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Mr. Jeong Hee Lee
Yonsei University, Republic of Korea

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0 Deep Learning
0 Drug Development
0 Technology Valuation
0 prediction model
0 Biotechnology valuation

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Journal article
Published: 30 July 2020 in Sustainability
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Due to recent advancements in industrialization, climate change and overpopulation, air pollution has become an issue of global concern and air quality is being highlighted as a social issue. Public interest and concern over respiratory health are increasing in terms of a high reliability of a healthy life or the social sustainability of human beings. Air pollution can have various adverse or deleterious effects on human health. Respiratory diseases such as asthma, the subject of this study, are especially regarded as ‘directly affected’ by air pollution. Since such pollution is derived from the combined effects of atmospheric pollutants and meteorological environmental factors, and it is not easy to estimate its influence on feasible respiratory diseases in various atmospheric environments. Previous studies have used clinical and cohort data based on relatively a small number of samples to determine how atmospheric pollutants affect diseases such as asthma. This has significant limitations in that each sample of the collections is likely to produce inconsistent results and it is difficult to attempt the experiments and studies other than by those in the medical profession. This study mainly focuses on predicting the actual asthmatic occurrence while utilizing and analyzing the data on both the atmospheric and meteorological environment officially released by the government. We used one of the advanced analytic models, often referred to as the vector autoregressive model (VAR), which traditionally has an advantage in multivariate time-series analysis to verify that each variable has a significant causal effect on the asthmatic occurrence. Next, the VAR model was applied to a deep learning algorithm to find a prediction model optimized for the prediction of asthmatic occurrence. The average error rate of the hybrid deep neural network (DNN) model was numerically verified to be about 8.17%, indicating better performance than other time-series algorithms. The proposed model can help streamline the national health and medical insurance system and health budget management in South Korea much more effectively. It can also provide efficiency in the deployment and management of the supply and demand of medical personnel in hospitals. In addition, it can contribute to the promotion of national health, enabling advance alerts of the risk of outbreaks by the atmospheric environment for chronic asthma patients. Furthermore, the theoretical methodologies, experimental results and implications of this study will be able to contribute to our current issues of global change and development in that the meteorological and environmental data-driven, deep-learning prediction model proposed hereby would put forward a macroscopic directionality which leads to sustainable public health and sustainability science.

ACS Style

Min-Seung Kim; Jeong-Hee Lee; Yong-Ju Jang; Chan-Ho Lee; Ji-Hye Choi; Tae-Eung Sung. Hybrid Deep Learning Algorithm with Open Innovation Perspective: A Prediction Model of Asthmatic Occurrence. Sustainability 2020, 12, 6143 .

AMA Style

Min-Seung Kim, Jeong-Hee Lee, Yong-Ju Jang, Chan-Ho Lee, Ji-Hye Choi, Tae-Eung Sung. Hybrid Deep Learning Algorithm with Open Innovation Perspective: A Prediction Model of Asthmatic Occurrence. Sustainability. 2020; 12 (15):6143.

Chicago/Turabian Style

Min-Seung Kim; Jeong-Hee Lee; Yong-Ju Jang; Chan-Ho Lee; Ji-Hye Choi; Tae-Eung Sung. 2020. "Hybrid Deep Learning Algorithm with Open Innovation Perspective: A Prediction Model of Asthmatic Occurrence." Sustainability 12, no. 15: 6143.

Journal article
Published: 14 July 2020 in Electronics
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Building a pattern detection model using a deep learning algorithm for data collected from manufacturing sites is an effective way for to perform decision-making and assess business feasibility for enterprises, by providing the results and implications of the patterns analysis of big data occurring at manufacturing sites. To identify the threshold of the abnormal pattern requires collaboration between data analysts and manufacturing process experts, but it is practically difficult and time-consuming. This paper suggests how to derive the threshold setting of the abnormal pattern without manual labelling by process experts, and offers a prediction algorithm to predict the potentials of future failures in advance by using the hybrid Convolutional Neural Networks (CNN)–Long Short-Term Memory (LSTM) algorithm, and the Fast Fourier Transform (FFT) technique. We found that it is easier to detect abnormal patterns that cannot be found in the existing time domain after preprocessing the data set through FFT. Our study shows that both train loss and test loss were well developed, with near zero convergence with the lowest loss rate compared to existing models such as LSTM. Our proposition for the model and our method of preprocessing the data greatly helps in understanding the abnormal pattern of unlabeled big data produced at the manufacturing site, and can be a strong foundation for detecting the threshold of the abnormal pattern of big data occurring at manufacturing sites.

