 8.9, 95% CI 3.1 to 14.7, P=.003) and at the end of treatment (adjusted mean difference: 10.6, 95% CI 3.9 to 17.3, P=.002). Use of health resources was not statistically significant between the groups in either the first week (P=.12) or the first treatment cycle (P=.13). Exploratory analysis findings demonstrated clinically important improvements (indicated by EORTC C-30 or BR-23 scale scores)-social, physical, role, and cognitive function were improved while pain, appetite loss, and systemic therapy side effects were reduced. CONCLUSIONS: Use of the app enabled patients undergoing systemic therapy for early stage breast cancer to better cope with symptoms which was demonstrated by a better global quality of life and summary score after the first week and by a better summary score at the end of treatment in the intervention group compared to those of the control group, but no change in the use of health resources was demonstrated.

Diagnosing brain tumours by routine blood tests using machine learning.

Routine blood test results are assumed to contain much more information than is usually recognised even by the most experienced clinicians. Using routine blood tests from 15,176 neurological patients we built a machine learning predictive model for the diagnosis of brain tumours. We validated the model by retrospective analysis of 68 consecutive brain tumour and 215 control patients presenting to the neurological emergency service. Only patients with head imaging and routine blood test data were included in the validation sample. The sensitivity and specificity of the adapted tumour model in the validation group were 96% and 74%, respectively. Our data demonstrate the feasibility of brain tumour diagnosis from routine blood tests using machine learning. The reported diagnostic accuracy is comparable and possibly complementary to that of imaging studies. The presented machine learning approach opens a completely new avenue in the diagnosis of these grave neurological diseases and demonstrates the utility of valuable information obtained from routine blood tests.

A topological approach to delineation and arrhythmic beats detection in unprocessed long-term ECG signals.

BACKGROUND AND OBJECTIVE: Arrhythmias are one of the most common symptoms of cardiac failure. They are usually diagnosed using ECG recordings, particularly long ambulatory recordings (AECG). These recordings are tedious to interpret by humans due to their extent (up to 48 h) and the relative scarcity of arrhythmia events. This makes automated systems for detecting various AECG anomalies indispensable. In this work we present a novel procedure based on topological principles (Morse theory) for detecting arrhythmic beats in AECG. It works in nearly real-time (delayed by a 14 s window), and can be applied to raw (unprocessed) ECG signals. METHODS: The procedure is based on a subject-specific adaptation of the one-dimensional discrete Morse theory (ADMT), which represents the signal as a sequence of its most important extrema. The ADMT algorithm is applied twice; for low-amplitude, high-frequency noise removal, and for detection of the characteristic waves of individual ECG beats. The waves are annotated using the ADMT algorithm and template matching. The annotated beats are then compared to the adjacent beats with two measures of similarity: the distance between two beats, and the difference in shape between them. The two measures of similarity are used as inputs to a decision tree algorithm that classifies the beats as normal or abnormal. The classification performance is evaluated with the leave-one-record-out cross-validation method. RESULTS: Our approach was tested on the MIT-BIH database, where it exhibited a classification accuracy of 92.73%, a sensitivity of 73.35%, a specificity of 96.70%, a positive predictive value of 88.01%, and a negative predictive value of 95.73%. CONCLUSIONS: Compared to related studies, our algorithm requires less preprocessing while retaining the capability to detect and classify beats in almost real-time. The algorithm exhibits a high degree of accuracy in beats detection and classification that are at least comparable to state-of-the-art methods.

An application of machine learning to haematological diagnosis.

