Cooperative Scenario Mining from Blood Test Data of Hepatitis B and C

2005 ◽  
pp. 312-335 ◽  
Author(s):  
Yukio Ohsawa ◽  
Hajime Fujie ◽  
Akio Saiura ◽  
Naoaki Okazaki ◽  
Naohiro Matsumura
Keyword(s):  
Hepatitis B ◽  
Test Data ◽  
Blood Test

Prediction of Recurrence in Patients with Stage III Colon Cancer Using Conventional Clinicopathological Factors and Peripheral Blood Test Data: A New Analysis with Artificial Intelligence

Oncology ◽  
2021 ◽  
Vol 99 (5) ◽  
pp. 318-326
Author(s):  
Yutaro Kamei ◽  
Tetsuro Takayama ◽  
Toshiyuki Suzuki ◽  
Kenichi Furihata ◽  
Megumi Otsuki ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Colon Cancer ◽  
Serum Albumin ◽  
Test Data ◽  
Predictive Factors ◽  
Blood Test ◽  
Stage Iii ◽  
Clinicopathological Factors ◽  
Stage Iii Colon Cancer ◽  
Staging Systems

Background: Survival rate may be predicted by tumor-node-metastasis staging systems in colon cancer. In clinical practice, about 20 to 30 clinicopathological factors and blood test data have been used. Various predictive factors for recurrence have been advocated; however, the interactions are complex and remain to be established. We used artificial intelligence (AI) to examine predictive factors related to recurrence. Methods: The study group comprised 217 patients who underwent curative surgery for stage III colon cancer. Using a self-organizing map (SOM), an AI-based method, patients with only 23 clinicopathological factors, patients with 23 clinicopathological factors and 34 of preoperative blood test data (pre-data), and those with 23 clinicopathological factors and 31 of postoperative blood test data (post-data) were classified into several clusters with various rates of recurrence. Results: When only clinicopathological factors were used, the percentage of T4b disease, the percentage of N2 disease, and the number of metastatic lymph nodes were significantly higher in a cluster with a higher rate of recurrence. When clinicopathological factors and pre-data were used, three described pathological factors and the serum C-reactive protein (CRP) levels were significantly higher and the serum total protein (TP) levels, serum albumin levels, and the percentage of lymphocytes were significantly lower in a cluster with a higher rate of recurrence. When clinicopathological factors and post-data were used, three described pathological factors, serum CRP levels, and serum carcinoembryonic antigen levels were significantly higher and serum TP levels, serum albumin levels, and the percentage of lymphocytes were significantly lower in a cluster with a higher rate of recurrence. Conclusions: This AI-based analysis extracted several risk factors for recurrence from more than 50 pathological and blood test factors before and after surgery separately. This analysis may predict the risk of recurrence of a new patient by confirming which clusters this patient belongs to.

CancerEMC: frontline non-invasive cancer screening from circulating protein biomarkers and mutations in cell-free DNA

2021 ◽  
Author(s):  
Saifur Rahaman ◽  
Xiangtao Li ◽  
Jun Yu ◽  
Ka-Chun Wong
Keyword(s):  
Test Data ◽  
Cancer Detection ◽  
Blood Test ◽  
Class Imbalance ◽  
Cancer Type ◽  
Protein Biomarkers ◽  
Class Imbalance Problem ◽  
Multiple Cancer ◽  
Cell Free Dna ◽  
Free Dna

Abstract Motivation The early detection of cancer through accessible blood tests can foster early patient interventions. Although there are developments in cancer detection from cell-free DNA (cfDNA), its accuracy remains speculative. Given its central importance with broad impacts, we aspire to address the challenge. Methods A bagging Ensemble Meta Classifier (CancerEMC) is proposed for early cancer detection based on circulating protein biomarkers and mutations in cfDNA from the blood. CancerEMC is generally designed for both binary cancer detection and multi-class cancer type localization. It can address the class imbalance problem in multi-analyte blood test data based on robust oversampling and adaptive synthesis techniques. Results Based on the clinical blood test data, we observe that the proposed CancerEMC has outperformed other algorithms and state-of-the-arts studies (including CancerSEEK published in Science, 2018) for cancer detection. The results reveal that our proposed method (i.e., CancerEMC) can achieve the best performance result for both binary cancer classification with 99.1748% accuracy (AUC = 0.999) and localized multiple cancer detection with 74.1214% accuracy (AUC = 0.938). For addressing the data imbalance issue with oversampling techniques, the accuracy can be increased to 91.4966% (AUC = 0.992), where the state-of-the-art method can only be estimated at 69.64% (AUC = 0.921). Similar results can also be observed on independent and isolated testing data. Availability https://github.com/saifurcubd/Cancer-Detection

scholarly journals An Augmented Model with Inferred Blood Features for the Self-diagnosis of Metabolic Syndrome