ACS Style

Jeong-Hee Lee; Jongseok Kang; We Shim; Hyun-Sang Chung; Tae-Eung Sung. Pattern Detection Model Using a Deep Learning Algorithm for Power Data Analysis in Abnormal Conditions. Electronics 2020, 9, 1140 .

AMA Style

Jeong-Hee Lee, Jongseok Kang, We Shim, Hyun-Sang Chung, Tae-Eung Sung. Pattern Detection Model Using a Deep Learning Algorithm for Power Data Analysis in Abnormal Conditions. Electronics. 2020; 9 (7):1140.

Chicago/Turabian Style

Jeong-Hee Lee; Jongseok Kang; We Shim; Hyun-Sang Chung; Tae-Eung Sung. 2020. "Pattern Detection Model Using a Deep Learning Algorithm for Power Data Analysis in Abnormal Conditions." Electronics 9, no. 7: 1140.

Journal article
Published: 22 July 2019 in Journal of Open Innovation: Technology, Market, and Complexity
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The financial valuation of a drug that is still under development is required for various purposes. The risk-adjusted net present value (r-NPV) method, which recently emerged in the biotech industry, uses the development attrition rate as a discount factor to reflect risk during each development phase. The r-NPV method was developed to overcome the disadvantages of the prevailing discounted cash flow and real options methods and considers drug type, as well as the stage of development in its approach. Using this method, the current study examines technology values in the biopharmaceutical industry and matches the clinical development periods and success rates of these new drugs by analyzing datasets from ClinicalTrials.gov and MedTrack DB. It thus provides support for an empirical valuation model for experts in the field. Notably, there is limited research on the attrition rate and development period of new substance drugs and the research results are not consistently presented. In addition to new substance drugs, further research is necessary to deepen understanding of the attrition rate and development period of biologically-based drugs because of their inherent physical and developmental differences. Similarly, research on performance specifics within drug class models would enable refinement of the model.

ACS Style

Jonghak Woo; Eungdo Kim; Tae-Eung Sung; Jongtaik Lee; Kwangsoo Shin; Jeonghee Lee. Developing an Improved Risk-Adjusted Net Present Value Technology Valuation Model for the Biopharmaceutical Industry. Journal of Open Innovation: Technology, Market, and Complexity 2019, 5, 45 .

AMA Style

Jonghak Woo, Eungdo Kim, Tae-Eung Sung, Jongtaik Lee, Kwangsoo Shin, Jeonghee Lee. Developing an Improved Risk-Adjusted Net Present Value Technology Valuation Model for the Biopharmaceutical Industry. Journal of Open Innovation: Technology, Market, and Complexity. 2019; 5 (3):45.

Chicago/Turabian Style

Jonghak Woo; Eungdo Kim; Tae-Eung Sung; Jongtaik Lee; Kwangsoo Shin; Jeonghee Lee. 2019. "Developing an Improved Risk-Adjusted Net Present Value Technology Valuation Model for the Biopharmaceutical Industry." Journal of Open Innovation: Technology, Market, and Complexity 5, no. 3: 45.

