scholarly journals dynaPhenoM: Dynamic Phenotype Modeling from Longitudinal Patient Records Using Machine Learning

2021 ◽  
Author(s):  
Hao Zhang ◽  
Chengxi Zang ◽  
Jie Xu ◽  
Hansi Zhang ◽  
Sajjad Fouladvand ◽  
...  
Keyword(s):  
Machine Learning ◽  
Disease Progression ◽  
Latent Class ◽  
Learning Algorithm ◽  
Transition Probability ◽  
Transition Probability Matrix ◽  
Patient Records ◽  
Disease Heterogeneity ◽  
Health Records ◽  
Underlying Pathophysiology

Identification of clinically meaningful subphenotypes of disease progression can facilitate better understanding of disease heterogeneity and underlying pathophysiology. We propose a machine learning algorithm, termed dynaPhenoM, to achieve this goal based on longitudinal patient records such as electronic health records (EHR) or insurance claims. Specifically, dynaPhenoM first learns a set of coherent clinical topics from the events across different patient visits within the records along with the topic transition probability matrix, and then employs the time-aware latent class analysis (T-LCA) procedure to characterize each subphenotype as the evolution of these learned topics over time. The patients in the same subphenotype have similar such topic evolution patterns. We demonstrate the effectiveness and robustness of dynaPhenoM on the case of mild cognitive impairment (MCI) to Alzheimer's disease (AD) progression on three patient cohorts, and five informative subphenotypes were identified which suggest the different clinical trajectories for disease progression from MCI to AD.

The process of sourcing and preparing electronic health records data to implement a machine-learning algorithm for early identification of maternal cardiovascular risk (Preprint)

2021 ◽  
Author(s):  
Nawar Shara ◽  
Kelley M. Anderson ◽  
Noor Falah ◽  
Maryam F. Ahmad ◽  
Darya Tavazoei ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cardiovascular Risk ◽  
Electronic Health Records ◽  
Patient Care ◽  
Early Identification ◽  
Health Data ◽  
Patient Specific ◽  
Patient Records ◽  
Health Records ◽  
Electronic Health

BACKGROUND Healthcare data are fragmenting as patients seek care from diverse sources. Consequently, patient care is negatively impacted by disparate health records. Machine learning (ML) offers a disruptive force in its ability to inform and improve patient care and outcomes [6]. However, the differences that exist in each individual’s health records, combined with the lack of health-data standards, in addition to systemic issues that render the data unreliable and that fail to create a single view of each patient, create challenges for ML. While these problems exist throughout healthcare, they are especially prevalent within maternal health, and exacerbate the maternal morbidity and mortality (MMM) crisis in the United States. OBJECTIVE Maternal patient records were extracted from the electronic health records (EHRs) of a large tertiary healthcare system and made into patient-specific, complete datasets through a systematic method so that a machine-learning-based (ML-based) risk-assessment algorithm could effectively identify maternal cardiovascular risk prior to evidence of diagnosis or intervention within the patient’s record. METHODS We outline the effort that was required to define the specifications of the computational systems, the dataset, and access to relevant systems, while ensuring data security, privacy laws, and policies were met. Data acquisition included the concatenation, anonymization, and normalization of health data across multiple EHRs in preparation for its use by a proprietary risk-stratification algorithm designed to establish patient-specific baselines to identify and establish cardiovascular risk based on deviations from the patient’s baselines to inform early interventions. RESULTS Patient records can be made actionable for the goal of effectively employing machine learning (ML), specifically to identify cardiovascular risk in pregnant patients. CONCLUSIONS Upon acquiring data, including the concatenation, anonymization, and normalization of said data across multiple EHRs, the use of a machine-learning-based (ML-based) tool can provide early identification of cardiovascular risk in pregnant patients. CLINICALTRIAL N/A

scholarly journals Classification of Diabetes using Random Forest with Feature Selection Algorithm

2019 ◽  
Vol 9 (1) ◽  
pp. 1295-1300 ◽  
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Random Forest ◽  
Electronic Health Records ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Health Records

Diabetes has become a serious problem now a day. So there is a need to take serious precautions to eradicate this. To eradicate, we should know the level of occurrence. In this project we predict the level of occurrence of diabetes. We predict the level of occurrence of diabetes using Random Forest, a Machine Learning Algorithm. Using the patient’s Electronic Health Records (EHR) we can build accurate models that predict the presence of diabetes.