2020 ◽  
Vol 59 (01) ◽  
pp. 018-030
Author(s):  
Tianshu Zhou ◽  
Ying Zhang ◽  
Chengkai Wu ◽  
Chao Shen ◽  
Jingsong Li ◽  
...  
Keyword(s):  
Metabolic Syndrome ◽  
Test Data ◽  
Classification Accuracy ◽  
Blood Test ◽  
Health Management ◽  
Fasting Blood Glucose ◽  
Density Lipoprotein ◽  
Developed Countries ◽  
The Third ◽  
Health Checks

Abstract Background and Objectives The penetration rate of physical examinations in China is substantially lower than that in developed countries. Therefore, an auxiliary approach that does not depend on hospital health checks for the diagnosis of metabolic syndrome (MetS) is needed. Methods In this study, we proposed an augmented method with inferred blood features that uses self-care inputs available at home for the auxiliary diagnosis of MetS. The dataset used for modeling contained data on 91,420 individuals who had at least 2 consecutive years of health checks. We trained three separate models using a regularized gradient-boosted decision tree. The first model used only home-based features; additional blood test data (including triglyceride [TG] data, fasting blood glucose data, and high-density lipoprotein cholesterol [HDL-C] data) were included in the second model. However, in the augmented approach, the blood test data were manipulated using multivariate imputation by chained equations prior to inclusion in the third model. The performance of the three models for MetS auxiliary diagnosis was then quantitatively compared. Results The results showed that the third model exhibited the highest classification accuracy for MetS in comparison with the other two models (area under the curve [AUC]: 3rd vs. 2nd vs. 1st = 0.971 vs. 0.950 vs. 0.905, p < 0.001). We further revealed that with full sets of the three measurements from earlier blood test data, the classification accuracy of MetS can be further improved (AUC: without vs. with = 0.971 vs. 0.993). However, the magnitude of improvement was not statistically significant at the 1% level of significance (p = 0.014). Conclusion Our findings demonstrate the feasibility of the third model for MetS homecare applications and lend novel insights into innovative research on the health management of MetS. Further validation and implementation of our proposed model might improve quality of life and ultimately benefit the general population.

scholarly journals Blood Biomarker Profiling and Monitoring for High-Performance Physiology and Nutrition: Current Perspectives, Limitations and Recommendations

2019 ◽  
Vol 49 (S2) ◽  
pp. 185-198 ◽  
Author(s):  
Charles R. Pedlar ◽  
John Newell ◽  
Nathan A. Lewis
Keyword(s):  
Test Data ◽  
Sports Medicine ◽  
Blood Test ◽  
High Performance ◽  
New Technologies ◽  
Individual Variability ◽  
Training Load ◽  
Clinical Population ◽  
Sports Science ◽  
Blood Biomarker

Abstract Blood test data were traditionally confined to the clinic for diagnostic purposes, but are now becoming more routinely used in many professional and elite high-performance settings as a physiological profiling and monitoring tool. A wealth of information based on robust research evidence can be gleaned from blood tests, including: the identification of iron, vitamin or energy deficiency; the identification of oxidative stress and inflammation; and the status of red blood cell populations. Serial blood test data can be used to monitor athletes and make inferences about the efficacy of training interventions, nutritional strategies or indeed the capacity to tolerate training load. Via a profiling and monitoring approach, blood biomarker measurement combined with contextual data has the potential to help athletes avoid injury and illness via adjustments to diet, training load and recovery strategies. Since wide inter-individual variability exists in many biomarkers, clinical population-based reference data can be of limited value in athletes, and statistical methods for longitudinal data are required to identify meaningful changes within an athlete. Data quality is often compromised by poor pre-analytic controls in sport settings. The biotechnology industry is rapidly evolving, providing new technologies and methods, some of which may be well suited to athlete applications in the future. This review provides current perspectives, limitations and recommendations for sports science and sports medicine practitioners using blood profiling and monitoring for nutrition and performance purposes.