Journal article
Published: 03 September 2018 in Sustainability
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This paper analyzes factors affecting pricing in patent licensing contracts in the biopharmaceutical industry based on a dataset that includes royalty-related data such as running royalty rate, up-front payment, milestones, and deal value. Data on drug candidates for 11 drug classes is obtained for regression analysis between royalty-related data and multiple input descriptors such as market factors, licensor factors, and licensee factor in order to derive the formula for predicting royalty-related estimates such as royalty rate, up-front payment, milestones, and deal value. Data is gathered from multiple sources including MedTrack and is processed through merging and cleaning. We found that the three most important factors in pricing in patent licensing in the biopharmaceutical industry are CAGR (Compound Annual Growth Rate), PDELR (Previous Deal Experience of Licensor), and AR (Attrition Rate). We found that factors in the formula used to estimate the license fee are totally different by drug class. We found that the three most important factors in the frequency in the formula used to estimate the license fee are PDELR, RnDLR (R&D Costs of Licensor), and PDELE (Previous Deal Experience of Licensee). This study suggests a method of estimating the proper royalty rate, up-front payment, milestones, and deal value of the drug candidates of 11 drug classes by using easily obtained input data.

ACS Style

Jeong Hee Lee; Eungdo Kim; Tae-Eung Sung; Kwangsoo Shin. Factors Affecting Pricing in Patent Licensing Contracts in the Biopharmaceutical Industry. Sustainability 2018, 10, 3143 .

AMA Style

Jeong Hee Lee, Eungdo Kim, Tae-Eung Sung, Kwangsoo Shin. Factors Affecting Pricing in Patent Licensing Contracts in the Biopharmaceutical Industry. Sustainability. 2018; 10 (9):3143.

Chicago/Turabian Style

Jeong Hee Lee; Eungdo Kim; Tae-Eung Sung; Kwangsoo Shin. 2018. "Factors Affecting Pricing in Patent Licensing Contracts in the Biopharmaceutical Industry." Sustainability 10, no. 9: 3143.

Journal article
Published: 02 August 2018 in Journal of Open Innovation: Technology, Market, and Complexity
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This research aimed to build a solid basis through analytic hierarchy process (AHP) analysis to develop a reliable and practical valuation model that reflects the characteristics of the biotech industry and propose a reference formula to estimate the license fee by drug class for potential business transactions. In this study, we reviewed 135 related studies and found 167 related determinants. We surveyed 25 or more specialists in the biopharmaceutical industries. The survey group consisted of National Research Institutes (‘Group 1’), Companies (‘Group 2’), and Government Agencies–Universities (‘Group 3’). The average of the total group and Group 3 showed the same tendency at a Level 3 ranking, where the priority in determining the license fee was arranged in the order of ‘the market factor, the technology factor, the financial factor, and the environmental factor’ in light of the factors, and ‘patent characteristics, licensee characteristics, and licensor characteristics’ for the characteristics. We noted that the patent characteristics were primarily significant in technology transactions and their contract fee in the groups (Total, Group 2 and Group 3), followed by licensee characteristics. In terms of the in-depth index, we noted that the development phase and attrition rate, intellectual property tradability, and licensee licensing experience, followed by quality of technology, were the most influential determinants.

ACS Style

Jeong Hee Lee; Tae-Eung Sung; Eungdo Kim; Kwangsoo Shin. Evaluating Determinant Priority of License Fee in Biotech Industry. Journal of Open Innovation: Technology, Market, and Complexity 2018, 4, 30 .

AMA Style

Jeong Hee Lee, Tae-Eung Sung, Eungdo Kim, Kwangsoo Shin. Evaluating Determinant Priority of License Fee in Biotech Industry. Journal of Open Innovation: Technology, Market, and Complexity. 2018; 4 (3):30.

Chicago/Turabian Style

Jeong Hee Lee; Tae-Eung Sung; Eungdo Kim; Kwangsoo Shin. 2018. "Evaluating Determinant Priority of License Fee in Biotech Industry." Journal of Open Innovation: Technology, Market, and Complexity 4, no. 3: 30.

