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Article ; Online: Adopting and expanding ethical principles for generative artificial intelligence from military to healthcare

David Oniani / Jordan Hilsman / Yifan Peng / Ronald K. Poropatich / Jeremy C. Pamplin / Gary L. Legault / Yanshan Wang

npj Digital Medicine, Vol 6, Iss 1, Pp 1-

2023 Volume 10

Abstract: Abstract In 2020, the U.S. Department of Defense officially disclosed a set of ethical principles to guide the use of Artificial Intelligence (AI) technologies on future battlefields. Despite stark differences, there are core similarities between the ... ...

Abstract	Abstract In 2020, the U.S. Department of Defense officially disclosed a set of ethical principles to guide the use of Artificial Intelligence (AI) technologies on future battlefields. Despite stark differences, there are core similarities between the military and medical service. Warriors on battlefields often face life-altering circumstances that require quick decision-making. Medical providers experience similar challenges in a rapidly changing healthcare environment, such as in the emergency department or during surgery treating a life-threatening condition. Generative AI, an emerging technology designed to efficiently generate valuable information, holds great promise. As computing power becomes more accessible and the abundance of health data, such as electronic health records, electrocardiograms, and medical images, increases, it is inevitable that healthcare will be revolutionized by this technology. Recently, generative AI has garnered a lot of attention in the medical research community, leading to debates about its application in the healthcare sector, mainly due to concerns about transparency and related issues. Meanwhile, questions around the potential exacerbation of health disparities due to modeling biases have raised notable ethical concerns regarding the use of this technology in healthcare. However, the ethical principles for generative AI in healthcare have been understudied. As a result, there are no clear solutions to address ethical concerns, and decision-makers often neglect to consider the significance of ethical principles before implementing generative AI in clinical practice. In an attempt to address these issues, we explore ethical principles from the military perspective and propose the “GREAT PLEA” ethical principles, namely Governability, Reliability, Equity, Accountability, Traceability, Privacy, Lawfulness, Empathy, and Autonomy for generative AI in healthcare. Furthermore, we introduce a framework for adopting and expanding these ethical principles in a practical way that has ...
Keywords	Computer applications to medicine. Medical informatics ; R858-859.7
Subject code	170
Language	English
Publishing date	2023-12-01T00:00:00Z
Publisher	Nature Portfolio
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Selected articles from the BioCreative/OHNLP challenge 2018

Sijia Liu / Yanshan Wang / Hongfang Liu

BMC Medical Informatics and Decision Making, Vol 19, Iss S10, Pp 1-

2019 Volume 3

Keywords	Computer applications to medicine. Medical informatics ; R858-859.7
Language	English
Publishing date	2019-12-01T00:00:00Z
Publisher	BMC
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Editorial

Yanshan Wang / Hua Xu / Ozlem Uzuner

BMC Medical Informatics and Decision Making, Vol 19, Iss S5, Pp 1-

The second international workshop on health natural language processing (HealthNLP 2019)

2019 Volume 3

Keywords	Natural language processing ; NLP ; Healthcare ; Electronic health records ; EHR ; Artificial intelligence ; Computer applications to medicine. Medical informatics ; R858-859.7
Language	English
Publishing date	2019-12-01T00:00:00Z
Publisher	BMC
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Special issue of BMC medical informatics and decision making on health natural language processing

V. G. Vinod Vydiswaran / Yaoyun Zhang / Yanshan Wang / Hua Xu

BMC Medical Informatics and Decision Making, Vol 19, Iss S3, Pp 1-

2019 Volume 3

Keywords	Computer applications to medicine. Medical informatics ; R858-859.7
Language	English
Publishing date	2019-04-01T00:00:00Z
Publisher	BMC
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Natural language processing for populating lung cancer clinical research data

Liwei Wang / Lei Luo / Yanshan Wang / Jason Wampfler / Ping Yang / Hongfang Liu

BMC Medical Informatics and Decision Making, Vol 19, Iss S5, Pp 1-

2019 Volume 10

Abstract: Abstract Background Lung cancer is the second most common cancer for men and women; the wide adoption of electronic health records (EHRs) offers a potential to accelerate cohort-related epidemiological studies using informatics approaches. Since manual ... ...

