scholarly journals Automated Extraction of Diagnostic Criteria From Electronic Health Records for Autism Spectrum Disorders: Development, Evaluation, and Application (Preprint)

2018 ◽  
Author(s):  
Gondy Leroy ◽  
Yang Gu ◽  
Sydney Pettygrove ◽  
Maureen K Galindo ◽  
Ananyaa Arora ◽  
...  
Keyword(s):  
Machine Learning ◽  
Electronic Health Records ◽  
Diagnostic Criteria ◽  
Large Scale ◽  
Autism Spectrum ◽  
Machine Learning Algorithms ◽  
Health Records ◽  
Rule Based ◽  
Electronic Health ◽  
Rule Based Approach

BACKGROUND Electronic health records (EHRs) bring many opportunities for information utilization. One such use is the surveillance conducted by the Centers for Disease Control and Prevention to track cases of autism spectrum disorder (ASD). This process currently comprises manual collection and review of EHRs of 4- and 8-year old children in 11 US states for the presence of ASD criteria. The work is time-consuming and expensive. OBJECTIVE Our objective was to automatically extract from EHRs the description of behaviors noted by the clinicians in evidence of the diagnostic criteria in the Diagnostic and Statistical Manual of Mental Disorders (DSM). Previously, we reported on the classification of entire EHRs as ASD or not. In this work, we focus on the extraction of individual expressions of the different ASD criteria in the text. We intend to facilitate large-scale surveillance efforts for ASD and support analysis of changes over time as well as enable integration with other relevant data. METHODS We developed a natural language processing (NLP) parser to extract expressions of 12 DSM criteria using 104 patterns and 92 lexicons (1787 terms). The parser is rule-based to enable precise extraction of the entities from the text. The entities themselves are encompassed in the EHRs as very diverse expressions of the diagnostic criteria written by different people at different times (clinicians, speech pathologists, among others). Due to the sparsity of the data, a rule-based approach is best suited until larger datasets can be generated for machine learning algorithms. RESULTS We evaluated our rule-based parser and compared it with a machine learning baseline (decision tree). Using a test set of 6636 sentences (50 EHRs), we found that our parser achieved 76% precision, 43% recall (ie, sensitivity), and >99% specificity for criterion extraction. The performance was better for the rule-based approach than for the machine learning baseline (60% precision and 30% recall). For some individual criteria, precision was as high as 97% and recall 57%. Since precision was very high, we were assured that criteria were rarely assigned incorrectly, and our numbers presented a lower bound of their presence in EHRs. We then conducted a case study and parsed 4480 new EHRs covering 10 years of surveillance records from the Arizona Developmental Disabilities Surveillance Program. The social criteria (A1 criteria) showed the biggest change over the years. The communication criteria (A2 criteria) did not distinguish the ASD from the non-ASD records. Among behaviors and interests criteria (A3 criteria), 1 (A3b) was present with much greater frequency in the ASD than in the non-ASD EHRs. CONCLUSIONS Our results demonstrate that NLP can support large-scale analysis useful for ASD surveillance and research. In the future, we intend to facilitate detailed analysis and integration of national datasets.

Application of Machine Learning in Chronic Kidney Disease Risk Prediction Using Electronic Health Records (EHR)

2021 ◽  
pp. 213-233
Author(s):  
Laxmi Kumari Pathak ◽  
Pooja Jha
Keyword(s):  
Machine Learning ◽  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Electronic Health Records ◽  
Bone Diseases ◽  
Machine Learning Algorithms ◽  
Health Records ◽  
Chronic Kidney Disease Risk ◽  
Electronic Health ◽  
Heart Disorders