Quick and accurate medical diagnoses are crucial for the successful treatment of diseases. Using machine learning algorithms and based on laboratory blood test results, we have built two models to predict a haematologic disease. One predictive model used all the available blood test parameters and the other used only a reduced set that is usually measured upon patient admittance. Both models produced good results, obtaining prediction accuracies of 0.88 and 0.86 when considering the list of five most likely diseases and 0.59 and 0.57 when considering only the most likely disease. The models did not differ significantly, which indicates that a reduced set of parameters can represent a relevant "fingerprint" of a disease. This knowledge expands the model's utility for use by general practitioners and indicates that blood test results contain more information than physicians generally recognize. A clinical test showed that the accuracy of our predictive models was on par with that of haematology specialists. Our study is the first to show that a machine learning predictive model based on blood tests alone can be successfully applied to predict haematologic diseases. This result and could open up unprecedented possibilities for medical diagnosis.

Classification of ischaemic episodes with ST/HR diagrams.

Coronary artery disease is the developed world's premier cause of mortality and the most probable cause of myocardial ischaemia. More advanced diagnostic tests aside, in electrocardiogram (ECG) analysis it manifests itself as a ST segment deviation, targeted by both exercise ECG and ambulatory ECG. In ambulatory ECG, besides ischaemic ST segment deviation episodes there are also non-ischaemic heart rate related episodes which aggravate real ischaemia detection. We present methods to transform the features developed for the heart rate adjustment of ST segment depression in exercise ECG for use in ambulatory ECG. We use annotations provided by the Long-Term ST Database to plot the ST/HR diagrams and then estimate the overall and maximal slopes of the diagrams in the exercise and recovery phase for each ST segment deviation episode. We also estimate the angle at the extrema of the ST/HR diagrams. Statistical analysis shows that ischaemic ST segment deviation episodes have significantly steeper overall and maximal slopes than heart rate related episodes, which indicates the explored features' utility for distinguishing between the two types of episodes. This makes the proposed features very useful in automated ECG analysis.

Modern parameterization and explanation techniques in diagnostic decision support system: a case study in diagnostics of coronary artery disease.

OBJECTIVE: Coronary artery disease has been described as one of the curses of the western world, as it is one of its most important causes of mortality. Therefore, clinicians seek to improve diagnostic procedures, especially those that allow them to reach reliable early diagnoses. In the clinical setting, coronary artery disease diagnostics are typically performed in a sequential manner. The four diagnostic levels consist of evaluation of (1) signs and symptoms of the disease and electrocardiogram at rest, (2) sequential electrocardiogram testing during the controlled exercise, (3) myocardial perfusion scintigraphy, and (4) finally coronary angiography, that is considered as the "gold standard" reference method. Our study focuses on improving diagnostic performance of the third, virtually non-invasive, diagnostic level. METHODS AND MATERIALS: Myocardial scintigraphy results in a series of medical images that are obtained by relatively inexpensive means. In clinical practice, these images are manually described (parameterized) by expert physicians. In the paper we present an innovative alternative to manual image evaluation-an automatic image parameterization on multiple resolutions, based on texture description with specialized association rules. Extracted image parameters are combined into more informative composite parameters by means of principal component analysis, and finally used to build automatic classifiers with machine learning methods. RESULTS: Our experiments with synthetic datasets show that association-rule-based multi-resolution image parameterization works very well for scintigraphic images of the heart. In coronary artery disease diagnostics we confirm these results as our approach significantly improves on clinical results in terms of diagnostic performance. We improve diagnostic accuracy by 17%, specificity by 12% and sensitivity by 22%. We also significantly improve the number of reliably diagnosed patients by 19% for positive diagnoses, and 16% for negative diagnoses, so that no costly further tests are necessary for them. CONCLUSIONS: Multi-resolution image parameterization equals or even betters that of the physicians in terms of the diagnostic quality of image parameters. By using these parameters for building machine learning classifiers, we can significantly improve diagnostic performance with respect to the results of clinical practice, affect process rationalization, as well as possibly provide novel insights into the diagnostic problems, features and/or processes.

Image processing and machine learning for fully automated probabilistic evaluation of medical images.