Journal article
Published: 13 March 2018 in Biomaterials Research
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The purpose of this paper is to provide technology trends and information regarding market and prospects in stents used for human blood vessels in Korea and the world. A stent is a medical device in the form of a cylindrical metal net used to normalize flow when blood or other bodily fluids such as biliary fluids are obstructed in blood vessels, gastrointestinal tracts, etc. by inserting the stent into a narrowed or clogged area. Stents are classified into vascular and non-vascular stents. The coronary artery stent is avascular stent that is used for coronary atherosclerosis. The demand is increasing for stents to treat diseases such as those affecting the heart and blood vessels of elderly and middle-aged patients. Due to the current shift in the demographic structure caused by an aging society, the prospect for stents seems to be very bright. The use of a stent designed to prevent acute vascular occlusion and restenosis, which is a side effect of conventional balloon angioplasty, has rapidly become popular because it can prevent acute complications and improve clinical outcomes. Since the initial release of this stent, there have been significant developments in its design, the most notable of which has been the introduction of drug-eluting stents (DES). Bioresorbable scaffolds (BRS) have the potential to introduce a paradigm shift in interventional cardiology, a true anatomical and functional “vascular restoration” instead of an artificial stiff tube encased by a persistent metallic foreign body. Data for this research were gathered from primary and secondary sources as well as the databases of the Korea Institute of Science Technology Information (KISTI) located in Seoul, Korea like KISTI Market Report. The sources used for primary research included the databases available from the Korea Institute of Science Technology Information, past industry research services/studies, economic and demographic data, and trade and industry journals. Secondary research was used to supplement and complement the primary research. Interviews were conducted with physicians and surgeons from the key hospitals and senior sale/marketing managers from stent product suppliers in South Korea. The global stent market is estimated at US $ 7.98 billion in 2016 and is expected to grow at a Compound Annual Growth Rate (CAGR) of 3.8% over the next 5 years. As of 2016, the global market for vascular stents is estimated at $ 7.22 billion, with coronary artery stents accounting for 67.3% of the vascular stent market. Among the coronary artery stents, BRS is notably expected to grow at an annual average rate of 8.8% by 2020, but the global adoption rate of BRS remains low at present. In the Korean market, stents for blood vessels account for most of the market, and the market size of stents for blood vessels in Korea was estimated to be 145 billion won as of 2016. In comparison to the sales growth rate of other medical devices, the future stent technology market is judged to be higher in growth potential.

ACS Style

Jeong Hee Lee; Eung Do Kim; Eun Jung Jun; Hyoung Sun Yoo; Joon Woo Lee. Analysis of trends and prospects regarding stents for human blood vessels. Biomaterials Research 2018, 22, 8 .

AMA Style

Jeong Hee Lee, Eung Do Kim, Eun Jung Jun, Hyoung Sun Yoo, Joon Woo Lee. Analysis of trends and prospects regarding stents for human blood vessels. Biomaterials Research. 2018; 22 (1):8.

Chicago/Turabian Style

Jeong Hee Lee; Eung Do Kim; Eun Jung Jun; Hyoung Sun Yoo; Joon Woo Lee. 2018. "Analysis of trends and prospects regarding stents for human blood vessels." Biomaterials Research 22, no. 1: 8.