Chronic kidney disease (CKD) is a disorder in which the kidneys are weakened and become unable to filter blood. It lowers the human ability to remain healthy. The field of biosciences has progressed and produced vast volumes of knowledge from electronic health records. Heart disorders, anemia, bone diseases, elevated potassium, and calcium are the very prevalent complications that arise from kidney failure. Early identification of CKD can improve the quality of life greatly. To achieve this, various machine learning techniques have been introduced so far that use the data in electronic health record (EHR) to predict CKD. This chapter studies various machine learning algorithms like support vector machine, random forest, probabilistic neural network, Apriori, ZeroR, OneR, naive Bayes, J48, IBk (k-nearest neighbor), ensemble method, etc. and compares their accuracy. The study aims in finding the best-suited technique from different methods of machine learning for the early detection of CKD by which medical professionals can interpret model predictions easily.

scholarly journals Dense phenotyping from electronic health records enables machine-learning-based prediction of preterm birth

2020 ◽  
Author(s):  
Abin Abraham ◽  
Brian L Le ◽  
Idit Kosti ◽  
Peter Straub ◽  
Digna R Velez Edwards ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Preterm Birth ◽  
Electronic Health Records ◽  
Prediction Models ◽  
Mode Of Delivery ◽  
Machine Learning Algorithms ◽  
Health Records ◽  
Electronic Health ◽  
Independent Cohort

Abstract: Identifying pregnancies at risk for preterm birth, one of the leading causes of worldwide infant mortality, has the potential to improve prenatal care. However, we lack broadly applicable methods to accurately predict preterm birth risk. The dense longitudinal information present in electronic health records (EHRs) is enabling scalable and cost-efficient risk modeling of many diseases, but EHR resources have been largely untapped in the study of pregnancy. Here, we apply machine learning to diverse data from EHRs to predict singleton preterm birth. Leveraging a large cohort of 35,282 deliveries, we find that a prediction model based on billing codes alone can predict preterm birth at 28 weeks of gestation (ROC-AUC=0.75, PR-AUC=0.40) and outperforms a comparable model trained using known risk factors (ROC-AUC=0.59, PR-AUC=0.21). Our machine learning approach is also able to accurately predict preterm birth sub-types (spontaneous vs. indicated), mode of delivery, and recurrent preterm birth. We demonstrate the portability of our approach by showing that the prediction models maintain their accuracy on a large, independent cohort (5,978 deliveries) with only a modest decrease in performance. Interpreting the features identified by the model as most informative for risk stratification demonstrates that they capture non-linear combinations of known risk factors and patterns of care. The strong performance of our approach across multiple clinical contexts and an independent cohort highlights the potential of machine learning algorithms to improve medical care during pregnancy.

scholarly journals Development of Phenotyping Algorithms for the Identification of Organ Transplant Recipients: Cohort Study

10.2196/18001 ◽  
2020 ◽  
Vol 8 (12) ◽  
pp. e18001
Author(s):  
Lee Wheless ◽  
Laura Baker ◽  
LaVar Edwards ◽  
Nimay Anand ◽  
Kelly Birdwell ◽  
...  
Keyword(s):  
Machine Learning ◽  
Electronic Health Records ◽  
Organ Transplantation ◽  
Learning Algorithms ◽  
Organ Transplant ◽  
Machine Learning Algorithms ◽  
Transplant Recipients ◽  
Health Records ◽  
Organ Transplant Recipients ◽  
Electronic Health

Background Studies involving organ transplant recipients (OTRs) are often limited to the variables collected in the national Scientific Registry of Transplant Recipients database. Electronic health records contain additional variables that can augment this data source if OTRs can be identified accurately. Objective The aim of this study was to develop phenotyping algorithms to identify OTRs from electronic health records. Methods We used Vanderbilt’s deidentified version of its electronic health record database, which contains nearly 3 million subjects, to develop algorithms to identify OTRs. We identified all 19,817 individuals with at least one International Classification of Diseases (ICD) or Current Procedural Terminology (CPT) code for organ transplantation. We performed a chart review on 1350 randomly selected individuals to determine the transplant status. We constructed machine learning models to calculate positive predictive values and sensitivity for combinations of codes by using classification and regression trees, random forest, and extreme gradient boosting algorithms. Results Of the 1350 reviewed patient charts, 827 were organ transplant recipients while 511 had no record of a transplant, and 12 were equivocal. Most patients with only 1 or 2 transplant codes did not have a transplant. The most common reasons for being labeled a nontransplant patient were the lack of data (229/511, 44.8%) or the patient being evaluated for an organ transplant (174/511, 34.1%). All 3 machine learning algorithms identified OTRs with overall >90% positive predictive value and >88% sensitivity. Conclusions Electronic health records linked to biobanks are increasingly used to conduct large-scale studies but have not been well-utilized in organ transplantation research. We present rigorously evaluated methods for phenotyping OTRs from electronic health records that will enable the use of the full spectrum of clinical data in transplant research. Using several different machine learning algorithms, we were able to identify transplant cases with high accuracy by using only ICD and CPT codes.