The paper presents results of our long-term study on using image processing and data mining methods in a medical imaging. Since evaluation of modern medical images is becoming increasingly complex, advanced analytical and decision support tools are involved in integration of partial diagnostic results. Such partial results, frequently obtained from tests with substantial imperfections, are integrated into ultimate diagnostic conclusion about the probability of disease for a given patient. We study various topics such as improving the predictive power of clinical tests by utilizing pre-test and post-test probabilities, texture representation, multi-resolution feature extraction, feature construction and data mining algorithms that significantly outperform medical practice. Our long-term study reveals three significant milestones. The first improvement was achieved by significantly increasing post-test diagnostic probabilities with respect to expert physicians. The second, even more significant improvement utilizes multi-resolution image parametrization. Machine learning methods in conjunction with the feature subset selection on these parameters significantly improve diagnostic performance. However, further feature construction with the principle component analysis on these features elevates results to an even higher accuracy level that represents the third milestone. With the proposed approach clinical results are significantly improved throughout the study. The most significant result of our study is improvement in the diagnostic power of the whole diagnostic process. Our compound approach aids, but does not replace, the physician's judgment and may assist in decisions on cost effectiveness of tests.

Computerized segmentation of whole-body bone scintigrams and its use in automated diagnostics.

Bone scintigraphy or whole-body bone scan is one of the most common diagnostic procedures in nuclear medicine used in the last 25 years. Pathological conditions, technically poor image resolution and artefacts necessitate that algorithms use sufficient background knowledge of anatomy and spatial relations of bones in order to work satisfactorily. A robust knowledge based methodology for detecting reference points of the main skeletal regions that is simultaneously applied on anterior and posterior whole-body bone scintigrams is presented. Expert knowledge is represented as a set of parameterized rules which are used to support standard image-processing algorithms. Our study includes 467 consecutive, non-selected scintigrams, which is, to our knowledge the largest number of images ever used in such studies. Automatic analysis of whole-body bone scans using our segmentation algorithm gives more accurate and reliable results than previous studies. Obtained reference points are used for automatic segmentation of the skeleton, which is applied to automatic (machine learning) or manual (expert physicians) diagnostics. Preliminary experiments show that an expert system based on machine learning closely mimics the results of expert physicians.

Transductive machine learning for reliable medical diagnostics.

In the past decades Machine Learning tools have been successfully used in several medical diagnostic problems. While they often significantly outperform expert physicians (in terms of diagnostic accuracy, sensitivity, and specificity), they are mostly not being used in practice. One reason for this is that it is difficult to obtain an unbiased estimation of diagnose's reliability. We discuss how reliability of diagnoses is assessed in medical decision making and propose a general framework for reliability estimation in Machine Learning, based on transductive inference. We compare our approach with a usual (Machine Learning) probabilistic approach as well as with classical stepwise diagnostic process where reliability of diagnose is presented as its posttest probability. The proposed transductive approach is evaluated on several medical data sets from the UCI (University of California, Irvine) repository as well as on a practical problem of clinical diagnosis of the coronary artery disease. In all cases significant improvements over existing techniques are achieved.

Transductive reliability estimation for medical diagnosis.

In the past decades, machine learning (ML) tools have been successfully used in several medical diagnostic problems. While they often significantly outperform expert physicians (in terms of diagnostic accuracy, sensitivity, and specificity), they are mostly not being used in practice. One reason for this is that it is difficult to obtain an unbiased estimation of diagnose's reliability. We discuss how reliability of diagnoses is assessed in medical decision-making and propose a general framework for reliability estimation in machine learning, based on transductive inference. We compare our approach with a usual (machine learning) probabilistic approach as well as with classical stepwise diagnostic process where reliability of diagnose is presented as its post-test probability. The proposed transductive approach is evaluated on several medical datasets from the University of California (UCI) repository as well as on a practical problem of clinical diagnosis of the coronary artery disease (CAD). In all cases, significant improvements over existing techniques are achieved.