FRI-016-Validation of the PREsTo machine learning algorithm for the prediction of disease progression in patients with primary sclerosing cholangitis

2019 ◽  
Vol 70 (1) ◽  
pp. e390-e391 ◽  
Author(s):  
John Eaton ◽  
Konstantinos Lazaridis ◽  
Pietro Invernizzi ◽  
Olivier Chazouillères ◽  
Gideon Hirschfield ◽  
...  
Keyword(s):  
Machine Learning ◽  
Disease Progression ◽  
Primary Sclerosing Cholangitis ◽  
Sclerosing Cholangitis ◽  
Learning Algorithm ◽  
Machine Learning Algorithm

Machine Learning Algorithms to Detect Subclinical Keratoconus: Systematic Review (Preprint)

2021 ◽  
Author(s):  
Howard Maile ◽  
Ji-Peng Olivia Li ◽  
Daniel Gore ◽  
Marcello Leucci ◽  
Padraig Mulholland ◽  
...  
Keyword(s):  
Machine Learning ◽  
Disease Progression ◽  
Learning Algorithm ◽  
Early Stage ◽  
Demographic Data ◽  
Learning Algorithms ◽  
Detailed Comparison ◽  
Machine Learning Algorithms ◽  
Machine Learning Algorithm ◽  
Clinical Measures

BACKGROUND Keratoconus is a disorder characterized by progressive thinning and distortion of the cornea. If detected at an early stage corneal collagen cross linking can prevent disease progression and further visual loss. Whilst advanced forms are easily detected, reliably identifying subclinical disease can be problematic. A number of different machine learning algorithms have been used to improve the detection of subclinical keratoconus based on the analysis of single or multiple clinical measures such as corneal imaging, aberrometry, or biomechanical measurements. OBJECTIVE To survey and critically evaluate the literature on algorithmic detection of subclinical keratoconus and equivalent definitions. METHODS We performed a structured search of the following databases: Medical Literature Analysis and Retrieval System Online (MEDLINE), Excerpta Medica Database (EMBASE), Web of Science and Cochrane from Jan 1, 2010 to Oct 31, 2020. We included all full text studies that have used algorithms for the detection of subclinical keratoconus. We excluded studies that did not perform validation. RESULTS We compared the parameters measured and the design of the machine learning algorithms reported in 26 papers that met the inclusion criteria. All salient information required for detailed comparison including diagnostic criteria, demographic data, sample size, acquisition system, validation details, parameter inputs, machine learning algorithm and key results are reported in this study. CONCLUSIONS Machine learning has the potential to improve the detection of subclinical keratoconus or early keratoconus in routine ophthalmic practice. Presently there is no consensus regarding the corneal parameters that should be included for assessment and the optimal design for the machine learning algorithm. We have identified avenues for further research to improve early detection and stratification of patients for early intervention to prevent disease progression. CLINICALTRIAL N/A

Predicting imminent disease progression in advanced colorectal cancer by a machine-learning algorithm.

2019 ◽  
Vol 37 (4_suppl) ◽  
pp. 645-645
Author(s):  
Yuri Kogan ◽  
Shmuel Shannon ◽  
Eldad Taub ◽  
Marina Kleiman ◽  
Moran Elishmereni ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Machine Learning ◽  
Disease Progression ◽  
Tumor Markers ◽  
Learning Algorithm ◽  
Diagnostic Algorithm ◽  
Predictive Values ◽  
New Approach ◽  
Single Marker