scholarly journals Development of Phenotyping Algorithms for the Identification of Organ Transplant Recipients: Cohort Study (Preprint)

2020 ◽  
Author(s):  
Lee Wheless ◽  
Laura Baker ◽  
LaVar Edwards ◽  
Nimay Anand ◽  
Kelly Birdwell ◽  
...  
Keyword(s):  
Machine Learning ◽  
Electronic Health Records ◽  
Organ Transplantation ◽  
Learning Algorithms ◽  
Organ Transplant ◽  
Machine Learning Algorithms ◽  
Transplant Recipients ◽  
Health Records ◽  
Organ Transplant Recipients ◽  
Electronic Health

BACKGROUND Studies involving organ transplant recipients (OTRs) are often limited to the variables collected in the national Scientific Registry of Transplant Recipients database. Electronic health records contain additional variables that can augment this data source if OTRs can be identified accurately. OBJECTIVE The aim of this study was to develop phenotyping algorithms to identify OTRs from electronic health records. METHODS We used Vanderbilt’s deidentified version of its electronic health record database, which contains nearly 3 million subjects, to develop algorithms to identify OTRs. We identified all 19,817 individuals with at least one International Classification of Diseases (ICD) or Current Procedural Terminology (CPT) code for organ transplantation. We performed a chart review on 1350 randomly selected individuals to determine the transplant status. We constructed machine learning models to calculate positive predictive values and sensitivity for combinations of codes by using classification and regression trees, random forest, and extreme gradient boosting algorithms. RESULTS Of the 1350 reviewed patient charts, 827 were organ transplant recipients while 511 had no record of a transplant, and 12 were equivocal. Most patients with only 1 or 2 transplant codes did not have a transplant. The most common reasons for being labeled a nontransplant patient were the lack of data (229/511, 44.8%) or the patient being evaluated for an organ transplant (174/511, 34.1%). All 3 machine learning algorithms identified OTRs with overall >90% positive predictive value and >88% sensitivity. CONCLUSIONS Electronic health records linked to biobanks are increasingly used to conduct large-scale studies but have not been well-utilized in organ transplantation research. We present rigorously evaluated methods for phenotyping OTRs from electronic health records that will enable the use of the full spectrum of clinical data in transplant research. Using several different machine learning algorithms, we were able to identify transplant cases with high accuracy by using only ICD and CPT codes.

scholarly journals De-identifying free text of Japanese electronic health records

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Kohei Kajiyama ◽  
Hiromasa Horiguchi ◽  
Takashi Okumura ◽  
Mizuki Morita ◽  
Yoshinobu Kano
Keyword(s):  
Machine Learning ◽  
Electronic Health Records ◽  
Conditional Random Fields ◽  
Short Term Memory ◽  
Personal Information ◽  
Pathology Report ◽  
Free Text ◽  
Health Records ◽  
Rule Based ◽  
Electronic Health