Abstract	Abstract Background Lung cancer is the second most common cancer for men and women; the wide adoption of electronic health records (EHRs) offers a potential to accelerate cohort-related epidemiological studies using informatics approaches. Since manual extraction from large volumes of text materials is time consuming and labor intensive, some efforts have emerged to automatically extract information from text for lung cancer patients using natural language processing (NLP), an artificial intelligence technique. Methods In this study, using an existing cohort of 2311 lung cancer patients with information about stage, histology, tumor grade, and therapies (chemotherapy, radiotherapy and surgery) manually ascertained, we developed and evaluated an NLP system to extract information on these variables automatically for the same patients from clinical narratives including clinical notes, pathology reports and surgery reports. Results Evaluation showed promising results with the recalls for stage, histology, tumor grade, and therapies achieving 89, 98, 78, and 100% respectively and the precisions were 70, 88, 90, and 100% respectively. Conclusion This study demonstrated the feasibility and accuracy of automatically extracting pre-defined information from clinical narratives for lung cancer research.
Keywords	Natural language processing ; Lung cancer ; Stage ; Histology ; Tumor grade ; Treatments ; Computer applications to medicine. Medical informatics ; R858-859.7
Subject code	610
Language	English
Publishing date	2019-12-01T00:00:00Z
Publisher	BMC
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Natural language processing of radiology reports for identification of skeletal site-specific fractures

Yanshan Wang / Saeed Mehrabi / Sunghwan Sohn / Elizabeth J. Atkinson / Shreyasee Amin / Hongfang Liu

BMC Medical Informatics and Decision Making, Vol 19, Iss S3, Pp 23-

2019 Volume 29

Abstract: Abstract Background Osteoporosis has become an important public health issue. Most of the population, particularly elderly people, are at some degree of risk of osteoporosis-related fractures. Accurate identification and surveillance of patient ... ...

Abstract	Abstract Background Osteoporosis has become an important public health issue. Most of the population, particularly elderly people, are at some degree of risk of osteoporosis-related fractures. Accurate identification and surveillance of patient populations with fractures has a significant impact on reduction of cost of care by preventing future fractures and its corresponding complications. Methods In this study, we developed a rule-based natural language processing (NLP) algorithm for identification of twenty skeletal site-specific fractures from radiology reports. The rule-based NLP algorithm was based on regular expressions developed using MedTagger, an NLP tool of the Apache Unstructured Information Management Architecture (UIMA) pipeline to facilitate information extraction from clinical narratives. Radiology notes were retrieved from the Mayo Clinic electronic health records data warehouse. We developed rules for identifying each fracture type according to physicians’ knowledge and experience, and refined these rules via verification with physicians. This study was approved by the institutional review board (IRB) for human subject research. Results We validated the NLP algorithm using the radiology reports of a community-based cohort at Mayo Clinic with the gold standard constructed by medical experts. The micro-averaged results of sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and F1-score of the proposed NLP algorithm are 0.930, 1.0, 1.0, 0.941, 0.961, respectively. The F1-score is 1.0 for 8 fractures, and above 0.9 for a total of 17 out of 20 fractures (85%). Conclusions The results verified the effectiveness of the proposed rule-based NLP algorithm in automatic identification of osteoporosis-related skeletal site-specific fractures from radiology reports. The NLP algorithm could be utilized to accurately identify the patients with fractures and those who are also at high risk of future fractures due to osteoporosis. Appropriate care interventions to those patients, not only the most at-risk patients but also those with emerging risk, would significantly reduce future fractures.
Keywords	Fracture identification ; Natural language processing ; Radiology reports ; Electronic health records ; Computer applications to medicine. Medical informatics ; R858-859.7
Subject code	006
Language	English
Publishing date	2019-04-01T00:00:00Z
Publisher	BMC
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Discovering associations between problem list and practice setting

Liwei Wang / Yanshan Wang / Feichen Shen / Majid Rastegar-Mojarad / Hongfang Liu

BMC Medical Informatics and Decision Making, Vol 19, Iss S3, Pp 13-

2019 Volume 22

Abstract: Abstract Background The Health Information Technology for Economic and Clinical Health Act (HITECH) has greatly accelerated the adoption of electronic health records (EHRs) with the promise of better clinical decisions and patients’ outcomes. One of the ... ...