Journal article
Published: 17 October 2016 in Journal of Open Innovation: Technology, Market, and Complexity
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This research seeks to answer the basic question, “What would be the most determining factors if I perform regression analysis using several independent variables?” This paper suggests the way to estimate the proper royalty rate and up-front payment using multiple data I can get simply as input. This research analyzes the dataset, including the royalty-related data like running royalty rate (back-end payments) and up-front payment (up-front fee + milestones), regarding drug candidates for specific drug class of anticancer by regression analysis. Then, the formula to predict royalty-related data is derived using the attrition rate for the corresponding development phase of the drug candidate for the license deal, TCT (Technology Cycle Time) median value for the IPC code (IP) of the IP, Market size of the technology, CAGR (Compound Annual Growth Rate) of the corresponding market and the revenue data of the license buyer (licensee). For the anticancer (antineoplastics) drug classes, the formula to predict the royalty rate and up-front payment is as follows. $$ \mathrm{Royalty}\ \mathrm{Rate} = 9.997 + 0.063\ *\ \mathrm{Attrition}\ \mathrm{Rate} + 1.655\ *\ \mathrm{Licensee}\ \mathrm{Revenue}\ \hbox{-}\ 0.410\ *\ \mathrm{T}\mathrm{C}\mathrm{T}\ \mathrm{Median} $$ $$ \hbox{-} 1.090\ *\ \mathrm{Market}\ \mathrm{Size}\ \hbox{-}\ 0.230\ *\ \mathrm{CAGR}\ \left(\mathrm{Formula}\ 1\right) $$ $$ \begin{array}{l}\mathrm{Up}\hbox{-} \mathrm{Front}\ \mathrm{Payment}\ \left(\mathrm{Up}\hbox{-} \mathrm{front} + \mathrm{Milestones}\right) = 2.909\ \hbox{-}\ 0.006\ *\ \mathrm{Attrition}\ \mathrm{Rate} + 0.306\ *\ \\ {}\mathrm{Licensee}\ \mathrm{Revenue}\ \hbox{-}\ 0.74\ *\ \mathrm{T}\mathrm{C}\mathrm{T}\ \mathrm{Median}\ \hbox{-}\ 0.113\ *\ \mathrm{Market}\ \mathrm{Size}\ \hbox{-}\ 0.009\ *\ \mathrm{C}\mathrm{AGR}\ \left(\mathrm{Formula}\ 2\right)\end{array} $$ In the case of Equations Equation 1 to estimate the royalty rate, it is statistically meaningful at the significance level of 1 % (P-Value: 0.001); however, in the case of Equations Equation 2 to estimate the up-front payment it is statistically not meaningful (P-Value: 0.288), thus requiring further study. This research is limited to the relationship between multiple input variables and royalty-related data in one drug class of anticancer (antineoplastics). Valuation for the drug candidate within a specific drug class can be possible, and the royalty rate can be a variable according to drug class and licensee revenue.

ACS Style

Jeong Hee Lee; Bae Khee-Su; Joon Woo Lee; Youngyong In; Taehoon Kwon; Wangwoo Lee. Valuation method by regression analysis on real royalty-related data by using multiple input descriptors in royalty negotiations in Life Science area-focused on anticancer therapies. Journal of Open Innovation: Technology, Market, and Complexity 2016, 2, 1 -22.

AMA Style

Jeong Hee Lee, Bae Khee-Su, Joon Woo Lee, Youngyong In, Taehoon Kwon, Wangwoo Lee. Valuation method by regression analysis on real royalty-related data by using multiple input descriptors in royalty negotiations in Life Science area-focused on anticancer therapies. Journal of Open Innovation: Technology, Market, and Complexity. 2016; 2 (1):1-22.

Chicago/Turabian Style

Jeong Hee Lee; Bae Khee-Su; Joon Woo Lee; Youngyong In; Taehoon Kwon; Wangwoo Lee. 2016. "Valuation method by regression analysis on real royalty-related data by using multiple input descriptors in royalty negotiations in Life Science area-focused on anticancer therapies." Journal of Open Innovation: Technology, Market, and Complexity 2, no. 1: 1-22.

