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Opinion: AI needs patients' voices in order to revolutionize health care

#artificialintelligence

"Listen to your patient; they are telling you the diagnosis," an aphorism attributed to Dr. William Osler, the founder of modern medicine, still holds true today. The disappearance of patients' stories from electronic health records could be one reason that artificial intelligence and machine learning have so far failed to deliver their promised revolution of health care. The medical industry's fascination with artificial intelligence is understandable. Advancements in medicine have dramatically improved patient outcomes, and there is every reason to believe that machine learning, deep learning, artificial intelligence, and the like will do the same. But before we jump on the AI bandwagon, I offer this caution: consider the source of the data it is dependent on.


Artificial intelligence needs patients' voice to remake health care - STAT

#artificialintelligence

"Listen to your patient; they are telling you the diagnosis," an aphorism attributed to Dr. William Osler, the founder of modern medicine, still holds true today. The disappearance of patients' stories from electronic health records could be one reason that artificial intelligence and machine learning have so far failed to deliver their promised revolution of health care. The medical industry's fascination with artificial intelligence is understandable. Advancements in medicine have dramatically improved patient outcomes, and there is every reason to believe that machine learning, deep learning, artificial intelligence, and the like will do the same. But before we jump on the AI bandwagon, I offer this caution: consider the source of the data it is dependent on.


Extract and visualize clinical entities using Amazon Comprehend Medical Amazon Web Services

#artificialintelligence

Amazon Comprehend Medical is a new HIPAA-eligible service that uses machine learning (ML) to extract medical information with high accuracy. This reduces the cost, time, and effort of processing large amounts of unstructured medical text. You can extract entities and relationships like medication, diagnosis, and dosage, and you can also extract protected health information (PHI). Using Amazon Comprehend Medical allows end users to get value from raw clinical notes that is otherwise largely unused for analytical purposes because it's difficult to parse. There is immense value associated with extracting information from these notes and integrating it with other medical systems like an Electronic Health Record (EHR) and a Clinical Trial Management System (CTMS).


Deep EHR: Chronic Disease Prediction Using Medical Notes

arXiv.org Machine Learning

Early detection of preventable diseases is important for better disease management, improved inter-ventions, and more efficient health-care resource allocation. Various machine learning approacheshave been developed to utilize information in Electronic Health Record (EHR) for this task. Majorityof previous attempts, however, focus on structured fields and lose the vast amount of information inthe unstructured notes. In this work we propose a general multi-task framework for disease onsetprediction that combines both free-text medical notes and structured information. We compareperformance of different deep learning architectures including CNN, LSTM and hierarchical models.In contrast to traditional text-based prediction models, our approach does not require disease specificfeature engineering, and can handle negations and numerical values that exist in the text. Ourresults on a cohort of about 1 million patients show that models using text outperform modelsusing just structured data, and that models capable of using numerical values and negations in thetext, in addition to the raw text, further improve performance. Additionally, we compare differentvisualization methods for medical professionals to interpret model predictions.


Medical Concept Embedding with Time-Aware Attention

arXiv.org Artificial Intelligence

Embeddings of medical concepts such as medication, procedure and diagnosis codes in Electronic Medical Records (EMRs) are central to healthcare analytics. Previous work on medical concept embedding takes medical concepts and EMRs as words and documents respectively. Nevertheless, such models miss out the temporal nature of EMR data. On the one hand, two consecutive medical concepts do not indicate they are temporally close, but the correlations between them can be revealed by the time gap. On the other hand, the temporal scopes of medical concepts often vary greatly (e.g., \textit{common cold} and \textit{diabetes}). In this paper, we propose to incorporate the temporal information to embed medical codes. Based on the Continuous Bag-of-Words model, we employ the attention mechanism to learn a "soft" time-aware context window for each medical concept. Experiments on public and proprietary datasets through clustering and nearest neighbour search tasks demonstrate the effectiveness of our model, showing that it outperforms five state-of-the-art baselines.


Death vs. Data Science: Predicting End of Life

AAAI Conferences

Death is an inevitable part of life and while it cannot be delayed indefinitely it is possible to predict with some certainty when the health of a person is going to deteriorate. In this paper, we predict risk of mortality for patients from two large hospital systems in the Pacific Northwest. Using medical claims and electronic medical records (EMR) data we greatly improve prediction for risk of mortality and explore machine learning models with explanations for end of life predictions. The insights that are derived from the predictions can then be used to improve the quality of patient care towards the end of life.


Predicting readmission risk from doctors' notes

arXiv.org Machine Learning

We develop a model using deep learning techniques and natural language processing on unstructured text from medical records to predict hospital-wide $30$-day unplanned readmission, with c-statistic $.70$. Our model is constructed to allow physicians to interpret the significant features for prediction.