645 Background: In advanced cancers, predicting disease progression just before its clinical manifestation enables an earlier switch to the next treatment line, preventing deterioration in the patient's state and potentially improving survival. Yet, given the ambiguity of current tumor markers in alerting to progression, physicians are unable to forecast this key event. We developed a diagnostic algorithm for announcing an approaching disease progression in late-stage colorectal cancer (CRC) patients by processing continuous carcinoembryonic antigen (CEA) input. Methods: Longitudinally measured CEA data of advanced CRC patients treated by standard 1st line chemotherapies, collected from 2 clinical trials (projectdatasphere.org), served for algorithm development by machine-learning and training assisted by receiver-operating-characteristic (ROC) analysis and correlation tests. Performance was validated by cross-validation techniques. Results: CEA and response evaluations of 489 CRC patients (median follow-up time: 168 days) were processed by the algorithm, predicting disease progression with 57% sensitivity (100/175 progression events) and 88% specificity (21/175 false positives). Positive and negative predictive values, accuracy and Cohen’s kappa were 64%, 84%, 79% and 0.46, respectively. The algorithm’s predictive power was superior to that of standard statistical analyses of these CEA data (e.g., ROC). Conclusions: Our study offers a new approach to using tumor markers as prognosticators. The algorithm-amplified ability of CEA to predict progression in CRC complements our recent findings in lung cancer, where integration of CEA and 4 other markers provided 66% sensitivity in predicting progression, surpassing the low capacity of each separate marker. Conceivably, future algorithm-integration of multiple markers in CRC may also exceed the limited signal of a single marker. Clinical use of our algorithm, amplifying weak marker signals of imminent progression, should allow physicians to reliably harness tumor markers for improving treatment and potentially extending survival in cancer patients.

scholarly journals Validation of a machine learning algorithm for early severe sepsis prediction: a retrospective study predicting severe sepsis up to 48 h in advance using a diverse dataset from 461 US hospitals

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Hoyt Burdick ◽  
Eduardo Pino ◽  
Denise Gabel-Comeau ◽  
Carol Gu ◽  
Jonathan Roberts ◽  
...  
Keyword(s):  
Machine Learning ◽  
Severe Sepsis ◽  
Electronic Health Records ◽  
Patient Outcomes ◽  
Learning Algorithm ◽  
Validation Dataset ◽  
Machine Learning Algorithm ◽  
Health Records ◽  
Testing Dataset ◽  
Electronic Health

Abstract Background Severe sepsis and septic shock are among the leading causes of death in the United States and sepsis remains one of the most expensive conditions to diagnose and treat. Accurate early diagnosis and treatment can reduce the risk of adverse patient outcomes, but the efficacy of traditional rule-based screening methods is limited. The purpose of this study was to develop and validate a machine learning algorithm (MLA) for severe sepsis prediction up to 48 h before onset using a diverse patient dataset. Methods Retrospective analysis was performed on datasets composed of de-identified electronic health records collected between 2001 and 2017, including 510,497 inpatient and emergency encounters from 461 health centers collected between 2001 and 2015, and 20,647 inpatient and emergency encounters collected in 2017 from a community hospital. MLA performance was compared to commonly used disease severity scoring systems and was evaluated at 0, 4, 6, 12, 24, and 48 h prior to severe sepsis onset. Results 270,438 patients were included in analysis. At time of onset, the MLA demonstrated an AUROC of 0.931 (95% CI 0.914, 0.948) and a diagnostic odds ratio (DOR) of 53.105 on a testing dataset, exceeding MEWS (0.725, P < .001; DOR 4.358), SOFA (0.716; P < .001; DOR 3.720), and SIRS (0.655; P < .001; DOR 3.290). For prediction 48 h prior to onset, the MLA achieved an AUROC of 0.827 (95% CI 0.806, 0.848) on a testing dataset. On an external validation dataset, the MLA achieved an AUROC of 0.948 (95% CI 0.942, 0.954) at the time of onset, and 0.752 at 48 h prior to onset. Conclusions The MLA accurately predicts severe sepsis onset up to 48 h in advance using only readily available vital signs extracted from the existing patient electronic health records. Relevant implications for clinical practice include improved patient outcomes from early severe sepsis detection and treatment.

scholarly journals Systematic human learning by literature and data mining for feature selection in machine learning

2021 ◽  
Author(s):  
Herdiantri Sufriyana ◽  
Yu Wei Wu ◽  
Emily Chia-Yu Su
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Prediction Model ◽  
Learning Algorithm ◽  
Human Learning ◽  
Conduct Literature ◽  
Time Varying ◽  
Clinical Prediction ◽  
Health Records ◽  
Development And Validation

Abstract We proposed a learning algorithm for human to conduct literature and data mining for causal factor discovery. The applicability is to select features for a machine learning prediction model, including but not limited to that using real-world, time-varying data from electronic health records. This protocol is relatively quick to find potentially actionable predictors for a clinical prediction while dealing with high dimensionality in big data. However, this protocol might not find a potentially novel cause, since this only exhaustively examines the existing evidences in a single study. The key stages consisted of systematic human learning, causal diagram construction, data preprocessing, causal inference modeling, and development and validation of a prediction model to describe the explainability.

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