Abstract Background Recently, more electronic data sources are becoming available in the healthcare domain. Electronic health records (EHRs), with their vast amounts of potentially available data, can greatly improve healthcare. Although EHR de-identification is necessary to protect personal information, automatic de-identification of Japanese language EHRs has not been studied sufficiently. This study was conducted to raise de-identification performance for Japanese EHRs through classic machine learning, deep learning, and rule-based methods, depending on the dataset. Results Using three datasets, we implemented de-identification systems for Japanese EHRs and compared the de-identification performances found for rule-based, Conditional Random Fields (CRF), and Long-Short Term Memory (LSTM)-based methods. Gold standard tags for de-identification are annotated manually for age, hospital, person, sex, and time. We used different combinations of our datasets to train and evaluate our three methods. Our best F1-scores were 84.23, 68.19, and 81.67 points, respectively, for evaluations of the MedNLP dataset, a dummy EHR dataset that was virtually written by a medical doctor, and a Pathology Report dataset. Our LSTM-based method was the best performing, except for the MedNLP dataset. The rule-based method was best for the MedNLP dataset. The LSTM-based method achieved a good score of 83.07 points for this MedNLP dataset, which differs by 1.16 points from the best score obtained using the rule-based method. Results suggest that LSTM adapted well to different characteristics of our datasets. Our LSTM-based method performed better than our CRF-based method, yielding a 7.41 point F1-score, when applied to our Pathology Report dataset. This report is the first of study applying this LSTM-based method to any de-identification task of a Japanese EHR. Conclusions Our LSTM-based machine learning method was able to extract named entities to be de-identified with better performance, in general, than that of our rule-based methods. However, machine learning methods are inadequate for processing expressions with low occurrence. Our future work will specifically examine the combination of LSTM and rule-based methods to achieve better performance. Our currently achieved level of performance is sufficiently higher than that of publicly available Japanese de-identification tools. Therefore, our system will be applied to actual de-identification tasks in hospitals.

Leveraging Large-scale Electronic Health Records and Interpretable Machine Learning for Clinical Decision Making at the Emergency Department: Protocol for System Development and Validation (Preprint)

2021 ◽  
Author(s):  
Nan Liu ◽  
Feng Xie ◽  
Fahad Javaid Siddiqui ◽  
Andrew Fu Wah Ho ◽  
Bibhas Chakraborty ◽  
...  
Keyword(s):  
Machine Learning ◽  
Emergency Department ◽  
Electronic Health Records ◽  
Large Scale ◽  
Black Box ◽  
Learning Tools ◽  
Health Records ◽  
Interpretable Machine Learning ◽  
Ed Visits ◽  
Electronic Health

BACKGROUND There is a growing demand globally for emergency department (ED) services. An increase in ED visits has resulted in overcrowding and longer wait times. The triage process plays a crucial role in assessing and stratifying patients' risks and ensuring that the critically ill promptly receive appropriate priority and emergency treatment. A substantial amount of research has been conducted on the use of machine learning tools to construct triage and risk prediction models; however, the black box nature of these models has limited their clinical application and interpretation. OBJECTIVE In this study, we plan to develop an innovative, dynamic, and interpretable System for Emergency Risk Triage (SERT) for risk stratification in the ED by leveraging large-scale electronic health records (EHR) and machine learning. METHODS To achieve this objective, we will conduct a retrospective, single-centre study based on a large, longitudinal dataset obtained from the EHR of the largest tertiary hospital in Singapore. Study outcomes include adverse events experienced by patients, such as the need for an intensive care unit, inpatient death, among others. With pre-identified candidate variables drawn from expert opinions and relevant literature, we will apply an interpretable machine learning-based AutoScore to develop three SERT scores. These three scores can be used at different times in the ED, i.e., upon arrival, during the ED stay, and at admission. Furthermore, we will compare our novel SERT scores with established clinical scores and previously described black box machine learning models as baselines. The receiver operating characteristic analysis will be conducted on the testing cohorts for performance evaluation. RESULTS The study is currently being conducted. The extracted data indicate approximately 1.8 million ED visits by over 810,000 unique patients. Modelling results are expected to be published in 2022. CONCLUSIONS The SERT scoring system proposed in this study will be unique and innovative due to its dynamic nature and modelling transparency. If successfully validated, our proposed solution will establish a standard for data processing and modelling by taking advantage of large-scale EHRs and interpretable machine learning tools.

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