Journal article
Published: 15 January 2016 in Journal of Open Innovation: Technology, Market, and Complexity
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This research seeks to answer the basic question, “How can we build up the formula to estimate the proper royalty rate and up-front payment using the data I can get simply as input?” This paper suggests a way to estimate the proper royalty rate and up-front payment using a formula derived from the regression of historical royalty dataset. This research analyzes the dataset, including the royalty-related data like running royalty rate (back-end payments) and up-front payment (up-front fee + milestones), regarding drug candidates for specific drug classes, like anticancer or cardiovascular, by regression analysis. Then, the formula to predict royalty-related data is derived using the attrition rate for the corresponding development phase of the drug candidate for the license deal and the revenue data of the license buyer (licensee). Lastly, the relationship between the formula to predict royalty-related data and the expected net present value is investigated. For the anticancer (antineoplastics) and cardiovascular drug classes, the formula to predict the royalty rate and up-front payment is as follows. $$ \mathrm{X}=\left(\mathrm{Attrition}\kern0.5em \mathrm{Rate}\kern0.5em \ast \kern0.5em \mathrm{Licensee}\kern0.5em \mathrm{Revenue}\right)/100 $$ $$ <\mathrm{Drug}\kern0.5em \mathrm{Class}:\kern0.5em \mathrm{Anticancer}\kern0.5em \mathrm{activity}\kern0.5em \mathrm{candidates}> $$ (1) $$ \begin{array}{l}\mathrm{Royalty}\kern0.5em \mathrm{Rate}=\left(1+\mathrm{a}*\mathrm{X}\right)/\left(\mathrm{b}+\mathrm{c}*\mathrm{X}\right)=\\ {}\left(1+\hbox{--} 5.14147\mathrm{E}\hbox{-} 09*\mathrm{X}\right)/\left(0.128436559+\hbox{--} 6.37\mathrm{E}\hbox{--} 10*\mathrm{X}\right)\end{array} $$ (2) $$ \begin{array}{l}\mathrm{Upfront}\kern0.5em \mathrm{payment}\kern0.5em \left(\mathrm{Up}\hbox{-} \mathrm{front}+\mathrm{Milestones}\right)=\left(\mathrm{a}+\mathrm{X}\right)/\left(\mathrm{b}+\mathrm{c}*\mathrm{X}\right)=\\ {}\left(\hbox{--} 133620928.7+\mathrm{X}\right)/\left(\hbox{--} 3.990489631+2.04191\mathrm{E}\hbox{--} 08*\mathrm{X}\right)\end{array} $$ $$ \mathrm{X}=\left(\mathrm{Attrition}\ \mathrm{Rate}\ *\ \mathrm{Licensee}\ \mathrm{Revenue}\right)/100 $$ $$ <\mathrm{Drug}\ \mathrm{Class}:\ \mathrm{Cardiovascular}\ \mathrm{activity}\ \mathrm{drug}\ \mathrm{candidates}> $$ (3) $$ \begin{array}{l}\mathrm{Royalty}\kern0.5em \mathrm{Rate}=\mathrm{y}0+\mathrm{a}/\mathrm{X}+\mathrm{b}/{\mathrm{X}}^2=\\ {}9.26\mathrm{e}+0+\left(-8.528+5\right)/\mathrm{X}+1.744\mathrm{e}+10/{\mathrm{X}}^2\end{array} $$ (4) $$ \begin{array}{l}\mathrm{Upfront}\kern0.5em \mathrm{payment}\kern0.5em \left(\mathrm{Up}\hbox{-} \mathrm{front}+\mathrm{Milestone}\right)=\mathrm{y}0+\mathrm{ax}+\mathrm{b}{\mathrm{x}}^2\\ {}=7.103\mathrm{e}+6+\left(\hbox{--} 3.990489631\right)*\mathrm{X}+\left(\hbox{--} 1.536\mathrm{e}\hbox{--} 12\right)*{\mathrm{X}}^2\end{array} $$ In the case of Equations Equation 2 and Equation 4, it is statistically meaningful (R2: 039–0.41); however, in the case of Equations Equation 1 and Equation 3, it has a weak relationship (R2: 022–0.28), thus requiring further study. This research is limited to the relationship between two drug classes—anticancer (antineoplastics) and cardiovascular—and royalty-related data. Valuation for the drug candidate within a specific drug class can be possible, and the royalty rate can be a variable according to drug class and licensee revenue.

ACS Style

Jeong Hee Lee; Youngyong In; Il-Hyung Lee; Joon Woo Lee. Valuations using royalty data in the life sciences area—focused on anticancer and cardiovascular therapies. Journal of Open Innovation: Technology, Market, and Complexity 2016, 2, 1 -25.

AMA Style

Jeong Hee Lee, Youngyong In, Il-Hyung Lee, Joon Woo Lee. Valuations using royalty data in the life sciences area—focused on anticancer and cardiovascular therapies. Journal of Open Innovation: Technology, Market, and Complexity. 2016; 2 (1):1-25.

Chicago/Turabian Style

Jeong Hee Lee; Youngyong In; Il-Hyung Lee; Joon Woo Lee. 2016. "Valuations using royalty data in the life sciences area—focused on anticancer and cardiovascular therapies." Journal of Open Innovation: Technology, Market, and Complexity 2, no. 1: 1-25.