Abstract	Abstract Background The Health Information Technology for Economic and Clinical Health Act (HITECH) has greatly accelerated the adoption of electronic health records (EHRs) with the promise of better clinical decisions and patients’ outcomes. One of the core criteria for “Meaningful Use” of EHRs is to have a problem list that shows the most important health problems faced by a patient. The implementation of problem lists in EHRs has a potential to help practitioners to provide customized care to patients. However, it remains an open question on how to leverage problem lists in different practice settings to provide tailored care, of which the bottleneck lies in the associations between problem list and practice setting. Methods In this study, using sampled clinical documents associated with a cohort of patients who received their primary care at Mayo Clinic, we investigated the associations between problem list and practice setting through natural language processing (NLP) and topic modeling techniques. Specifically, after practice settings and problem lists were normalized, statistical χ2 test, term frequency-inverse document frequency (TF-IDF) and enrichment analysis were used to choose representative concepts for each setting. Then Latent Dirichlet Allocations (LDA) were used to train topic models and predict potential practice settings using similarity metrics based on the problem concepts representative of practice settings. Evaluation was conducted through 5-fold cross validation and Recall@k, Precision@k and F1@k were calculated. Results Our method can generate prioritized and meaningful problem lists corresponding to specific practice settings. For practice setting prediction, recall increases from 0.719 (k = 2) to 0.931 (k = 10), precision increases from 0.882 (k = 2) to 0.931 (k = 10) and F1 increases from 0.790 (k = 2) to 0.931 (k = 10). Conclusion To our best knowledge, our study is the first attempting to discover the association between the problem lists and hospital practice settings. In the ...
Keywords	Problem list ; Practice setting ; Topic modeling ; Statistical χ2 test ; TF-IDF and enrichment analysis ; Computer applications to medicine. Medical informatics ; R858-859.7
Subject code	360
Language	English
Publishing date	2019-04-01T00:00:00Z
Publisher	BMC
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Computational drug repurposing based on electronic health records

Nansu Zong / Andrew Wen / Sungrim Moon / Sunyang Fu / Liwei Wang / Yiqing Zhao / Yue Yu / Ming Huang / Yanshan Wang / Gang Zheng / Michelle M. Mielke / James R. Cerhan / Hongfang Liu

npj Digital Medicine, Vol 5, Iss 1, Pp 1-

a scoping review

2022 Volume 8

Abstract: Abstract Computational drug repurposing methods adapt Artificial intelligence (AI) algorithms for the discovery of new applications of approved or investigational drugs. Among the heterogeneous datasets, electronic health records (EHRs) datasets provide ... ...

Abstract	Abstract Computational drug repurposing methods adapt Artificial intelligence (AI) algorithms for the discovery of new applications of approved or investigational drugs. Among the heterogeneous datasets, electronic health records (EHRs) datasets provide rich longitudinal and pathophysiological data that facilitate the generation and validation of drug repurposing. Here, we present an appraisal of recently published research on computational drug repurposing utilizing the EHR. Thirty-three research articles, retrieved from Embase, Medline, Scopus, and Web of Science between January 2000 and January 2022, were included in the final review. Four themes, (1) publication venue, (2) data types and sources, (3) method for data processing and prediction, and (4) targeted disease, validation, and released tools were presented. The review summarized the contribution of EHR used in drug repurposing as well as revealed that the utilization is hindered by the validation, accessibility, and understanding of EHRs. These findings can support researchers in the utilization of medical data resources and the development of computational methods for drug repurposing.
Keywords	Computer applications to medicine. Medical informatics ; R858-859.7
Subject code	001
Language	English
Publishing date	2022-06-01T00:00:00Z
Publisher	Nature Portfolio
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: A clinical text classification paradigm using weak supervision and deep representation

Yanshan Wang / Sunghwan Sohn / Sijia Liu / Feichen Shen / Liwei Wang / Elizabeth J. Atkinson / Shreyasee Amin / Hongfang Liu

BMC Medical Informatics and Decision Making, Vol 19, Iss 1, Pp 1-

2019 Volume 13

Abstract: Abstract Background Automatic clinical text classification is a natural language processing (NLP) technology that unlocks information embedded in clinical narratives. Machine learning approaches have been shown to be effective for clinical text ... ...