The approximate formulas predicting personal somatic telomere length using patient blood test data

2019 ◽  
Vol 97 (11) ◽  
pp. 1090-1093
Author(s):  
Toyoki Maeda ◽  
Takahiko Horiuchi ◽  
Naoki Makino
Keyword(s):  
Telomere Length ◽  
Test Data ◽  
Blood Cells ◽  
Blood Test ◽  
White Blood Cells ◽  
Laboratory Data ◽  
Biological Aging ◽  
Differential Prediction ◽  
Test Items ◽  
Peripheral Leukocytes

Biological aging underlies lifestyle-related diseases. It can be assessed by measuring personal somatic cell telomere length. However, measuring the telomere length is laborious, and its clinical surrogate parameters have not been developed. This study analyzed the correlation between telomere length in peripheral leukocytes and laboratory data to select test items relating closely to biological aging. We established formulas from these clinical data to predict the personal telomere length. The subjects were patients having visited Kyushu University Beppu Hospital from 2012 to 2015. Two hundred and thirty-two patients were enrolled. The blood data were collected and telomere lengths were measured by Southern blotting method. The patients showed significant correlations between the telomere length and several blood test data with a sex-related difference. Candidate formulas are as follows: Predicted telomere length (kb) in men = 8.59 − 0.037 × Age (years) + 0.024 × Hemoglobin (g/dL); Predicted telomere length (kb) in women = 4.83 − 0.019 × Age (years) + 0.23 × Albumin (g/dL) + 0.0001 × White blood cells (/mm3) + 0.0020 × Red blood cells (× 104/mm3) + 0.0032 × Total cholesterol (mg/dL). Thus, the derived formulas allow for the accurate differential prediction of telomeric length in male and female patients.

scholarly journals Identification of Markers Predicting Clinical Course in Patients with IgG4-Related Ophthalmic Disease by Unbiased Clustering Analysis

2020 ◽  
Vol 9 (12) ◽  
pp. 4084
Author(s):  
Kinya Tsubota ◽  
Yoshihiko Usui ◽  
Rey Nemoto ◽  
Hiroshi Goto
Keyword(s):  
Cluster Analysis ◽  
Clinical Features ◽  
Test Data ◽  
Extraocular Muscle ◽  
Blood Test ◽  
Roc Curves ◽  
High Serum ◽  
Serum Ige ◽  
Serum Igg4 ◽  
Ophthalmic Disease

Purpose: To describe the clinical features of patients with immunoglobulin G4 (IgG4)-related ophthalmic disease (IgG4-ROD) grouped by unbiased cluster analysis using peripheral blood test data and to find novel biomarkers for predicting clinical features. Methods: One hundred and seven patients diagnosed with IgG4-ROD were divided into four groups by unsupervised hierarchical cluster analysis using peripheral blood test data. The clinical features of the four groups were compared and novel markers for prediction of clinical course were explored. Results: Unbiased cluster analysis divided patients into four groups. Group B had a significantly higher frequency of extraocular muscle enlargement (p < 0.001). The frequency of patients with decreased best corrected visual acuity (BCVA) was significantly higher in group D (p = 0.002). Receiver operating characteristic (ROC) curves for the prediction of extraocular muscle enlargement and worsened BCVA using a panel consisting of important blood test data identified by machine learning yielded areas under the curve of 0.78 and 0.86, respectively. Clinical features were compared between patients divided into two groups by the cutoff serum IgE or IgG4 level obtained from ROC curves. Patients with serum IgE above 425 IU/mL had a higher frequency of extraocular muscle enlargement (25% versus 6%, p = 0.004). Patients with serum IgG4 above 712 mg/dL had a higher frequency of decreased BCVA (37% versus 5%, p ≤ 0.001). Conclusion: Unsupervised hierarchical clustering analysis using routine blood test data differentiates four distinct clinical phenotypes of IgG4-ROD, which suggest differences in pathophysiologic mechanisms. High serum IgG4 is a potential predictor of worsened BCVA, and high serum IgE is a potential predictor of extraocular muscle enlargement in IgG4-ROD patients.

Reactivation of Hepatitis B Viral Infection in Inactive HBsAg Carriers Following Anti-Tumor Necrosis Factor-α Therapy

2009 ◽  
Vol 36 (11) ◽  
pp. 2416-2420 ◽  
Author(s):  
SOO-JIN CHUNG ◽  
JA KYUNG KIM ◽  
MIN-CHAN PARK ◽  
YONG-BEOM PARK ◽  
SOO-KON LEE
Keyword(s):  
Hepatitis B ◽  
Liver Function ◽  
Blood Test ◽  
Tumor Necrosis ◽  
Normal Liver ◽  
Normal Liver Function ◽  
Hbsag Carriers ◽  
Tnf Α ◽  
Factor Α ◽  
Necrosis Factor

