scholarly journals Efficient Prediction of Vitamin B Deficiencies via Machine-Learning Using Routine Blood Test Results in Patients With Intense Psychiatric Episode

2020 ◽  
Vol 10 ◽  
Author(s):  
Hidetaka Tamune ◽  
Jumpei Ukita ◽  
Yu Hamamoto ◽  
Hiroko Tanaka ◽  
Kenji Narushima ◽  
...  
Keyword(s):  
Machine Learning ◽  
Blood Test ◽  
Vitamin B ◽  
Test Results ◽  
Routine Blood ◽  
Routine Blood Test ◽  
Efficient Prediction

scholarly journals Efficient Prediction of Vitamin B Deficiencies via Machine-learning Using Routine Blood Test Results in Patients With Intense Psychiatric Episode

2019 ◽  
Author(s):  
Hidetaka Tamune ◽  
Jumpei Ukita ◽  
Yu Hamamoto ◽  
Hiroko Tanaka ◽  
Kenji Narushima ◽  
...  
Keyword(s):  
Machine Learning ◽  
Blood Test ◽  
Psychiatric Symptoms ◽  
Vitamin B ◽  
Test Results ◽  
Learning Models ◽  
Routine Blood ◽  
Routine Blood Test ◽  
Vitamin B Deficiency ◽  
Machine Learning Models

AbstractBackgroundVitamin B deficiency is common worldwide and may lead to psychiatric symptoms; however, vitamin B deficiency epidemiology in patients with intense psychiatric episode has rarely been examined. Moreover, vitamin deficiency testing is costly and time-consuming, which has hampered effectively ruling out vitamin deficiency-induced intense psychiatric symptoms. In this study, we aimed to clarify the epidemiology of these deficiencies and efficiently predict them using machine-learning models from patient characteristics and routine blood test results that can be obtained within one hour.MethodsWe reviewed 497 consecutive patients deemed to be at imminent risk of seriously harming themselves or others over 2 years in a single psychiatric tertiary-care center. Machine-learning models (k-nearest neighbors, logistic regression, support vector machine, and random forest) were trained to predict each deficiency from age, sex, and 29 routine blood test results gathered in the period from September 2015 to December 2016. The models were validated using a dataset collected from January 2017 through August 2017.ResultsWe found that 112 (22.5%), 80 (16.1%), and 72 (14.5%) patients had vitamin B1, vitamin B12, and folate (vitamin B9) deficiency, respectively. Further, the machine-learning models were well generalized to predict deficiency in the future unseen data, especially using random forest; areas under the receiver operating characteristic curves for the validation dataset (i.e. the dataset not used for training the models) were 0.716, 0.599, and 0.796, respectively. The Gini importance of these vitamins provided further evidence of a relationship between these vitamins and the complete blood count, while also indicating a hitherto rarely considered, potential association between these vitamins and alkaline phosphatase (ALP) or thyroid stimulating hormone (TSH).DiscussionThis study demonstrates that machine-learning can efficiently predict some vitamin deficiencies in patients with active psychiatric symptoms, based on the largest cohort to date with intense psychiatric episode. The prediction method may expedite risk stratification and clinical decision-making regarding whether replacement therapy should be prescribed. Further research includes validating its external generalizability in other clinical situations and clarify whether interventions based on this method could improve patient care and cost-effectiveness.

scholarly journals Diagnosing brain tumours by routine blood tests using machine learning

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Simon Podnar ◽  
Matjaž Kukar ◽  
Gregor Gunčar ◽  
Mateja Notar ◽  
Nina Gošnjak ◽  
...  
Keyword(s):  
Machine Learning ◽  
Brain Tumour ◽  
Brain Tumours ◽  
Blood Test ◽  
Neurological Diseases ◽  
Blood Tests ◽  
Routine Blood ◽  
Routine Blood Test ◽  
Tumour Diagnosis ◽  
Neurological Patients

Abstract 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.

scholarly journals Complementing chronic frailty assessment at hospital admission with an electronic frailty index (FI-Laboratory) comprising routine blood test results

2020 ◽  
Vol 192 (1) ◽  
pp. E3-E8 ◽  
Author(s):  
Hugh Logan Ellis ◽  
Bettina Wan ◽  
Michael Yeung ◽  
Arshad Rather ◽  
Imran Mannan ◽  
...  
Keyword(s):  
Hospital Admission ◽  
Blood Test ◽  
Frailty Index ◽  
Test Results ◽  
Routine Blood ◽  
Routine Blood Test ◽  
Frailty Assessment

Identification of smokingstatus from routine blood test results using deep neural network analysis.

2018 ◽  
Author(s):  
Neil Skjodt ◽  
Polina Mamoshina ◽  
Kirill Kochetov ◽  
Franco Cortese ◽  
Anna Kovalchuk ◽  
...  
Keyword(s):  
Neural Network ◽  
Network Analysis ◽  
Blood Test ◽  
Deep Neural Network ◽  
Test Results ◽  
Neural Network Analysis ◽  
Routine Blood ◽  
Routine Blood Test

Predicting abnormal laboratory blood test results in the intensive care unit using novel features based on information theory and historical conditional probability (Preprint)

2021 ◽  
Author(s):  
Camilo E. Valderrama ◽  
Daniel J. Niven ◽  
Henry T. Stelfox ◽  
Joon Lee
Keyword(s):  
Machine Learning ◽  
Intensive Care ◽  
Conditional Probability ◽  
Blood Test ◽  
Laboratory Tests ◽  
Conditional Entropy ◽  
Admission Diagnosis ◽  
Test Results ◽  
Abnormal Laboratory ◽  
Blood Tests

BACKGROUND Redundancy in laboratory blood tests is common in intensive care units (ICU), affecting patients' health and increasing healthcare expenses. Medical communities have made recommendations to order laboratory tests more judiciously. Wise selection can rely on modern data-driven approaches that have been shown to help identify redundant laboratory blood tests in ICUs. However, most of these works have been developed for highly selected clinical conditions such as gastrointestinal bleeding. Moreover, features based on conditional entropy and conditional probability distribution have not been used to inform the need for performing a new test. OBJECTIVE We aimed to address the limitations of previous works by adapting conditional entropy and conditional probability to extract features to predict abnormal laboratory blood test results. METHODS We used an ICU dataset collected across Alberta, Canada which included 55,689 ICU admissions from 48,672 patients with different diagnoses. We investigated conditional entropy and conditional probability-based features by comparing the performances of two machine learning approaches to predict normal and abnormal results for 18 blood laboratory tests. Approach 1 used patients' vitals, age, sex, admission diagnosis, and other laboratory blood test results as features. Approach 2 used the same features plus the new conditional entropy and conditional probability-based features. RESULTS Across the 18 blood laboratory tests, both Approach 1 and Approach 2 achieved a median F1-score, AUC, precision-recall AUC, and Gmean above 80%. We found that the inclusion of the new features statistically significantly improved the capacity to predict abnormal laboratory blood test results in between ten and fifteen laboratory blood tests depending on the machine learning model. CONCLUSIONS Our novel approach with promising prediction results can help reduce over-testing in ICUs, as well as risks for patients and healthcare systems. CLINICALTRIAL N/A

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