Abstract	Abstract Background Automatic clinical text classification is a natural language processing (NLP) technology that unlocks information embedded in clinical narratives. Machine learning approaches have been shown to be effective for clinical text classification tasks. However, a successful machine learning model usually requires extensive human efforts to create labeled training data and conduct feature engineering. In this study, we propose a clinical text classification paradigm using weak supervision and deep representation to reduce these human efforts. Methods We develop a rule-based NLP algorithm to automatically generate labels for the training data, and then use the pre-trained word embeddings as deep representation features for training machine learning models. Since machine learning is trained on labels generated by the automatic NLP algorithm, this training process is called weak supervision. We evaluat the paradigm effectiveness on two institutional case studies at Mayo Clinic: smoking status classification and proximal femur (hip) fracture classification, and one case study using a public dataset: the i2b2 2006 smoking status classification shared task. We test four widely used machine learning models, namely, Support Vector Machine (SVM), Random Forest (RF), Multilayer Perceptron Neural Networks (MLPNN), and Convolutional Neural Networks (CNN), using this paradigm. Precision, recall, and F1 score are used as metrics to evaluate performance. Results CNN achieves the best performance in both institutional tasks (F1 score: 0.92 for Mayo Clinic smoking status classification and 0.97 for fracture classification). We show that word embeddings significantly outperform tf-idf and topic modeling features in the paradigm, and that CNN captures additional patterns from the weak supervision compared to the rule-based NLP algorithms. We also observe two drawbacks of the proposed paradigm that CNN is more sensitive to the size of training data, and that the proposed paradigm might not be effective for complex ...
Keywords	Clinical text classification ; Natural language processing ; Electronic health records ; Machine learning ; Weak supervision ; Computer applications to medicine. Medical informatics ; R858-859.7
Subject code	006
Language	English
Publishing date	2019-01-01T00:00:00Z
Publisher	BMC
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Rare disease knowledge enrichment through a data-driven approach

Feichen Shen / Yiqing Zhao / Liwei Wang / Majid Rastegar Mojarad / Yanshan Wang / Sijia Liu / Hongfang Liu

BMC Medical Informatics and Decision Making, Vol 19, Iss 1, Pp 1-

2019 Volume 11

Abstract: Abstract Background Existing resources to assist the diagnosis of rare diseases are usually curated from the literature that can be limited for clinical use. It often takes substantial effort before the suspicion of a rare disease is even raised to ... ...

Abstract	Abstract Background Existing resources to assist the diagnosis of rare diseases are usually curated from the literature that can be limited for clinical use. It often takes substantial effort before the suspicion of a rare disease is even raised to utilize those resources. The primary goal of this study was to apply a data-driven approach to enrich existing rare disease resources by mining phenotype-disease associations from electronic medical record (EMR). Methods We first applied association rule mining algorithms on EMR to extract significant phenotype-disease associations and enriched existing rare disease resources (Human Phenotype Ontology and Orphanet (HPO-Orphanet)). We generated phenotype-disease bipartite graphs for HPO-Orphanet, EMR, and enriched knowledge base HPO-Orphanet + and conducted a case study on Hodgkin lymphoma to compare performance on differential diagnosis among these three graphs. Results We used disease-disease similarity generated by the eRAM, an existing rare disease encyclopedia, as a gold standard to compare the three graphs with sensitivity and specificity as (0.17, 0.36, 0.46) and (0.52, 0.47, 0.51) for three graphs respectively. We also compared the top 15 diseases generated by the HPO-Orphanet + graph with eRAM and another clinical diagnostic tool, the Phenomizer. Conclusions Per our evaluation results, our approach was able to enrich existing rare disease knowledge resources with phenotype-disease associations from EMR and thus support rare disease differential diagnosis.
Keywords	Data-driven approach ; Rare disease ; Knowledge enrichment ; Differential diagnosis ; Computer applications to medicine. Medical informatics ; R858-859.7
Subject code	006 ; 610
Language	English
Publishing date	2019-02-01T00:00:00Z
Publisher	BMC
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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