Objective.To investigate whether anti-tumor necrosis factor-α (TNF-α) therapy can influence the reactivation of hepatitis B virus (HBV) infection in inactive HBsAg carriers.Methods.The medical records of 103 patients [59 with ankylosing spondylitis (AS), 41 with rheumatoid arthritis (RA), 2 with juvenile RA, and 1 with psoriatic arthritis] who had been treated with anti-TNF-α therapy were reviewed retrospectively. Data on seropositivity of HBV, HBV load, and serum aminotransferases prior to and after initiation of anti-TNF-α therapy were obtained.Results.Eight patients were inactive HBsAg carriers, and all of them had normal liver function and undetectable HBV load prior to anti-TNF-α therapy. Reactivation of hepatitis B occurred in 1 patient during the course of anti-TNF-α therapy. After the third infusion of infliximab 5 mg/kg at Week 6, a blood test showed that the patient had normal liver function. When the patient returned for the fourth infusion of infliximab at Week 14, a blood test showed markedly elevated aspartate aminotransferase (AST)/alanine aminotransferase (ALT) levels (457 and 1054 IU/l, respectively) and increased viral DNA by HBV polymerase chain reaction (PCR). The fourth infliximab infusion was canceled, and entecavir 0.5 mg/day was prescribed. Then AST/ALT levels began to decrease and returned to normal range after 3 months. Followup HBV PCR showed negative results.Conclusion.We found 1 HBV reactivation case among 8 inactive HBsAg carriers following anti-TNF-α therapy. This finding supports the prophylactic use of antiviral agents in HBV carriers, even if they have normal liver function or an undetectable viral load.

scholarly journals Correlation between immune-related adverse events and prognosis in patients with various cancers treated with anti PD-1 antibody.

2020 ◽  
Author(s):  
Hiroshi Matsuoka ◽  
Takahiro Hayashi ◽  
Karen Takigami ◽  
Kazuyoshi Imaizumi ◽  
Ryoichi Shiroki ◽  
...  
Keyword(s):  
Adverse Events ◽  
Test Data ◽  
Blood Test ◽  
Response Rate ◽  
Progression Free Survival ◽  
Therapeutic Effects ◽  
Cancer Type ◽  
Free Survival ◽  
Pre Treatment ◽  
Grade 3

Abstract Background Immune checkpoint inhibitors (ICIs) targeting programmed cell death protein 1 (PD-1) are used for the treatment of various cancer types. However, immune-related adverse events (irAEs) occur in patients treated with ICIs. Several small-scale studies have reported the onset of irAEs and therapeutic effects of ICIs. Here we report a large-scale retrospective study covering a wide range of cancers. We evaluated irAEs and the therapeutic effects of ICIs and determined whether irAEs could be predicted. Methods This study included patients treated with the anti-PD-1 antibodies nivolumab or pembrolizumab at Fujita Health University Hospital between December 2015 and March 2019. We retrospectively reviewed the electronic medical records for age, cancer type, pre-treatment blood test data, presence or absence of irAE onset, type and severity of irAEs, outcome of irAE treatment, response rate, progression-free survival and overall survival. Results The subjects were 280 patients including. The overall incidence of irAEs was 41.1% (115 patients), and the incidence of severe irAEs of grade 3 and higher was 2.8% (eight patients). The most common irAEs were skin disorders, thyroid disorders and interstitial pneumonia. Patients with irAEs were significantly older than those without irAEs (69.7 versus 66.0 years, P=0.02). The objective response rate (ORR) in patients with irAEs was 30.4%, which was significantly higher than in patients without irAEs (12.7%; P<0.01). Both the median overall and progression-free survival were significantly longer in patients with irAEs. Based on the blood test data obtained before ICI therapy, hypothyroidism, thyroid-stimulating hormone levels and thyroglobulin antibody levels were associated with the onset of irAEs. In many patients with irAEs of Common Terminology Criteria for Adverse Events Grade 3 or higher, re-administration of ICIs was difficult, and their outcomes were poor. In contrast, many patients with irAEs of a lower grade were able to resume ICI therapy. Conclusion Although the onset of irAEs was difficult to predict based on pre-treatment tests. Because ICIs were more effective in improving outcomes in patients with irAEs, the continuation of ICI therapy might be beneficial with the control of symptoms after the onset of irAEs.

scholarly journals Covid-19 clinical data analysis using Ball Mapper

2020 ◽  
Author(s):  
Pawel Dlotko ◽  
Simon Rudkin
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Data Analysis ◽  
Clinical Data ◽  
Test Data ◽  
Blood Test ◽  
Preliminary Work ◽  
Clinical Data Analysis ◽  
The Moment

AbstractIn this note we provide a result of analysis of blood test data from patients with SARS-Cov-2 using Ball Mapper Algorithm. We observe that patients with the virus and in particularly patients who end up in Intensive Care Unit have quite narrow values of those parameters. Please note that this is a preliminary work and it need to be validated on much larger dataset which we are trying to acquire at the moment.

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